The compute consumption patterns of large models on the two sides have split into two completely different tracks:

In the U.S., roughly 90% of leading large-model Token go into writing code, finding bugs, automated testing, and engineering orchestration. In China, more than 55% of large-model Token flow directly into short-drama production.

One side uses compute to replace senior software engineers earning $200,000 to $300,000 a year, saving money through enterprise software subscriptions; the other uses compute to mass-produce feed slices from hundreds of daily short-drama episodes, cashing out through same-day paid traffic and top-up revenue.

Most people’s first reaction to this comparison is mockery, often with a little self-deprecation: they are rebuilding the software substrate, while we are chasing quick money in down-market entertainment. It feels less respectable.

But after spending so long inside content production and technical workflows, after doing the math and watching render jobs run, I’ve reached the opposite conclusion: compute has never cared what looks respectable. Behind the roar of the machines, there is only the coldest business ROI.

The starkly different Token consumption paths in China and the U.S. have nothing to do with which side has better technical taste. They are simply the smartest money in each market voting for the fastest monetization channel available there.

01Silicon Valley’s Ledger: Paying for Expensive Talent

Why was the U.S. able to produce Cursor?

The logic is actually very clean: an experienced Silicon Valley engineer often costs $200,000 to $400,000 a year.

For Silicon Valley tech companies, labor is one of the most expensive items on the balance sheet. If a tool can save an engineer two hours of boilerplate code a day or one low-level syntax hunt, companies will pay $20 or $200 a month for it without blinking.

This logic works smoothly because it is rooted in the mature U.S. enterprise-service market: companies are very willing to buy software, decision chains are short, and the savings from better R&D efficiency can be absorbed directly by high-ACV enterprise services. Translate “30% higher productivity per person” into a financial statement, and it becomes a very clear cost-saving calculation.

The Token burned by Cursor are, in essence, converting relatively cheap electricity and chip depreciation into the most expensive engineering hours in the world.

02China’s Ledger: Only Businesses That Pay Back the Same Day Dare to Burn Token Openly

Shift the scene to China, and the logic of selling productivity software becomes much heavier.

In China, enterprise services, collaboration tools, and enterprise-grade Agents often collect payment on quarterly or even yearly cycles. Sales has to crawl through long bidding processes, engineering has to handle customized delivery, and then everyone waits through a long receivables cycle. That process simply cannot offset the millions in daily GPU depreciation sitting in the data center.

Short dramas are a completely different species. Many people see them as video versions of melodramatic wish-fulfillment stories, but look deeper and the industry is fundamentally a high-frequency paid-traffic business with extremely fast turnover.

Its operating cycle has been compressed to an extreme: scripts are broken into dozens or hundreds of standardized shots in the morning; AI tools run unattended to batch-generate visuals and voiceovers; in the afternoon, editors assemble dozens of feed ads whose first three seconds are packed with conflict; at night, the backend shows top-up conversion rates and same-day ad ROI; winners get extra paid-traffic budget before dawn, and losers are killed that same night.

On the production floor: automation tools batch-generate shots, while editors pick usable clips directly from rows of storyboard thumbnails

In a traditional live-action short drama, once the crew starts shooting, locations, costumes, props, makeup, and actors’ fees mean that even an extremely compressed production still costs tens of thousands to over a hundred thousand yuan, with production cycles measured in weeks.

Move into an AI workflow, and virtual characters are locked down with reference images, while scenes and camera movement are handed to the model for batch processing. Production costs are cut by more than half; more importantly, the frequency of testing new plotlines rises to the level of days or even hours.

In a business where input and output can close the loop within 24 hours, producers’ attitude toward Token changes completely: compute is no longer an R&D expense; it becomes a raw material on the assembly line. Burn an extra 10,000 yuan of Token, and if it finds a shot combination with a higher completion rate, the ad account may earn back 30,000 yuan that same day.

There is not much romantic baggage behind short dramas eating 55% of the compute. The reason is very practical: right now, this is one of the very few industrial arteries in China that can turn compute into same-day cash at high frequency.

03Why Seedance? The Real Wall for Latecomers Is Post-Training Data

Once you understand the business loop, it becomes clear why Seedance can own this scene so tightly.

From the outside, people looking at AI video often like to compare base-model parameter counts, physical simulation, or whether the image reaches 4K. But if you actually try to make dramas with open-source or general public video models, you hit a wall very quickly.

Short dramas ask for something very specific from the image: they do not demand the complex lighting artistry of theatrical cinema, but a character’s face absolutely cannot change across dozens of consecutive shots; viewers can ignore occasional background flaws, but story-driving actions such as pushing open a door, turning the head, or hardening the eyes must land with precise rhythm inside two seconds.

Character Master: short dramas can tolerate background clipping, but the protagonist’s face cannot change across dozens of shots

Generic open-source models can often produce a beautiful empty shot with nice light and shadow, but once specific character drama enters the frame, limbs deform or the model fails to deliver the right emotional response.

Seedance’s moat is built exactly here. Anyone can rent GPUs and compute clusters; the real gap lies in the “post-training data” produced every day by tens of thousands of professionals on the production front line.

Put plainly, post-training data is humans on the front line telling the model “what looks good, what passes review, and what keeps viewers watching.”

On a real short-drama editing desk, to get a three-second slap-in-the-face or turn-around shot, an editor will usually generate several versions in a row:

If the movement looks natural and the eyes feel right, the editor clicks Download/export, drags it into Jianying, and cuts it into the finished episode; if the face warps or the walk clips through the geometry, the editor takes one glance and, without hesitation, clicks “Regenerate” or deletes it outright; if the lip shape is wrong, the editor circles the mouth area on the frame and runs one targeted inpainting pass.

These actions may look like routine editorial picking and choosing, but in the platform backend, every “final download and use” is an extremely valuable positive sample, and every “drop it immediately and reroll” is a precise negative sample.

Interaction scene: shot-by-shot, action-by-action checking; every manual reroll feeds the model the most realistic negative samples of micro-expression and rhythm

This kind of data simply cannot be crawled from the open web.

The open-source community can scrape every HD film and short video on the internet to train a base model, but those videos contain only finished cuts, not rejected takes. Public data can never tell you which tiny bodily twitch humans will kill on sight, or which shift in the eyes keeps viewers through the third second.

Every day, hundreds or thousands of short-drama teams spend real money burning Token on the platform, working overnight and nitpicking every shot. It is equivalent to countless professional editors paying out of pocket to perform high-intensity targeted tuning for the model.

As massive amounts of hands-on feedback flow back into the model, Seedance’s feel for the niche question of “how short dramas should be shot” starts to resemble that of a seasoned assembly-line assistant director.

Later competitors may buy the same GPUs and train base models with similar parameter counts, but without this group of professional users who find faults on the production line every day and calibrate the model’s feel with real business metrics, their generated images will always carry a layer of awkward guesswork.

04Two Realities, Two Species

America grew Cursor; China produced Seedance.

This is not about which track is technically superior. It is simply the inevitable result of two different sets of economic factor endowments:

The U.S. uses compute to replace extremely expensive high-end knowledge work, turning programmers into super-individuals who can operate like one-person companies; China uses compute to reconstruct a down-market content industry obsessed with turnover, breaking traditional film and television workflows into a digital assembly line priced by the second.

Many people still treat short dramas as fast food. But Hollywood’s studio system also first crawled out of nickelodeons and low-cost cheap genre films.

In the early stages of an industry, roughness often means stronger vitality.

When 55% of China’s large-model compute is doing brute-force calculations every day for action consistency, shot language, and audiovisual rhythm, the post-training barrier fed by real money is already much thicker than outsiders imagine.

The business world never lacks respectable blueprints. More often, the real way to nail technology into reality is to turn the roar of machines into cash that genuinely moves across the books every morning.

One is the candid snapshot where someone’s facial expression went rogue or an awkward pose was caught. It’s too funny to delete, yet slightly too chaotic to post on public feeds. The other is the dream shot where the scenery is stunning and the subject looks great, but the entire background is overrun by random tourists and selfie sticks, reducing an editorial scene to an overcrowded tourist snapshot.

You don’t actually need tedious manual retouching. By approaching these images through generative AI, flawed outtakes can turn straight into custom messaging stickers, and ordinary crowded photos can have every tourist removed while pulling in genuine cinematic film lighting. Paired with outfit-checking before you pack and immortalizing quirky trip blunders into 3D fridge magnets, these four workflows are the most practical.

Here are the final outputs and structured prompts ready for you to copy and apply directly.

01Turn Awkward Outtakes Directly into Custom Reaction Memes

Everyone saves countless reaction stickers made from other people’s memes in group chats, but using your own face delivers the sharpest humor. Skip stale text like “Good morning” or “Received.” Weave in playful micro-actions and contemporary cultural humor to maximize the contrast.

Take this six-panel sheet created from candid outtakes as an example:

No studio session is necessary — an ordinary casual snapshot is all it takes:

Open ChatGPT and upload your photo:

Copy this prompt and swap out the physical descriptors with your own:

Key technique: describe humor through concrete gestures (like “examining a French fry with a magnifying glass”) rather than generic cute poses, preventing the model from generating bland caricatures. Once generated, crop each frame for your messaging stickers.

02Prevent Packing Regrets Preview Three Outfits Before Leaving

The most frustrating travel packing mistake is lugging a heavy suitcase of clothes, only to find upon arriving at the hotel that none of them match or a vital pair of pants was left behind.

You don’t need to buy new clothes just for a trip. Take a single full-body portrait of yourself, and lay out the candidate garments flat on your bed to photograph them:

Always label each piece with clean letters A, B, C, D — vision models track structured letters reliably without confusing garments.

Apply this structured prompt:

Resulting preview:

A white tee with denim jeans keeps things crisp and clean, while throwing on the green utility jacket adds dimension; pairing the striped shirt with a brown skirt shifts the look into relaxed leisure.

Virtual fitting works best to audit palette harmony and macro silhouettes. Running this check before leaving lets you filter out incompatible clothes early, cutting down your luggage weight significantly.

03Turn Inverted Umbrellas Into Bespoke Souvenir Magnets

Generic tourist attraction fridge magnets cost a small fortune and almost always feature the same cookie-cutter monumental gates.

In reality, the travel moments that stick in your memory are often the unscripted blunders — like coastal gale-force gusts turning a yellow umbrella inside-out:

Hand the subject, inverted umbrella, red-roof lighthouse, and orange drink over to AI to produce a vintage graphic illustration:

Generated graphic layout:

Inscribing “Seaside Day” on top and “Heavy Winds” across the banner anchors genuine personal memories far better than any commercial landmark name.

You can also ask the model to render a polymer clay sculpture preview to inspect physical tactile dimensionality:

Bringing this into the real world is effortless: color-print the flat vector layout onto ordinary paper, cut along the contour, and affix it to self-adhesive magnetic sheeting. Your fridge holds a one-of-a-kind travel memory.

04Rescue Scenic Travel Shots Erase Crowds and Grade for Cinema

You hike to an iconic vantage point in peak golden light, the framing is ideal and energy is high, only for the background to be swarmed by dozens of backpackers and selfie sticks, reducing an editorial shot to a crowded tourist scene.

Take this raw capture where the background is completely clogged with tourists:

Don’t waste hours manually painting with an eraser tool. Feed the image into an editing model to purge every passerby, reconstruct the architectural environment, and tune the cinematic lighting in a single pass:

To get genuine cinematic qualities, avoid vague phrases like “cinematic masterpiece” — models need specific lighting cues, architectural synthesis, and color grade parameters.

Copy this prompt and adapt the subject positions and scenery to match your environment:

Generative editing excels at background synthesis, but occasionally re-interprets facial features slightly. Always zoom in to verify facial integrity first. This approach is built for creative travel photography rather than unaltered archival records.

Don’t rush to delete those scenic travel captures spoiled by tourist crowds — save them and let AI run this workflow.

You are chatting smoothly in the ChatGPT web app, the plan is settled, and then everything gets stuck in relay mode: the AI cannot see any local code. It says, “send me that config file,” so you switch to the editor, find it, copy it, and paste it in. It gives you a troubleshooting command, so you switch to Terminal, run it, then copy the error back. After a few rounds of window switching, half the time is gone to passing messages back and forth.

To let AI touch a local computer, many people either open a full coding agent like Codex. Anyone who has used it knows how precious that quota is; burning it just to read a log or inspect a config is overkill. Or they spend half a day wiring up a local MCP server and then have to keep maintaining it.

Today I tried the Remote Desktop Commander plugin. It turns the ChatGPT chat box into a lightweight assistant that can touch your local computer.

After connecting it, I sent one line in the chat: “It should be connected now. Try again.”

It replied directly: the device was online, ping succeeded, and it could read local project files smoothly.

The later automatic edits and environment tests were still running, but the core wall was already gone: the ChatGPT chat box no longer has to stare at your computer from the other side of the screen.

01What It’s Actually Good For

First, be clear about what this is: the plugin does not click around your screen for you. It is a real remote MCP, or Model Context Protocol, pipe.

OpenAI’s official plugin page is explicit about the capabilities: read local files, run terminal commands, and manage background processes. You start a small background agent on your computer, and ChatGPT sends file and command requests through a secure cloud channel back to your local machine.

The most common and smoothest community use cases are the mechanical chores that used to burn human time:

Check project state without manually sending files: let it inspect local Git history and read the latest error log. After a plan is settled, let it create a file in a local test directory and run a verification. Keep these small file, config, and log tasks inside ordinary ChatGPT conversations, and save expensive Codex quota for the genuinely complex development work.

02Get Started in 5 Minutes

You do not need to buy a server or wrestle with complicated API keys. Two steps are enough.

1Start the local agent: make sure Node.js 18 or later is installed. Open Terminal and run the command below. A browser authorization page opens automatically, and Terminal shows a pairing code. If the codes match, click Verify Device. When Terminal says Device ready, you are done. Note: keep this Terminal window open. If you close it, the device goes offline.

2Install the ChatGPT plugin: search for Remote Desktop Commander in the ChatGPT sidebar or app marketplace and add it. Or open the official setup page and install it from there. Authorize with the same account you just paired.

3Run a safe test: open a new conversation and start with a read-only instruction. If it reports real file names, the channel is fully connected.

Then create a test file in an empty directory, read it back, confirm everything is correct, and only then hand it a real project.

03Pitfalls and Boundaries That Matter

The practical value of treating the ChatGPT chat box as an entry point to your computer is simple: it erases the old mechanical loop of switching windows, pasting code, and relaying logs. You no longer need to hand-build and maintain MCP. When you need it, connect it and try it.

Hunting down APKs, figuring out 32-bit vs 64-bit, copying to a USB stick, granting permissions, clicking through installs one by one, then configuring sources. No single step is hard. Together they cost you an evening.

What is TVBox? It puts the newest shows, movies and variety programs on your TV the moment they come out, which is why it ends up on almost every TV.

This time I handed that evening to Codex. I typed on my Mac, and it connected to the TV and installed everything itself.

01Before You Start: Get These 3 Things Ready

1A Mac with Codex installed (Claude Code works too)

2The TV and the Mac on the same Wi-Fi

3Developer mode and network debugging turned on for the TV

Developer mode is usually unlocked by tapping the build number repeatedly under “Settings → About”. If you can’t find it, send your TV model to Codex and ask. Mine is a TCL Thunderbird, and most Android TVs work. I also did this remotely on a friend’s Sony TV, and it worked there too.

02Copy This Message and Let AI Install TVBox

Connecting to the TV, installing TVBox, configuring sources: say it all in the first message. Below is what I actually sent, lightly tidied. You can copy it as is:

Once you send it, the TV pops up “Allow USB debugging?”. Tick “Always allow” and press Allow. That’s the only time in this whole tutorial you pick up the remote. Just this once.

19 minutes later, it handed back this:

When watching, switch sources under TVBox “Settings → Line selection”. If one won’t load, try the next.

03Want Something Else? One Sentence Is Enough

The rules were set in the first message. Just keep going in the same conversation; it remembers the earlier requirements. Everything below is what I actually typed.

Apple TV: for Apple’s exclusive shows

Why install it: Apple’s own exclusives only stream here. Severance, Ted Lasso and other top-rated shows are all Apple’s, with good picture quality and no ads.

Once installed, sign in with a QR code. If it’s a black screen the first time, tell it, and it restarts the app.

Netflix: hand the error to AI

If Netflix throws an error, give the error code to the AI. It fixes it on its own and swaps in a version that fits.

Douyin: scroll with the remote

The official TV version has been discontinued, so it installed a third-party app called myDV. Scroll up and down with the remote.

AirScreen and VLC: easier casting

Install AirScreen and VLC together, for casting and playing local videos.

Finally: clear out old versions and leftovers

That freed up 1.4 GB.

04If a Computer Can Control a TV, What Else Can It Do?

Installing apps is only the start.

If a computer can install apps, press buttons and read the screen, then having AI build a little remote-control game for the TV, play the family album at startup, or pop up a reminder after the kids have watched for an hour is just a few more sentences.

Things you wouldn’t have dared imagine, you can now just say out loud and try.

Enzo · 09.26

What You Need

• A Gemini AI Pro or Ultra plan (Spark is only available on these two tiers)
• An existing Facebook account
• A Visa card, as a backup
• A clean proxy node

Sign-Up Steps

1. Open Gemini Spark: gemini.google.com/spark
2. Enter the prompt: Open http://muse.ai/join
3. Click “Allow”: a browser running in a native US environment opens in the current window
4. Sign up with email: in that browser, enter your email and the verification code
5. Link Facebook: use an existing account, which lets you skip the card check
6. Enter the invite code: Settings > Redeem Code, within 48 hours of signing up

Invite code: GGO9WT

It adds an extra 1 billion tokens, which lasts a long time. You don’t get them without the code.

Troubleshooting

• Stuck in the queue: your proxy node isn’t clean. Switch to a clean node and try again
• Card check pops up: any Visa card works. You’re charged $1, refunded after a week
• Invite code invalid: each code has a limited number of uses, so it may be full

You could say he was the gateway teacher for an entire generation of video lovers. Countless people who wanted to learn editing or become travel creators fell into the craft by watching those videos — sitting at a computer, masking frame by frame, trying again and again to imitate that rhythm. Those seamless transitions through clouds and oceans, those speed ramps locked to the beat, were so recognizable that the whole circle simply called them the “Kolder style.”

That was the coming-of-age story for a lot of creators who fell in love with editing. To trick the viewer’s eye for just a few seconds, people stayed up all night inside editing software, tuning speed curves and calculating parallax. That almost monastic craft used to be one of the highest barriers in the video industry.

And nobody expected that the same person who once pushed the craft to its ceiling would release a new film not long ago.

The title was blunt enough to carry a cold kind of surrender: AI is officially better than me....

The film opens with a heavy confession:

“It seems like AI is officially replacing me. Maybe not all of me, but a large part of who I am, and what I make a living from, is being replaced.”

A few surreal traversal shots flash by at the start. Sitting on top of an iron-ore train crossing the Sahara, he shakes his head at the screen — even someone who has spent more than a decade doing top-tier editing does not know where he would begin if he had to make those images by hand.

When the person who inspired countless people to edit admits that his signature craft has been reset to zero, many people assume this is another tragedy of the craftsperson being eliminated by the algorithm.

But if you watch the main film alongside the 15-minute behind-the-scenes documentary that followed it, you find that the real point is the exact opposite:

The times really have changed. AI is ruthlessly killing every technical barrier. But real creation has not changed one bit.

01It Wasn’t AI That Killed the Barrier
It Was the Technical Leveling That Keeps Happening

Two years ago, when that crooked AI video of “Will Smith eating spaghetti” first appeared online, anyone who made a living from craft probably felt the chill in their bones:

“At the time I honestly felt like this was the beginning of the end. If the tool can make this without me, then what am I even doing here?”

That fear was painfully real. Everyone who stands on skill has some pride inside: “I can make something other people cannot.” We spent ten years eating bitterness, surviving countless all-nighters, and finally built a technical amulet that let us stand on our own. Then, in the blink of an eye, an algorithm flattens that barrier with two lines of text.

Anyone would go through a phase of resentment when their sense of value and identity suddenly loses weight.

But: “There are only two kinds of mind: one that blames the world, and one that accepts what is here.”

Why accept it? Look back at the evolution of tools over the past fifteen years, and the answer was already written into history:

“Gear gets cheaper, the barrier gets lower, and more people can make things. That has always been good.”

See it that way and what killed the technical barrier was never just today’s AI. It was the technical leveling that happens again and again.

Fifteen years ago, we used a $300 plugin to flip the table on Hollywood’s $100,000 machines. Later, we used a laptop keyboard to save the cost of an orchestra, and algorithms to save the all-night labor of an editing assistant. Every time a new technology reshaped the industry, as long as we were the ones holding the new weapon, we sincerely praised progress.

But history has its rhythm: the cheap weapon that once let you overthrow someone else will eventually become the blade an algorithm uses to overthrow you.

That is not the end. It is just the inevitability of technical leveling.

You cannot drag the world backward, and complaining does nothing. Every creator who wants to go far sooner or later has to face this fact: software fluency, shortcut memory, the hand speed of all-night rotoscoping — those were always skilled labor, not art itself.

02Only 15% of the Film Was Actually AI-Generated

So how was that 15% made? The workflow was actually very simple, and extremely restrained:

The remaining 85% still had to be shaped by hand:

Writing the script and refining the plan took weeks.

To capture a few big aerial shots, the drone pilot pushed a Mavic 3 to its top speed chasing a train at full tilt. One shaky hand and the aircraft would have vanished into the empty Sahara for good.

Rough cuts, color grading, frame-by-frame adjustments, and on-location sound design still had to happen.

“Brainstorming stories and images, getting stuck, and then breaking through — that is still the creative process I have always known.”

03Creation Is a Spectrum
The End of AI Flooding Is a Mediocre Sea of Trash

Now, whenever people see an AI-labeled film online, many instinctively call it “AI trash.”

And yes, the plastic visual effects mass-produced by models are flooding the internet. But everything depends on which end of the spectrum you stand on.

At one end is the fully lazy operator. Type “help me shoot a sci-fi blockbuster” into a prompt box, and watch whatever the model spits out. That kind of intentionless cyber-trash has no life beyond diluting attention.

At the other end is someone working with extremely specific composition and emotion: holding real light, dust, sweat, and risk from a desert shoot, and handing only the hardest small visual puzzle to AI.

Tools can be abused without restraint, or controlled with very strong intent.

AI can generate a pile of things you do not care about in thirty seconds. It can also help you realize a picture in your head that would once have been impossible even if you spent everything you had.

What it takes away is the manual labor of operating software. But “what exactly do you want to say to the world?” is something no algorithm can calculate for you.

04The Scarce Thing Was Never Skill
It Was Your Perspective and Lived Experience

The legendary music producer Rick Rubin has a line that sets the frame for this whole storm:

“Art is not in the tools, materials, or equipment. It is in the way you see the world.”

Rick Rubin himself can barely play any instrument, and he does not even really understand every fader on a mixing console. Yet he helped make some of the greatest records in pop music history, because he has top-tier aesthetics, taste, and instinct.

AI is essentially pushing more creators into a position like Rick Rubin’s.

In the past, we had to grind through three to five years of 3D modeling, particle effects, and color-grading software before we could barely reproduce the images in our heads. For many people, that long physical grind wore away most of the original impulse to express anything.

Now the technical threshold has collapsed to zero. But once “making it” is no longer hard, beautiful images themselves lose value fast. Audiences everywhere will quickly become numb to flawless pure-AI spectacle.

When that day arrives, what scarce thing actually decides the outcome?

It is your perspective. It is your particular way of seeing the world.

And what shapes that perspective is lived experience that is real.

The film finally stops in front of that enormous monolith in Mauritania. It feels like the end of an old chapter, and the opening of a new one.

The times have changed, but creation has not. The technique is dead, but the human is alive.

· The End ·

Most people actually have a pile of idle “supercomputing power” sitting in their pockets: if you subscribe to ChatGPT Pro, OpenAI allocates 200 high-end GPT-6 Pro deep-reasoning queries every week (even the $100 tier gets 50 per week). But most of the time, this quota sits completely dormant. People get used to doing everything end-to-end inside Codex, letting the model think all the way through from scratch, and chewing through their allowance before they even realize it.

The crucial detail: your ChatGPT web quota and your Codex quota are tracked completely separately.

Previously, the reason people rarely used the web app for system planning was that connecting it to GitHub only gave it visibility into the code—it had zero access to live production data:

• Code can only tell the model how the system is “theoretically” supposed to run;
• Production databases and logs tell the model what actually happens every day: how many requests come in, which tables are slowest, and which model endpoints are quietly burning cash.

Without visibility into real data, any plan the model comes up with is likely detached from reality.

Today I saw blogger Khazix share a very clever workaround: open a read-only data window (MCP) to your server, pipe live data to web ChatGPT, and let your included Pro quota spend the time on the brain-melting planning. Once the plan is locked down, toss it back to Codex with one click just to write the code. You don’t need to be deeply technical to follow along.

Assign the Task to Codex: Build a Read-Only Data Channel

Don’t be intimidated by terms like “MCP”—at its core, it’s just a secure tunnel that allows AI to read data from your server. You don’t need to write code yourself; just prompt Codex directly:

Here is a reference prompt you can give Codex: “Add a read-only production business data MCP server to our system for ChatGPT to use, allowing GPT-6 Pro to securely query live data across all servers. Requirements: least privilege, strictly read-only, auditable, no impact on production performance, and absolutely no exposure of secret keys or sensitive data.”

Be crystal clear on security details here: zero write permissions to prevent accidental modifications by the AI; authentication tied directly to existing enterprise SSO (Khazix simply reused his company’s Feishu login) to ensure only you can query the data.

Once deployed, the read-only channel automatically handles data masking and tamper-proofing:

Connect the Channel to ChatGPT

Open settings in the ChatGPT web app or desktop client, go to the plugins panel, click “New Plugin”, enter the service URL you just deployed, choose OAuth for authentication, complete authorization, and you’re set once the list displays “Connected”.

Open the Floating Window for Deep Planning with Dual Plugins

You don’t have to keep switching back and forth between your browser and IDE while coding. Inside the Codex interface, press Option + Command + N or click the chat bubble in the top-left corner to bring up the native ChatGPT floating window on the right.

Click the plus sign next to the input box and enable both plugins—the official GitHub plugin plus the read-only data plugin you just configured—then set the model to Pro.

Then send it your prompt directly: “Help me thoroughly analyze the code and log data in our system. Without compromising quality, identify every opportunity where we can aggressively cut costs.”

Once submitted, GPT-6 Pro enters deep thinking. In testing, it reasoned for a full 44 minutes, cross-referencing call traces, duplicate requests, and actual expenses across every endpoint before delivering an exhaustive audit report:

Import into Codex with One Click: Hands-Off Coding

Once the plan is ready, you don’t need to copy and paste documentation manually. Just click the small “Add to Codex” icon in the top-right corner of the floating window:

The entire conversation context and optimization plan will attach directly to Codex’s main input box.

Next, switch the model in Codex to GPT-6 Astra (with reasoning effort set to high), and send a dead-simple execution command:

“Help me verify and implement every worthwhile optimization mentioned here, then ship them all together.”

Once the instruction is sent, you can step away and do other things. Following the ready-made high-quality plan, Codex will automatically modify files, run local test suites, and pass CI checks:

When the task finishes, changes across more than 20 files are committed, and the PR merges cleanly into production. A major task that could easily have burned 10% to 20% of your quota ended up using only about 4% of your weekly Codex allowance:

A Quick Reference for the Workflow

Condensing the entire workflow down to four simple steps you can follow:

1. Open the channel: Have Codex write a read-only MCP server for you, lock permissions down strictly to read-only, and bind your enterprise SSO or personal login;
2. Connect the plugin: Add a custom plugin in ChatGPT settings, enter the URL, and complete OAuth authorization;
3. Do the planning: Press Option + Command + N in Codex to open the floating window, mount both the GitHub plugin and your data plugin, and select the Pro model to carry out thorough deep planning;
4. Pure execution: Click “Add to Codex” in the top-right corner of the floating window, switch to the Astra model, and send a single prompt letting it focus purely on modifying code and running tests.

To save quota, some people turn to third-party reverse proxies to bridge the web interface into their IDE. These shady proxies are notorious for being flagged by OpenAI as suspicious activity, putting accounts at risk of bans or stealth model degradation.

The beauty of Khazix’s approach is that it relies entirely on officially supported OpenAI features—the official Quick Chat window and the official MCP plugin ecosystem—without any rule-breaking or policy violations.

Offload the brain-draining planning to the high-tier model bundled with your web subscription, and leave the coding implementation to Codex. With a clear division of labor, you squeeze every bit of value out of an otherwise idle Pro subscription, while saving your pricey Codex quota for where it counts the most.

This article is adapted from Khazix’s hands-on walkthrough and mirrored from my original WeChat post.

Replying to a long message on WeChat, writing the Friday status report, jotting down a quick to-do — once there are enough characters, my fingers ache. Sending a voice message instead is easier, but dropping a 60-second clip into a work group chat annoys people. And the dictation built into the operating system captures every “um” and “uh” and every garbled restart, so I end up back at the keyboard cleaning it up, which takes longer than just typing the thing myself.

That is why I had been using Typeless. It genuinely is clever. Unlike ordinary voice typing, it works out what you meant to say and turns the filler and the mid-sentence corrections into one clean, readable paragraph.

I used it for a year and never subscribed.

The reason is simple: the free tier caps you at 8,000 words a week, and the output is slow. Every time I finished speaking and let go of the key, it sat there for several seconds before slowly producing the text, which is more than enough to derail a train of thought. Lifting the cap and getting faster output costs 12 to 30 US dollars a month, which adds up to a couple of hundred over a year. I only wanted to save myself some keystrokes; I had no interest in paying that every month for a small typing utility.

Then NetEase Youdao launched Bagesuo (bug.youdao.com). It is completely free and the text comes out fast, so it has taken over as my main tool.

Really Just Two Things: It Is Fast, and It Is Free

Bagesuo runs on Mac and Windows, and works as soon as you download it.

Two things won me over:

1. The text appears almost instantly. Hold the shortcut, say your piece, let go, and the words land at the cursor within about a second. There is no spinner, no dead air, so your train of thought stays intact.
2. It is completely free, with no word limit. No carefully counting what is left of the weekly quota, and no “faster processing” perk locked behind a paid tier.

It is also not a conventional pinyin IME. It sits in the background instead of taking over your keyboard. Whether the cursor is in WeChat, Feishu, a browser tab, Notion or a notes app, you hold the shortcut, speak, release, and the text goes straight in.

How It Compares with the Alternatives

The obvious question is how it stacks up against Doubao or the dictation your system already ships with. The short version:

• Built-in dictation: a plain recorder. It types exactly what you said, filler and misheard words included. I rarely use it.
• Doubao IME: quick and pleasant for pinyin typing, but its voice input is still literal transcription. It will not tidy up what you meant, and it is a full IME that wants global keyboard access.
• Typeless: still more refined when the logic runs long and complicated, which is why I have not deleted it. But it is slow, capped and expensive. For the 90 percent of everyday messages and notes, free and near-instant Bagesuo is already enough.

On privacy, the site states that nothing is retained in the cloud, that audio is not used for training, and that history stays on your own machine. For routine everyday input that is fine by me.

Wrapping Up

For most people, voice input is about saving effort. No rationing words, no waiting around for text to appear, no paying every month for the privilege — just get my words down quickly and that is enough.

If long WeChat messages and weekly reports wear you out, and you would rather not touch the keyboard, it is worth installing. It does save a fair amount of hassle.

1. Being a Specialist Isn’t Destiny — It’s a Trick the Assembly Line Invented

School and work hand us the same rulebook: pick one trade and guard it for life — be a programmer, an accountant, a salesperson. Show interest in several things at once and you get written off as restless, someone who cannot sit still long enough to get good at anything.

But look back two centuries and “one person, one trade, forever” turns out to be a very recent oddity in human history. It came out of the Industrial Revolution, a division-of-labor trick designed to turn workers into interchangeable parts on a line. Even modern schooling was originally built to mass-produce people who follow instructions and stay inside the process.

Before that, the person everyone admired was the generalist who could make a whole thing. Craftsmen, farmers, explorers — nobody lived as a narrow slice of a process. When Leonardo apprenticed in a workshop to learn painting, he came out the other side having picked up anatomy, engineering, optics, and bronze casting along the way. Nobody forced him to choose one of them and go get a certificate in it. Being able to finish a complete thing mattered far more than guarding a credential.

Left: the assembly line at Ford’s Highland Park plant, 1913 — the moment a “job” was broken down into interchangeable steps. Right: Leonardo’s study of the shoulder and neck, c. 1510 — one man who was painter, anatomist, and engineer at once. In the span of a century, humans were reengineered from “making whole things” into “minding one step.” Both images are public domain, via Wikimedia Commons.

When large models arrived, the first thing to lose value was not depth of study — it was the dull, repetitive, context-stripped act of operating. If everything you stand on is being a little faster than the next person inside one piece of software, getting swept away by new tooling is only a matter of time.

2. Don’t Refuse the Tools, and Don’t Turn Yourself into an Algorithm Either

Faced with new tools, most people instinctively bolt to one of two extremes.

One camp calls itself pure. They recoil from AI, convinced that touching a machine costs you your soul, forever talking about returning to nature and getting away from screens. The funny part is that they turn right around and scroll their phones until they cannot stop. As long as you spend your day reading things online, your attention is already on the algorithm’s payroll. That kind of refusal is less clarity than a flattering pose.

The other camp runs the opposite way: automation zealots. They would happily outsource every act of thinking to a tool, endlessly tinkering with management templates and building “second brains,” losing their temper at the smallest friction in daily life. Every meal, every walk gets run through a cost-benefit check. It looks clever, and it usually ends with their own capacity to feel and to judge quietly wiped along with the friction.

Both sides are off. The people who are most at ease — and most creative — tend to swing between the two: on one side a life that runs warm and real, walks, an aimless book, staring out a window, keeping their instincts about people and living intact; on the other side back at the desk, using AI without apology to blast through the dull work at full speed. Darwin put in about three serious working hours a day, spent much of the rest pacing a path behind his house and listening to his wife read aloud, and still wrote nineteen books. Being able to move freely between the real world and hard tools is what lets a person live unclenched.

What actually opens up a gap is knowing how to live a life of contrast. On one side, walking, drifting, paying real attention. On the other, sitting down and pushing the tools as far as they go. The tension between them is the best fuel creativity has.

3. Every Tool Depreciates — Crossing Disciplines Is the Real Edge

Pry apart the things you can actually do and they fall into completely different layers:

Layer 01 · Bedrock — Understanding What People Actually Want

How to say a thing clearly, how to tell a good story, how to make someone feel sincerity and worth. There is no standard formula here, and no machine will ever compute it cleanly. As long as people are still dealing with people, this layer will never go out of date.

Layer 02 · Edge — Those “Pointless Hobbies” You Genuinely Love

Philosophy, lifting, code, drawing, running a business — anything you honestly care about belongs here. Pulled out one at a time you might lose to a specialist, but blend them together and you become instantly recognizable. A fitness coach who has read some philosophy and a coach who only counts your reps are two different people in other people’s eyes.

Layer 03 · Craft — The Basics of Getting Work Out of Specific Software

Retouching photos, memorizing shortcuts, wrangling some back-office tool. This used to look like the most valuable layer, because grinding the skill in took years. Now AI produces something comparable in seconds. If your entire net worth is “fluent in one piece of software,” that is far too much risk in one place.

What makes a person irreplaceable was never how slick they are with the tools on top. The understanding underneath, crossed with a set of loves nobody else has in that combination — that is where the gap actually opens.

4. Rough Drafts Are Fine — AI’s Ceiling Is Your Own Taste

Somebody always plays with AI for two days, then sneers that the thing only produces garbage.

Here is the plain truth: first drafts of anything are rough, and that has nothing to do with AI. The real threshold is whether you yourself know what “good” looks like, and whether you are willing to spend the attention to grind it there.

AI is a mirror. It holds no preferences of its own and has no taste to speak of. If you take whatever the machine spits out first and ship it without even reading it closely, the one settling for less is not the machine.

People who actually use it well never expected one leap to the finish. They treat it like a quick-handed intern with no read on the room. A new hire cannot possibly know your standards on day one. You have to say plainly what you want, point at what is wrong when the work comes back, correct course step by step — until the two of you finally land somewhere that clicks.

5. Hand Off the Drudgery, and You’ll See Where You’re Irreplaceable

Take a blank sheet of paper. List the small dull tasks you do every day, one by one, and look at which ones you could hand straight to AI or a script.

Doing this is not about becoming hands-off tomorrow. Its real value is forcing you to work out where the machine can actually take over, and which parts have to stay with your own taste and instinct.

Once that line is drawn, the noise settles. Let the tools chew through the repetitive work; the attention and hours you get back go straight to the things you genuinely care about and want to pour yourself into.

None of this means focus and depth stopped mattering. The scientists gnawing on hard problems, the craftspeople who spend a lifetime on one discipline — they command respect in any era. What is being reshuffled was never real professional depth. It was the cog model that treated people as consumable: mind one monotonous link in the chain, know nothing upstream or downstream, and stay swappable at any moment.

And for people who are naturally curious about many things, this reads more like an exoneration two hundred years late. Inside the old division of labor they took the brunt of it, hearing the same lecture their whole lives: you never stick with anything, jack of all trades, master of none. And honestly, it was hard back then — grinding the basics of any single craft took years. Wanting to build a website meant years of code; wanting to make a poster meant years of drawing; wanting to run a business meant getting tangled in procedure. Split your attention and you genuinely could not beat someone who had practiced one move for a lifetime.

AI flipped that rulebook over. It works like a construction crew that charges nothing, taking the most punishing, most time-consuming groundwork of any craft to a solid seventy or eighty percent. At that point “how do I type it out line by line” stops being the chasm, and the instinct for what to make, how to join unrelated things together, and what actually counts as good becomes the scarcest thing you own. All those supposedly scattered “pointless hobbies” — the odd books you read, your feel for beauty, your nose for technology, your grasp of business — turn out to be exactly what protects you. With AI as the lever, one person can string the loose beads into a necklace and finish, start to end, what used to take a small team.

So stop feeling guilty about your wide curiosity. The world is turning back from the cold assembly line toward valuing whole things made well. Hold on to your taste, knead your many interests into a skill nobody else has, and let AI chew the grunt work — running an entire thing yourself, living as someone with blood in their veins who cannot be swapped out, is the long game available to every one of us.

Background reference: Dan Koe’s video If You Have Multiple Interests, Please Take Advantage of the Next 10 Years.

1. The “BlackBerry Moment” of Efficiency: Why 99% of AI Narratives Miss the Point

Over the past two years, the tech and venture capital worlds have fallen into a collective bout of cognitive mania and exhaustion: foundational model vendors grind night and day burning compute, racing on reasoning, and gaming benchmark leaderboards, while application founders and enterprise clients are left deeply disoriented. The market is flooded with repackaged AI copy-polishers, code completions, and meeting-notes generators. It looks like a flourishing ecosystem, but user retention is miserable and willingness to pay is paper-thin. Traditional tech giants have equipped their employees with every conceivable "AI assistant," yet their income statements and total factor productivity show no qualitative leap whatsoever.

Why is this happening? Because today's commercial narrative around large language models is, in almost 99% of cases, chained to a single word: "efficiency."

Yet if you leaf back through the history of commercial technology, you will find a startling, widely overlooked law of history: whenever a general-purpose technology truly matures and explodes into mass adoption, people stop talking about efficiency altogether.

Wind the clock back twenty years to the eve of the mobile internet. Before the iPhone, the undisputed king of mobile devices was the physical-keyboard BlackBerry. What did the industry elites and tech media obsess over day in and day out? Nothing but "efficiency"—reading and answering corporate email on the go, approving business documents at the airport gate, checking calendars and contacts in the palm of your hand. In the eyes of every investor and analyst at the time, the entire point of the mobile internet was to build a "pocket office" that would double the productivity of business professionals.

Two answers to the same niche. On the left is the BlackBerry: a screen barely large enough to read an email, with a third of the body surrendered to a physical keyboard—it took "fast typing" to the extreme because it believed the purpose of mobility was work. On the right is the original 2007 iPhone, where the physical keyboard was swallowed whole by a glass touchscreen, identifiable as the first-generation model by its silver aluminum bezel and black plastic base. Derided at launch for being slow to type on and ill-suited for business, that very screen went on to house short-form video, mobile gaming, and live streaming. Keyboards serve efficiency; screens serve desire. The side that bet on efficiency lost the entire era. Left: BlackBerry 8900, photo by Beamish4, CC BY-SA 4.0. Right: Original iPhone, photo by Carl Berkeley, CC BY-SA 2.0. Both images are photographs of actual devices, not promotional materials; the right image shows an unlit screen, identified by the silver aluminum bezel, bottom black plastic cutout, and absence of a front-facing camera.

Yet once 3G and 4G networks blanketed the world, and multi-touch smartphones truly matured and penetrated every corner of the globe, who went around talking about "mobile working" every day? Everyone knew a smartphone could handle office tasks and send emails, but that had become an invisible, taken-for-granted utility, like running water. What were the super-apps that actually swallowed the lion's share of time for billions of people and propped up multi-trillion-dollar commercial empires?

They were TikTok (and Douyin), Honor of Kings and Genshin Impact, Instagram, YouTube, streaming video, and live broadcasts—all entertainment, all social interaction, all an outlet for psychological desire and lived experience!

Why can't the efficiency narrative sustain the endgame of a technology cycle? Because efficiency gains face hard physical constraints imposed by human biology and social organization, whereas the expansion of entertainment and desire knows no ceiling whatsoever.

Consider this: even if a super-AI tool arrived today that boosted a knowledge worker's efficiency by a flat 100x, could they actually accomplish 100 times more than before? Absolutely not. Because the responsibility an individual can bear, the decision-making risk they can digest, and the psychological load they can handle per unit of time are strictly limited. No one can sign off on 100 high-stakes business decisions involving tens of millions of dollars in a single day, nor can anyone shoulder ultimate accountability for the success or failure of 100 projects in 24 hours. If society simply makes slide deck creation 100 times faster, the net result will merely be 100 times more garbage slide decks that nobody reads, sending marginal utility cratering to zero.

Turn your gaze to entertainment and psychological consumption, however, and the ceiling vanishes in an instant.

Twenty years ago, in the early days of the PC internet, spending more than 10 hours a day online was branded by mainstream society as "severe internet addiction," even spawning Yang Yongxin-style electroconvulsive therapies. Today, outside of 8 hours of sleep, almost every modern person spends 16 hours continuously online—scrolling short clips, playing games, watching streams, and interacting on social media. The digital realm has seamlessly blended into the background environment of human existence, and nobody bothers arguing anymore whether this qualifies as an "addiction."

Human civilization's underlying impulse has never been to work for work's sake. The ultimate purpose of technological efficiency gains is to ransom humanity from grinding mechanical labor, freeing up more time to splurge on entertainment, experience, and emotional sustenance.

Look back at today's LLM ecosystem: when the entire industry is still patting itself on the back because "code generation rates improved by 30%" or "document summarization was shaved down by 5 minutes," it proves we are still mired in the AI era's "BlackBerry Moment." The so-called "productivity" displayed by large models today is nothing more than a temporary mask borrowed from the old industrial world, before this frontier technology has found its native soul and true host. We have not even cleared the early days of stage one.

2. Why the Application Explosion Cannot Be Rushed

If today's frenzy is merely the opening moments of the cycle, where exactly are we standing? In a recent interview, Professor Zeng, former Chief Strategy Officer at Alibaba, proposed an analytical lens that cuts through the cyclical fog: an "industrial history perspective."

Professor Zeng divides the evolution of a general-purpose technology (GPT) into three rigorous stages: Stage 1 is infrastructure rollout; Stage 2 is the explosive boom of applications; and Stage 3 is the native era, where business paradigms and organizational structures are completely remade by the new technology.

In the podcast, electricity's development was drawn as an analogy for AI: "In 1892, Edison invented the light bulb, lighting up that one-square-kilometer patch of Manhattan... and then around 1910 there was a substantial breakthrough... roughly when the power grid covered major American cities, and finally that year saw two major applications emerge... the washing machine and the refrigerator."

Edison publicly demonstrated his carbon-filament incandescent lamp at Menlo Park in 1879; the day human civilization switched on its first central DC power distribution network, illuminating Manhattan's financial district with the commissioning of the Pearl Street Station, was September 4, 1882. The year 1892 mentioned in the interview was actually when J.P. Morgan orchestrated the merger of Edison General Electric with Thomson-Houston, formally giving birth to General Electric (GE).

Correcting these spoken historical inaccuracies is by no means nitpicking, because the actual history makes Professor Zeng's thesis even more profound and striking:

From the moment the first commercial power station began supplying electricity in 1882 to the launch of the first commercially sold electric washing machine, the Thor (1908, though built in 1907), humanity endured a grueling 26-year trek. The timeline for other killer appliances that truly entered millions of households and reshaped how humans live and entertain themselves is even more staggering:

• Commercial Radio Broadcasting (KDKA): Emerged in 1920, a full 38 years after the first power station went online. It was the spread of radio that brought music, ballgame commentary, and soap operas into living rooms in real time for the first time, inaugurating the modern entertainment industry;
• Residential Room Air Conditioners: Carrier invented industrial dehumidification air conditioning in 1902, but the first standalone room air conditioner designed for standard residential bedrooms did not arrive until 1931—a gap of 49 years;
• Commercial Black-and-White Television: The US FCC did not officially grant commercial television licenses until 1941, a staggering 59 years after Edison threw the switch at Pearl Street.

The way electricity truly penetrated households was not by saving on power bills, but by capturing the evening. On the left is a promotional photo from radio's early years: three figures kneeling on a rug, turning the tuning knob, with the "morning glory" horn flared open toward them (the flare's top was cropped out in the original negative)—notice their posture, mirroring how humans once gathered around a campfire. On the right, circa 1958, a father crouches before the TV to change channels, four children sprawl across the floor, and the mother reads a comic version by the sofa—the television sits more central than the fireplace. More than thirty years separate the two photographs, yet living room furniture was being rearranged in the exact same direction: not toward greater efficiency, but toward a screen. Left: Photo by Bain News Service, mid-1920s, Library of Congress, public domain. Right: Photo by Evert F. Baumgardner, circa 1958, US National Archives, public domain. Both are genuine period photographs, though staged promotional/documentary shots rather than candid captures.

Across this timescale, an intriguing phenomenon emerges: the adoption path of electricity also perfectly validates the great migration from an "early efficiency narrative" to a "mature entertainment empire!" Factories initially hooked up to electricity so textile workers could spin cloth through the night, so mines could be illuminated, and so electric streetcars could run (pure productive efficiency). But what truly transformed electricity into the indispensable lifeblood of modern civilization, and what consumed vast shares of American disposable income, were Hollywood film projectors, radios broadcasting around the clock, home televisions that emptied the streets every evening, and the dazzling rollercoasters of Disneyland!

3. “Application-Specific Knowledge” Hard-Won Through Trial and Error Is Technology’s True Compass

Confronted with the half-century timeline of the electrical revolution, many instinctively counter: "The industrial era required mining coal, pouring concrete, and laying copper wire—heavy physical assets. Today is made of digital bits, software distribution takes seconds, and AI can't possibly take that long."

This objection sees only the "laying of pipes" while ignoring the "exploration of human nature." Shipping code may take a single second, but the trial-and-error feedback loop of figuring out what humans actually want cannot be compressed by any amount of compute.

Stage 2 (the application explosion) is by no means an inconsequential warm-up lap. Without the chaotic, short-lived, and often sacrificial trial-and-error of Stage 2, underlying technology would have no clue where to erect the native edifice of Stage 3.

Foundational technology has no eyes of its own; "application-specific knowledge," forged through trial and error at the application layer, is the only compass capable of guiding technology's evolution.

Look back at ByteDance during the mobile internet era: when Zhang Yiming founded the company in 2012, he had not foreseen the short-video empire that would rule the world five years later. In its first few years, the ByteDance team pumped out over a dozen seemingly trivial, lowbrow utility and meme communities—Neihan Duanzi, Gaoxiao Jiongtu, Neihan Manhua, and Jinri Toutiao. It was precisely through these tens of thousands of points of friction with real users that ByteDance's industrial-scale engineering stumbled upon and perfected its master key: mobile recommendation algorithms and feedback loops. Around 2016, when 4G data rates plummeted, smartphone cameras and processors received massive upgrades, and the short-video wave surged forward, ByteDance seamlessly fused its battle-tested recommendation algorithms with short video, giving birth to the global phenomena of Douyin and TikTok.

Could Douyin or TikTok have been invented in 2012? Impossible. Even if you had handed that 2012 team ten thousand of the latest GPUs, without the nourishment of Toutiao's massive text-and-image dataset, and without thousands of encounters touching the grassroots entertainment psychology of everyday people, no one could have conjured that vertical-swiping black hole out of thin air.

This is the reality the AI industry must face today: the thousands of seemingly mindless AI chat interfaces, crude anime image generators, and companion toys scoffed at by mainstream elites are not worthless venture bubbles—they are the "cognitive R&D cost" the industry must pay for the next generation of truly great native applications. Technology cannot skip the graveyard of Stage 2 to leap straight into the promised land of Stage 3.

4. The “Browser”: Protocols and Super-Gateways in the Agent Era

"Right now, there isn't even an embryonic Yahoo in sight. That's why I say we haven't even seen the browser yet. In other words, we are still waiting for a browser to emerge."

I suspect many people's immediate reaction is to take this literally: does it mean building a "new browser" packed with AI plugins for our phones and laptops? Or creating an Agent directory or app store?

That is an exceedingly superficial misreading of business history.

What was the true historical achievement when Marc Andreessen and his team developed NCSA Mosaic and the Netscape Navigator? Before the browser, the internet was an academic archipelago rife with arcane command-line utilities (FTP, Gopher, Telnet, Usenet), navigable only by the few geeks who understood how to configure IP addresses and ports to exchange data. The genius of the browser was that it defined a unified rendering engine and hyperlinking standard (HTML/HTTP), abstracting the entire complex network topology beneath a pane of glass that any ordinary person could navigate with the click of a finger. The browser served as the universal protocol and runtime environment of the Web era.

The "browser" we are clamoring for today is, in essence, the universal interaction protocol and master gateway of the Agent era.

Examine today's AI agent ecosystem: it almost perfectly recreates the chaos and fragmentation on the eve of the browser's birth in 1993. OpenAI pushes its own GPTs and Assistants API; Anthropic pushes Claude Projects and Computer Use; open-source frameworks build their own walled gardens and reinvent wheels. Every agent is an isolated island, incapable of communicating with others, invoking capabilities across boundaries, or sharing memory and context. Meanwhile, everyday users are frightened off by code repositories that demand API key configurations and complex local environments.

This "super-gateway and communication protocol of the Agent era" represents the most critical and lucrative ecological throne in the entire history of AI. Its arrival will definitively end the chaotic era of model balkanization.

Yet there is a deeper dimension to consider: the commercial essence of this super-gateway for the Agent era can never be a lightweight "yellow pages directory"; it must be a heavy-duty "trust and asset settlement layer."

Why? Because web pages in the Web 1.0 era were "read-only and weakly interactive." Back then, if you clicked the wrong link in Yahoo's directory, the worst outcome was closing the tab and wasting two minutes of your life. The cost of error was virtually zero.

Agents are a completely different beast: "How do you know which agent actually works? Would you dare hand a critical task over to an agent? Would you trust an agent with your wallet? Would you trust an agent with your personal privacy? Where does that trust come from? And how do I know the fees it charges me are legitimate?"

Follow these questions to their logical conclusion, and the reality hits hard: the moment agents can invoke tools to book hotels, execute contracts, and manipulate financial accounts, prompt injection, unauthorized API calls, and hallucinated executions cease to be technical talking points and become direct losses on the balance sheet. When that happens, there is only one question that truly matters: who picks up the tab?

The cost of an agent's mistakes is catastrophic property, legal, and reputational disaster.

Therefore, the core competitive advantage of the future "browser" that commands the landscape will not be boasting that "we connect to 100,000 agents." It will be its ability to provide sandboxed security isolation, fine-grained permission controls, cross-agent reputation verification, and financial-grade underwriting with upfront breach indemnification. Its role will be a trinity: the graphical shell of an OS, the Visa/Mastercard of the modern financial system, and the Swiss Re of liability insurance. Whoever solves the trust dilemma so humans can comfortably turn their backs to an agent will hold the key to the next multi-trillion-dollar commercial empire.

5. Big Tech’s “Genetic Self-Destruction”: Why Old Species Never Cross Technological Cycles

As such a future-defining super-niche emerges, a pressing question arises: can today's tech giants (Microsoft, Google, Meta, Amazon, Alibaba, Tencent)—swimming in capital, hoarding the world's top compute and talent—leverage their immense resources to incubate this new species directly?

For Big Tech to cross this cycle through internal spin-outs is, in terms of commercial genetics, a direct violation of the laws of nature.

Looking back over thirty years of business history, the undisputed rulers of Stage 1 almost universally faced catastrophic destruction once Stage 3 arrived:

• AOL (America Online): The uncontested ruler of the dial-up era, commanding a market capitalization of $222 billion in 1999. After the rise of broadband and the open web, it was sold off for scrap to Verizon in 2015 for $4.4 billion—a 98% destruction of wealth;
• Yahoo: Defined the portal and directory era of Stage 1 of the internet, with a market cap topping $125 billion in early 2000. It stumbled at every turn as algorithmic search and social networks took over, until its core assets were similarly absorbed by Verizon in 2017 for $4.83 billion;
• BlackBerry (RIM): The uncrowned king of feature phones and physical-keyboard mobile productivity, reaching a market cap of $80 billion in 2008. In the face of the multi-touch and mobile entertainment tsunami unleashed by the iPhone, its hardware DNA rapidly pushed it to irrelevance.

Why do the giants closest to the future, sitting on the largest cash reserves, so often become the most tragic dinosaurs at the turning point of a cycle?

The root cause does not lie in executive IQ or work ethic. It lies in the fact that the organizational structures and product forms of Big Tech are themselves fossils of the previous PC and mobile eras, carrying an irreconcilable "genetic rejection mechanism" within:

1. The Matricidal Business Model Dilemma: A corporation's lifeblood is determined by its current core cash cow. Google's hundreds of billions in annual profits are built on users typing keywords and clicking on ten sponsored blue links; e-commerce giants' hundreds of billions in profits are built on the "traffic tax" merchants bid for shelf exposure. Yet the ultimate mission of a genuinely native Agent gateway is to kill the search box, kill the digital shelf, and eradicate all redundant informational friction in between, fulfilling user intent directly. Asking Big Tech to pour all its energy into developing a product that shatters 90% of its existing revenue stream at the root is like asking someone to slash their own carotid artery just to master an ultimate martial art;
2. The Bureaucratic Immune Backlash: Big Tech is riddled with rigid job ladders, quarterly OKRs/KPIs, convoluted reporting structures, and delicate balances of departmental self-interest. The moment any truly disruptive innovation shows the slightest sign of cannibalizing legacy profits or threatening layoffs in established units, veteran departments deploy a thousand unassailable bureaucratic excuses—compliance reviews, legal risks, ROI calculations—to quietly smother it in the crib. Kodak invented the digital camera first, only to shelve it to protect film profits; Nokia built a cutting-edge touch-tablet prototype, only to cling to Symbian's departmental fiefdom;
3. The Clash of Talent and Cultural DNA: Companies building foundational models in Stage 1 revere scientists who publish top-tier academic papers and systems engineers who tune multi-thousand-GPU clusters. In contrast, teams building native applications and brand-new gateways in Stages 2 and 3 require "product obsessives" and growth hackers who are exquisitely attuned to subtle human emotions and capable of tapping into mass-market psychological cravings. These two groups are fundamentally incompatible in their values, aesthetic tastes, and decision-making cadences. Putting a cohort of researchers pursuing academic purity in the same room with product managers obsessed with making users addicted generates enough organizational friction to paralyze the entire company.

Consequently, the giants can use their formidable balance sheets to retreat behind the scenes and provide compute utilities (which remains a colossal business). But the next generation of super-applications and ecological gateways that define the future are destined to emerge outside the establishment, growing wild across the frontier, forged by new teams unburdened by legacy baggage.

6. The Dynamo Paradox: Hanging Motors on Old Line Shafts Will Never Yield Productivity

If the preceding insights speak to macro strategy, another observation points squarely at the organization itself: organization and strategy are two sides of the same coin; before you talk strategy, you must talk organization.

"Before discussing strategy, you must discuss organization. Organization has become far more important for the CEO." The two are "the yin and yang, impossible to separate entirely"—both are expressions grown from the exact same new DNA.

This seemingly counterintuitive argument rests on a famous theoretical cornerstone in modern economic history: the renowned "Paul A. David Dynamo Paradox" (Paul A. David, 1990).

Nobel laureate Robert Solow famously quipped: "You can see the computer age everywhere but in the productivity statistics." Seeking to unravel this puzzle, Stanford economic historian Paul A. David spent years combing through nearly half a century of historical archives on American industrial electrification.

He uncovered a startling historical fact: Edison built his Pearl Street Station in 1882, but it was not until the 1920s—a full forty years later—that electricity produced a steep upward surge on America's industrial productivity curve. Throughout those four decades, US industrial productivity barely budged.

What was the reason? The answer was suspended right from the ceilings of those old factories: the heavy iron line shaft.

In the 19th-century steam age, a factory could only afford a single, cumbersome central steam engine. To continuously transmit power from that engine to hundreds of lathes and stamping presses across the plant, a crisscrossing transmission system of cast-iron shafts, gears, and spinning leather belts had to be mounted across the ceilings—the so-called "line shaft." Because every machine had to be physically coupled to the line shaft to operate, the factory's physical layout was subjected to brutal constraints: machines had to be arranged in rigid parallel rows hugging the shafts; factories had to be built as narrow multi-story buildings so power could be transferred vertically; daylight was obstructed, belts spun at breakneck speeds, noise was deafening, and hauling materials between floors was convoluted and wildly inefficient.

During the first thirty years of electrification, what the vast majority of clever, conservative factory owners did was simply dismantle the steam engine, bolt a large electric motor into its spot, and continue driving the same creaking overhead line shafts and belts! The power source was cleaner, but the factory's physical layout, machinery placement, and workers' assembly workflows remained unchanged down to the millimeter. This sort of patch-up "improvement" yielded negligible efficiency gains.

The real productivity miracle occurred the moment an entirely new species arrived, such as Ford's Highland Park Plant: Henry Ford and his team tore down every last line shaft and drive belt from the ceilings! Exploiting the distributed nature of electricity, they fitted every single lathe and stamping press with its own dedicated fractional motor. Liberated from the location of central power, factories were flattened into sprawling, single-story complexes; equipment was arranged unidirectionally, strictly following the sequential manufacturing process of automotive parts. Thus was born the world's first modern moving assembly line, and the production time for a Model T plummeted from 12 hours to just 93 minutes!

The technology was already there; the return was waiting on reorganization.
The time constant of organizational inertia is far longer than that of technological diffusion.

Hold this mirror of industrial history up to the present day, and you will find that the absurd behavior of most enterprises precisely mirrors the tragedy of a century ago: today, 90% of companies plugging in LLMs and rolling out Copilots are doing nothing more than hanging a new motor on an old line shaft!

Corporate organizational charts still follow the hierarchical reporting trees of the industrial age; departmental silos remain impenetrable; performance review cycles and bureaucratic sign-offs stay locked in stone. All they have done is issue a ChatGPT account to every workstation, or embed an AI sidebar in their clunky legacy internal software. An employee might churn out a report draft in one second with AI, but their manager still spends half an hour reviewing it. Workflows have not shortened by a single step, and boundaries of accountability have not been reimagined. This sort of superficial "digital embellishment" will never produce a fundamental leap in productivity.

In the industrial age, the "job title" is that ubiquitous line shaft!

The industrial age locked individuals into rigid job titles like "customer service rep," "finance supervisor," and "operations manager" because transmitting information was extraordinarily expensive. Division by job title was necessary to define who sees what data, who reports to whom, and who oversees which section of the assembly line. A job title is a mechanized, artificial barrier erected around scarce human cognitive capacity.

Yet in a future where intelligence is ubiquitous, hierarchical structures organized around job titles have become a noose choking efficiency. In a truly disruptive, AI-native organization, the atomic unit is no longer the "job title," but the "task." Future organizational architectures will be highly fluid: when a new business objective emerges, the system dynamically orchestrates two or three core human experts alongside dozens of silicon-based agents with specialized skills, self-assembling into an agile task swarm based on context. Once the task is completed, the swarm immediately disbands, feeding accrued data back into the global memory bank. Unless you smash the rusty line shaft of "job titles" dangling from the ceiling of modern enterprise, a true AI productivity explosion will remain pure fantasy.

7. Black Hole Effect vs. Price Wars: A Capability Moat Is Not a Profit Moat

The conversation featured two judgments regarding the competitive landscape of foundational LLMs. Both are deeply insightful in their respective contexts, but when placed side-by-side on the same logical table, they reveal a massive theoretical tension:

Proposition 1 (Compounding Intelligence and the Black Hole Effect): Leading frontier model labs have begun using their most capable models to generate high-quality synthetic data, correct errors, and train the next generation of models, creating an "AI training AI" compounding flywheel. Professor Zeng argues that this widens the gulf between the leader and everyone else, generating an absolute gravitational pull akin to a cosmic black hole that irreversibly accelerates the inward drain of top talent and capital.

Proposition 2 (Homogenized Oligopolies of Public Infrastructure): Professor Zeng then points out that as foundational models become public infrastructure, their ultimate commercial model will inevitably be "an oligopoly under heavy government regulation." And the defining trait of oligopolistic competition is "extreme commoditization"—products can be swapped out by downstream customers at will, forcing players to subsist on wafer-thin margins driven by minor differentiators, with any expectation of windfall profits gone.

If the black hole effect holds true and the leader possesses an uncatchable self-evolutionary flywheel, the endgame should be a "winner-take-all" cosmic singularity, leaving no room for a stalemate among competing oligopolists. Conversely, if the ultimate outcome is a commoditized public utility where downstream customers swap vendors at will, it proves the black hole's gravity has a ceiling and the frontrunner's technical moat is nowhere near as insurmountable as imagined.

The master key to untangling this contradiction lies in separating two concepts routinely conflated in business: an "absolute technical capability moat" versus a "genuine commercial pricing power moat."

The black hole effect acts upon the "absolute ceiling of capability," while commoditized competition acts upon the "pricing power of commercial profits." Not only is there no guaranteed positive conversion between the two, they are often brutally decoupled.

A frontier lab may pull three strides ahead of its rivals in arcane mathematical proofs, theoretical physics reasoning, and ultra-long logic chains, but what determines whether downstream enterprise customers open their wallets is never whether the frontrunner scored a 99; it is how close the runner-up is to the customer's everyday "good-enough line"!

For more than 85% of real-world commercial scenarios—pre-sales support, reporting spreadsheets, junior-level coding, contract review, copywriting—an open-source or second-tier model scoring an 85 clears the threshold of daily commercial viability. Once the runner-up crosses that bar, downstream customers will never pay a 10x token premium over the long haul for those extra 14 points of brilliance, even if the top model is smart as Einstein. The outsized technical profits that the frontrunner burned astronomical compute to manufacture will instantly collapse down to the industry's "marginal cost of inference" in a brutal price war.

In this era, a lead in raw algorithmic and model capability may last only a few months. Betting your commercial moat on a "generational technology lead" is fragile in the extreme; true moats can only ever be built on deep domain integration that code cannot replicate, proprietary data flywheels, and punishing organizational switching costs.

8. Will the Corporation Really Go Extinct?

In this envisioned future of organizations, collaborative entities "will certainly not be called companies," evolving instead into nimble, boutique, all-partner New Labs where human experts collaborate intimately with digital workers.

We must draw a sober distinction between what is destined to die and what can never disappear.

Over a century of industrial civilization, large corporations supported bloated cadres of middle management fundamentally because the information bandwidth of the human brain is limited. Executive orders had to be relayed down layer by layer through directors and managers, while front-line reports had to be aggregated upward through the same tiers. Middle management was, in essence, a physical amplifier and filter for information. But when multimodal agents can surface enterprise-wide operational context, financial data, and customer feedback in real time before everyone's eyes, traditional middle-management roles dedicated to supervising, relaying messages, and compiling formatted reports will lose all economic justification.

Yet the "corporation," as the greatest institutional invention of modern commercial civilization, will never disappear as a legal entity. None of the three pillars sustaining the modern corporate institution can be replaced by current AI technology:

1. The Irreplaceability of the Legal Subject of Liability: When an autonomous agent defaults on tens of millions in commercial trade payables or causes a catastrophic failure in an automated medical setup, to whom does the court issue a subpoena? An algorithm cannot be sent to prison, and a task pipeline cannot serve as the debtor in an enforcement proceeding. The civil and commercial legal order of modern society must trace liability back to a recognized legal person endowed with civil capacity;
2. "Limited Liability" as the Ultimate Shield for Human Exploration: Over the past three hundred years, humanity dared to shoulder immense risks—charting oceanic trade routes, laying railways, and building semiconductors—primarily because of the institutional protection of "limited liability." It allows founders and shareholders to cap their downside at their invested capital; even if the venture fails completely, debt will not wipe out their families or burden generations to come. If we were truly to regress to classical partnerships where partners bear unlimited joint and several liability, no one in an AI era rife with sudden technological disruptions would dare shoulder the ruinous risks of capital-heavy innovation;
3. The Trust Anchor of Commercial Contracts: Multinational procurement teams and large enterprise clients trust you with their core lifelines because of your business license, paid-in capital, third-party compliance audits, and long-standing commercial reputation. No serious enterprise will sign a life-or-death contract with a stream of virtual code floating across the internet.

Reality provides the clearest proof: OpenAI and Anthropic, operating on the global frontier today, may run extremely flat and lean internal operations, but both have encased themselves within corporate entities incorporated in Delaware—OpenAI's for-profit arm has converted into a Delaware Public Benefit Corporation (PBC), and Anthropic is likewise a PBC. What is crumbling to dust is the "bureaucratic management regime" of the industrial era; what remains indestructible is the "legal liability bedrock" of the market economy.

9. Endgame Deductions for the Future

Synthesizing this historical depth, theoretical analysis, and real-world tension, and stepping away from short-term noise, I have deduced five high-conviction directional theses for commercial execution over the next three to five years:

1. The Next Trillion-Dollar Application Will Not Come from Efficiency Tools, but Will Explode in "Native Entertainment and Psychological Consumption"

From radio and television in the electrical revolution to TikTok and gaming in the mobile internet era, every technological wave reaches mass maturity only when it *kills the efficiency narrative*.

Current attempts to use AI for writing code, polishing slide decks, and refining weekly status reports are merely transitional relics akin to BlackBerry's "pocket office." Efficiency gains run up against a physical ceiling, but human desire, emotional resonance, and immersive experiences have no bounds. The next super-species born of the AI era will harness emergent model capabilities and multimodal interaction to flourish in virtual companionship, high-agency interactive storytelling, adaptive gaming, and entirely new forms of entertainment.

2. The Super-Niche of the Agent Era Is "Unified Protocols + Trust Guarantees," Not Shallow Directories

What Professor Zeng means by "waiting for a browser" is a unified runtime environment and interaction protocol capable of ending today's fragmented agent balkanization. But the core moat of this gateway will not lie in how many features it lists; it will lie in whether it can underwrite unauthorized agent actions and property risks. Players that become OS-level gateways in the future will inevitably operate as both graphical control hubs and credit-settlement institutions.

3. Big Tech Will Defend Compute Utilities, but Is Destined to Lose Dominance over Next-Gen Consumer Gateways

Giants like Microsoft, Amazon, Google, and Alibaba possess unmatched capital expenditure budgets and data center assets, making their transition into the "compute waterworks" of the AI era completely natural. Yet in the battle for the super-native gateway closest to the consumer, their legacy cash cows—search advertising, take rates on digital shelves, platform revenue cuts—will prove to be their most lethal curse. Disruptive gateways must burst out from the periphery, propelled by rogue upstarts free from legacy baggage.

4. The Real Battleground for Enterprises Is "Flattening Organizational Line Shafts," Not "Which Model You Bought"

Revisiting Paul A. David's dynamo paradox, every manager must wake up to this truth: paying for model API access is merely window dressing—hanging a motor on an old line shaft. What will truly separate enterprise winners from losers in the future is not who accesses the newest model release, but who dares to smash the bureaucratic silos built around "job titles" first, reorganizing operations into an agile, task-driven collaborative network. The dividends of organizational re-engineering will dwarf any pure technology stack selection.

5. The Radical Revaluation of Individual Worth: Rote Experience Drops to Zero; Problem-Framing Intuition Soars

A word to young people: "Relatively speaking, experience—especially experience that can be codified into knowledge—is no longer valuable, because it has all been absorbed by large models."

Every skill that can be codified into an SOP manual or reproduced through explicit rules is seeing its market value collapse. The last redoubt of human indispensability has been forced back onto two fundamental things: first, the razor-sharp intuition to originally define a great problem amidst ambiguity and chaos; second, the courage to make decisive calls under incomplete information and take full accountability for the consequences.

10. No Need to Jump the Gun: Watch the Direction of the Tide First

Professor Zeng confessed that two or three years ago, he gave a whole talk titled "Let's Embrace Yahoo." But early this year, even he felt conflicted: "How have we fallen back to the browser phase—something that feels even earlier than Yahoo?" His assessment was pushed significantly earlier: we are still waiting for a browser to emerge.

"Let's embrace Yahoo": Today's arena hasn't even been leveled yet, and the so-called endgame has barely begun. Back in 1993, among the students celebrating through the night in the computer labs of the University of Illinois over the Mosaic browser, not a single one could have predicted whether Google, Facebook, or ByteDance would rule the internet twenty years later—because back then, not even the seeds of those companies had been sown.

Since we stand at the very beginning of a long cycle, jumping the gun, agonizing with anxiety, and making perverse compromises for short-term gimmicks are all meaningless. Spend less time guessing who is half a step ahead today, and focus more on grounding yourself in how underlying relations of production, organizational forms, and business models are being irreversibly restructured. As Professor Zeng concluded: "We used to lean toward reducing uncertainty, but at this stage, we actually need to embrace it. Uncertainty is the greatest opportunity."

Reference: *Zhang Xiaojun | Business Interviews*, Episode 153: "Discussing the Industrial History Perspective with Zeng Ming"

Below, I start with buying crypto on OKX, then configuring your IP, opening a card, funding it, and paying directly on the official ChatGPT website. How to handle failed payments is covered at the end. The entire process uses your own account and your own card—no third-party top-ups, no shared subscriptions.

Five-Step Process

Price Quick Reference

Swipe left and right on mobile to view the full table.

Pro 20x has a tax-inclusive listed price of ₱9,990, with a final settlement price of around ₱8,919, which comes out to roughly $144–150.

If you want to keep things simple, route everything through the Philippines—the tradeoff is paying about a dozen dollars more for Pro 5x. If you want the absolute best price on every tier, open a separate window with an Egyptian IP specifically for 5x.

OKX: Prepare USDT

Mainland Chinese users can register; you can verify your identity with a passport or a Chinese National ID. C2C trading supports WeChat, Alipay, and bank card payments. The USDT you buy will be deposited into the card later.

Before You Register

• Both passports and National IDs work. Passports are more widely accepted and can be used on the other platforms below as well. Sometimes it gets routed to manual review—just wait for the results.
• Don’t use temporary SMS verification numbers. You’ll need to receive SMS messages for risk control verification and account recovery later, so use a real number you maintain long-term.
• You must complete identity verification (KYC); without it, you can’t buy crypto via C2C.
• Download only from the official website or official app stores; don’t install third-party “Lite” versions.

Registration and Buying Crypto

1. Register using the sign-up link below.
2. Complete identity verification: take a photo of your passport and complete facial recognition. Review times vary depending on the on-screen status.
3. Link your own payment method, then place an order in C2C to buy USDT.
4. Once you receive the coins, withdraw them to your payment card’s deposit address. Always start with a small test amount.

How Much to Prepare Initially

Swipe left and right on mobile to view the full table.

Add a small buffer on top of the tier you plan to subscribe to, and test with a small amount first. There's no need to transfer a large sum just to test a card.

RoxyBrowser: Browser and Network Environment

When paying, try to avoid nodes shared by many people. Prepare a clean Philippine IP, then create a separate profile window in RoxyBrowser to isolate cookies and your browser environment.

Registration and Setup

1. Register via the link below, download the client, and log in. The free tier provides 2 profile windows.
2. Create a new window, then configure the browser environment and proxy IP separately. Use a PH node for the Philippines region.
3. Open the IP check page to verify the actual outbound location—don’t just rely on the node’s label.
4. Use a stable environment for registration, payment, and renewals; avoid switching back and forth.

If you don’t have a Philippines node, there’s a workaround: RoxyBrowser lets you purchase residential Philippines IPs directly; alternatively, you can log in via a US node and switch the region to the Philippines on the upgrade page, which usually pulls up peso pricing as well.

Even more important than “how pure the IP is” is stability. Use the same window and the same IP region for registration, initial top-up, and renewals. Don’t jump between Hong Kong one minute and the US the next—hopping regions triggers risk controls far more easily than poor IP quality.

Plasma One: Registration, Funding, and Cashback

Plasma One is a stablecoin Visa card funded with USDT and denominated in USD. It has dedicated cashback tiers for AI subscriptions: for services like OpenAI, Claude, and Cursor, the Core card gives 5% back, the Platinum card gives 10%, and the $8 ChatGPT Go plan is even fully reimbursed.

Plasma is invitation-only; you need an invite code to sign up. You can pick any of the following codes:

If you use the registration link, the code will populate automatically; you can also download the app first and enter it manually. Codes have usage limits and are first come, first served.

Four Items to Prepare

Swipe left and right on mobile to view the full table.

Registration and Identity Verification

1. Download Plasma One from the App Store and verify the developer information.
2. Register with your email, enter the referral code, and provide a phone number you use long-term.
3. Take a photo of your passport and complete facial recognition to pass KYC.
4. Upload your proof of address, ensuring your name and address match the submitted statement.
5. Wait for review, which can take anywhere from a few minutes to several days.

If rejected, check the reason first instead of repeatedly submitting the same identity documents. Mainland KYC has tightened over the past two years, so occasional rejections are normal—wait at least 24 hours before trying again.

Funding and Card Activation

1. In the app, tap Add Funds to view currently supported cryptocurrencies, networks, and deposit addresses.
2. Select the ARB (Arbitrum) network, and choose the exact same network when withdrawing on OKX. The network names on both sides must match.
3. Test with a small amount first to confirm the network, address, and arrival before transferring the main funds.
4. Deposit at least $100, then activate the Core card.
5. Once the card is active, head to the ChatGPT checkout page to review and pay.

Alternative Payment Card: Bybit

If Plasma’s verification doesn’t go through, you can switch to Bybit. Register using a Taiwan IP and verify using a Chinese passport. It features a 0 card issuance fee, 0 annual fee, can be bound to WeChat, Alipay, and Apple Pay, and works for direct payment on the ChatGPT official site.

Bybit Application Steps

1. Open Click Here to Register on Bybit with a Taiwan IP connected.
2. Verify identity using a Chinese passport. A National ID can pass as well, but a passport is more reliable.
3. Fill in a residential address in Taiwan (generate one using Google Maps or the tool at the bottom; proof of address will not be required later).
4. You can use a Chinese email, and +86 mobile numbers are supported. But when registering overseas accounts, it’s best to use an overseas number—it makes subsequent SMS risk verification smoother.
5. Finally, enter the invitation code ZK7ZZ5X to receive mutual rewards.
6. Activate the virtual card → deposit USDT → select this card when paying on ChatGPT.

Maintain a Taiwan IP throughout registration and KYC; don’t switch back to mainland China halfway through. Policies for welcome packages and card airdrops change frequently—refer to what’s currently shown in the app.

How to Choose Other Cards

Swipe left and right on mobile to view the full table.

Enter referral code ZK7ZZ5X on the Bybit form (mutual rewards apply); reward eligibility is subject to the current promotion.

Whether a card can make payments and what price appears on the page are two separate issues. The card solves the payment itself; regional pricing depends on your IP and checkout settings.

Subscription Cashback: By Region and Card Tier

Low-price regions are only the first layer of savings. Use the right card, and you can get a portion of your subscription fee back.

Swipe left and right on mobile to view the full table.

Three things determine whether you can claim these cashbacks:

Always pay directly on the official website (Click Here to Subscribe on ChatGPT Official Site)—don’t use App Store or Google Play in-app purchases, and avoid third-party top-ups. With in-app purchases, the merchant of record is Apple, not OpenAI, so you lose both the regional discount and the cashback.

Route through Apple Pay or Google Pay if necessary. Some cards get blocked when charged directly through the card network, but routing through Apple/Google Pay goes through much more smoothly.

Check current promotions in your card’s app before subscribing; cashback is calculated by “region × card tier,” and rules vary by region.

Subscribing to ChatGPT: How to Choose Across the Three Tiers

Plus · Philippines Region

1. Log in to ChatGPT and go to Upgrade to Plus.
2. Check the plan, currency, and price. The listed price should be ₱982–999 / month.
3. Enter your card details and your actual billing address, confirm the tax and total amount, then pay.

If the price doesn't match, check your outbound IP first; it could also be a pricing adjustment or a different account region.

Pro 20x · Philippines Region

1. Select Pro 20x on the Upgrade page; the tax-inclusive listed price is ₱9,990 / month.
2. You can subscribe to Plus or 5x first, then upgrade to 20x. Make sure you understand the prorated difference and effective date when upgrading.
3. The final charge will be around $144–150; verify the amount and complete payment.

Pro 5x · Egypt Region

1. Create a new window in RoxyBrowser, and set the proxy IP to Egypt (EG).
2. Select Pro 5x; the listed price is EGP 4,736 / month, approx. $88.
3. Use a supported card and your real billing information, verify the total, and pay.

How to Fill Out the Billing Address

Enter the country, state, city, and ZIP code separately. A mismatch between the city and ZIP code easily triggers an AVS (Address Verification System) failure.

If you're unfamiliar with formatting English addresses, you can use this to map out the fields.

Payment Failed: Stop First, Then Troubleshoot

OpenAI processes payments via Stripe. Their risk controls evaluate whether your IP country matches your billing country, IP reputation (datacenter vs. residential), browser fingerprint, card BIN, address verification, and payment attempt frequency.

If a payment fails, don't spam retries.
Pause for a bit first.

Swipe left and right on mobile to view the full table.

If an error occurs, you must wait before trying again. Spamming payment attempts will render that card completely unusable in the short term, and the cost of waiting is far lower than the hassle of swapping cards.

Preventative measures you can take upfront: keep a fixed environment in an anti-detect browser, using the same profile window and IP region for registration, payment, and renewals; ensure the city and ZIP code match on your real billing address; keep at least 1.2x the monthly fee in your card balance to prevent renewal declines; start small before upgrading; keep your identity details consistent and avoid submitting KYC repeatedly.

Before renewal, check your card’s status and ensure you have sufficient balance, then glance at the transaction notification after the charge goes through.

FAQ

Refer to the checkout page for exact prices, and check the app for current cashback promotions.

The reason is simple. Once I started running multiple AIs, I lost track of who was actually working.

When I only ran Claude Code, one window was enough. Later, when Codex and OpenCode were running alongside it, I constantly had to hunt back and forth to see who was working, who was waiting on me, and which files had been modified.

Orca eliminates these exact little headaches.

Running Multiple AIs at Once Is Finally Less Chaotic

Orca isolates each task in its own copy of the project. Even with multiple AIs editing at once, they won’t immediately collide over the same files.

The sidebar shows who’s running and who’s waiting on a reply. Just click in to pick up the conversation.

There’s a specific term for this in Git, but beginners don’t need to learn it upfront. All you need to know is that keeping tasks separate makes troubleshooting much easier when things break.

Click Directly on the Page, Skip Explaining It Twice

When building web pages, I used to take a screenshot, circle the spot, and head back to the terminal to explain which button in the top-right corner I meant.

In Orca, you can click page elements directly. It hands the code snippet and a screenshot of that specific element straight to the AI.

This feature is called Design Mode. You don’t have to memorize the name—in practice, it just means one less screenshot to take and fewer wasted words.

Terminal, Web Page, and Files—All in One Place

Once the AI finishes making changes, I can inspect the terminal, web page, files, and diffs within the exact same task.

The five or six windows I used to keep open are cut in half. Individually, each feature feels ordinary, but bringing them together is what actually saves effort.

Know at a Glance Whether Long-Running Tasks Have Stopped

Orca displays usage and reset times for Claude and Codex, and lets you switch Codex accounts directly.

The most annoying part of long-running tasks is discovering thirty minutes later that you hit your rate limit early on. This feature isn’t flashy, but it genuinely cuts down on wasted waiting time.

No More Hunting Around to See What Changed

Once the AI is done, you can see exactly which files it touched. If something looks off, leave an inline comment and send it back to fix it. GitHub check results can live on the same screen, too.

It’s Not Magic, Either

Orca won’t make Claude or Codex any smarter. The mistakes models make are still there—once changes are finished, you still have to inspect the files and run tests.

The more tasks you run at once, the heavier the hit on memory and CPU. Old workspaces need cleaning up, too. It currently has 57.5k GitHub stars and updates rapidly. High popularity doesn’t mean every release is rock-solid, and bumping into bugs occasionally is completely normal.

It runs on macOS, Windows, and Linux, though real-world experience may vary across platforms.

If you usually only run one AI at a time, Ghostty, cmux, or a standard terminal is already enough. Orca will feel too heavy for that kind of workflow.

How Beginners Should Try It

Don’t start by fiddling with configs, and don’t fire up ten AIs all at once.

Connect whichever tool you already have working—Claude Code or Codex. Open a project, and hand it just one small task.

Look for just three things: do the tasks avoid conflicting with each other, does clicking elements on the page save you an extra detour, and can you understand what the AI touched much faster after it finishes?

If none of those three things make your life easier, there’s no reason to switch tools just for 57.5k stars.

I hopped from Warp to tmux, then to cmux, and finally landed on Orca. The reason isn’t mysterious. It saves me from hunting down windows, guessing which AI is stuck, and worrying about conflicting changes tangling together.

You don’t need to constantly chase new tools. When your headaches change, that’s when you switch.

Wyckoff, Livermore, value investing, hot money sentiment, fundamental plays—they’ve watched every course and taken every note. Sitting quietly during post-market reviews, everything makes sense. But the moment live trading begins, their mind goes completely blank. You understand every principle, yet you lose money on every real trade. Almost every trader who’s been through it knows the feeling.

The Biggest Gap Between Retail and Institutions Was Never Strategy—It’s Process

Everyday retail trading is completely fragmented: blindly scanning news before the open, making spur-of-the-moment intraday decisions, and jotting down careless post-market notes with zero progress to show for it. Institutions stay consistent not because of superior talent, but because they follow a fixed process: filtering opportunities pre-market, trading with discipline intraday, and rigorously correcting mistakes post-market. Quants go even further—they run entirely on data.

Scattered techniques will never beat a systematic set of rules.

Ask AI Directly If You Should Buy, and It’ll Just Tell You What You Want to Hear

In a rush to take a position, plenty of people ask AI point-blank: Can I buy this stock? The AI’s response is always well-organized and looks professional. Follow it a few times, and you end up losing money without a clue why.

It has no trading framework, no data verification, and no risk management logic. If you want to go long, it digs up a pile of bullish reasons; if you want to buy the dip, it downplays every risk. Every line sounds reasonable, but all it’s doing is amplifying your emotional trading.

The Two Fatal Flaws of Retail Traders: No Data, No Process

Plenty of people obsess over trading strategies to the point of delirium: studying trends today, sentiment tomorrow, and value the day after. All these methods pile up in their heads, tangled and contradictory, completely falling apart the moment the market moves against them. Beyond that, there are two fatal flaws that are the hardest to fix.

First, no reliable data sources. The pre-market news, bullish headlines, and thematic hype you scroll through are all stale, second-hand noise that the market has already digested. Institutions finished positioning long before you arrived; jumping in on the news simply means you’re holding the bag.

Second, no fixed execution process. Strategies are static, but people are alive—and emotional. Without hard rules to constrain you, even the best technique goes straight out the window the second impulse takes over intraday.

That’s why I built an open-source toolkit called trading-research. It doesn’t recommend stocks, and it doesn’t trade automatically. What it does is organize solid strategies from across the market, connect them with public data that anyone can access, and set up a complete workflow covering pre-market, intraday, and post-market routines.

Sixteen Strategies: First, Stop Them Fighting Each Other

Online material is too fragmented: a little trend-following here, some volume-price analysis there, a bit of value over there—conflicting with each other and completely disjointed.

I spent two and a half months iterating through over 50 versions up to v2.57. I integrated 16 strategies—including value investing (Huayuan narrative valuation), Livermore trend-following, Wyckoff volume-price, hot money sentiment, options Gamma, US supply chains, A-share short-term trading, and Hong Kong market frameworks—stripping away platitudes and mysticism to keep only hard, executable rules. The most crucial part was resolving strategy conflicts: without decoupling them, packing in more systems only makes them unusable.

Many people lose money because they apply the wrong framework: using long-term logic for A-share ultra-short-term trading, or forcing short-term sentiment onto US equities. This tool matches scenarios and allocates weights accordingly. When trading A-share ultra-short term, it prioritizes hot money and volume-price logic while long-term frameworks automatically go silent; when trading the US AI supply chain, supply chain dynamics and risk control logic take precedence while short-term sentiment steps aside; when planning long-term positioning, it focuses on value and fundamental analysis, filtering out short-term noise.

The right method for the right market.

Pre-Market, Intraday, and Post-Market: Dedicated Handling for Each Stage

Pre-Market: Screen Opportunities with Data

Hooked up to authoritative public data sources across A-shares, Hong Kong stocks, and US markets. Original earnings reports, exchange filings, SEC disclosures, capital flows, options structures, sector sentiment, macroeconomic data—covering as much as possible. Pre-market is no longer about scrolling short videos or making blind guesses based on rumors; it’s about vetting risks and spotting opportunities with real data, narrowing the information gap with institutions right at the source.

Intraday: Execute by the Rules

Strategies are codified into rigid operational standards: entry conditions, position sizing, stop-loss levels, and exit plans when you’re wrong are all locked in ahead of time. Common pitfalls like making impulse intraday calls, chasing rallies, catching falling knives, and stubbornly holding losing positions are shut down by strict rules.

Post-Market: Review and Correct Mistakes

Comes with built-in decision tracking, hypothesis validation, and error post-mortems. Why did a trade win or lose? Where did the logic fail? How do you improve next time? Everything is documented and traceable, so you’re no longer scribbling a couple of careless review notes and walking into the exact same traps the next day.

It Answers a Few Concrete Questions

What are the specific opportunities and risks? Where are the operating boundaries right now? What conditions would prove my thesis wrong?

If there isn’t enough evidence, it won’t draw a conclusion; if data is missing, it flags it plainly. It doesn’t manufacture a false sense of security, and it doesn’t indulge impulsive trading.

How to Get Started

No complex commands, and no need to learn an entire technical stack first. Just hand it directly to your agent (Claude Code, Cursor, both work):

Help me install the skill at https://github.com/enzoyoyo/trading-research, mount it to my local skills directory according to the README, and run a self-check.

Most core features work right out of the box with no API keys or configuration required. For advanced multi-market feeds and official US market data, simply configure read-only credentials as needed. Everything is stored locally from start to finish—never commit them to your repository.

Boundaries

• No automated live trading; no broker order-execution APIs
• No stock picks, no price predictions, no guaranteed returns
• No win-rate figures, no get-rich-quick marketing
• Strictly focused on data aggregation, strategy execution, trading constraints, and post-market review support

It’s designed for anyone looking to break the habit of emotional trading and build their own complete trading framework. Strategies are just tools; only repeatable processes and strict discipline will hold up over time. If you have feedback after using it, feel free to open an issue in the repo.

Repository: https://github.com/enzoyoyo/trading-research

This article is for sharing trading insights and introducing investment research tooling only; it does not constitute any investment advice. No stock picks, no promised returns. The stock market carries risk—invest with caution. Make all trading decisions independently, and trade at your own risk.

Honestly, my expectations were rock bottom. When you buy this kind of thing, you generally know what you’re getting into—if a dirt-cheap product works stably for even half a day or a full day, I can live with that. After all, it’s exploited using a session key bug. I only bought it because Liandong Xiaopu claimed to offer buyer protection, and I figured that whatever else happened, the merchants here were at least vetted.

Instead, it died almost immediately after I logged in—practically gone in seconds.

After jumping through all kinds of hoops, I only got half of it back. If a few dozen yuan went down the drain in seconds, whatever—but when hundreds of yuan vanish instantly, and enough people are affected, the nature of the problem changes completely. That’s why I had to write this up so everyone can steer clear. Don’t harbor any wishful thinking—it only fuels the arrogance of these crooked sellers.

Rounds of Back-and-Forth

Spending two days to claw back half my money doesn’t mean I accept the outcome.

Along the way, the crooked seller even tried to trick me into canceling the dispute.

Luckily, the complaint portal was still accessible. Don’t withdraw anything until the money actually lands in your account.

I Wasn’t the Only One in the Group

In the same group, someone repeatedly spammed “Refund my money.” Another user bluntly stated that right after paying and logging in, it died—it was ridiculously fast. Plenty of people in the group were demanding refunds.

“First-Login Guarantee” and “You Lost the Gamble” Are the Same Playbook

Some people in the group took the seller’s side, pushing talking points like paid shills. One remarked, “It clearly stated it’s only guaranteed for first login; you chose to gamble.” Another chimed in, “You lost the gamble, now you’re throwing an impotent tantrum.” Someone else fired back rhetorically, “Paying 280 for something worth $300—did you not stop to think before buying?”

They shift the issue from “Was the product ever delivered?” to “Was the buyer being greedy?” It makes victims second-guess themselves and makes bystanders feel a few hundred yuan isn’t worth pursuing.

And just four words—“first-login guarantee”—absolve them of all after-sales responsibility?

Isn’t this the textbook scenario straight out of e-commerce consumer fraud cases? Merchants hide behind claims like “no after-sales disputes supported once the transaction is complete” to deny refunds, which is legally null and void to begin with.

I knew the price was low, and I was willing to shoulder risks that were clearly disclosed. But the crux of the dispute is still delivery: was the item actually delivered or not?

Does Dying in Seconds Count as Delivery?

I definitely didn’t buy through official channels, and my expectations were as low as half a day or a day. If it got revoked after I actually used it, I would have written it off as paying for a temporary service. Plus, the platform is backed by a registered company, which is why I was willing to take a chance. I never expected it would still turn out like this.

It dies almost immediately after login, and then they shut down support with “first-login guarantee” and lecture the customer on top of that? That’s what infuriates me.

Even Registered Companies Play This Game

If You Haven’t Recovered Your Money Yet, Follow This Order

First, preserve all original evidence. Keep the product page, order number, payment records, delivered credentials, expiration notices, chat logs, after-sales tickets, and refund records. Save the original full-resolution screenshots separately.

Next, file a dispute directly through the payment transaction. In your WeChat Pay bill, go to “Questions about this transaction,” select the reason closest to your situation, and clearly explain what was purchased, what was promised, the actual delivery outcome, and your refund request. Don’t exaggerate, and don’t present guesses as facts.

After that, you can report it on Heimao Tousu (Black Cat Complaint) or other consumer complaint platforms.

It’s Just a Few Hundred Yuan—Can You File a Police Report?

Yes, you can.

I’ve already paid the tuition for trying to cut corners. If you haven’t recovered your money either, don’t waste your breath arguing in the group to the bitter end. Save your evidence, go through payment channels, and file a police report if necessary. When everyone gives up on a few hundred yuan, what the seller walks away with is no longer just a few hundred yuan.

In the End, I Bought Grok at Full Price Myself

Both figures are genuine—they just reflect different points in time and different scopes. The $60 is my actual credited amount, while the $25 + $25 currently on the official site is the benchmark new applicants should refer to today.

The Current Rules First

As of August 11, 2026, the benefits listed on the official Nebius Builder Program website include

• $25 Nebius Token Factory credit
• $25 Tavily credit
• $1 Nebius Academy certification offer
• Office hours and Builder community access

The official site designates this promotion as an early preview and explicitly notes that benefits, eligibility, and availability may continue to change.

This means older tutorials can still help you understand the application and redemption process, but they cannot be taken as a guarantee of what you will actually receive. Once you open the page, always go by whatever rules the official website displays that day.

The Old Workflow I Followed

The six workflow screenshots below come from the original post by linux.do user zhangshiwu, while the balance screenshot is from my own account. Older campaign codes, promo codes, names, and account identifiers have been redacted.

Step 1: Fill Out the Builder Program Form

Head to the Builder Program, scroll down to the application form, and fill in your name, email, and the project you plan to try out.

After submitting, you’ll receive a confirmation email from the Builder Program. Recent emails explicitly state $25 in Token Factory and $25 in Tavily.

Step 2: Claim via the Email Link

The older workflow redirected to a Nebius promo codes page to enter your name and email once again. That campaign code is now obsolete; the screenshot is only meant to show what the page looks like.

After submitting, you will receive a one-time promo code by email. Don’t forward your code, and don’t include it in public screenshots.

Step 3: Sign Up for Token Factory

Open Nebius Token Factory to register an account. The older workflow typically involved two requirements:

1. Sign in using Gmail or GitHub
2. Link a bank card and complete a $0 verification

A $0 verification is typically just the issuing bank and platform confirming the card is valid; it doesn’t mean you will never be billed later. Even after claiming your credits, keep a close eye on your balance, pricing, and billing settings—don’t simply ignore it because it’s labeled free.

Step 4: Enter the Promo Code

Once inside Token Factory, click your avatar in the top right corner and select Top up. The tenant, organization, and balance details in the screenshot have been redacted.

Select With promo code, then enter the code from the email.

Is It Still Worth Applying?

If you just want to try an OpenAI-compatible API, the current $25 Token Factory credit is enough to run plenty of small tasks. Create an API key first, run a few text requests, check the models, speed, and balance deductions, and then decide whether to stick with it.

Don’t register in bulk, don’t buy or sell promo codes, and don’t try to bypass card linking or account restrictions. The platform’s current models, pricing, and promotional terms may all be adjusted.

What I can confirm is that my account indeed shows $60.00. As for what new applicants will receive, please refer to whatever the official Builder Program website and emails specify at that time.

FAQ

How much do you get if you apply now?

As of August 11, 2026, the perks listed on the official site include a $25 Nebius Token Factory credit, a $25 Tavily credit, a $1 Nebius Academy certification offer, plus office hours and Builder community access. The official site designates the promotion as an early preview, meaning perks, eligibility, and availability can change at any time. The $60 credited to my account was simply the record from this particular application and doesn’t guarantee you can still get the same amount now.

Do you need to link a card?

Yes. Registering for Token Factory requires linking a bank card and completing a $0 verification. This is usually just the card issuer and platform verifying that the card is valid, not a guarantee that you’ll never be billed later. After claiming your credits, you should still watch your balance, pricing, and billing settings—don’t ignore them completely just because it says free.

Can you still follow the older tutorial?

It works for understanding the general application and redemption workflow, but it can’t be taken as a guarantee of how much credit you’ll actually receive now. The campaign codes in the older workflow are expired, and both pages and perks keep changing. For every step, it’s best to check whatever rules the official website displays that day before deciding whether to follow along.

How are the Tavily credits split?

The official benefits list separates Token Factory and Tavily into two independent $25 credits rather than a single quota split between them. This time, I only went down the Token Factory track; I didn’t explore how the $25 for Tavily is redeemed and used, so if you need that, I recommend checking directly on the official site. For more practical guides on similar subscriptions and credits, check out How to Save on AI Subscriptions, or visit the AI Subscriptions: Saving Money and Pitfalls topic for more posts.

The promotion lead and legacy workflow screenshots come from the original post by linux.do user zhangshiwu. Current benefit terms are sourced from the official Nebius Builder Program website, verified on August 11, 2026. The cover image for this post was typeset by AI, with the amount based on an authentic screenshot of my actual balance.

The entire short was made in front of a computer. A few years ago, saying that would have sounded like an exaggeration. One person could edit or do VFX, but conjuring actors, streets, weather, and camera movements out of thin air—and tying them together on a timeline—usually took a whole crew. Now, someone sitting at a keyboard can make cinematic shots directly.

People at Keyboards Are Starting to Make Cinematic Shots

In online showcase demos for Seedance 2.5, a fifteen-second desert monster battle is split across six shots in a single generation. A fifteen-second skincare commercial is broken into eight cuts, with three characters taking turns interacting with the product before finishing on a close-up. A fifteen-second CGI product ad packs in ten cuts.

These are official demos released by the platform itself—not independent reviews, and certainly no guarantee that anyone clicking a button will get the same outcome. But compared to the early days of making a still image move, the model is clearly handling far more. Directing, cinematography, production design, acting, VFX, and editing used to be separate jobs; now they’re all crammed into the exact same generation task.

People are moving quickly on the commercial side too. Accounts producing AI reality shows—teachers and students playing hide-and-seek, sea survival challenges—are churning out an episode every five days on average and landing brand deals. By comparison, a public post-mortem of a real-life hide-and-seek shoot detailed about twenty camera operators, multiple editors, two to three weeks of shooting, and roughly three months for the full team to produce one episode.

Money Becomes One Generation at a Time

Efficiency went up, but the bills piled up just as fast. In the public tests back then, a single fifteen-second 4K video cost nearly 100 yuan—six times more expensive than 720p. Regenerate a shot ten times, and that’s 1,000 yuan. 10,000 yuan can buy a camera, or it can buy 100 clicks on the generate button. In the past, that choice was a no-brainer. But now that an ad shot often carries a 10,000-yuan budget anyway, people are starting to hesitate.

You save on actors, locations, and gear, only for the cost of rerunning prompts ten or twenty times to show up. If you want to burn fewer credits, you first have to make it crystal clear what you want.

My approach on this short shared similarities with those cases: keep the character consistent, lock down the location, maintain continuity of props, let shots unfold chronologically, and decide on the audio in advance.

First, Create a Face That Won’t Change

I started by using GPT Image 2 High to create a character master. The first version was too bland, so I scrapped it and started over, finally settling on a cropped gray tank top, light blue jeans, black canvas sneakers, a black cord necklace, and a windblown side ponytail.

When storyboarding, this image was the sole character reference. Four shots: tying hair, feeding the cat, hanging laundry, and the front porch. The lead keeps moving toward screen right, while a white cup, wooden clothespins, light-colored clothes, and the cat appear sequentially across different segments. Whatever action one segment ended on, the next picked up from that exact posture.

The Prompt, Like a Production Document

The prompt was no longer just a single visual description—it was more like a miniaturized production document. First lay out the scene, then specify which reference assets to use and what must be locked down, choreograph actions and camera setups second by second, write out the audio separately, and finally add what must not appear.

Some commercial examples label the raw video as @video_1, then lock down the desktop, blue pen, powered-off monitor, screen light reflections, and handheld tracking trajectory item by item. The model is only permitted to add the creature and its cast lighting and shadows—no arbitrary color changes, speed ramping, or reframing. Audio is separately restricted: live sound only, no music.

Examples like these can be referenced directly, and I handled my own piece the exact same way: leave room for the model to improvise where it can, and write out everything that must not change item by item.

Next came the video stage.

4K or 1080p?

What AI calls 4K isn’t the same as 4K shot on a camera. When a camera increases resolution, it captures the real scene in finer detail; when a generative model enhances quality, it synthesizes eye reflections, hair strands, and material textures. These details look more realistic, but they didn’t necessarily exist in the original frame.

Right now, Seedance 2.0 supports 4K, while 2.5 doesn’t yet.

The Face Was Blocked by the Platform

I originally planned to feed both the character master and the opening storyboard frames into Seedance 2.0 so all four clips would carry the same face. But whenever you generate video containing a human face, Seedance requires identity verification first, meaning face assets cannot simply be uploaded directly. Even though this face was freshly generated by a model and this person doesn’t exist in reality, the system still treated it as a real individual’s face. Without verification, image-to-video was completely blocked. The final character look and four storyboard frames were finished, but right when I needed them most, I couldn’t use them.

That restriction annoyed me at the time, but looking back, you can’t simply blame the platform. Once anyone can generate cinematic shots, faces and voices turn into callable assets. The internet is already flooded with unauthorized voiceovers and deepfake swaps. Face licensing is no longer just a copyright issue to sort out after release—it has moved straight into the production pipeline.

In the end, I gave up on reference images and switched all four segments entirely to text-to-video. The trade-off was clear: the face would drift, and the wardrobe might change too.

Without Images, Relying on Text to Identify Her

With no images to reference, I had to make sure the text could identify her as closely as possible. All four prompts repeated the exact same character description under an internal codename, KDV-01: age, face shape, eyes, nose, skin tone, build, hairstyle, tank top, jeans, shoes, necklace—not a single detail missed.

KDV-01, Korean female around 25 years old, oval face, almond eyes, fair warm skin tone. Long wavy black hair, messy side-swept low ponytail. Faded gray sleeveless crop tank top, loose high-waisted light-blue jeans, black canvas sneakers, black cord necklace.

Describing the character wasn’t enough. I broke down the ten seconds and told the model what happened in each slice.

First 2.8 seconds: she sits on a concrete curb fixing her side ponytail; the camera just started rolling, focus hunting back and forth between her face and hand.

Next 2.7 seconds: she drops her hands, pauses, then stands up. The camera reframes half a beat late, exposure briefly brightening.

Remaining time: she walks screen right into a narrow alley. The camera follows slightly slow, ending on her continuing to walk rightward.

The timing wasn’t meant for the model to hit cues down to the second like an actor; I used it to check whether I had crammed too many actions into a single prompt, while giving each segment an ending that could link cleanly into the next.

Audio was written separately. Birdsong, wind, a distant scooter, faint neighborhood chatter, the rustle of clothes and leaves, shoes on concrete. No music, no voiceover, no post-production sound effects.

Then it handed me four clips, each 10.1 seconds long.

Between the Storyboard and the Final Cut, the Weather Changed

The two images below come from the same cat-feeding shot. The first is the storyboard I made: sunlight filtering through the leaves, the lead’s face clearly defined, the cat walking fully into the frame.

The final cut didn’t follow the plan. The weather turned overcast, the image skewed teal-gray, and only half of the cat entered the frame, smeared with motion blur. The woman’s face changed, the black cord necklace turned into a dark choker, and a brown leather belt materialized on her jeans out of thin air.

I looked back and forth between the two images a few times and kept the final cut. The storyboard looked gorgeous—lighting, subject, and cat were all in the right place, and because of that, it felt like a staged production still. The final cut’s overcast sky, the hands lagging half a beat behind, and the cat that never fully stepped into frame felt much closer to what I originally had in mind. When friends film everyday life, they don’t wait for the cat to strike a pose before pressing record.

Across the four clips, plenty still didn’t match up: the face wasn’t quite the same, there were slight wardrobe variations, and the weather jumped around. Fortunately, the character consistently moved to the right, and the props and actions carried over. Set against this kind of blurry, handheld footage with no clear narrative, none of it felt out of place.

The four clips were generated separately and spliced together with help from AI at the end. No music, no LUTs, no noise reduction, and no added grain—just four frames of black at the very end.

Forty Seconds, Pieced Together

From that same batch, there were two even shorter clips, just eight seconds each. I generated them separately and they didn’t make the final cut, but I actually felt they were closer to the realism I was after. A wider aspect ratio, more noticeable handheld camera motion, like someone really pulled out a camera by the roadside and casually filmed a clip. Here they are to watch together.

The newly released Seedance 2.5 can now generate up to thirty seconds continuously, taking in up to fifty images, audio clips, and videos at once, with some demos even staging six camera cuts in a single run. Unfortunately, the showcase output is only 720p and 24fps. It looks fine on a phone, but on a large screen or in wide shots, there’s still a noticeable lack of detail, especially with frame stutter.

Relay Platforms Swap Models; Official Subscriptions Are More Reliable

There are plenty of platforms now, like Lovart, plus all kinds of relay sites. The catch behind that same button is that just because the UI lists a particular model doesn’t mean it’s actually generated by that model at that resolution. Some platforms swap models or throttle specs behind the scenes, leaving users with nothing more than a playable video. That’s why I insist on specifying the exact model, resolution, and a ban on silent downgrades right in my prompts, stick to official subscriptions whenever possible, and check the file metadata once it downloads. If you don’t do this, you’ll get burned without even knowing it.

Generating cinema from behind a keyboard has already arrived. Solo creators can take on monster films, commercials, and reality shows that once required massive teams—or, like me, craft a slice of cinematic footage.

Yet once you hit generate, creators still face plenty of choices: which version to burn cash on, what details count as authentic, which face you’re allowed to use, whether the model got swapped out, and whether to fix a botched shot or keep it.

See a good article? Save it first. Hear a sharp insight? Jot it down fast. Switch to a new tool? I can’t resist redesigning my categories, tags, and search setups all over again. Producing content, building workflows, organizing knowledge bases—none of this is foreign to me.

Sometimes looking at my knowledge base, it really does look like a subway map: categories constantly branching, tags growing ever more granular, interconnected in every direction.

But whether these tools actually work comes down to just one thing for me now: when it’s time to write an essay, build a product, or solve a problem, does all that stuff I diligently saved actually help?

Dan Koe recently wrote a long essay that opened with just two sentences:

“If something requires deliberate effort to remember, it’s not important. If it’s important, you’ll naturally remember it.”

That stung a bit.

It definitely nailed some of my bad habits, but I also think it’s wrong.

It Makes Some Sense, but It’s Also Wrong

I don’t entirely agree with Dan Koe.

You forget important things just the same. You forget them over time, when you’re burned out, or when too many things pile up. A name, a commitment, or even a book that genuinely shaped you—a few years later, all that might be left is a hazy impression. Forgetting something doesn’t mean it wasn’t important, nor does it mean you didn’t learn it in earnest.

I dug through some research papers and looked up whatever I could find online. To put it bluntly: rereading something over and over usually just makes it look more familiar. If you really want to remember something longer, you have to close the original, try explaining it yourself, and see where you got it wrong. Feeling like you “got it” is a completely different thing from actually being able to explain it and put it to work.

So that opening statement is simply flawed.

Yet why did it still hit a nerve? Because I do this all the time: mistaking “saved” for “understood,” and casually telling myself I learned a little more today.

In reality, nothing probably happened in between.

Hitting Save Brings Instant Peace of Mind

Halfway through an article, thinking you might need it later, you hit bookmark and instantly feel at ease. Before you’ve even finished reading, that nagging fear of “what if I can’t find this later” disappears. And once it’s safely tucked away, it’s that much easier to tell yourself: I’ll read it later.

Over time, your bookmarks, read-later apps, and note vaults become stuffed with things you plan to “read carefully later.” They sit like a stack of unopened deliveries—too precious to throw out, yet none urgent enough to actually unbox.

The next time I search for material, these bookmarks resurface in bulk, reminding me how much I still haven’t read and haven’t digested. The more I save, the heavier that sense of debt becomes.

I used to believe that saving first could never hurt. Only later did I realize that once the stash piles up, just deciding what to read first is exhausting enough.

Christian Tietze calls this the “Collector’s Fallacy.” Simply put: knowing where something is stored is a far cry from actually understanding it.

I’ve messed with plenty of systems. Categorization, search, AI retrieval—they can certainly surface old notes. But that only proves I’ve encountered the idea before; it doesn’t mean it has become my own.

Search can retrieve the original wording, but it can’t think through the real questions for me: Do I actually buy this? Does it line up with my experience? What’s the real cost of putting it into practice?

In the end, you still have to think through those things yourself.

Especially when creating content, there’s always far more material than you can ever get through, and these takes constantly contradict one another. In the end, which claim I stand behind and which I reject is a choice I have to make. Tools can lay the materials on the table, but they won’t make that decision for me.

You Only Know What You’re Missing When You Need to Use It

There’s one point in Dan Koe’s essay I strongly agree with: most people get the order of learning completely backwards. Telling people to “delete your note-taking system” was just him being deliberately provocative.

When most people want to learn something new, their first reflex is to track down a comprehensive course. They start from the basics, underlining conscientiously, terrified of missing a single concept. They watch a ton and take plenty of notes. Yet the moment they close the course, they still have no idea where to start.

Actually building something is a completely different story.

When I try to put a visual together, I quickly hit a wall with some feature I don’t know how to use. Halfway through drafting an essay, I realize an earlier section made no sense at all. Hand a product to users, and an entry point I thought was crystal clear turns out to be completely invisible to them.

Looking up information at that point is different: whatever you find gets applied immediately. When there’s a real problem right in front of you, what to learn and where to fill the gaps becomes obvious. You test it right away, and you instantly know what’s off.

As the American Psychological Association notes on deliberate practice: practice requires clear goals and immediate feedback on where you went wrong. Mere repetition has limited value.

This also explains why things you look up on the fly often stick better than what you once painstakingly memorized. It solved an immediate problem, and you applied it right then and there. Next time a similar situation pops up, your brain recalls it far more readily.

So now when I learn something, I try to start with a concrete problem to solve. Without a specific problem, no matter how much material you consume, it dissolves within a couple of days, leaving behind only an ever-growing pile of notes.

Of course, you shouldn’t swing to the other extreme and assume every reading session has to yield an immediate payoff. Novels, history, poetry—they don’t necessarily have any immediate utility. They might take years to shape someone’s judgment, or simply make a person a little less coarse. If every page required a tangible deliverable, reading would just become another job.

When picking up a skill, learning with a problem in mind is usually faster. Reading casually out of pure curiosity the rest of the time is completely fine, too.

After Saving Notes, You Still Have to Put Them to Work

Dan Koe laid into the “second brain” pretty aggressively. But keep reading, and you’ll find he isn’t actually against using tools to take notes. He used Roam Research himself, created digital knowledge products, and was pitching a new vault tool right in that essay.

Which is pretty amusing: tearing down the second brain in the opening, then promoting a brand-new database tool by the end.

Tools are obviously useful. They help you store material, surface related items, and slowly accumulate scattered thoughts over time. When Andy Matuschak wrote about “evergreen notes,” he pointed out that writing a note is just the start—you have to keep revising it, cross-reference it with past notes, and ideally work it into new essays and projects. Just archiving it doesn’t cut it.

Saving a note into a database just means it isn’t lost yet. It only starts becoming useful when I rewrite it in a draft or use it to overturn an earlier judgment.

I used to obsess over how to capture faster, file more accurately, and search reliably. Now I care much more about one thing: once I save this, will I actually ever use it again?

I ask myself what this material will actually do next: Go into the draft I’m currently writing? Tweak a product spec? Compare it against an old idea? If I can’t answer that at all, then no matter how sleek the system is, it’s just helping me stack clutter more neatly.

So I’ve set a blunt rule for myself. Every time I save something that feels important, I force myself to write a single line: How does this relate to something I’m actively trying to solve right now? If I can’t write that one sentence, I don’t save it.

If it genuinely feels useful, I put it to work in whatever I’m doing as soon as possible. Even if it’s just weaving it into a paragraph or revising an assumption, that’s far more useful than filing it away by the book.

As for AI, I’m happy to let it search, categorize, flag where evidence is missing, or sketch out a few structural options. But what I actually believe is still something I have to articulate myself while writing.

Getting stuck halfway through a sentence usually shows me I never really understood it in the first place. It’s annoying, but getting stuck tells you exactly where your thinking falls short.

I Don’t Want to Remember Everything I Read Anymore

It’s impossible anyway, and it turns reading into just another exam. How much you retain from a book depends not only on technique, but on how much it connects to your life right now.

From now on, when I come across a great article, I’ll ask first: What can I actually do with this today? As for how to never forget it—that’s just not that important anymore.

If nothing comes to mind right away, that’s fine. Just read it and move on; there’s no need to cram everything into your bookmarks.

Most books will be forgotten sooner or later. But a single sentence from one might keep me from making a foolish choice next time, help me cut a line of fluff when writing, or give me a little more patience when someone else is stressed. Maybe that’s what truly stays with you.

By then, I won’t even remember which page it came from.

And that’s fine.

The book has already done its job.

Every AI is smart on its own, but put them together and things go sideways fast. Who is editing which file? What step is the last task on? How do you cleanly feed what Gemini extracted into Claude Code? When one task gets stuck, should the other two keep running?

A lot of people say AI still isn’t capable enough. After using it for a while, I realized that the busiest person was actually me: the models write code while I shuttle information back and forth; the models wait for instructions while I chase down progress; each model remembers only its own conversation, leaving me to piece the whole project back together.

Multica aims to tackle precisely this: acting as the squad leader for a bunch of AI coders.

It doesn’t compete with Claude Code or Codex. Instead, it pulls already-capable AI tools into a unified task system, issuing them badges, scheduling their tasks, and tracking their progress. You create an Issue on the board and assign it to an AI; the corresponding Agent picks up the task, the local daemon on the bound computer invokes the underlying AI CLI, reports status during execution, and returns the results when finished.

Above is the official Multica board interface. Humans and AI share the same task board, allowing you to delegate work to an AI employee just like assigning tasks to a coworker.

Multica’s code repository is public, and it supports self-hosting. However, it uses a modified Apache 2.0 license with additional commercial and branding terms, so you can’t equate “public code” with “free for all uses forever.”

The tool lineup also changes quickly. Verified as of July 17, 2026, the official Providers Matrix lists 16 built-in tools, including Claude Code, Codex, Cursor, Hermes, OpenClaw, Grok, and others; Gemini does not appear in this matrix as an independent provider. My Gemini researcher is a personal workflow setup—it doesn’t mean everyone gets the same combination out of the box.

After running multica setup, the daemon detects recognizable AI CLIs in your computer’s PATH and registers them as runtimes. You still have to create the actual AI employees yourself and bind them to specific runtimes; installing the software doesn’t automatically assemble the tools on your machine into a team.

My Team Setup

I set up a six-member team in Multica. The screenshot below shows my division of labor: one dispatch hub plus five specialized AIs. I clearly defined what each one handles and what is strictly off-limits.

Dispatcher: Dispatch Router

Only distributes tasks and monitors status without touching the work itself. When a request comes in, it first determines who should take it: if information lookup is needed, it calls the researcher; if architecture design is needed, it calls the architect; only when it’s time to write code does it hand off to the engineer.

Researcher: Gemini Context Scanner

Dedicated to reading ultra-long documents and scanning multiple files. I hand off long, sprawling context to it—like reading an entire project directory in one go, or chewing through a 3,000-line config file—and have it extract what’s relevant to the current task.

Scout: Grok Web Scout

Responsible for real-time web searches, browsing websites, and checking the latest updates on X. For instance, if a framework just dropped an update yesterday that our project depends on, I have the scout dig up the original announcement, official docs, and developer discussions first, then bring back usable material.

Architect: Fable Lead

Responsible for solution design, tech stack selection, and risk assessment. I gave it one hard-and-fast rule: don’t touch the code. It can analyze the project structure, point out which modules need changes, and outline the implementation order, but it cannot touch or edit files along the way.

Engineer: Codex Engineer

The only role on the team allowed to write code. It turns the architect’s proposal into actual code, fills in tests, fixes bugs, and delivers reviewable results. Concentrating write permissions into a single role ensures you don’t have three or four AIs modifying the same batch of files at once, leaving everyone confused about who changed what.

Reviewer: Sol Reviewer

Independent review, dedicated to nitpicking code and making the final pass-or-reject call. It also only reviews without writing; when it spots an issue, it bounces the task back to Codex Engineer instead of trying to patch things itself while pointing out flaws.

Why break things down so granularly? Because AI has a common bad habit: ask it to architect, write code, add tests, and review its own output all at once, and it will cut corners along the way, cobbling together something that runs but is a nightmare to maintain. Splitting design, implementation, and review gives every stage a clear owner, making it much easier to pinpoint where things went wrong.

Task statuses in Multica move from queued, dispatched, and running to completed, failed, or cancelled. The AI can report progress during execution and raise blockers when it hits a wall. The dashboard makes it much easier to see where things are stuck, but that doesn’t mean you can blindly leave tasks running overnight; unclear task descriptions, underlying tool errors, or environment issues will cause the AI to fail just the same.

I also really like its Skills system, though one point from the original text needs correcting: completing a task successfully doesn’t magically turn the output into a Skill on its own. You have to package verified methods into a Skill or import an existing one, then attach it to the relevant Agent. Whenever a similar deployment, check, or operational task comes up later, other roles on the team can reuse it without having to be taught from scratch every time.

Is the Code Secure?

Above is my runtime environment panel. The background daemon runs on my MacBook, and the panel shows eight AI tools online and on standby.

Multica’s server is responsible for storing and coordinating projects, issues, task statuses, and other metadata; the actual invocation of AI CLIs happens via the local daemon on the bound machine. The server doesn’t execute code on behalf of Agents, nor does it dig directly into your local directories to do work.

However, “local execution” doesn’t mean “data never leaves your computer.” If the underlying call goes to a remote model, the prompts, file snippets, and context needed to complete the task may still be transmitted according to the policies of the respective AI tools and model providers. For proprietary company code, client data, secrets, and internal projects, you still need to audit each tool’s permissions and data policies individually.

The original screenshot also shows device names, workspaces, and daemon details. They aren’t passwords, but they are operational traces worth scrubbing before publishing. A screenshot proves which runtimes were online at the time; a single image alone cannot prove a cross-device task actually ran to completion.

Managing More Than Just One Machine

This is one feature of Multica that genuinely surprised me: it doesn’t just manage the machine right in front of you.

Above is my own Runtimes panel. There are two devices listed: one named enzo, which is my work computer; the other is enzodeMacBook-Pro-2.local. Both show as online in the screenshot, and you can see at a glance what runtimes and tools are available on each machine.

You can run one daemon on your office Mac and another on your home PC. When traveling, opening Multica lets you see the online status of both machines without having to SSH in first to run status commands.

Agents on your work machine can handle one class of tasks, a beefier desktop at home can take on builds, and tools on your laptop can handle document scanning. When you need more computing power, you can simply add another idle machine into the runtime pool.

There’s an important caveat here: tasks don’t automatically drift to another machine just because it’s faster. Each Agent is bound to a runtime you select, and tasks must be assigned according to that relationship—there is no automatic load balancing.

So “managing all compute from a single dashboard” is more accurately understood as: you can monitor runtime status, activity, and usage across multiple machines in one view, but the actual task execution still falls on each bound computer and its underlying tools.

How Does It Compare to Hermes and OpenClaw?

They occupy completely different niches; one doesn’t simply replace the other.

Multica: Managing a Team

Multica is an AI team management platform. It handles task distribution across multiple AIs and humans: who is doing what, how far along things are, which task is stuck, and which Skills belong to which Agents. It fits people who are already running multiple AI tools simultaneously and need to break down tasks and track status.

Hermes: Long-Term Execution with Context

Hermes is an AI agent equipped with persistent memory, Skills, subagents, channels, and automation capabilities. I prefer to think of it as a long-term personal assistant—the longer you work together, the more memories and reusable methods it accumulates.

Hermes’s MoA isn’t “multi-model voting” as the original text claimed, either. It has multiple reference models analyze a problem in parallel before handing the results to an aggregator model, which generates the final response and calls tools when needed. Running multiple models generally increases invocation volume, but costs depend on the models, turns, and configuration—you can’t just slap a fixed “80x” multiplier on it.

OpenClaw: Hooking AI into Chat Interfaces

OpenClaw is a self-hosted personal AI assistant and messaging gateway. A typical use case is connecting AI to chat channels, letting you assign tasks, manage schedules, triage inboxes, or run background automations via chat messages. It can participate in coding tasks too, but its strength lies in acting as a messaging gateway and personal automation hub.

The original text mentioned WeChat, and this needs clarification: OpenClaw’s WeChat integration relies on an external plugin—it doesn’t come out of the box by default in every installation environment.

The Bottom Line on How to Choose

If you need to “split five tasks among three AIs and watch who’s stuck and who’s finished,” that’s Multica; if you need to “fire off a message in a chat app and have an AI get things done,” that’s OpenClaw; if you need an Agent with persistent memory, Skills, and subagents, Hermes is the better fit.

They can also work together. Multica’s current list of built-in providers includes Hermes and OpenClaw. Once the respective CLIs are installed and recognized by the runtime, they can plug into Multica as underlying tools. Multica handles tasks and status, while Hermes or OpenClaw tackles what they do best.

Does It Burn Through Cash?

One look at my usage dashboard makes it clear why some people call this way of working “paying to work.” Sol Reviewer alone logged 167.1M tokens, way ahead of the pack.

Tokens are the basic unit of measurement models use when processing text. The common rule of thumb that “four English characters equal roughly one token” is only a rough guideline for English; Chinese, code, punctuation, and different models can all vary wildly. More importantly, having only the raw token total without the model breakdown, prompt-to-completion ratios, cache pricing, and billing periods means you can’t simply convert 167.1M directly into an exact bill in RMB.

This setup can certainly get expensive, but it depends on what you use it for. If you only rely on a single model to write the occasional script or squash bugs, sticking with your existing tools is far less hassle. Bolting on an entire team management system just to get one AI working only adds configuration and maintenance overhead.

Multica’s value shines in multi-role collaboration. When a project reaches a certain scale—requiring task decomposition, role specialization, progress tracking, and the repeated reuse of verified methods—this management layer starts making sense. What it saves isn’t just a handful of model calls, but all the time I’d otherwise spend alt-tabbing between windows, re-explaining context over and over, and manually chasing down tasks.

Another perspective is treating it as tuition. Practicing how to orchestrate AI collaboration early on feels a lot like learning how to manage an engineering team ten years ago. But paying tuition has to deliver positive feedback: the output has to be usable, exposed problems need to be fixable, and the next run has to be more reliable than the last. If you’re just spinning up multiple models only to get redundant answers and end up redoing everything yourself, you’re just setting money on fire.

My cost-saving strategy is tiered routing: long documents go to models that are better suited for reading and reasonably priced, real-time searches go to Grok, critical design proposals go to the architect, final code is written by Codex Engineer, and Sol Reviewer only steps in when an independent sanity check is truly needed.

Hermes MoA has multiple reference models analyze the same problem before an aggregator synthesizes the results; Multica’s division of labor is more about having distinct roles handle search, planning, coding, and review separately. The two approaches solve different problems and have entirely different cost structures.

Beginner’s Guide: Launching Your AI Crew in Five Steps

Here is a step-by-step guide from scratch. Multica is still iterating, and the installation methods and Providers Matrix may change, so always refer to the current official documentation during actual setup.

Step 1: Install Multica

On Mac or Linux with Homebrew, enter the following in your terminal:

brew install multica-ai/tap/multica

Without Homebrew, use the official install script:

curl -fsSL https://raw.githubusercontent.com/multica-ai/multica/main/scripts/install.sh | bash

On Windows, use the PowerShell install script. Once installed, run multica version to confirm the command works properly.

Step 2: Initialize

Enter the following in your terminal:

multica setup

It connects to Multica Cloud, opens your browser to complete login, saves your credentials, and starts the daemon. Once running, the daemon detects recognizable AI CLIs on your machine and registers the corresponding runtimes.

Step 3: Confirm Your Computer Is Online

Open the Multica web dashboard and navigate to Settings → Runtimes. Your computer should appear in the list with an online status. What you see here is the connection status of the runtime, which is also the execution environment you’ll select when creating AI employees later.

If it’s offline, go back to your terminal and run:

multica daemon start

Then check the status with multica daemon status.

Step 4: Create Your First AI Employee

Go to Settings → Agents, click New Agent, and select the provider, model, and role description in order. For example, if you already have Claude Code installed, name it something like “Engineer Wang,” then bind it to the runtime that just came online.

Repeat this step to create multiple Agents. Bind each Agent to a specific runtime and underlying tool, then configure its responsibilities, permissions, and Skills as needed.

Step 5: Assigning Work

Head back to the board, create a new Issue, clearly describe what needs to be done, and select the corresponding AI employee under assignee.

The task enters the queue and gets dispatched to the Agent. Once execution starts, status and progress appear in the activity feed; completions, failures, cancellations, or blockers are all logged. Human and AI actions weave together in the same activity stream, looking very much like a team chat group.

If you want to go fully self-hosted, you need to think of the server side and the local execution layer separately. The official Self-host Quickstart uses Docker or Kubernetes to deploy the server; once the server is running, execute:

multica setup self-host

Self-hosting replaces only the server layer; the actual Agents are still executed by your local daemon and AI tools. Don’t mix this up with the regular Cloud initialization command.

Final Thoughts

I’d recommend beginners test the waters with five to ten simple tasks first, just to see if the work these AIs produce meets a “usable and shippable” standard. Don’t throw your most critical project at them right out of the gate.

If you only use AI occasionally, opening Claude Code, Codex, or any single tool directly is much simpler. Multica introduces the overhead of managing Agents, runtimes, task states, and Skills—there’s no point in building a team just for the sake of having a team.

But if you’ve already been battered by juggling multiple terminal windows, manually chasing down progress day in and day out, and repeatedly retyping the exact same context into different chat windows, Multica fills that exact void. It turns “one person juggling five terminals” into “one manager watching a single board.”

As for burning through tokens, my stance remains the same as at the start: it’s entirely up to you. If you feel a project is worth practicing on early, go for it, but you have to get positive returns. If the output is usable and iterable, and the pitfalls you hit can be turned into Skills you can reuse next time, then those tokens were well spent.

Burning cash with zero output and nothing reusable left behind—that really is just “paying to work.”

Fact Check & References

• Multica Cloud Quickstart
• Multica Providers Matrix
• Multica Self-host Quickstart
• Hermes Mixture of Agents
• OpenClaw WeChat Channel Documentation

One prompt spelled out everything: focal length, color temperature, dust in the air, character mood, camera speed. I plugged it in as-is, and the footage looked okay. The person walked, curtains moved, light came in from the window.

But when I put it next to a single line—“The girl hangs up the phone, pushes open the door, and runs out into the rain”—I couldn’t see where that massive paragraph had won.

What’s more troublesome is that every once in a while, it really does produce a gorgeous shot. Once the pretty picture is done, you still have no idea which phrase did the trick. Next time you switch characters or swap rooms, you have to drag the entire block over again and try your luck.

It’s a bit like buying a lottery ticket. Even if you win, you can’t explain how the numbers came together.

I’ve Been Writing Shorter and Shorter Prompts

Seedance 2.0 can already take text, images, audio, and video simultaneously. If an image can do the job, let the image do it. Model capabilities and billing methods will keep evolving; refer to the official site when you use it for exact details on supported inputs and pricing.

When I have a consistent character reference image, I stop writing about how old she is, her hairstyle, or which jacket she’s wearing. When the platform supports reusing a “subject,” I just reuse it. The same goes for scenes. A single image already establishes the space, textures, lighting, and character proportions; writing it all out again only blurs the focus.

Save text for changes not present in the image.

Here is how I write them now:

Medium close-up. She hangs up the phone without putting it down. She stares out the window, pausing for two seconds, as the sound of rolling cart wheels comes from the end of the hallway.

That’s it. Shot scale, action, pauses, sound—all of them can be checked. Did she pause for two seconds? Did her gaze shift over? Did the rolling wheels keep up? Once it generates, you can tell at a glance.

“Melancholic, poetic, cinematic, warm sunlight, dust, memory” aren’t forbidden words, of course. It’s just that they can’t take action for the character. If cutting those words suddenly leaves the person in the shot with nothing to do, then that shot wasn’t clearly thought through to begin with.

I handle sound separately, too. Since there’s still editing ahead, I only let the model handle dialogue, ambient noise, and foley. The doorbell, rain, shoes splashing in water—these grow out of the visuals. Don’t rush to generate BGM, or your pacing can easily get locked into an ill-fitting track.

Take the girl rushing out of the convenience store into the rain. The character image tells the model who she is; the scene image tells it what the door and the street look like. The text only says: The phone rings, she checks the screen, her expression changes, and she pushes the door open and runs out.

I don’t push for a final cut on round one, either. I check four things first: Did the face change? Is the sequence of actions right? Did the camera jump erratically? Did the key sounds keep up? Whichever fails is the one I fix.

A Few Lessons Burned Through Credits

Make a storyboard, but make it for yourself first.

It helps me decide the shot sequence, character blocking, and where to cut. But toss an entire storyboard crammed with arrows, curves, tables, and tiny text at a model, and you’ll often end up talking past each other. The model sees a complex image; it won’t necessarily follow the reading order in your head.

Break key shots into individual reference images, and write out the sequence in short sentences. It feels clunkier, but it’s much more solid.

When I write timecodes, it’s mainly to stop myself from cramming too many actions into a single shot.

“0 to 3 seconds close-up, 3 to 6 seconds zoom out” can help me lay out the order, but you can’t count on the model to cut on the dot at the 3-second mark every time. Stuffing five or six shots into a dozen seconds usually ends with none of them turning out clear.

I don’t obsess over 720p versus 1080p from the start, either.

When testing characters, actions, and camera moves, low-res is plenty as long as you can see whether the face changed or the action drifted off course. There’s no need to make the model churn out 1080p every round. On platforms that deduct tokens, points, or credits by resolution, generating low-res first is far cheaper and saves waiting time.

Once the shot is locked down, feed the selected clip to tools like Topaz Video AI for upscaling and sharpening. If the action is wrong, Topaz can’t save it anyway, so I’d rather spend my credits upfront testing a few more rounds.

I also have a very crude check now: if I can’t even get through reading my own prompt, I probably haven’t seriously thought through what should happen in that shot.

That style of writing is just piling on words in hopes that a few of them might accidentally click. Once finished, even you can’t tell which line is worth keeping.

Why There Are Flowers Behind the Door

What these practices cut out is rework: you don’t have to keep re-describing the character, you don’t have to hunt for conflicts inside bloated prompts, and you don’t have to roll the dice on high-res renders.

Once shots start rendering quickly, I soon hit a wall with a different set of questions:

Why does she run out after taking that call? Why did the cart in the hallway make her pause? Why should the audience keep following her?

AI can list twenty answers. I have to delete nineteen of them.

I’d rather take a sheet of paper and draw three boxes.

The first: she stands outside the door.

The second: she pushes the door open and goes in.

The third: no one is in the room, but the table is covered with flowers.

Seedance can give me ten ways to push open a door, and ten expressions after seeing the flowers.

But I need to know why it’s this door, why there are flowers inside, and why she didn’t cry when she saw them.

Next time I write a Seedance prompt, I’ll probably write it even shorter.

A character image, a scene image, a single line of action. What little time is left, I want to save for the flowers behind the door.

FAQ

Why Do Prompts Get Worse as They Get Longer?

Long prompts can occasionally yield gorgeous shots, but you can never tell which phrase actually made it happen. Switch characters or swap rooms, and you have to drag the entire spell over again to try your luck—a bit like buying a lottery ticket. Even if you win, you can’t explain how the numbers came together. Short, inspectable, reusable prompts are what actually work.

Should Characters and Scenes Still Be Written into the Prompt?

Once you have reliable reference images, you don’t need to write them anymore. The character image already establishes how old she is, her hairstyle, and which jacket she’s wearing; the scene image already makes the space, textures, lighting, and character proportions clear. Writing it all again only blurs the focus. Text should be reserved solely for changes not present in the image: actions, pauses, sound.

Should You Generate Low-Res Previews First?

Yes. When testing characters, actions, and camera moves, low-res is plenty as long as you can tell whether the face changed or the action drifted off course; there’s no need to output 1080p every round. Most platforms deduct tokens, points, or credits by resolution, so generating low-res first is cheaper and cuts down the wait. Once the shot is locked down, hand it off to Topaz Video AI for upscaling—though if the action is wrong, upscaling won’t save it anyway.

To see how the same mindset applies to shots with a cinematic film look, check out AI Can Already Shoot with a “Cinematic Film” Look; to systematically learn how to write shot-by-shot prompts and build complete storyboards, see the preview page for the Higgsfield AI Video Course. For more hands-on AI video creation reviews, see our AI Video Creation topic.

Source note: Descriptions of multimodal input in this article reference ByteDance Seed team's official introduction to Seedance 2.0; timecodes, low-res previews, and Topaz post-upscaling reflect personal workflow experience. Platform billing methods vary, and post-upscaling cannot fix an action that was generated wrong in the first place.

Another “god-tier card”?

4% cash back, AI subscription rebates, a ChatGPT Go subsidy, spending directly via Visa from your stablecoin balance, and Apple Pay support.

It definitely sounds easy to get hyped about.

But after digging through the rules, the card didn’t feel that magical to me.

It works, and it has a sweet spot.

But it’s not the kind of card you can swipe mindlessly where the more you spend, the more you make.

For Core users, spending around $1,000 a month is about right.

Because the first $1,000 gets you 3%, and the cash back rate drops after that.

For Platinum users, the truly comfortable range is the first $10,000.

The first $1,000 gets 4%, and it stays at 3% up to $10,000—past that, it’s not nearly as sexy.

Whether mainland Chinese users can register comes down to whether your proof of address matches what you entered. The passport itself is rarely the blocker.

This point is crucial.

A lot of tutorials frame it like some esoteric ritual, but when people actually get stuck, it’s almost always because the name, address, and proof of address don’t line up.

Speaking of cash back, skip the promo posters and look straight at the tiers.

Here’s how the base cash back looks for Core:

First $1,000: 3%.

Up to $2,000: 2%.

Up to $3,000: 1%.

Over $3,000: 0.25%.

So if you’re just after the cash back, Core’s sweet spot is keeping it under $1,000 a month.

For example, if you already have regular USD expenses—AI subscriptions, foreign software, routine small purchases—running them through this card to pocket 3% works well enough.

But forcing thousands of dollars of spend just to chase cash back? Not worth it in my view.

Platinum looks a bit more impressive.

First $1,000: 4%.

Up to $10,000: 3%.

Up to $20,000: 2%.

Over $20,000: 1%.

The catch is that Platinum doesn’t come free.

The current rules require locking up 100,000 XPL for 12 months, which will later increase to 150,000 XPL.

At that point, it’s not just getting a regular card.

You’re taking on the price volatility of a stash of XPL in exchange for higher cash back and perks.

So my take is: unless you have steady, large-volume USD spending, or you were already planning to lock up XPL, don’t let Platinum’s 4% go to your head.

Just start with Core to test the waters.

The card also has a pretty practical little perk: Core offers up to an $8/month ChatGPT Go rebate.

I actually find this more legit than a lot of the splashy marketing claims.

Because AI subscriptions are something you’re paying for anyway, so every bit saved counts.

Back to registration.

Looking through feedback on X, people with mainland Chinese passports definitely have gotten approved.

Just don’t take that to mean “any mainland user can breeze through without effort.”

KYC was never a tutorial-following game to begin with.

The details you enter, the info on your ID, and what’s on your proof of address all need to corroborate one another.

That’s the whole point.

If you want to give it a try, this is roughly the sequence:

1. Download Plasma One and register an account.

During sign-up, it will ask you to select a region. The most common route in the community is choosing Hong Kong.

But by no means should you interpret this as grabbing any random Hong Kong address to fill in.

If they ask for proof of address later on, you have to be able to provide documentation that matches that exact address.

Without authentic, verifiable documents, don’t force it.

2. Complete ID verification.

Select China as the issuing country.

Use a passport if possible.

Some say national ID cards work, but I wouldn’t call that a reliable path. A passport at least aligns better with English naming.

3. Fill in your address.

Don’t get sloppy here.

If you selected Hong Kong earlier, provide a corresponding Hong Kong address.

More crucially, your subsequent proof of address must show the exact same address.

A missing floor, a mismatched unit, or inconsistent name formatting can all cause problems.

4. Upload proof of address.

This is where people trip up most often.

Ideally, your documents should be from the last 3 months: bank statements, utility or telecom bills, government correspondence, or a lease agreement.

The file must be complete and clear, with your name and address clearly legible.

Don’t gamble with screenshots, half-cropped images, or blurry photos.

5. After submitting, don’t rush to close the verification page.

A detail many in the community have noted is that after uploading your documents, you’ll enter a screen showing the review is in progress.

Don’t instinctively tap the close button in the top-right corner.

Wait a moment and let it redirect back to the home screen on its own.

If everything went smoothly, you should usually see that the card is ready to activate.

This is community experience, not a magic button.

What really determines the outcome is whether your information actually lines up.

If it fails, don’t rush to open a new account right away.

Check these things first:

Does the name match the passport?

Does the address match the proof of address?

Is the proof of address dated within the last 3 months?

Is the document complete and legible?

Have the same ID and the same face already been submitted repeatedly?

I know there are plenty of wilder suggestions circulating online.

Like buying fake proof of address, doctoring documents, or burning and remaking accounts on failure.

That might look like a shortcut in the short term, but over time it turns your financial accounts into disposable trash.

If you’re just looking to churn once and bail, that’s up to you.

But if you actually want to use the card long-term, I wouldn’t recommend playing that game.

The app looks roughly like this.

It’s nothing complicated—just standard screens for balances, cards, cash back, and transfers.

How would I personally use it?

Pretty simple.

Start with Core.

Pocket the 3% on the first $1,000 each month.

Route AI subscriptions through it where possible, like smaller plans such as ChatGPT Go.

No rush on Platinum.

Unless you already have heavy USD spending and are fine locking up XPL for 12 months.

Otherwise, locking up a large chunk of volatile assets just for 4% is unnecessary.

Don’t view the cash back as US dollars, either.

It’s paid out in XPL.

It might look like a gain today, but nobody can guarantee where the price goes down the road.

So the people this card suits best aren’t the ones who jump in the second they see the words “god-tier card.”

It’s much better suited for people who already spend in foreign currencies, already pay for AI subscriptions, and can tolerate the risks of stablecoin products.

If your needs happen to align with that, it’s worth a shot.

If not, don’t run around creating work for yourself just to chase some cash back.

I’ll leave the sign-up link here.

App Store link: https://apps.apple.com/app/plasma-one-stablecoin-card/id6759033493

Tested and verified working as of May 2026. Primary route: “Register China region first → Switch to Turkey via web”, backed by gift cards.

Apple’s regional pricing, exchange rates, and risk control rules fluctuate constantly. Before you begin, refer to whatever is shown on account.apple.com on the day. This guide only guarantees this workflow works as of May 2026.

A Turkish Apple ID can save you serious money. Here is a quick price breakdown:

If you want to compare prices first, check appstoreprice.org to pull the regional prices for your target apps so you know what you are aiming for before doing anything.

Here is the high-level workflow:

[Clean email + Chinese phone number]
        │
        ▼
 ① Register a clean China region Apple ID
        │  (Browser incognito mode)
        ▼
 ② Switch region to Turkey on the web via account.apple.com/tr/
        │  Select None for payment method
        ▼
 ③ Sign out on iPhone → Switch App Store storefront → Sign in with new account
        │  Do NOT reverse this order!
        ▼
 ④ Download a free app to lock in the region
        │
        ▼
 ⑤ Buy gift card (oyunfor recommended) → Redeem
        │
        ▼
 ⑥ Subscribe to ChatGPT Plus / iCloud / Apple Music inside App Store

The whole process takes around 2 hours. Have these ready before starting:

• An email address never used for any Apple ID: Gmail or Outlook preferred; 163 or 126 also works. QQ Mail’s success rate dropped significantly in 2026—avoid it if possible
• A +86 phone number that can receive SMS text messages normally
• A stable non-mainland proxy set to a US or Japan node. It must be in global mode—this is crucial for whether the None option shows up later
• A Visa or Mastercard credit card (CMB cards tested working) to buy gift cards later. If you don’t have one, Alipay via SEAGM works too
• Do not use your primary iCloud account. Create a dedicated fresh account to avoid any issues with your main account’s data

The same mindset applies to other app subscriptions: Subscribing to Claude Code Max via Nigeria covers the exact same regional pricing arbitrage applied to a different product.

Must-Read Before You Start

There is a critical change in iOS 26.4: Apple removed the account-switching shortcut inside the App Store and merged it into “Settings → Apple Account → Media & Purchases”.

The biggest pitfall is iOS 26’s new risk controls: if you sign in to the new account without switching the App Store region first, the system will most likely bounce your account right back to the China region, making it much harder to change again. Do not mess up the sequence in Step 3.

On account seasoning: many online tutorials say new accounts need to age for 3–7 days before topping up. In my testing, that’s unnecessary. As long as your IP is solid (stable global proxy, clean node) and you follow the procedure, topping up and subscribing on day one works without issues. Every single one of my own accounts was used on the day of registration, and all are running normally.

The payment card determines whether your account survives. The vast majority of banned account cases I’ve seen were caused by dirty gift cards (especially shady BINs), having nothing to do with whether the account was seasoned. So where you get your card matters far more than waiting a few days.

Step 1 · Register a Clean China Region Account

Open your browser in incognito mode, visit account.apple.com, and click “Create Your Apple ID”.

Key details to fill in:

• Set Country/Region to China mainland first (we will switch to Turkey later)
• Use the clean email address you prepared
• Enter your +86 phone number
• Set the date of birth so you are over 18
• Complete both email and SMS verification

Once registered, keep the browser window open, do not log in on your iPhone’s App Store, and proceed directly to the next step.

Step 2 · Switch Region to Turkey via the Web

The core step of the entire workflow. Before starting, confirm two things:

• Proxy is switched to global mode with a US or Japan node
• Browser is still in incognito mode

Open the Turkey-specific entry point: account.apple.com/tr/

Going straight to account.apple.com also allows you to change regions, but the /tr/ path has a more straightforward interface that saves detours.

After signing in with the China account you just registered:

1. Go to “Personal Information” → “Country/Region” → click “Change”
2. Select Türkiye / Turkey and agree to the terms
3. On the payment method page → select None
4. Use a generator to create a full Istanbul billing address: 1ktools Turkey Address Generator
5. Keep the phone number as +86; no need to change it
6. Save, and wait until the top of the account page displays Türkiye to confirm success

“None” Option Not Showing Up?

The most common roadblock in 2026. Troubleshoot in this order:

Step 3 · Switch App Store Region on iPhone (Sequence Is Extremely Critical)

Mess up the order in this step and your account is ruined. Pay close attention.

Correct sequence:

  Sign out of old account ──▶ Switch App Store storefront ──▶ Sign in with new account
                              (itms-apps link)

Wrong sequence (triggers risk control, bouncing you back to China):

  Sign out of old account ──▶ Sign in with new account directly ──▶ System boots you back to China

Step 1: Sign out of your original account first

iPhone Settings → tap Apple Account at the top → Media & Purchases → Sign Out → confirm sign out

Step 2: Switch region first, then sign in (DO NOT reverse this)

After signing out, do not sign in to the new account yet. Open Safari, copy and paste this link into the address bar, and open it:

itms-apps://itunes.apple.com/WebObjects/MZStore.woa/wa/resetAndRedirect?dsf=143480&cc=tr

Seeing a “Cannot Connect to App Store” popup is normal; ignore it. Once this step is done, the App Store storefront has switched to Turkey.

Step 3: Go back and sign in with the new account

Go back to “Settings → Apple Account → Media & Purchases”, enter the Turkey account credentials, and sign in.

After signing in, open the App Store and verify: the interface changes to English, and your profile page shows Türkiye / Turkey. It’s only successful if these match.

If you get bounced straight back to the China store, the region switch likely didn’t take. Redo from step 1.

Step 4 · Download a Free App to Lock In the Region

This step is mandatory. Skip it, and the region will revert on its own.

Search for any free app in the Turkish App Store (WhatsApp, Spotify, etc.). Tap “Get”, and a prompt will appear asking you to complete your account information.

• Select None again for the payment method
• Fill in the address again (generate another set using the generator from Step 2)
• Use pinyin or English for the name—do not use Chinese characters

Once the download completes, your region is locked in.

Step 5 · Buy Gift Cards to Top Up (oyunfor Recommended)

The Turkey region does not accept domestic Chinese credit cards directly for app subscriptions, so topping up relies on gift cards. A 500 TL card roughly covers one month of ChatGPT Plus, which is a sensible denomination to start with.

This is the single most critical link in the entire process. No matter how careful you were earlier, buy the wrong card and your account is toast. Almost every account ban I’ve seen was caused by dirty cards. The channel determines everything.

A side-by-side comparison of three mainstream channels:

Why Choose oyunfor

oyunfor is a veteran local Turkish gaming card vendor: direct sales, clean card stock, and legitimate supply channels. Cards bought here rarely run into issues during Apple’s risk audits. Cheap cards on Xianyu are mostly black-market or gray-market cards; risking your entire account just to save a few bucks is not worth it.

URL: oyunfor.com

Step-by-step instructions:

1. Register an account. Chinese mobile numbers can register directly. Use Gmail for the email (163/126 does not work; QQ untested)
2. Log in, search for “Apple Store iTunes Gift Card”, or go straight to oyunfor.com/apple-store
3. Select the 500 TL denomination and add it to your cart
4. Select Visa/Mastercard credit card for payment; CMB Visa tested working
5. After payment clears, the redemption code will be sent to your registered email, usually within a few minutes

Fees: card payments incur a 2.49% transaction fee. For 500 TL, the actual charge comes out to around ¥77.66 (subject to exchange rate fluctuations).

Do not use a proxy or IPv6 during checkout on oyunfor, or payment will fail. Just pay over a direct domestic connection.

If you don’t have a credit card, use SEAGM. Just search for “iTunes Gift Card Turkey”. It supports Alipay, requires no proxy, and costs only 1–2 yuan more while saving you hassle. URL: seagm.com

Redeeming the Card Code

iPhone Settings → tap Apple Account at the top → Media & Purchases → Redeem Gift Card or Code → paste code.

It may ask for an address again during redemption; fill it in normally (using the address from the generator earlier).

If None refused to appear earlier, it usually shows up automatically after this step: once there is a balance, Apple considers you a Turkish user, locking in the region for good.

Step 6 · Subscribe to ChatGPT Plus

This is the core reason most people bother with the Turkey region in the first place.

1. Search and download the official ChatGPT app on the App Store
2. Log in with your OpenAI account (not your Apple ID—your actual ChatGPT account)
3. Tap Settings → Upgrade to Plus → confirm the price shows 500 TL ≈ ¥78
4. Select this tier → tap Subscribe
5. It will most likely prompt “Account information not verified.” Don’t panic; tap Continue, and a form will appear asking you to complete your details
6. Enter the address generated earlier, and enter your +86 domestic phone number; no SMS verification will be requested
7. Once you see the subscription confirmation screen, you’re all set

If the price displays in USD or RMB, the region switch didn’t take. Go back and check Step 3 and Step 4.

An Apple ID is strictly bound to a single ChatGPT account. Changing the linked account is a major hassle, so log in with your primary ChatGPT account right from the beginning.

An Alternative Route: Register a Turkey Account Directly (Faster but Lower Success Rate)

If you don’t want to bother with the region-switching process, you can also select Turkey directly during registration. Users on LINUX DO verified this works:

1. Register directly on account.apple.com, setting Country/Region directly to Turkey
2. Do not use a proxy (doing so often causes it to fail)
3. Don’t use QQ Mail; fill in other details using the generator
4. Use your domestic +86 phone number
5. If it says “Your account cannot be created at this time”, switch browsers or try incognito mode again

If successful, skip straight to switching regions in Step 3. If it fails, stick to the Step 1–2 region-switching route—don’t bang your head against the wall.

FAQ

Is region switching still possible?

Yes. As of May 2026, the “Register China region first → Switch to Turkey via web” route is verified working. However, iOS 26.4 moved the account-switching entry into “Settings → Apple Account → Media & Purchases”, and added new risk controls: if you sign in to the new account without switching App Store regions first, the system will almost certainly boot your account back to the China region. Following the exact order in Step 3 is more critical than ever.

Do I need to switch cards first?

You don’t need to swap your primary card just for the region switch—switching regions is free; you just select None for payment method. Where a card actually comes into play is topping up later: the Turkey region doesn’t accept domestic Chinese credit cards for app subscriptions, leaving you to rely on gift card balances or linking a virtual card like Wise or N26. What determines whether your account stays safe isn’t your everyday bank card; it’s the channel you use to buy gift cards.

How should I choose gift cards, and will I get burned?

Comparing the three channels: oyunfor is direct-sale, has clean card stock, carries the lowest dirty card risk, and supports Visa / Mastercard; SEAGM supports Alipay, requires no proxy, and costs a yuan or two more but is hassle-free; Xianyu is cheap but carries the highest dirty card risk. Nearly every ban case I’ve seen was caused by card issues, not account seasoning, so I wouldn’t recommend skimping on a few bucks here.

How much can I actually save compared to the official local store?

Compared to the China store: iCloud 2TB drops from ¥68/mo to roughly ¥25, saving 63%; ChatGPT Plus drops from ¥158/mo to roughly ¥78, saving 50%; Apple Music drops from ¥10/mo to roughly ¥5, saving 50%. For more guides on saving on similar subscriptions, check out the AI Subscription Savings & Pitfalls hub.

What should I do if risk controls trigger (and I’m booted back to the China region)?

If None never appears under payment methods, troubleshoot in order: verify whether your proxy is in global mode, check node stability, ensure account balance is zeroed out, and confirm there are no active subscriptions. If it still doesn’t appear, jump straight to buying and redeeming a gift card; once you have a balance, the system usually recognizes you as a Turkish user automatically. If you were booted back to China, chances are you reversed the sign out → switch region → sign in sequence in Step 3—just start over from scratch. Avoid switching regions back and forth more than 1–2 times a year, as repeated switching tightens risk controls.

Things Nobody Bothers to Tell You

These are peripheral tips learned the hard way after running into real-world pitfalls.

• Do not bind a domestic Chinese credit card directly to subscribe in the Turkey region—it will throw an “Invalid Payment Method” error. If you want to bind a card, use a virtual card (Wise / N26, or routes that don’t rely on Chinese credit cards like How to Use Plasma One Without Losing Money), or stick to gift card balances for maximum safety
• After switching regions, Apple Music comes with a default 6-month free trial—remember to cancel manually before it expires
• ChatGPT Plus must be subscribed to inside the iOS app (via Apple balance), not on the web. The web version doesn’t accept Apple balance and charges standard pricing
• Some China-exclusive apps aren’t available in the Turkish App Store (WeChat, Taobao, Alipay, etc.), so think carefully before switching regions on your daily driver. I recommend using an old iPhone as a dedicated Turkey device while keeping your new phone on the China store
• Turkish gift card prices fluctuate with the Turkish lira exchange rate; they are usually cheapest at the beginning of the month and tick up toward the end due to the monthly billing rush

Follow this procedure, and as of May 2026, it is verified working. If problems arise, check the sequence in Step 3 and the card source in Step 5 first—that’s where most issues come from.

Put simply: you give Codex a goal, and it can run for extended periods working toward it. When the current turn finishes, it doesn’t wait for you to prompt “continue”—it determines on its own whether the task is done. If not, it keeps going.

This isn’t a theoretical feature. People are already running it in practice.

Peter Steinberger had a Goal run for 11 hours 31 minutes. Yannik (@ynkzlk) also shared a more comprehensive case study: his Codex /goal ran continuously for 6 hours 44 minutes. Midway through, he closed his laptop, pausing it for over 5 hours. When he came back, Codex resumed automatically and finished the task.

This is worth paying attention to, not because “Agents don’t need sleep.” Machines don’t sleep anyway.

What’s far more interesting is: what kind of task can you actually hand off to an Agent to run unattended for this long?

Enabling /goal First

You need Codex CLI 0.128.0 or higher.

codex update
codex --version

Then enable goals:

codex features enable goals

You can also edit your config manually:

~/.codex/config.toml

Add:

[features]
goals = true

If you don’t want to see the experimental feature warning every time, you can add:

suppress_unstable_features_warning = true

Right now, this is primarily available in the terminal Codex CLI; the desktop app hasn’t caught up yet.

What Does /goal Actually Do?

Yannik’s case study is particularly informative.

He handed Codex a voice interview system in a TypeScript monorepo and tasked it with verifying and fixing multiple end-to-end scenarios.

The final results:

• Total runtime: 6 hours 44 minutes
• Actual model compute time: about 41 minutes
• Cumulative input: about 6.8 million tokens
• Cache hit rate: about 94%
• Final status: TASK_COMPLETE
• All four target end-to-end scenarios passed

The most interesting part was its recovery capability.

He shut his laptop at minute 57. Over 5 hours later when he returned, Codex had already resumed execution on its own. No reprompting, no manual recovery steps. It injected a developer message into the thread along the lines of “continue working toward current goal,” and then kept running.

So the value of /goal isn’t just “automatically clicking continue.”

It’s more like adding a goal-persistence layer for long-running tasks in Codex: the goal remains, the state remains, and the next turn keeps revolving around that goal.

The Key Isn’t /goal—It’s Writing a Goal That Looks Like a Contract

A lot of people will misuse this feature.

For example, writing something like:

/goal Optimize this project for me

That’s basically handing over the steering wheel and hoping the car miraculously ends up where you had in mind.

That’s not how Yannik approached it. His /goal prompt was around 600+ words with a clear structure covering:

• A clear objective
• Which files to read first
• Working rules
• Guardrails on what bad paths to avoid
• Verification methods
• Output requirements
• done_when—the explicit criteria for what counts as done

The most important piece here is done_when.

Without a clear definition of done, an Agent easily runs into two problems:

• Declaring completion too early
• Spinning its wheels, repeatedly “optimizing”

So /goal isn’t a magic spell. What it really thrives on is an engineering contract.

The clearer you write it, the better it can execute independently. The vaguer you write it, the easier it goes off track.

A More Reliable Way to Write /goal

Don’t just write a one-line goal. Include at least these sections:

/goal Complete [Task Name].

Goal:
[What is the final deliverable this time]

Read first:
- [Key File 1]
- [Key File 2]
- [Relevant docs / tests / configs]

Working rules:
- Understand the existing implementation before modifying
- Do not modify unrelated files
- Do not add new long-term feature flags
- Do not use --no-verify to bypass checks
- Preserve existing user changes

Completion criteria done_when:
1. [User-facing outcome is usable]
2. [Relevant tests pass]
3. [build / lint / typecheck pass]
4. [None of the specified failure modes introduced]
5. Upon completion, explain what was changed, how it was verified, and any remaining risks

Pause if any of the following occur:
- Login, CAPTCHA, payment, or secret keys required
- Production environment operations required
- Test environment unavailable
- Requirements conflict with each other

This template might look tedious, but it saves you hours of babysitting down the road.

What Tasks Are Well-Suited for /goal?

Tasks with clear boundaries, clear acceptance criteria, and a process that can be automatically verified.

For example:

• Fixing a well-defined bug
• Building a small feature
• Getting a batch of tests to pass
• Verifying and fixing several end-to-end scenarios
• Cleaning up lint errors, type errors, or build errors
• Executing according to an existing SPEC.md or task plan

What these tasks have in common is that the Agent can determine on its own “what is still needed to reach completion.”

What Tasks Are Not a Good Fit?

This part is even more critical.

Not suitable:

• Exploratory tasks
• Tasks with ill-defined success criteria
• Security-sensitive paths
• Tasks dependent on external systems where you haven’t verified connectivity
• Small tasks that could easily be wrapped up interactively within ten minutes

Yannik’s rule of thumb is practical: if a task wouldn’t naturally span across more than two sessions, you probably don’t need /goal.

Don’t use a new feature just for the sake of using a new feature.

Regular Codex is enough for short tasks. Write a SPEC first for ambiguous tasks. Keep humans at the gates for high-risk tasks.

My Take on /goal

The shift with /goal isn’t about “letting Agents run longer.”

Running longer isn’t hard. Running for a long time without drifting off course is.

Previously, humans were more like supervisors: watching it work, stepping in when seeing an error, and tossing in a “continue” whenever it stopped short.

Now, the human role looks more like an architect: define the goals, boundaries, acceptance criteria, and failure modes clearly upfront, then let the Agent execute on its own.

This actually raises the bar for the user.

You can’t just toss out feature requests anymore. You have to be able to break a fuzzy idea down into a contract that an Agent can independently execute, verify, and close out.

If you can’t write that, don’t blame the Agent when it goes off the rails.

The Shortest Practical Advice

If you want to try /goal, I suggest starting with this kind of task:

/goal Fix failing tests in the current project.

Run tests first, locate root causes, and only modify relevant files.
Completion criteria: all originally failing tests pass; no new failing tests introduced.
If external accounts, secret keys, or production environment permissions are required, pause and explain.
Final output: root causes, modifications made, verification commands, and remaining risks.

Don’t jump right in by asking it to “refactor the entire project.”

Start by having it tackle small, verifiable, longer-running tasks. Once that’s stable, expand the scope.

References

• OpenAI Codex GitHub Release: rust-v0.128.0
• Felipe Coury: /goal lands in Codex CLI 0.128.0
• Greg Brockman: built-in Ralph loop++
• Yannik (@ynkzlk): 6-hour 44-minute /goal case study
• Tecton & Tide: From SPEC.md to /goal: My Codex + GPT-5.5 Workflow
• Michael Guo: Chinese summary on /goal and engineering contracts

Video: Luo Fuli — OpenClaw, Agent Frameworks: The AI Paradigm Has Already Changed Dramatically. Source: Zhang Xiaojun Podcast Link: https://www.youtube.com/watch?v=V9eI-t3TApE

Don’t treat this interview as just a pitch for OpenClaw. Luo Fuli is talking about something far more concrete: the center of gravity in AI competition is shifting away from raw models toward the combined efficiency of models, agent frameworks, post-training, and organizational design. The model is no longer the sole protagonist—the system is.

Here are a few takeaways I jotted down after listening. The quotes are all from the video; the takeaways are my own.

The Model Is the Engine, the Framework Is the Car

When looking at LLMs in the past, people were used to obsessing over parameters, leaderboards, and pre-training—whoever was bigger and scored higher was crowned the winner. In the agent era, the math doesn’t work that way. A mid-tier model placed in a capable enough framework—paired with memory, tools, task decomposition, multi-model collaboration, and long-horizon execution—can pull off things that previously seemed far out of its league.

Capability is no longer locked solely inside model weights; it’s also embedded in system orchestration. The engine matters, of course, but if the transmission, suspension, navigation, and driver-assist are all garbage, even the most powerful engine will just spin its wheels.

A Complete Reversal in Three Days

The part where Luo Fuli talks about OpenClaw is the most interesting. At first, she was dismissive, writing it off as just Claude Code bolted onto instant messaging, maybe even tainted with marketing hype and hand-waving. A lot of technical people react like this: the second they see productized packaging, their instinct is to dismiss it as impure.

Once she actually got her hands on it, her perspective shifted day by day:

• Day one: she felt it had a “soul.” In plain terms, the context orchestration was meticulously crafted; when small details like time, memory, prompts, and interaction patterns are stacked together, the entire experience transforms.
• Day two: she started delegating non-coding tasks to it, like team management, screening for curiosity, and organizational design.
• Day three: she handed it research tasks and discovered it could co-construct User Agents and fit right into post-training data and RL workflows.

It took a researcher just three days to go from looking down on a tool to overhauling her judgment. That’s more convincing than any product marketing.

The Value of an Agent Framework Is Covering Weaknesses

She said something I strongly agree with:

The greatest value of an agent framework isn’t showing off fancy tricks—it’s covering the model’s weaknesses.

When discussing AI, many people still like to ask, “Which model is the strongest?” It’s not an invalid question, but it’s no longer enough. The question we should be asking is: in a specific task, can the model be reliably amplified by the system? Can it retain long-term context, find its own tools, turn failures into lessons, and transfer conclusions from one task to the next?

If it can’t do that, it’s just a chatbot. If it can, it finally starts looking like real productivity.

Why It’s Easier to Blow Up in the Chinese-Speaking World First

My take is that the hunger for efficiency is much stronger in China. Anything that saves headcount, saves time, or turns a single person into a small team gets pushed into real-world scenarios much faster here.

That’s also where the risk lies. Many people treat agents like some magic silver bullet for automation, only to run the new tools with their same old processes. Agents force you to rewrite your workflows: checkpoints that once required human supervision get inspected autonomously by the system; task delegation that once relied on gut intuition shifts to scheduling driven by memory, context, and feedback loops; managers asking for status updates gets replaced by agents reporting exceptions on their own. If the organization doesn’t adapt and just tacks on an AI tool, the efficiency gains will be minimal.

Post-Training Is Becoming the Main Battleground

Luo Fuli touched on GPU compute allocation. She noted that a reasonable ratio across research, pre-training, and post-training might be 3:1:1.

What lies behind this ratio is that in the agent era, post-training and research exploration are no longer just cosmetic polish applied before a model ships. Pre-training used to be laying the foundation while post-training was interior decoration. Not anymore:

Post-training today is more like converting a house into a self-operating factory.

You have to train it how to use tools, how to plan across long horizons, how to interact with its environment, and how to recover from failures. This directly alters resource allocation—and it reorganizes teams.

Big-Company Resources Don’t Equal Speed

Building agents requires multi-system coordination: models, data, RL, infra, product, toolchains, and evaluation all have to move in sync. If a team is still split across traditional big-tech functional silos, it will quickly grind to a halt. Luo Fuli mentioned that even though MiMo and MiClaw sit inside a tech giant, they effectively operate like a startup.

Big companies have resources, but resources don’t automatically translate to speed. The true rarities are judgment and reaction time.

Proving Yesterday’s Self Wrong Every Day

She had another line:

Every single day, you’re proving yesterday’s self wrong.

Amid this wave of AI shifts, this isn’t cheap self-help fluff. A judgment you formed yesterday might expire today; a tool you just mastered gets absorbed by the framework tomorrow; a capability you thought demanded human touch gets reliably half-automated by an agent the day after. The most dangerous people right now are those who’ve toyed with AI a little and assume they’ve got it all figured out.

Three Things That Will Widen the Gap Going Forward

The gap won’t come from whether you know how to write prompts, nor will it come from subscribing to the most expensive models. It will come down to:

1. Whether you can break tasks down into systems rather than one-off Q&As;
2. Whether you can crystallize experience into skills, memory, and workflows rather than starting from scratch every time;
3. Whether you can accept that your process from yesterday is already outdated—and overhaul it immediately.

The biggest takeaway for me from this interview is that agents aren’t just another product category—they’re a whole new way of organizing work.

It’s the Same Story for Content Production

In the past, content teams competed on individual talent: who picked the best topics, who edited the cleanest, who wrote the sharpest copy, who understood the algorithms. The next phase is about competing on systems: how trending topics are captured, how assets are ingested, how scripts are generated, how audience comments feed back into topic selection, how viral structures are codified into templates, how personas across different accounts are isolated, and how distribution across platforms is handled.

Humans still matter, but human value will concentrate heavily on judgment, taste, trade-offs, and taking responsibility. Repetitive labor will become increasingly worthless.

Stop Just Asking “Which Model Is Stronger”

What really struck me about this interview is that she treats AI neither as magic nor as run-of-the-mill software. She examines how system boundaries are shifting: which capabilities stem from the model, which from the framework, which from organization, and which from environmental feedback.

Does my current way of working need to be rewritten by agents? That’s the question I’ve been turning over in my head ever since listening.

The original log comes from a user who got it to work once; I’ve reorganized the accounting and steps in sequential order. This isn’t a battle-tested, long-term stable solution—it’s better viewed as a “working sample” rather than a standard answer to copy blindly. Nigerian subscription prices, exchange rates, and Apple risk control rules all change, so go by the actual charges at the time before proceeding.

Who This Is For

• People who want to use Claude Code Max long-term, but don’t want to pay US prices
• People willing to wrestle with Apple accounts, payment pipelines, proxies, and browser environments
• People mentally prepared for account bans, refunds, and re-registration
• People who already have an established Claude account, or are willing to resolve account issues separately

If you just want low cost, low risk, and minimal maintenance, this route might not be for you. What it saves is the subscription price; what it costs is time, hassle, and risk control overhead.

On paper, it definitely saves money.

Here’s how the math looked back then:

• Timon virtual card: around 195 RMB
• MTN phone number: around 95 RMB
• Initial deposit: $85, about 616 RMB at an exchange rate of 7.25
• Opening a new Timon Blue virtual card: $5
• Claude 5x actual charge: around $76.97

Compared to the US price of $125, the price difference at the time was enough to cover the card purchase costs, so looking at the first month alone, the math works out.

But this isn’t a “no-brainer bargain.” It has three real prerequisites:

1. You can stomach Apple payment risk controls
2. You can accept account environment isolation
3. You can handle the loop of re-linking cards, re-logging into Apple IDs, or even opening new cards when issues arise

What You Need to Prepare

• A Nigerian Apple ID
• A Timon virtual credit card
• A matching MTN phone number
• Around $85 in initial funds
• An iPhone or iPad, used for App Store in-app purchases
• A Claude account that’s as clean as possible, with an established account preferred
• A US residential IP solution (for example, cliproxy, priced around $3.8/month)
• An anti-detect browser, RoxyBrowser
• A tool capable of chain proxying, such as Clash Verge
• Willingness to complete KYC facial recognition

The original log didn’t go into how to choose sellers, and I haven’t screened them for you either. Check transaction histories and reviews yourself before ordering.

Follow This Sequence

1. First, register and sign in to a Nigerian Apple ID. Sign out of the App Store on your phone, then sign in with the Nigerian account. The App Store account and iOS system account can be from different regions and use different IDs without interfering with each other.
2. Buy the Timon card and MTN phone number. After paying, you usually need to provide an email, receive two verification codes, and then complete KYC facial verification. In the original log, this step was finished within a day.
3. Deposit funds into the Timon USD wallet first. This was done via a third-party seller top-up: $85 at a 7.25 exchange rate. Timon has a USD wallet, a Naira wallet, and a USD credit card; balances are tracked separately, but you can transfer between them.
4. Open a new Timon Blue virtual card. The path is Timon → Virtual Card; card creation costs $5. Make sure there’s money in the wallet first, otherwise the currency selection might not even appear.
5. Fund the virtual card. The path is Card → Fund → $80. Topping up a USD card from a USD wallet usually has no transaction fee; if you top up Naira first and then convert to USD, you’ll pay an extra $0.50 fee, and the exchange rate is worse.
6. Make a small purchase in the Nigerian App Store first. This “warms up” the payment pipeline; in the original log, the user bought an app under a few dollars just to confirm the account and card could be billed normally.
7. Log into Claude on iOS and subscribe via the Nigerian region. One rule-of-thumb trick: temporarily change the phone’s time zone to Pacific Time, country to the United States, and language to English. There’s no evidence whether this is strictly necessary; I treat it as optional.
8. Purchase Claude 5x. In the original log, the actual charge was $76.97, leaving $3.03 on the card afterward. Once purchased, log out of Claude and revert the phone’s original time zone, language, and region settings.
9. Clean up legacy Claude traces on your computer before logging into Claude Code. If you previously used direct account login or OAuth, clean up the environment first. The easiest approach is to rename ~/.claude entirely and reinstall a fresh environment. Pay special attention to these paths:
   • ~/.claude/config.json
   • ~/.claude/telemetry/
   • ~/.claude/.credentials.json
10. Complete the web login using a clean network and an anti-detect browser. Don’t use your regular Chrome environment directly. When Claude Code logs in, it pops up the default browser; copy that redirect link into the anti-detect browser to finish authentication, and do your best to keep your regular Google accounts and everyday browser fingerprints out of the mix.

The Biggest Pitfalls Here

• Apple payment risk controls on the first large transaction This looks more like Apple payment risk controls rather than an issue with Claude itself. There are two ways to handle it: contact Apple Support and wait 48 to 72 hours, or do nothing and keep waiting. Don’t repeatedly retry subscribing in the meantime, or the risk controls might escalate.

• Don’t leave money on the card long-term Nigerian virtual cards carry the risk of being reclaimed. The safe approach is to deposit funds only right before billing and avoid sitting on a balance normally.

• Converting Naira to USD is more expensive If you use a seller who can only top up Naira, converting it to USD usually dings you with another transaction fee and exchange rate loss, which isn’t necessarily more cost-effective than depositing USD directly.

• Guilt by association from IPs and old environments Matching IPs may be part of correlation profiling, though whether it’s a primary factor is still inconclusive. From a safety perspective, it’s best not to mix old accounts, old browsers, and old networks.

• This path offers no guarantee of long-term stability Even the author of the original log noted that this was just “getting it to work once,” without any long-term testing. Platform risk controls, exchange rates, seller quality, payment pipelines—if any single point changes, the entire method could fail.

FAQ

Is the Nigerian region still stable right now?

It shouldn’t be considered a stable solution—it’s just a sample that worked once, without standing the test of time. If any single link in platform risk controls, exchange rates, seller quality, or payment pipelines changes, the whole method could stop working. Don’t treat it as a standard answer to copy blindly.

What card is required?

You need a Timon virtual credit card paired with an MTN phone number, verified through KYC facial recognition. The card and phone number together cost around 290 RMB at the time, and opening a Timon Blue virtual card costs another $5.

How much is the difference compared to the US?

The US Claude 5x plan is around $125, while the Nigerian route actually charged $76.97. That leaves a monthly price difference of around $48, which basically covers one-off costs like buying the number and card issuance. This isn’t a long-term guarantee—it’s just the ledger recorded this time around.

How do you recover if hit by risk controls?

The first large transaction easily triggers Apple payment risk controls; it isn’t necessarily an issue with Claude itself. There are only two ways to handle it: contact Apple Support and wait 48 to 72 hours, or do nothing and keep waiting. Don’t repeatedly try to subscribe during this period, or the risk controls might escalate.

Is this suitable for long-term use?

If you just want to save a few bucks, it’s not worth the hassle. If you use Claude Code heavily every month and can stomach environment isolation, proxy maintenance, and the occasional card re-issuance, the math holds up—treat it as a “project,” not as “buying a subscription.” For a similar angle, you can also check out the Hands-on Guide to Switching a Mainland China Account to a Turkish Apple ID, or head over to the Saving Money and Avoiding Pitfalls on AI Subscriptions topic for more practical walk-throughs like this.

My Advice

If you’re planning to use Claude Code Max long-term, this method is worth a shot, but treat it as a “project,” not as “buying a membership.”

In practice, stick to three principles:

• Keep your payment environment and daily environment separate
• Top up only right before billing; don’t leave large balances behind
• Keep screenshots and invoices for every step so it’s easier to contact Apple Support if risk controls hit

If you’re only looking to save a few bucks, don’t bother. If you use Claude Code heavily every month and can accept account isolation, proxy upkeep, and occasionally opening a new card, the math checks out. If you’re actually going to jump in, test with a small amount first before committing to the full subscription—don’t pile all the variables together right from the start.

If you’d rather go the Apple region-switching route instead of third-party virtual card top-ups, check out the Hands-on Guide to Switching a Mainland China Account to a Turkish Apple ID; if you want to compare AI subscriptions side-by-side to find the best deal first, How to Save Money on AI Subscriptions covers that much more comprehensively.

Who this is for

• People who dictate a lot of content on a regular basis
• People who want to turn voice directly into usable drafts
• People who use voice to write notes, emails, and coding prompts
• People already using Doubao, SuperWhisper, or Typeless who constantly feel something is “off”

Don’t look for an all-in-one input tool. Pairing them by use case is a much safer bet.

For short-voice scenarios like chatting, searching, or asking AI questions, prioritize tools like Doubao: free, fast on-screen output, and instant feedback.

For long-form voice scenarios, such as organizing dictations ranging from several minutes to tens of minutes, prioritize long-form voice input tools like SuperWhisper. Its edge goes beyond transcription to include post-processing, formatting, custom prompts, and history retention.

If you insist on keeping just one, SuperWhisper can barely cover short voice, though it’s on the slower side; using Doubao for long-form voice carries noticeably higher risks.

What you need to prepare

• Clarify your primary scenario: quick chats, or long-form output
• A short-voice tool: e.g., Doubao
• A long-form voice tool: e.g., SuperWhisper
• If you write code or specialized content heavily, prepare custom dictionaries, domain-specific vocabularies, and prompt templates
• Think through your budget expectations first
  • SuperWhisper’s monthly fee already includes LLMs and ElevenLabs transcription
  • Lifetime license is around $250, dropping to $150 with an educational discount
  • Spokenly membership is $10/month, but excludes polishing model costs, making it potentially 2 to 3 times more expensive than SuperWhisper overall
  • Typeless is $144 annually, around $72 with an educational discount, and around $30 monthly

Follow this order

1. Split tools by task first. Use short-voice input methods for chatting, searching, and quick queries. Use long-form voice input methods for long notes, emails, and lengthy prompts.
2. Check three things first for long-form voice: whether it keeps history, whether it supports extended continuous recording, and whether it allows custom prompts. Without these three, long-form drafting is basically unreliable.
3. If you mainly write long-form content, try SuperWhisper first. My take is that it is the most well-rounded for long-form voice: high accuracy, no 10-minute time limit, and it connects to LLMs for post-processing.
4. If speed is your main priority, look into Typeless. Its short-voice performance is faster, but it has a fatal flaw: recordings over 10 minutes get cut off without any clear warning. Lose focus for a second while dictating, and the second half is gone.
5. If you want to bring your own API keys to cut costs, take a look at Spokenly—but don’t just look at the headline membership price. It doesn’t include polishing model costs, and the ElevenLabs recording quota might run short. Whether it’s cheap depends on your monthly recording volume, not the unit price.
6. Don’t expect ChatGPT’s or Gemini’s built-in voice input to work as long-form voice tools. They are practically unusable for long recordings, falling short on accuracy, continuous recording, and cross-app versatility.

The biggest pitfalls here

• Assuming “can record audio” equals “suitable for long-form voice”
• Looking only at recognition accuracy while ignoring history retention and session recovery
• Overlooking formatting capabilities, ending up with an unparagraphed, unformatted wall of spoken text
• Using short-voice tools like Doubao for long recordings, losing both the audio and transcript when switching apps or getting killed in the background
• Recording for more than 10 minutes with Typeless, having the second half cut off with zero warning
• Windows users blindly copying other people’s setups SuperWhisper’s Windows version is stripped down and lacks elevenlabs-scribe. I haven’t tested it on Windows myself, so check the feature comparison on their official website before installing

My recommendation

If you only reply to messages, search for stuff, and query AI each day, Doubao is plenty.

If you rely on dictation every week to produce finished drafts, don’t skimp on tool costs—go straight for a long-form voice input tool, and keep a separate short-voice tool on hand. Keep short and long completely separate; don’t expect a single tool to do it all.

A solid, workable setup is simple: Doubao for speed, SuperWhisper for stability. The former solves input efficiency; the latter handles draft quality and data security. Under heavy use, the latter is what truly decides whether you can keep writing long-term.

The materials I reviewed include Anthropic’s official announcement, model documentation, and model card, plus several third-party leaderboards and user feedback. The practical takeaways mostly center on pricing, context window, output speed, coding capabilities, vision capabilities, billing changes, and when different access points become available.

Who This Is For

• Anyone already using Claude 4.6 who is debating whether to switch to 4.7
• People using coding environments like Claude Code, Cursor, and Copilot
• Anyone needing long context, image understanding, and continuous execution for complex tasks
• Anyone sensitive to token costs who worries a model switch will blow up their bill

If you’re just doing casual chatting or light Q&A, I haven’t seen enough evidence to prove that 4.7 is definitely more cost-effective.

Claude Opus 4.7 doesn’t blow 4.6 away across the board. The capabilities are stronger, but the trade-offs are also much more apparent.

Here are the hard facts we can confirm:

• Pricing unchanged: $5 input / $25 output
• Context unchanged: 1M
• Max output unchanged: 128k
• Output speed around 50 tps, roughly 30% faster than 4.6
• Noticeably enhanced coding and vision processing
• Burns through more tokens, especially in English and coding scenarios
• Long-context attention performance: user feedback suggests it’s not very stable
• Tone and voice: some feel it sounds more like GPT

If you mainly use it for complex coding, long-horizon tasks, and autonomous execution, 4.7 is worth trying. If your top priorities are long-context stability and cost control, it won’t necessarily feel better than 4.6.

What You Need to Prepare

• An entry point that can call Claude Opus 4.7: web interface, API, Claude Code, Cursor, OpenRouter, etc.
• Budget expectations: while the unit price hasn’t increased, total token consumption may go up
• Usage goals: be clear first on whether you want “higher task completion rates” or “more stability and cost savings”
• A fallback plan: if 4.7 doesn’t work out, the rollback command is /model claude-opus-4-6[1m]

A few new features rolled out alongside this release:

• /ultrareview: online review, billed separately; Pro and Max get 3 free trials
• auto mode: capable of autonomous decision-making and continuous execution, opened to Max users for the first time
• Added xhigh between high and max

I’m reserving judgment on the actual value of xhigh; it feels more like product tiering and a marketing play.

Follow This Sequence

1. Check first whether your core workload is coding or complex execution. If so, this release deserves your closest attention. 4.7’s autonomous coding has leveled up—it handles harder, longer-horizon tasks and proactively verifies output before reporting back. For anyone writing code, refactoring code, or running multi-step tasks, this shift is far more useful than benchmarks.
2. Next, see whether you rely on visual inputs. 4.7 raises the image processing ceiling to 2576 pixels on the long edge—3 times that of 4.6. For screenshot analysis, UI inspection, and image content understanding, this improvement is immediately noticeable.
3. Factor in costs—don’t just look at official unit pricing. Hands-on user feedback shows that tokenizer adjustments make 4.7 burn through more tokens: roughly 6% more for Chinese, 59% more for English, and 21% more for Python. If you write heavy amounts of code or read extensive English documentation, your cost per task will be higher than on 4.6.
4. Run small-scale tests first on long-context tasks. The context window is still 1M, but both MRCR benchmarks and community feedback show that 4.7’s attention performance drops under ultra-long contexts. “Being able to fit it all in” doesn’t mean “it still reliably grasps the key points.”
5. Keep an eye on multipliers and promos when choosing an entry point:

• Web / App: live
• OpenRouter Chat and API: live
• Claude Code: live
• Cursor: live, limited-time half-price
• Augment: live, half-price before 260430
• Windsurf: live, works even on trial accounts
• droid: live, 1x multiplier
• Copilot: live, with 4.7 at a 7.5x multiplier, possibly 15x after 260430; billed per prompt, capped at only medium, 200k context, and requires a Pro+ membership—the $10 Pro tier does not have 4.7

The Most Common Pitfalls

• Assuming unchanged pricing means unchanged costs What actually costs money is “unit price × total tokens.” The problem with 4.7 isn’t the sticker price; it’s that it eats more tokens.

• Assuming a 1M context window means you can blindly dump in documents User feedback suggests the exact opposite. For ultra-long context scenarios, test recall, localization, and citation accuracy on your own materials before deciding how much to feed in.

• Assuming high benchmark rankings mean it’s stronger across all tasks Leaderboards like LMArena, ArtificialAnalysis, ARC-AGI, and HLE are good references, but they can’t substitute for your actual workloads. On benchmarks like NYT Connections, 4.7’s score was less than half of 4.6’s.

• Mistaking new modes for must-haves /ultrareview, auto, and xhigh are all new additions, but not everyone needs them. Confirm whether your workflow actually benefits before deciding to migrate.

My Recommendation

If you’re a heavy code user—especially doing complex refactoring, long-horizon bug fixing, and automated verification in Claude Code or Cursor—4.7 is worth testing first. Its real strength lies in its ability to get work done, not in being a more dazzling conversationalist.

If you care more about long-context stability, predictable bills, and a natural writing style, don’t rush to switch entirely. The safest bet is running both models in parallel: hand complex coding to 4.7, and leave long-document retrieval and everyday conversation on 4.6 for now.

Do you want stronger execution, or lower cost? 4.7 is the former.

Who This Is For

• People who rely heavily on AI coding tools like Claude Code, Cursor, or Copilot day-to-day
• Anyone nearing their monthly or billing-cycle limit with deliverables still pending
• Teams where developers rely on AI as their primary engine and noticeably slow down when it’s cut off
• Anyone who wants to prepare a fallback plan in advance instead of panicking when their quota hits zero

Don’t treat AI coding tools as your entire assembly line. They’re accelerators. When your quota runs dry, switch to a degraded workflow immediately: prioritize delivery first, efficiency second, and developer experience last.

The video didn’t cover tool configuration, pricing tiers, rate-limit rules, or reset cycles. Pricing, quota, and hard caps depend on your account tier, subscription plan, and the models you call—check your own account dashboard for the specifics.

What You Need to Prepare

• A local development environment that can still actually run code
• A working terminal, editor, and Git setup
• At least one fallback option, such as:
  • An alternative AI coding tool
  • A local model
  • Plain manual coding
• A broken-down task list showing what must be finished today versus what can wait
• Project documentation, command logs, and commit history so you can keep moving without AI

Follow This Sequence

1. First, confirm whether you’ve actually hit the limit. Check product notifications, your account page, and CLI output. Don’t mistake network drops, auth failures, or model outages for an exhausted quota.
2. Immediately reprioritize your tasks. Split your workload into three buckets: must ship, can simplify, and can pause. Once your quota is gone, the worst mistake is sticking to your old, AI-dependent pace.
3. Tackle what you can move forward without AI first. Copy tweaks, style adjustments, minor bug fixes, config updates, writing tests, tidying directory structures, and updating docs—these deliver immediate progress toward shipping.
4. When you do have to write code, shrink the scope of your changes. Don’t touch huge features in one go—focus on a single function, an API endpoint, or one page. When coding by hand, smaller diffs mean less rework.
5. Lock in every step with Git. Commit every time you fix a small chunk to keep things from spiraling out of control. Without AI, people are much more prone to making messy, gut-feeling changes.
6. Until your quota resets, avoid high-uncertainty exploration. Major refactors, migrating to unfamiliar frameworks, or integrating complex automation might be cheap when AI is assisting you, but doing them purely by hand comes with a massive cost.

Common Pitfalls

• Misdiagnosing the root cause Not every error is quota-related. Check network connectivity, auth status, and model uptime first.

• Sticking to the “let AI draft it first” habit When your quota is gone, your workflow has to change with it, or you’ll stay completely blocked.

• Switching tools on the fly without migrating context If you don’t carry over your prompts, project conventions, directory layout, and command history, any new tool will require a cold restart.

• Getting emotional and hacking away at massive blocks of code This is when you’re most likely to break code that was otherwise ready to ship.

• Wishful thinking about reset times When limits refresh—whether hourly or per billing cycle—varies by plan. Check your account page for your specific tier.

My Advice

If you truly rely on Claude Code as your daily driver, prepare a “blackout plan” in advance: backup tools, common commands, project specs, QA checklists, and a minimum viable deliverable. That way, when your quota runs out, you only slow down—you don’t grind to a halt.

Your boss isn’t afraid of you writing code by hand; they’re afraid that without AI, you can’t deliver anything at all. Whether the project can keep moving forward when AI is unavailable matters far more than knowing how to use AI.

The reason isn’t complicated. Many models have never actually “read the full text”—they just get a title, a URL, and a snippet of a few dozen words back from a search engine, then rely on the model itself to fill in the rest. When the factual foundation is that thin, the longer the output gets, the higher the odds of hallucination.

I’ve tiered the products and search setups I’ve used based on whether they can pull the full text, whether they can trace sources, and whether that attribution is precise enough. Anyone doing research, writing analysis, or looking up new material can use this to pick their tools.

Who this is for

• People who regularly use AI to look up recent information, policy updates, and model news
• People sourcing topics, writing reports, or covering industry trends
• People who’ve already noticed that the same model can be brilliant one moment and completely off the rails the next
• People trying to decide whether to use web UIs, APIs, Research modes, or external retrieval tools

If most of what you ask about is common knowledge, history, or basic concepts, this won’t be as helpful.

Don’t conflate model capability with search capability. A lot of the time, the retrieval pipeline is just too weak; it’s not that the model itself is bad.

A simple rule of thumb:

1. If it only gets snippets instead of the full text, don’t expect it to answer new topics reliably
2. If it can fetch the full text but only links back to the top of the page, it works, but you’ll still have to do a lot of manual checking
3. If it can grab the full text and pinpoint the exact sentence, its practical value jumps significantly
4. For sites with aggressive anti-scraping like X or WeChat Official Accounts, many approaches fail by default
5. For specialized content like Reddit replies or YouTube subtitles, not every search source can retrieve them

So when asking about anything new after 2025, prioritize tools that can “fetch full text + trace sources.” If it’s just a standard web search endpoint, the output might look complete, but in reality it’s likely just “100 words of information + 900 words of hallucinated filler.”

What you’ll need

• Your regular LLM portal: ChatGPT, Claude, Gemini, or Codex all work
• At least one research tool capable of fetching full text
• The habit of manual verification
• The baseline expectation that “search results do not equal facts”

Here is how I tier them right now, based on my own usage records prior to March 2026:

• Tier 1: NotebookLM Web, Perplexity API
• Tier 2: Gemini Deep Research Web, GPT Deep Research Web, Claude Research Web, Manus Wide Research Web
• Tier 3: Tavily Extract API, Jina Reader API, Exa API, Grok Web
• Tier 4: Gemini Web
• Tier 5: GPT API / Web / Pro Web, Codex, Claude API / Web / Code, Gemini Deep Think Web, OpenRouter’s :online
• Tier 6: Antigravity, Gemini API, Gemini CLI
• Tier 7: Domestic Chinese official search pipelines

I only kept track of one pricing note: the Perplexity API opened in September 2025. I haven’t calculated the exact billing breakdown, so check their official site for current rates before using it.

Follow this order

1. First, determine whether what you’re asking about is “post-training new information.” If it’s a model update, product change, policy adjustment, or site rule update that happened after 2025, don’t assume the model knows it.
2. Check whether your tool gets snippets or full text. If it only retrieves a dozen search results with thirty- or forty-word snippets each, treat the answer as a lead, not a conclusion.
3. Prioritize options that can fetch the full text. In my experience, NotebookLM Web and the Perplexity API are closest to being truly usable; web-based Research products rank next; standard search-augmented chat products trail further behind.
4. Check whether source attribution is granular enough. Jumping to the exact sentence is best; landing at the top of the article is second best—the former makes verification much cheaper. Gemini Web supports sentence-level deep links via #:~:text=, which helps with manual verification.
5. Don’t blindly trust results from specialized sites. Google has an easier time retrieving YouTube subtitles and Reddit comments; for sites with anti-scraping like X or WeChat Official Accounts, many approaches simply break down.
6. Finally, do your own cross-verification. Open at least 2 to 3 links to confirm that the content cited by the model is actually there—don’t just judge by how convincing the writing looks.

Common pitfalls

• Confusing “web access” with “knowing how to research” These two things are worlds apart. Having web access doesn’t mean being able to read full text, let alone quote accurately.

• Letting your guard down the moment you see source links Some tools only link to homepages rather than specific sentences. You have no idea how the model connected the dots in between.

• Assuming the API is always better than the web app My experience is the exact opposite: quite a few API pipelines have weaker retrieval, and some only return temporary redirect URLs instead of real sources.

• Using domestic Chinese search pipelines for English AI topics If a topic already has a high density of English information, the results will take a noticeable hit. That’s why I separated domestic Chinese pipelines into their own tier.

• Mistaking model degradation for a prompt issue Sometimes it’s not that your prompting is bad—it’s that the retrieval foundation simply isn’t there. Prompts can only fine-tune; they can’t fix missing information.

My recommendations

If you’re genuinely using AI to track new developments and make informed calls, divide the labor among your tools clearly first:

• For everyday Q&A, standard models are plenty
• For retrieving new information, use Research tools that can fetch full text
• For high-stakes content, rely on the double safety net of “model summary + manual click-through”
• When dealing with content like X, WeChat Official Accounts, or community forum replies, default to doubting whether the scrape was complete

Here’s another practical rule: if an answer doesn’t cite specific source sentences, or if you can’t click through to verify them, treat that response as a rough draft at best, not a conclusion.

A lot of what gets labeled as “the model getting dumber” is simply starving it of enough information in the first place. Fix retrieval first; then we can talk about boosting intelligence.

The two things I find most practical about this list: each subreddit includes an activity reference, and it pulls together all the common areas—including model discussions, prompt engineering, AI writing, text-to-image, text-to-video, agents, IDEs, coding, and machine learning.

If you usually only keep tabs on the official websites of a few major LLMs, this list will help expand your information sources from “official announcements” to the “front lines of real users.”

Who This Is For

• Anyone who wants to track AI tool trends without getting dragged around by fragmented daily news
• Anyone looking for real user feedback, pricing rants, and failure post-mortems
• Creators brainstorming topics, product researchers, and competitive analysts
• Anyone hunting for prompts, workflows, plugins, and open-source solutions
• People whose English reading skills are average, but who still don’t want to miss out on firsthand discussions

Reddit is good for three things: gauging hype, reading candid feedback, and finding concrete workflows.

When you actually start using it, don’t try to binge everything at once. Pick subreddits based on your goals first, check their activity, and then decide whether to subscribe long-term. Communities like r/ChatGPT, r/LocalLLaMA, r/ClaudeAI, r/GeminiAI, r/StableDiffusion, r/aivideo, r/AI_Agents, r/cursor, and r/ClaudeCode are already enough to cover most common needs.

For translation, I use the Chrome extension “Immersive Translate.” The free built-in GLM translation engine reads much more naturally than Microsoft or Google. I haven’t specifically tested its free quota, stability, or regional restrictions, so check the extension’s documentation yourself before installing.

What You’ll Need

• An internet connection that can access Reddit reliably
• A browser, preferably Chrome
• The Chrome extension: “Immersive Translate”
• A basic categorization system—otherwise, a giant pile of bookmarked links will quickly become useless

I recommend setting up your resource library along these categories first:

• Model discussions: ChatGPT, Claude, Gemini, DeepSeek, Grok, Perplexity
• Open source & local deployment: LocalLLaMA, Ollama, MistralAI
• Prompts & workflows: PromptEngineering, ChatGPTPromptGenius, MCP, RAG, LangChain
• Image & video: Midjourney, CivitAI, StableDiffusion, ComfyUI, aivideo
• Agents & development: AI_Agents, Cursor, ClaudeCode, Codex, GithubCopilot, VibeCoding

Do It in This Order

1. Define your goal first. Are you looking for “model reputation,” “specific workflows,” or “content topics”? Different goals point to completely different subreddits.
2. Check macro activity first. Use visitor traffic and subscriber counts as a reference. For instance, ChatGPT-related subreddits have massive volume, making them great for seeing mainstream issues; subreddits like LocalLLaMA and ComfyUI are far better for tracking open-source trends and tinkering details.
3. Drill into niche subreddits. Head to WritingWithAI for writing; check Midjourney, CivitAI, and AIArt for text-to-image; look at StableDiffusion, aivideo, and Runwayml for text-to-video; check AI_Agents for agents; and head to Cursor, ClaudeCode, and Codex for development.
4. Lower the barrier with a translation plugin. If reading English feels like a chore, just turn on “Immersive Translate.” Lowering the friction of browsing is far more practical than brute-forcing your way through raw text.
5. Save only high-value posts. Prioritize complete workflows, cost breakdowns, post-mortems, tool comparisons, and documented limitations. Don’t hoard purely emotional rants or tribal fanboy posts.
6. Periodically revisit high-activity subreddits. Communities like r/ChatGPT, r/LocalLLaMA, r/StableDiffusion, and r/cursor evolve rapidly, making them great candidates for a long-term watchlist.

The Most Common Pitfalls

• Chasing hype while ignoring signal quality Large subreddits have tons of people, but also tons of noise. Endless complaints, flex screenshots, and tribal arguments don’t mean there’s actionable information.

• Equating member count with credibility Popularity only proves it’s buzzing; it doesn’t mean the judgments are accurate. When it comes to pricing, capability boundaries, ban risks, and commercial licensing limits, you’d better cross-reference with official documentation.

• Believing “if they got it running, I can too” Tools like ComfyUI, local models, and agent workflows often come with hidden costs like GPU requirements, operating systems, dependency conflicts, and regional network issues. Posts only give you the entry point; you have to fulfill the implementation prerequisites yourself.

• Hoarding too many bookmarks and never using them If you subscribe to dozens of subreddits all at once, you’ll most likely end up just scrolling a feed rather than building a knowledge base.

• Overlooking risk-focused subreddits Subreddits focused on complaints, jailbreaks, and controversies might look purely “negative,” but they are great for rounding out your understanding of limits, costs, and risks.

My Advice

Treat this list as an “information index,” not a “must-read-everything checklist.”

For everyday use, keeping 6 to 10 subreddits is plenty:

• 2 for general discussion
• 2 to 3 for specific model verticals
• 1 to 2 for image/video
• 2 for agents/development
• 1 for complaints or controversies

If you’re doing content creation or product research, focus primarily on four types of posts: real-world case studies, failure post-mortems, cost changes, and user complaints. They’re far more useful than feature announcements and much closer to real decision-making scenarios.

I curated this list by hand, and subreddit rules, activity levels, and content quality constantly change. When you come across information you plan to base decisions on, don’t take it at face value. Click in, review posts from the past week and month, form your own judgment, and only then decide whether to track it long-term.

Information was all over the place during launch week, with official pages, leaderboards, forum feedback, and scattered first impressions all tangled together. I sorted through it across specs, pricing, access points, quotas, context windows, speed modes, and running costs, keeping only what can actually be verified.

Who This Is For

• Anyone who wants to try GPT-5.4 without reading through a pile of release docs first
• People already running models in ChatGPT, OpenRouter, or Codex extensions
• Anyone who tracks API costs and wants to know whether the 1M context window is actually practical
• Anyone trying to figure out whether a Pro subscription, the API, or third-party access makes the most sense for them

If you only chat occasionally, focusing on channels and limitations is enough; if you’re a heavy writer, analyst, or programmer, focus on pricing and context costs.

The most straightforward change in this GPT-5.4 generation is that it rounded out its specs: context reaches 1M, maximum output remains 128k, and the training data cutoff is 2025-08-31. On paper, it catches up with long-context contenders like Gemini and Claude, but the trade-offs are also clearer—specifically, long-context input will be more expensive.

On pricing, gpt-5.4/thinking is $2.5 per million input tokens and $15 per million output tokens; gpt-5.4-pro is $30 per million input tokens and $180 per million output tokens. Compared to the previous generation, output unit prices went up by 43% across the board. If your workflow involves “reading long documents + generating long answers,” your bills will get ugly fast.

For regular users just looking to test it out, start with the ChatGPT web/app or LMArena; if you’re already used to working with APIs, consider OpenRouter. Pro isn’t something everyone should jump to—it only makes sense if you truly use it at high volume and are willing to keep paying for a top-tier model.

What You Need to Prepare

• A clear use case: clarify whether it’s chat, writing, code, or long-document analysis first
• An account for your access point:
  • ChatGPT website or app
  • LMArena
  • OpenRouter
  • Codex extension or app
• Some cost awareness: especially regarding input costs past 272k context
• Grounded expectations: leaderboard rankings don’t guarantee your actual experience will feel noticeably better

The sources I cross-referenced are OpenAI’s official landing page, developer docs, Pro documentation, and system card. If you plan to use the API, verify parameters directly against official docs instead of relying on second-hand summaries.

Follow This Order

Step one: pick your access point.

If you just want an initial feel for how it performs, go straight to the ChatGPT website or app. Free accounts can use 5.4 too, but the quota is small. This is fine for testing the waters, not for heavy reliance.

If you want to run side-by-side comparisons for free, check out LMArena. It already has 5.4 live, but has output length restrictions, making it better for quick head-to-head evaluations rather than running complex tasks.

If you’re already in the API ecosystem, OpenRouter is the smoothest route. It added two model endpoints:

• openai/gpt-5.4
• openai/gpt-5.4-pro

Step two: confirm whether you actually need a 1M context window.

On paper, 1M sounds great, but not every scenario justifies the price tag. A key detail: in the Codex extension and app, context is capped at 258k by default; some users reported that you can push it to 1M by editing configuration files, but anything past 272k will be billed at double the quota.

The plain-English translation is simple: just because you can turn it on doesn’t mean you should. If you’re just writing code, editing a few documents, or analyzing a few dozen pages of material, 258k is often plenty. 1M only delivers real value when working across a vast number of files, massive knowledge bases, or ingesting huge batches of material at once.

Step three: decide whether to go with Pro.

What can be confirmed right now:

• Pro subscribers get unlimited access
• Plus cannot use Pro
• Some say Business is still capped at 15 queries per month, but I haven’t received official confirmation

I couldn’t verify the Business quota details. If you’re on a team account, check your admin console or official documentation before upgrading based on a random forum post.

Step four: if you’re a coder, take a look at /fast mode next. Codex added a /fast mode that burns 2x the quota for roughly 1.5x the speed according to official claims, though some users measured it closer to 1.35x in practice. Standard accounts support /fast as well.

Best use cases:

• Rapid switching between small tasks
• Fast trial-and-error
• When you care more about waiting less than burning quota

If you’re already pinched on quota, don’t turn it on.

Where People Stumble Most

The first pitfall: using leaderboards as a reason to buy.

Benchmark results at launch: 7th overall on LMArena, 5th on LiveBench, 1st on FrontierMath, while Code Arena and Artificial Analysis hadn’t fully indexed it yet. Leaderboards are fine to look at, but don’t treat them as your final decision factor. Math problems, synthetic benchmarks, and real-world day-to-day feel are completely different things.

The second pitfall: underestimating output costs.

The most glaring issue this generation is that output unit prices remain painfully high. If you frequently have the model generate long answers, long reports, or extensive code, costs will run higher than you expect. Especially with Pro, output at $180 per million tokens is not “casual experimenting” money.

The third pitfall: treating 1M as the default setup.

Long context doesn’t come for free. Past 272k, input pricing doubles. If you don’t have a clear upside, turning it on forcefully is just volunteering for a bigger bill.

The fourth pitfall: assuming the Codex line and the chatbot line are completely in sync. gpt-5.4-codex wasn’t released this time around; OpenAI now seems to ship Codex and chatbots along separate tracks, neither waiting for the other. That is, updates you see in ChatGPT won’t necessarily be reflected in the coding product line at the same time.

My Advice

If you just want to test out the new model, start with ChatGPT’s free tier or LMArena—there’s no rush to pull out your wallet.

If you’re an API user, gpt-5.4/thinking is the more practical starting point. It’s already far from cheap, but at least it stays within an acceptable range for trial and error. Stress-test it against your real-world tasks first before deciding whether to jump to Pro.

If you’re a heavy coder, run with Codex’s default context first, and only consider bumping it to 1M when you genuinely run out of context. Don’t max out costs right out of the gate just to have the “top-tier spec.”

If you’re handling corporate accounts or team procurement, ignore the forum chatter about Business rate limits and verify them directly in your admin console or the official docs.

This generation of models is best suited for people who know exactly why they need it. When your use case isn’t crystal clear, don’t let specs and leaderboards sway you. Run the numbers first, then scale up.

These prices and shared seat rates are a snapshot I took in late February 2026 from forum flea markets and Xianyu, reorganized around “how to buy, is it worth it, and where things easily go wrong.” AI subscription prices change frequently, so verify the current official price one more time before placing an order.

Who This Is For

• Anyone planning to use tools like ChatGPT, Claude, Gemini, or Cursor long-term
• Anyone on a budget who doesn’t want to constantly fiddle around just to save a few bucks
• Anyone who frequently looks for shared seats on Xianyu, forums, or flea markets
• Anyone who needs APIs, long context windows, image generation, or speech-to-text

If you just want to test things out occasionally, paying the official monthly price directly is the least hassle. If you’re a heavy daily user, only then consider group subscriptions or third-party top-ups.

First choose based on your use case, then choose based on your risk tolerance:

1. Just want stability with minimal hassle: prioritize official pricing, or personal group subscriptions from forum members with clear posting histories.
2. Looking to cut costs: check forum group subscriptions first, then Xianyu. Deals close fast on Xianyu, but there are far more pitfalls stemming from information asymmetry.
3. Need APIs, 4K image generation, or long context: don’t just look at memberships—many capabilities are only available via API. Big Banana 4K is a case in point: Pro, Ultra, AI Studio, NotebookLM, and Flow don’t include it; it’s only available via the paid API.
4. Seeing a price noticeably below market rate: first suspect the wrong version, wrong quota, too short a warranty, or that it’s simply not the same product.
5. Cheap doesn’t mean cost-effective: monthly passes missing a few days, warranties that only cover the first login, or dragged-out refunds when issues arise all eat up your time.

What You Need to Prepare

• A clear use case: chatting, coding, image generation, video creation, or speech-to-text—don’t mix them up when buying
• Payment method: ideally get your own U-card or a bank card supporting foreign currencies with normal exchange rates
• An acceptable risk boundary: can you accept throwaway monthly accounts, shared quotas, or having your chat history wiped?
• Pre-purchase checklist:
  • Is it an official account, family plan, group subscription, or API relay?
  • Is the quota dedicated or shared?
  • How long is the warranty?
  • How many days are left when delivered?
  • Does the warranty only cover the first login?
  • Does it require a specific network environment, or risk getting banned easily?

For the virtual card route, pre-activated cards on Xianyu cost about ¥16 to open, with a top-up exchange rate of 8—not cost-effective, but hassle-free. A $1 card costs about ¥8, usually only suitable for passing trial verification.

Follow This Order

1. First, confirm whether you are buying a “membership” or an “API” This step is the most critical. Many people mix up memberships and APIs when comparing prices, and end up buying the wrong thing. For example:

   • Google Ultra officially costs $250/month, half off for the first three months. The focus is on membership perks like Gemini, NotebookLM, and Google Drive—it does not include APIs.
   • GCP 300 trial accounts are a different route: a $300 credit to use within 90 days, which can run Gemini API, Opus API, servers, and more, but it easily gets banned in a subpar environment.

2. Next, check value for money by product

   Product	Official Price	Typical Second-Hand / Group Price	Notes
   Google Ultra	$250/month (half price for first 3 months)	Xianyu 5-person group ~¥260, personal group ¥150–180	Below ¥230 usually has a catch
   ChatGPT Business	$30/month	Admin ¥12, member ¥8 (1-month trial)	Monthly throwaway, chat history not kept
   Claude Code 20x	$200/month (shared quota ~$2700)	Forum 3-person group ¥500, 4-person group ¥350	Split by quota
   Claude Code Pro	$20/month	Nigerian App Store ~¥87	Apple ID route
   Cursor	——	Pro trial ¥12 (7 days), Business pre-made account ¥88	Short trial period
   Perplexity	$20/month or $200/year	Voucher code ¥50, annual membership ~¥140	Better value annually
   Manus	$20/month = 4000 points	Trial accounts sold separately based on points and duration	Works out to ~$5/1,000 points

3. If you need image and video generation, calculate separately

   • Big Banana 4K: official price for nano-banana-pro-4k is $0.24/image, and $0.15/image for nano-banana-2-4k; API relay is commonly ¥0.2/generation, while upstream wholesale is around ¥0.1/generation
   • Sora2: 10 free generations per day in the app; beyond that, a 10-second clip is about $0.4/generation; API is $1.2 for 720p 12s, and $6 for 1080p 12s
   • Seedance 2.0: Jimeng officially prices by package; amortized over an annual plan, the cost for a 10-second clip ranges from roughly ¥0.84 to ¥1.5

4. If you need speech-to-text, buy separately

   elevenlabs-scribe API tiers:

   Tier (Official / Xianyu)	Credits	Approx. Transcription Time
   $5 / ¥25	40,000 credits	~20 hours
   $11 / ¥45	110,000 credits	——
   $99 / ¥200	500,000 credits	——

5. Finally, choose your purchasing channel

   • First choice: Official pricing
   • Second choice: Personal group subscriptions on forums
   • Third choice: Xianyu The forum flea market is the main trading area; if you can’t see it, you need to join the trade group first. This path filters out some of the low-quality sellers.

The Most Common Pitfalls

1. Bait-and-switch pricing: putting an ultra-low price in the title to lure you in, then claiming it’s out of stock, the link was wrong, or the stable version costs more.
2. Incomplete billing periods: you buy a monthly pass, but only 25 days are left when it’s delivered, shaving off a sixth of its actual value.
3. Passing off inferior tiers as premium ones: masquerading Enterprise editions as personal accounts, or selling cheap APIs that are actually reverse-engineered workarounds.
4. Diluted quality: among cheap Claude APIs, some are actually reverse proxies via AWS/Kiro or similar providers, potentially loaded with system prompts, nerfed responses, or truncated context.
5. Warranties that are too short: “first-login only” or “3-day only” warranties signal that the account gets banned easily by nature, regardless of how generous the seller seems. GCP 300 trial accounts are a classic example.
6. Dragging out refunds: the seller neither rejects nor approves the request, stalling you for a week until it auto-refunds. You get your money back, but your time is wasted, and you can’t even leave a negative review.
7. Judging by surface-level reputation alone: transaction counts, Yu Xiao Pu store tiers, profile pictures, and bios don’t matter much. Look at the positive feedback rate and what negative reviews actually say; ideally, the positive rating should be between 98% and 99%. Pay even closer attention to how the seller responds to negative reviews—that’s basically the treatment you’ll get when things go south.

FAQ

Official Pricing, Group Subscriptions, or Xianyu: Which Should You Choose?

Choose first by your use case, then by your risk tolerance: if you just want to test things out occasionally, paying the official monthly price directly is the least hassle; if you’re a heavy daily user, only then consider group subscriptions or third-party top-ups. The priority for buying channels is official pricing first, personal forum group subscriptions second (from members with clear posting histories), and Xianyu a distant third—deals close fast on Xianyu, but there are far more pitfalls stemming from information asymmetry.

Which Pitfalls Must You Avoid at All Costs?

Bait-and-switch pricing in titles claiming out-of-stock, monthly cards delivered with days missing, Enterprise versions masquerading as personal accounts, cheap APIs that are actually AWS / Kiro reverse proxies (which may come with system prompts, nerfed responses, or truncated context), “warranties” covering only the first login or just three days (GCP 300 trial accounts are a prime example), and sellers dragging out refunds for a week by neither approving nor rejecting. For reputation, don’t look at transaction counts or levels—look at the positive feedback rate (ideally 98%–99%) and how the seller responds to negative reviews.

Between Region Switching and Group Subscriptions, Which Is More Stable?

The “Apple account route” in the price table is itself a region-switching method—like Claude Code Pro on the Nigerian App Store for ~¥87—which has a completely different pricing structure from group subscriptions. If you want a detailed walkthrough on region switching, see the Practical Guide to Switching a Mainland China Account to a Turkish Apple ID; the “Official > Forums > Xianyu” risk hierarchy in this article applies just the same. For more roundups like this, check out the AI Subscriptions: Saving Money and Avoiding Pitfalls topic.

My Recommendations

If you’re a power user, prioritize stable options instead of constantly swapping accounts, group subscriptions, and API relays every single day. What you save isn’t just a few bucks—it’s your attention span.

Here’s how I break down my actual recommendations:

• Daily chatting and office work: start with accessible, low-friction options like ChatGPT Business or Claude Code Pro
• Heavy coding and long context: step up to Claude Code 20x or buy the Claude API separately
• Heavy Google ecosystem users: Google Ultra is a better fit, especially if you actually take advantage of the Gemini, NotebookLM, and Google Drive combo
• Image and video generation: calculate the cost per generation directly—don’t fool yourself into thinking one membership will cover everything
• Whenever you see “ultra-low price, instant delivery, first login warranty only”: Treat it as high-risk by default; don’t gamble on luck

Pay official pricing whenever you can, stick to forums over Xianyu when you can’t, and get your own card instead of relying on third-party top-ups long-term. Much of what passes for “saving money” only looks good on paper; what you actually lose is your time, chat history, stability, and peace of mind.