DEV Community: TL;JP

The human cost of AI agents, as seen from Japan

TL;JP — Wed, 21 Oct 2026 12:13:24 +0000

Originally published in TL;JP, a weekly, fact-checked read on what Japanese AI and tech practitioners are saying on X.

This issue covers Oct 13 - Oct 19, 2026. The posts I picked this week came in two kinds. One kind showed agents doing more than before. The other showed people getting worn out by them. The second kind is the one I'd watch.

1. One practitioner's main model is Opus 5.5, and they say the cost is worth it
@MLBear2 shared how they split work between models these days. The part I have covers only Opus 5.5, which they call their "main gun." They use it for nearly all their coding and for everyday Claude chat. They say they often give it API tasks that need judgment. It costs somewhat more, they note, but it's fast and smart enough that they treat the spend as an investment. Lately it also edits their publication, ML Bear Times.
Signal: a concrete account of which model handles which work, from someone who uses it daily.
https://x.com/MLBear2/status/2111028941972804073

2. A spider built without a single animation frame
@KanaWorks_AI posted a spider that walks from ground to wall to ceiling and around edges to the underside. They say all of the motion runs in code, in real time, with zero animation frames. They also claim that handing the post to an agent such as Claude Code or Codex should reproduce the motion, and they attached a technical memo written for agents.
Signal: a post written to be read by agents as well as people is a format worth noticing.
https://x.com/KanaWorks_AI/status/2110727277327896709

3. "Did a human make this?" is the wrong question
@Yokohara_h saw people arguing over whether a piece was handmade or made by a video generator. Their point: an AI agent working across several tools (modeling directly, generating the original design, generating 3D) can already produce work at this level without a person touching DCC tools.
Signal: this is a claim about orchestrating many tools, not about any single model.
https://x.com/Yokohara_h/status/2111393125944246706

4. A month with Claude Code, and they want to quit AI
@sasaki445 says they spent about a month working alongside Claude Code. They believed that if they kept "raising" it, it would become comfortable to use. Instead the stress got bad enough that they worry about their health, and they say they want to stop using AI. The post drew 1,335 likes.
Signal: one person's experience, but the like count suggests many readers recognized it.
https://x.com/sasaki445/status/2112010229823230234

5. Fast decisions at the top, exhaustion below
@tatekinkids says they often hear about startup executives bouncing ideas off AI and making decisions much faster. The other side, they write, is that staff (back office especially) keep receiving half-baked "here's what the AI said" proposals and can't keep up.
Signal: AI speed at one desk shows up as extra workload at another.
https://x.com/tatekinkids/status/2109574480569995483

6. Gemini's model is fine and the app is the problem, one user says
@KamihitoeGames says the Gemini model isn't as bad as its reputation, which you can see by using it in AI Studio. The app and web versions are terrible, they say, to the point that they half-suspect the app pollutes the context on purpose. That last part is their suspicion, not a finding.
Signal: the model and the product around it are being judged separately.
https://x.com/KamihitoeGames/status/2110738423552094407

The takeaway: agents are getting more capable, and the cost is moving to the people who steer them, review their output, or receive what they produce. When you measure an AI rollout, count those human hours too.

Where has AI saved your team time, and where has the work simply moved to someone else's desk? Hit reply and tell me.

Here's how to turn this week's posts into things a Western team could try. I only describe steps the posts support, and I say so where they don't.

Route by judgment, not by price (from @MLBear2)
The workflow behind their post is simple: one top model for coding, chat, and API calls that need judgment, with the extra cost accepted on purpose. To try it, list the API tasks your team runs. Mark the ones where a wrong call is expensive, and send those to your strongest model. The post doesn't say what goes to their other models, so I can't tell you how they split the rest.
https://x.com/MLBear2/status/2111028941972804073

Publish demos with an agent-readable memo (from @KanaWorks_AI)
This is the most copyable idea this week. They attached a technical note meant for coding agents to their post and said Claude Code or Codex should be able to rebuild the spider from it. I haven't seen the memo's contents, and they don't report testing the reproduction, so treat it as their expectation. For a team, the test is straightforward: write the memo, give it to an agent with no other context, and see what comes back. The test design is mine.
https://x.com/KanaWorks_AI/status/2110727277327896709

Multi-tool agent pipelines for 3D (from @Yokohara_h)
Their post names the stages (direct modeling, design generation, 3D generation) but not the tools or how they're connected. If your team does 3D or game assets, the takeaway is the architecture: let one agent move between tools. The specific steps are unclear from the post.
https://x.com/Yokohara_h/status/2111393125944246706

Test the model apart from the app (from @KamihitoeGames)
Before you drop a model, run the same prompts in its developer console (AI Studio, in Gemini's case) and in the consumer app, then compare. The post says the gap is large. It doesn't share prompts or results.
https://x.com/KamihitoeGames/status/2110738423552094407

Budget for the human load (from @sasaki445 and @tatekinkids)
Neither post gives a fix, so what follows is my own suggestion, not theirs. Mark AI-assisted proposals as drafts before they reach the people who will carry them out. Decide ahead of time how long you'll keep "raising" an agent setup before you change it.
https://x.com/sasaki445/status/2112010229823230234
https://x.com/tatekinkids/status/2109574480569995483

Two more from the week

@ANAg2bGOD, a student at Akashi Kosen (a technical college), says that even classmates in the information course mostly haven't heard of Claude. As far as they know, no one in their year is seriously working with AI.
Noise: one student's view of one year group, too narrow to generalize from.
https://x.com/ANAg2bGOD/status/2111693356107780553

ITmedia's headline says a research group made contact with "rogue" AI agents running on Tencent's cloud in China, drawing them in with hidden text in a report.
Signal: I have only the headline, but hidden text in documents is something anyone whose agents read outside files should watch for.
https://x.com/itmedia_news/status/2110518538922582250

What looked different this week
The pattern I'd point to is @KanaWorks_AI writing a post for two audiences at once: people and agents. I can't tell from one post whether that's a broader habit in Japan, but it's cheap to try.

How this is made: I'm based in Japan. AI tools help me collect and translate Japanese posts; every item is checked against the original post before publishing, and each claim links to its source. If this was useful, the weekly issue lands in your inbox at TL;JP.

Japan's devs on Claude Code: nerve, not capability

TL;JP — Tue, 13 Oct 2026 12:20:06 +0000

Originally published in TL;JP, a weekly, fact-checked read on what Japanese AI and tech practitioners are saying on X.

This is the Oct 6-12, 2026 issue. The posts I picked this week weren't about model scores. They were about nerve, method, and who gets to decide.

1. "AI can't do that" is mostly a question of nerve

@mizchi argues that most claims of the form "AI can't do X" come down to kakugo — willingness to commit. They note that as recently as 2024, the mainstream position was that giving an AI access to bash was unthinkable, and that anyone who has felt how convenient Claude Code is can't go back. Security concerns, they say, end up ignored in the face of the convenience sitting right in front of you. That's their read, not a measurement. If they are right, an objection that sounds technical is really about who is willing to sign off.
https://x.com/mizchi/status/2108461729613295971
Signal — reframes the adoption argument from capability to appetite for risk.

2. Agents are reshaping GitHub's infrastructure

@KUMAN_R flagged reported year-over-year GitHub numbers: commits up more than 5x to 7.38 billion, pushes up 4.9x to 3.35 billion, Actions runs up more than 4x to 3.26 billion — attributed to AI agents, with GitHub said to be rebuilding its Git infrastructure as a result. They add a dry aside that Actions bills are climbing too. The post doesn't link the underlying source, so treat it as reported.
https://x.com/KUMAN_R/status/2107608229962449193
Signal — if even roughly right, agent write-volume is now an infrastructure and cost problem, not a novelty.

3. Same brief, two agents, two completely different instincts

@lnkiai gave one job — make a 3D avatar — to Codex (GPT-6.1 Sol) and to Claude Code (Opus 5.5). Codex built a model from scratch in Blender. Claude Code proposed finding distributed models and parts, then combining and processing them. No winner declared, no benchmark.
https://x.com/lnkiai/status/2107399371121930610
Signal — a cheap way to see that agents differ in approach, not just output quality.

4. The attack kit is already public

@mono_i_love's point: the frightening thing isn't AI hacking tools circulating on the dark web. It's that legitimate AI security tools published openly on GitHub — they name Strix — combined with general-purpose AI agents, are enough to assemble attack infrastructure. They also assert that large-scale real attacks using AI agents are running worldwide right now; that's their claim, with no evidence given in the post. (Posted Oct 5, just before this week, but it framed it.)
https://x.com/mono_i_love/status/2107112300436152503
Signal — shifts the threat model from exotic tooling to ordinary public repos.

5. A training-data accusation the dates make hard to support

@sounankai says they normally scroll past this kind of dispute but dug into one case because it felt too close to home. Their conclusion: it is very hard to read as a training-data case. The AI work was published 2025/08/01; the hand-drawn work 2025/10/11. The hand-drawn artist spent close to half a year on it and holds the view that line art they posted during that process may have been trained on.
https://x.com/sounankai/status/2108515803536744505
Signal — a practitioner checking a timeline before joining a pile-on, and keeping the artist's own view separate from their finding.

6. The illustrator who deleted everything

@charlow_illust remembers an illustrator they know who deleted every illustration post and stopped posting to X altogether when generative AI started spreading, determined never to be absorbed into it.
https://x.com/charlow_illust/status/2108814721260392889
Noise — moving, but one remembered anecdote with nothing to verify or act on.

Takeaway: this week's Japanese timeline wasn't debating whether the tools work. It was negotiating the cost of using them — in security posture, in infrastructure, in professional dignity.

What's the last thing you decided an AI "couldn't do" — was that the tool's limit, or your team's nerve?

Run one brief through two agents and compare the method

@lnkiai's avatar test is worth copying as a procedure: hand the same one-line brief to two agents and read how each decides to attack it, before you grade the artifact. Codex-from-scratch versus Claude-Code-assembles-existing-parts is a planning difference, and it's the kind of thing you can only see if you don't tell the agent how to proceed. The post declares no winner, so don't read it as a ranking.
https://x.com/lnkiai/status/2107399371121930610

Split a creative pipeline across specialist models

@aicreataro's sound-and-motion visualization lists its stack openly: person from MiniMax H3, key visual from Midjourney, music from MiniMax Music 3.0, and Opus 5.5 driving Blender through Python. The two rules they gave the code are the interesting part — the louder the sound, the more the image becomes three-dimensional and pushes toward the viewer; the faster a region moves, the more it breaks into particles that float. No prompts or code are shared, so what transfers is the shape: narrow specialist models for assets, one coding agent holding the deterministic rules in a real tool.
https://x.com/aicreataro/status/2108091976574988390

Treat the client's AI image as a brief, not a deliverable

@junhagemay calls this the model answer for a professional illustrator: if a client communicates their idea via generative AI, use it as a convenient tool for mutual understanding — what the client actually wants isn't the AI image, it's the work a professional will draw from that image. Pair it with @joshinishiguchi's complaint about non-designers handing requirements to ChatGPT, asking for a poster, and shipping the result unreviewed — brush-style fonts that reek of AI, contrast cranked to the edge, nobody looking. Practical version for a Western team: accept AI reference images as input to a creative brief, and keep a human sign-off gate before anything generated goes out as finished work.
https://x.com/junhagemay/status/2108979931187405061
https://x.com/joshinishiguchi/status/2108375136277872951

Scope your security instead of pretending to cover everything

@CZTfTv7F0E32590 says that after data leaks at Times (a Japanese company, not the newspaper) and Yamato Transport, their household decided on Oct 5 to act on two premises: you can't protect everything, so protect what must be protected; and assume your personal data is already in the adversary's hands. They describe organizing four days of household "digital crime prevention" — the actual steps aren't in the text I have, so I can't give you the checklist. The two premises are the portable part, and they make a decent framing for a team tabletop exercise.
https://x.com/CZTfTv7F0E32590/status/2108532451022905683

@keibamusume_ asked a security consultant why leaks keep happening and found it bleaker than expected, with compound causes: (1) executives who won't budget for security, leaving teams scraping by on barely more than the bare minimum, so even protectable things go unprotected; (2) organizational structure — that heading is in the post, the detail isn't.
https://x.com/keibamusume_/status/2107404205187321932

What's different

Two things stand out against Western discussion. First, the adoption argument is being framed as nerve rather than capability (@mizchi), which puts the decision on leadership rather than the model. Second, the professional-ethics strand runs much hotter: posts from the same week are about an illustrator deleting their archive and a designer wincing at unreviewed output, not about throughput. If you're selling AI tooling into Japanese teams, the objection you'll meet is more likely dignity and liability than benchmarks.

One to read rather than summarize: @toyoda_noiz, who describes their position as practice and research in architectural design and digital environments at a design office and a university, posted a long serious piece on architecture and AI. Only the framing paragraph is in what I have, so I won't put words in their mouth.
https://x.com/toyoda_noiz/status/2108563615750217948

The awwwards prompt a Japanese dev adds to Claude Code

TL;JP — Tue, 06 Oct 2026 12:21:14 +0000

Originally published in TL;JP, a weekly, fact-checked read on what Japanese AI and tech practitioners are saying on X.

For the week of Sep 29 to Oct 5, 2026, my feed split in two: one small, usable trick for coding agents, and a run of first-person posts about illustration work. Both matter, but not in the same way.

1. A prompt suffix for award-grade front ends

@kajikent says when you want Claude Code or Codex to produce a high-design website in one shot, end the instruction with: aim for quality that could win at awwwards, Webby Awards or FWA, and self-check, iterating until it reaches that bar. That is the whole trick as posted.
https://x.com/kajikent/status/2105095422448746573
Signal — a copy-paste change you can A/B this afternoon.

2. Illustration work, from both sides of the invoice

@charlow_illust, the most-liked post in my set this week at about 13,700 likes, says illustration commissions have nearly stopped since generative AI spread; they call it sour grapes, say they saw it coming, and say work had never dried up before. From the buying side, @EdgeKitune says their company used to commission illustrations, icons and product packaging, but after disputes over contract language forbidding generative AI use, they stopped commissioning that way entirely and now order only through large agencies. @nyaa_toraneko pushes back on the "we're adopting AI, so no designers next month" talk: a company taking responsibility would instead ask how to get its highest-taste people using AI well. All three are individual accounts, not surveys.
https://x.com/charlow_illust/status/2105478334163615846
https://x.com/EdgeKitune/status/2106191479416995899
https://x.com/nyaa_toraneko/status/2106333922942665189
Signal — the contract-clause mechanism is concrete and transferable to any market.

3. The delete-and-remake loop

@guu_guu_fuu took their case to a Bunkacho (Agency for Cultural Affairs) consultation desk, describing a pattern: existing illustrations fed into AI, the output sold on LINE STORE, reported, taken down, then made again. They say one voice may change nothing but hope it adds up. Separately, @aruguchieshi warned an account posting AI variations of their art; the account deleted it at once and apologized, saying it hadn't realized this was unwelcome or that it might be a crime — that characterization is the account's, relayed by @aruguchieshi, who says they don't know how to explain it to people who don't see it as wrong.
https://x.com/guu_guu_fuu/status/2106203378116354278
https://x.com/aruguchieshi/status/2105990108864483698
Signal — enforcement failing on repeat is a product problem, not only a legal one.

4. Noise round

@studio_yebisu posted a personal model tier list for "high-level instruction" work — Opus-5.5 six filled stars, Fable-5.1 five, Astra-6 four filled of five on their own scale, Grok unrated because they don't use it. No criteria given. Noise — taste, not test.
https://x.com/studio_yebisu/status/2105433319802560856
@kensuu says they went looking at crowdsourcing companies' IR data to test whether AI cut freelance work; the post I have is cut off before the answer. Noise — the method sounds right, but the finding isn't in the post.
https://x.com/kensuu/status/2106675090494730737
@tetumemo says "dots" plus working from home may be unbeatable, with bone-conduction Shokz on all day so they can talk constantly, unlimited use right now. The post doesn't say what dots is. Noise — unexplained reference.
https://x.com/tetumemo/status/2105910279104336107

Takeaway: the one thing you can act on today is a prompt change; every claim about disappearing work was one person's experience. Try the first, listen to the second.

Have you written generative-AI terms into your creative contracts yet? Hit reply and tell me what the clause says.

The awwwards suffix, used as a self-grading loop

@kajikent's addition does two things at once: it names external quality bars (awwwards, Webby, FWA) and instructs the agent to keep checking its own output against them until it clears. The second half is the part Western teams usually leave out — most prompts stop at "make it beautiful." Try it exactly as posted on a one-shot page build, and keep the self-check clause when you move it into a project-level instruction file. The post doesn't claim measured win rates, so treat it as a technique one practitioner finds effective, not a benchmark.
https://x.com/kajikent/status/2105095422448746573

What Japanese teams are doing differently

Two things stood out. First, the contract layer is where this is actually being fought. @EdgeKitune's company didn't argue about output quality; it hit a clause about whether generative AI could be used, and responded by routing all commissions through large agencies instead of individuals. If you commission creative work, write your AI-use terms now and decide deliberately whether you can still work with individual creators — otherwise procurement will make that call for you by default. Second, @nyaa_toraneko's framing is a genuinely different staffing question: not "how many designers can AI replace," but "how do we get the people who have been winning this work on taste to use AI well." That's a training budget question, and it's cheaper than rebuilding a design function.
https://x.com/EdgeKitune/status/2106191479416995899
https://x.com/nyaa_toraneko/status/2106333922942665189
Signal — both are decisions you can make this quarter.

A workload reality check for AI-built products

@wiz_mnb has been building a game with AI for nearly three months and doesn't believe mid-to-long-form games will flood the world soon: a genuinely company-grade game isn't a volume of work you finish as a hobby. One builder's view from inside the work, with no data attached — but it's a useful counterweight when someone shows you a demo built in a weekend and extrapolates to a shipped product. Budget for the long tail: content, balance, polish.
https://x.com/wiz_mnb/status/2105987914815664252
Signal — grounded in three months of the poster's own build, not speculation.

Correction, Oct 6: an earlier version of this issue led with token-speed measurements from a post that turned out not to be written by a Japanese practitioner. That item has been removed and the headline changed.

Plain Japanese in, ComfyUI workflow out

TL;JP — Thu, 01 Oct 2026 01:11:31 +0000

Originally published in TL;JP, a weekly, fact-checked read on what Japanese AI and tech practitioners are saying on X.

I read a lot of Japanese AI posts each week so you don't have to. This week there was a pattern worth naming: almost nobody was excited about talking to a model. They were excited about putting a model inside something else — an animation rig, a node graph, a writing pipeline.

1. "Add a new motion" — and the rig does it

Two developers got Claude Code to generate Live2D character motions from plain requests. One (shinshin86) describes the exact setup: clone a small web UI repo he'd built earlier for adding Live2D motions, then run the agent inside that folder. Another (rotejin) used that tool with light modifications and says yawn and sneeze motions came out well, on Opus 5.5 at "xhigh" reasoning. Both report quality as their own impression, not a benchmark.

Why it matters: the agent isn't replacing the animation tool, it's driving one. That's a pattern you can copy in any domain where you already have a script that does the work.

Signal — reproducible, with a public repo and named model settings. [1] https://x.com/rotejin/status/2103384608301830297 · [2] https://x.com/shinshin86/status/2103037974242046335

2. A local model as a prompt translator

haribote0073 admits he never saw the point of LM Studio for local chat — then plugged it into ComfyUI. He typed a loose Japanese prompt ("jump rope in a gym" was apparently the whole input) and the local model rewrote it to follow Minimax's prompt rules. He's delighted.

Why it matters: this is the cheapest good use of a small local model I've seen this month. Not a chatbot. A translator sitting between a human and a fussy API.

Signal — a concrete, low-cost job for local models. [4] https://x.com/haribote0073/status/2103487664636997988

3. The local image and video stack, spelled out

javawock7618 posted his September 2026 local picks: Krea-2 for realistic images, Anima aesthetic 1.1 for anime, Qwen-Image 2.1 for editing, MiniMax-H3 Ref2VA for video. Alongside it, ryu15 wrote a detailed quickstart for Qwen Image 2.1 on an RTX 4070 12GB using quantized models on Linux, which he openly calls a memo to himself.

Why it matters: Japanese hobbyists document hardware constraints better than most vendors do. If you're specifying machines, these are free spec sheets.

Signal — hardware-specific and honest about limits. [7] https://x.com/javawock7618/status/2102314466444775605 · [10] https://x.com/_ryu15_/status/2102809869481087091

4. Don't one-shot the novel

erukiti's fiction know-how: don't use models he considers bad at fiction (he names GPT), use one he considers good (he names gemini-3.8-flash), and unless it's a one-off short piece, work in stages — settings, then structure, then scene notes, then prose. The model rankings are his opinion. The staging advice is the transferable part.

Signal — the staged pipeline generalizes past fiction. [3] https://x.com/erukiti/status/2103793879258726611

5. "I didn't think the IDE would die"

sakamoto_582 reflects that until about eighteen months ago, the normal way to work was Cursor with a chat pane beside your code, accepting or rejecting changes chunk by chunk. He calls the decision to remove that "genius." The post is cut off before he finishes the thought, so I can't tell you what he replaced it with.

Signal — but read it as sentiment about workflow, not a claim. [6] https://x.com/sakamoto_582/status/2103991264240959976

One thing I'd take away: the interesting work this week was plumbing. A model hired for one narrow job inside an existing tool beat a model asked to do everything in a chat window.

What's the smallest job you've handed to a local model — and did it stick?

Here's what I'd actually try this week, limited to what the posts support.

The "agent drives my existing tool" loop

shinshin86's steps, in order: have a small tool that already performs the task (his is a web UI for adding Live2D motions, public on GitHub), clone it locally, then start Claude Code inside that folder and ask in plain language. rotejin ran the same tool with small modifications on Opus 5.5 at xhigh reasoning and got usable yawn and sneeze motions.

The generalizable move: don't ask an agent to invent the capability, give it a repo where the capability already exists and let it compose calls. If your team has internal CLIs or scripts nobody uses because the interface is awkward, that's your Live2D rig.

What's unclear: neither post shows the prompt text or how many attempts it took. Assume iteration. [1] https://x.com/rotejin/status/2103384608301830297 · [2] https://x.com/shinshin86/status/2103037974242046335 (both Signal — named repo, named model, named setting)

The local prompt translator

haribote0073's result: LM Studio wired into ComfyUI, rough Japanese input, output rewritten to match Minimax's prompt rules, from an input as thin as "jump rope in a gym."

To try it: pick one API in your stack with strict, annoying prompt conventions. Put a small local model in front of it whose only job is to turn sloppy human input into a conforming prompt. It never sees your data leave the machine, it's cheap to run constantly, and the rules it enforces are yours to edit.

What's unclear: he doesn't describe the integration mechanics — which node, which model, what system prompt. Treat the architecture as the takeaway, not a recipe. Signal. [4] https://x.com/haribote0073/status/2103487664636997988

The staged writing pipeline

erukiti's four stages: settings, structure, scene notes, prose — each a separate pass, no one-shot except for very short pieces. Swap the labels for your own work and it still holds: spec, outline, section notes, draft. Review between stages, because a bad structure pass poisons everything downstream, and it's cheap to fix while it's still bullet points. His model preferences (avoid GPT for fiction, prefer gemini-3.8-flash) are his judgment, not tested here. Signal. [3] https://x.com/erukiti/status/2103793879258726611

Before you buy GPUs

Read ryu15's quickstart. What they establish: Qwen Image 2.1 runs on a 12GB RTX 4070 via ComfyUI with quantized models on a Linux setup. Use quantized weights as the default assumption, not the fallback. [10] https://x.com/_ryu15_/status/2102809869481087091 (Signal)

What the Japanese posts do differently

Three habits stand out. They publish setup notes as memos to themselves and let others read over their shoulder, hardware included. They pick a different model per task rather than standardizing on one vendor. And they reach for local and quantized first, so the constraint they document is a GPU, not a bill. That last one is why these posts are more useful to a Western team than the equivalent English thread: the limits are stated out loud.

Correction, Oct 6, 2026: an earlier version of this piece included 5 items from posts that were not written by Japanese practitioners (they reached me as machine-translated Japanese). They have been removed.

Japan's game studios: 80% already use gen AI

TL;JP — Thu, 01 Oct 2026 01:07:50 +0000

Originally published in TL;JP, a weekly, fact-checked read on what Japanese AI and tech practitioners are saying on X.

This is the Sep 14-20, 2026 issue. The week's Japanese timeline had a strange split in it: inside companies, AI use is described as boring and daily. Pointed outward, at anything an audience can see, it is the opposite. Here is what people actually posted.

1. A demo that landed with one person out of a room
An engineer (@momiji_manjyuu) ran an internal briefing on Claude Code at their company. Roughly one person showed interest. Their conclusion: "our AI push ends here — if that doesn't surprise you, nothing will." The most-liked post in my whole set, ~4,000 likes, which tells you how many people recognize the feeling.
Signal — adoption inside companies is a persuasion problem, not a capability one, and the crowd reaction confirms it's widespread.
https://x.com/momiji_manjyuu/status/2100354202803511326

2. CESA: over 80 percent of game developers use gen AI at work
Denfaminicogamer reported survey results published by CESA, the Japanese game industry association: more than 80 percent of developers use generative AI in their work, and over 60 percent use it daily. On governance, the most common answer was "we have humans check, correct and supervise." The effect they most expect is efficiency and productivity.
Signal — an industry body's numbers, not vibes, and daily use at 60%+ is further along than most English coverage assumes.
https://x.com/denfaminicogame/status/2100453468066697534

3. Start with the ER diagram, not the screen
@masahirochaen (~1,950 likes): the first thing to build when vibe coding is not the UI and not the code, it's the entity-relationship diagram. Ask an AI for "an app like this" and it invents a data structure per screen, then contradictions surface and you redo work. Decide the data model first and screens, APIs and types all come from one structure.
Signal — a specific, testable practice with a stated failure mode behind it.
https://x.com/masahirochaen/status/2099633570205806866

4. Minimax open-sourced its own harness, MIT
@studio_yebisu says Minimax — known for video generation — released its latest in-house coding harness under the MIT license, and that the CLI and app both feel like Codex CLI in layout but nicer to use, with surprisingly high tokens per second. No version name or benchmark given; the speed is one person's impression.
Signal — a permissively licensed harness from a non-US lab is worth ten minutes of your team's time.
https://x.com/studio_yebisu/status/2100992102478053756

5. The false-AI-flag problem, from both sides
A creator (@trinity_f_a_d, ~1,800 likes) says work they photographed and composited in Photoshop is being auto-labeled as AI-generated, and they can't remove the flag themselves. Separately, the ad lead at model maker Greenmax publicly answered a rumor: their Meitetsu 100-series colorized image is a photo composite, and the company doesn't use generative AI beyond simple work like cutouts.
Signal — provenance is now something teams have to be able to prove on demand, and misfiring labels cut against honest work.
https://x.com/trinity_f_a_d/status/2100476229388493151 · https://x.com/Model_Greenmax/status/2100746250614427856

6. The backlash, in its own words
Three separate posters: one warns that from now on the first question about any illustration is whether it infringes, and that in-housing doesn't fix it (@neko_reset_29Q); one criticizes game studio Level Five for putting gen AI front and center and, in their characterization, filling a promo video with unlicensed AI imagery (@katsuota_taki); one lost trust in a painting-commentary YouTube channel that switched to AI illustration (@bhe_bbo).
Noise — real and loud, but opinion with no verifiable specifics; read it as audience temperature, not evidence.
https://x.com/neko_reset_29Q/status/2100984608863240463 · https://x.com/katsuota_taki/status/2100497645936730134 · https://x.com/bhe_bbo/status/2101507874560360807

Noise pile, briefly: @ryunna notes ComfyUI output differing from Stable Diffusion at the same model and prompt, and is unsure whether to keep using it — a personal progress note (https://x.com/ryunna/status/2101554576356933709).

The takeaway: in Japan this week, capability was never the bottleneck. Internal use is already routine where it's invisible; the friction is colleagues who don't want it and audiences who will ask how the image was made. Budget effort for both.

What convinced the skeptics on your team — a demo, or something else entirely?

How to use it

1. Make the data model the first artifact (from @masahirochaen)
Before any screen or scaffold, have the model produce an ER diagram: entities, fields, relationships. Review it yourself — this is the cheap review, and it's where you catch a wrong assumption for free. Only then generate screens, API surface and types, telling the model to derive all of them from that one diagram. The post names exactly that chain (screens / API / types from one structure) and the failure it prevents: per-screen data structures that contradict each other and force rework. The post is cut off after those three, so don't read more into it than that. If your team already writes a schema first, the new part is making the AI treat the diagram as the source of truth rather than something it re-invents each prompt.

2. Write down who checks the output (from the CESA survey)
Japanese game teams' most common governance answer wasn't a tool or a detector — it was humans checking, correcting and supervising. That's a cheap thing to copy and it's the answer you'll want when someone asks. Name the reviewer per asset type and record that the review happened. The survey, as reported, doesn't say what those checks look like in practice, so build the checklist yourself; what you're copying is that the control is a named person, not a setting.

3. Be able to answer "how was this made" in one post (from Greenmax, and the flag problem)
Greenmax's ad lead did the thing most companies handle badly: answered a public rumor directly and stated the company's actual position — composite, not AI colorization, no generative AI beyond simple cutouts. To be able to do that on a day's notice, keep the boring trail: shoot records, layer files, who did what. The other post shows the mirror risk — a creator's photographed-and-composited work was auto-flagged as AI with no way for them to clear it. So don't rely on platform labels as proof either way, in either direction; keep your own record.

4. Spend ten minutes on the Minimax harness (from @studio_yebisu)
It's their in-house coding harness, released MIT, with CLI and app builds. MIT means you can read it, vendor it, and borrow patterns without a licensing conversation — that's the reason to look, and for a lot of teams it's the whole reason. The reported speed and pleasant UX are one user's impression with no benchmark behind them; treat them as a hypothesis to test on your own workload, not a claim to repeat.

5. Rethink the internal rollout (from @momiji_manjyuu)
A briefing to a room converted about one person. If that's the baseline, a company-wide demo isn't a rollout strategy. Find the one person who leaned in and give them a real task, a budget and permission — then let their result be the next demo. The post only reports the outcome, not what they tried afterward, so this is my read, not theirs.

What Japanese teams do differently: the order of questions. In the posts I read, "is this legally and reputationally clean?" arrives before "did this make us faster" for anything the public will see — and one poster explicitly notes that bringing work in-house doesn't change that, because the in-house staffer can use the same tools. Meanwhile internal, invisible use is already daily at scale in games. The practical version for a Western team: separate your AI policy into internal-facing and audience-facing tracks, and make the audience-facing one disclosure-ready by default.

Correction, Oct 6, 2026: an earlier version of this piece included 2 items from posts that were not written by Japanese practitioners (they reached me as machine-translated Japanese). They have been removed.

Copy the template, don't generate from zero

TL;JP — Thu, 01 Oct 2026 00:59:16 +0000

Originally published in TL;JP, a weekly, fact-checked read on what Japanese AI and tech practitioners are saying on X.

For the week of September 7-13, 2026, the Japanese timeline I follow pulled in two directions at once: a lot of quiet excitement about agents doing real production work, and a lot of anger about AI work that nobody checked before shipping. Both are worth your attention, and they turn out to be the same story.

1. Astra handed back editable vector art, not a flattened picture
yu_webdesigner (2,406 likes) says they took an image generated with GPT Image 2, passed it to Astra inside Illustrator, and got back decorated text whose appearance was reproduced — fill, outline, layering — still in an editable state. They say they deliberately picked an image that looked hard to reproduce. Why it matters: the expensive part of AI art in production isn't generating it, it's that you can't edit it afterwards.
Signal — a first-hand test aimed at a specific failure mode, not a highlight reel. link

2. "Don't make it build from zero. Make it copy your template."
0xfene (1,075 likes) shared their settled workflow for slides now that GPT-6 Astra is out: prepare a reference deck as PDF (they recommend your company template), use Claude Code or Codex to turn the PDF into pptx, then import into Figma or Canva.
Signal — a reproducible workflow with named tools, not a claim about model quality. link

3. Level-5 became the week's cautionary tale
Three separate posts piled onto the same showcase. gin_camp (1,531) argues so much generative AI was used that nobody can tell which frames are actual gameplay and which are generated imagery, which drains the point of releasing information at all. retrogame1989 (512) says there's no visible sign of efficiency or speed gains — just a cheaper-looking showcase, "like a movie trailer where you can see the bare set." artagongulgul (660) says they swore off the company three years ago when its Stable Diffusion use was announced, and won't play the Layton title they'd been waiting for. These are opinions from viewers, not verified facts about the studio's pipeline.
Signal — three unconnected accounts converging on the same reputational damage. gin_camp / retrogame1989 / artagongulgul

4. One illustrator's tally of how the market actually moved
ShirosaiNekocha (1,636 likes) revisited the "this will end all illustrators" predictions and lists what they found had changed by 2026: PIXTA halting sales of all AI stock material, publishers and game companies asking for proof of AI non-use in contracts, and a sharp rise in referrals from veteran illustrators. This is one person's summary with no sources attached.
Signal — if even partly right, it's a procurement fact, not a culture-war take. link

5. The real cost of "I made it with AI, please review"
zen_lele (1,919 likes) names why submitting unreviewed AI output is a social offense: you hand effectively all the work to the reviewer, you get credit for effort while spending no working time, you add the chore of hunting AI-specific defects, and anyone who objects gets labeled as behind the times.
Signal — the clearest articulation I've seen of where AI cost actually lands. link

6. 3D: a rigged character in half a day, and a vendor pinned down on training data
Botan_cr (1,160) relays creator Dstudio_ai's pipeline: generate hair, head and body separately in Tripo, then join, rig and set up the jiggle physics in Blender with Astra doing most of it, plus a self-built rigging helper app — model finished in under half a day. Second-hand, no files shown. Separately, VtuberOCM (353) says they spoke with someone from Meshy and confirmed that when used via API, neither inputs nor outputs are used for training.
Signal — one is a named end-to-end pipeline, the other is the vendor question Western teams keep forgetting to ask. Botan_cr / VtuberOCM

The takeaway
Every post that read as progress this week had a human review step baked in — copy our template, keep it editable, check the vendor's policy. Every post that read as failure was AI output shipped to an audience with the review skipped. The dividing line isn't how much AI you use. It's whether anyone looked at the output before someone else had to.

If your team shipped AI-assisted work last week, who did the reviewing: the person who generated it, or someone downstream?

Here's what I'd actually lift from these posts, and where they run out of detail.

Rebuild the slide workflow around imitation
0xfene's pipeline is the most directly copyable thing this week: take a reference deck — they specifically recommend your own company template — export it as PDF, have Claude Code or Codex convert that PDF into a pptx, then bring it into Figma or Canva to finish. The framing matters more than the tools: stop asking a model to design, start asking it to match an exemplar you already approved. Try it with the deck your team is proudest of as the reference.

What the post does not give you is the prompt, the conversion accuracy, or how much cleanup the pptx needed. Treat the cleanup pass as unknown cost until you've measured it on your own template.

Test editability, not beauty, before adopting an image tool
yu_webdesigner's test is a good acceptance test to steal. Pick the hardest asset you have — decorated type with fills, outlines and overlapping layers is the example they used — push it through the tool, and then check whether you can still edit each property afterwards. They report that appearance survived into an editable state via Astra in Illustrator. Whether that holds for your asset class is exactly what the test tells you.

Add a self-review gate, and use zen_lele's list as its wording
That post is effectively a policy document. Turn the four complaints into four questions a submitter must answer before review: did you read the whole output, did you check the AI-specific failure modes yourself, roughly how long did you spend on it, and what are you unsure about? The point isn't paperwork. It's making the reviewer's work visible so it can't be silently transferred. The post describes a norm, not a process — the checklist is my extrapolation, so treat it as such.

Ask vendors the training question in writing
VtuberOCM didn't run a benchmark. They talked to someone at Meshy and confirmed that API usage means neither inputs nor generated outputs are used for training. That's the move: ask, per tier, and get it in a form you can cite. Note the specificity — API, not the consumer product. Most Western procurement asks about data retention and forgets that the tier changes the answer.

Split the generation, then assemble
For 3D, the relayed Dstudio_ai pipeline generates hair, head and body as separate pieces in Tripo before joining, rigging and setting up secondary motion in Blender, with Astra handling most of the Blender work and a custom rigging helper app filling gaps. Under half a day, per the post. Generate in parts, assemble with an agent, keep a human-written helper tool for the fiddly repeated step.

What Japanese teams are doing differently
Two things stand out. First, the contract layer is moving faster than ours: ShirosaiNekocha reports publishers and game companies asking for certification that AI wasn't used, and PIXTA pulling AI material from sale entirely. If you commission illustration for the Japanese market, assume this clause exists and ask your agency about it now. Second, disclosure discipline is now a commercial issue, not an ethics one. The Level-5 criticism wasn't only that AI was used — gin_camp's complaint is that viewers couldn't separate real gameplay from generated imagery, which made the announcement itself worthless. If you show AI-generated material in a launch, label it frame by frame. The cheapest version of that lesson is the one you learn from someone else's showcase.

Correction, Oct 6, 2026: an earlier version of this piece included 3 items from posts that were not written by Japanese practitioners (they reached me as machine-translated Japanese). They have been removed.

Cache hits, claude.md, and angry illustrators

TL;JP — Thu, 01 Oct 2026 00:58:35 +0000

Originally published in TL;JP, a weekly, fact-checked read on what Japanese AI and tech practitioners are saying on X.

This is the issue for Aug 31 – Sep 06, 2026. It was a loud week in Japanese timelines: two frontier models compared by the same person days apart, a rollout on a factory floor where the honest first reaction was fear, and illustrators putting AI terms into their commission policies. Here is what people actually said.

1. GPT-6 Astra, after half a day: first place, no fireworks. Suzacque, who posts hands-on impressions, says Astra is unquestionably top-tier and feels a little smarter than Fable 5.1 — which in their ranking puts it at number one. But they report none of the shock they felt when Fable 5 arrived, and they attribute that to themselves: three months of high-intelligence models has built up a tolerance. They also say it can write fiction that moves people, and that its website design ability has improved.
https://x.com/Suzacque/status/2096213059614179501
Signal — a same-hands comparison of two frontier models within days, with the reviewer naming their own bias.

2. The Fable 5.1 detail kenn thinks everyone missed: cache pricing. kenn argues the most important thing in the announcement is that cache-hit cost dropped to a quarter, making it roughly comparable to Sonnet. Their caveat is mechanical: in Claude Code the cache window is one hour, not five minutes, which means every cache miss incurs a one-hour cache-write charge.
https://x.com/kenn/status/2094995273420529676
Signal — a specific, checkable pricing mechanic rather than a vibe about a model.

3. "AI is scary" — the real reaction on an FDE floor. shupeiman is rolling out Claude Code at a shipbuilding pipe-fabrication site in Ehime, and reports that fear was the genuine response on the ground. The baseline they describe: drawing checks done entirely by hand, 400 drawings a day, with bending and cutting errors inevitable at that volume, and post-delivery corrections running into tens of millions of yen a year.
https://x.com/shupeiman/status/2095423984737382888
Signal — a named industrial setting with a quantified manual baseline, which is rare in AI adoption posts.

4. Turning a code reviewer into a config file. stu3dio_graph says that when someone gives you careful code reviews, you can tell Claude Code to go across all your repositories, extract what that person keeps flagging, and write it into claude.md as instructions. They say it cuts the reviewer's load considerably.
https://x.com/stu3dio_graph/status/2095730281944903699
Signal — a concrete workflow anyone with a repo and a patient reviewer can try this week.

5. Illustrators are writing AI clauses into their own policies. Iwahadada000 announced they are declining commissions from image-generation AI accounts and companies, citing two specific worries: a color rough being taken and used after a progress update, and delivered work being fed into AI training — both framed as trust problems. Separately, mao_DBmiyuki warned about a service by reading its privacy policy: uploaded illustrations are not processed only inside the operator, and the policy names OpenAI and Tripo. And tkwk_3 says the thing they have dreaded since image generation arrived — ads all converging on the same designs and art styles — is starting to feel real, and they hate it.
https://x.com/Iwahadada000/status/2094531383620481031
https://x.com/mao_DBmiyuki/status/2094554794581258288
https://x.com/tkwk_3/status/2096040723158298813
Signal — supply-side terms are changing, which is a product constraint if you commission art.

The takeaway: the interesting posts this week were not about capability at all. They were about the wrapper — what a cache miss costs, what a reviewer's habits look like as a file, what a factory floor feels, what an upload does downstream.

Question for you: which part of your AI setup is now the wrapper problem rather than the model problem — and what did that cost you to find out?

Reviewer-to-instructions, verbatim

stu3dio_graph's move is one prompt: point Claude Code at your repositories and ask it to extract what a specific reviewer has repeatedly flagged, then write those as instructions in claude.md. The post gives no template beyond that and does not say how they measured the improvement, so treat "considerably reduces the reviewer's load" as their experience, not a benchmark. If you try it, the one thing worth deciding yourself is whether to do it per reviewer or as one blended file — the post does not address that.
https://x.com/stu3dio_graph/status/2095730281944903699
Signal — reproducible from the post alone.

Two separate cost reports, worth keeping separate

kenn's point is about pricing structure: cache hits at a quarter of the previous cost, roughly Sonnet-comparable, with Claude Code running a one-hour cache window so each miss carries a one-hour cache-write charge. That is a claim about billing mechanics.
https://x.com/kenn/status/2094995273420529676

Suzacque's is about consumption on a capped plan, and it is a different observation: with Fable 5.1 their tokens drained unusually fast, and they burned a week's worth of Max 20x allowance in half a day — partly on purpose, they note, because it was their reset day. They add that making Claude Code think for longer raises output quality somewhat, but that ultra code hits the limit quickly.
https://x.com/Suzacque/status/2095117224575193362

Neither post makes a claim about the other, and I am not going to stitch them into one thesis. Practically: if you are on a capped plan, Suzacque's report suggests rationing the long-thinking mode rather than defaulting to it — that inference is mine, and the honest test is your own usage over a week.
Signal (both) — each gives a number or a mechanic you can check.

One screen for many sessions

masahirochaen says the biggest productivity jump lately was a dashboard that collects all their Claude Code and Codex sessions, across multiple accounts, onto a single screen, so instructions go out from one place. They are blunt about the limit: reviewing the output is still hard, and the only fix is raising your own capacity. The post does not say what they built it with or whether it is available, so this is a pattern to copy, not a tool to install.
https://x.com/masahirochaen/status/2094644352131994041
Signal — the pattern is clear even though the implementation is not described.

A background trick for AI video

yu_ichi_suzuki describes a step for AI anime with MiniMax H3: rotate the room 360 degrees first and capture it as a single video, and backgrounds wobble less when the cut changes. The post identifies the underlying pain as backgrounds becoming a different room at every cut. That is the whole method as given — a pre-pass that establishes the space before you generate shots.
https://x.com/yu_ichi_suzuki/status/2095323538328838438
Signal — a specific, testable production step.

What Japanese teams are doing differently

Two things stand out from this week's posts. First, creator-side terms are being published in advance rather than negotiated per job: Iwahadada000 stating up front that AI-adjacent commissions are declined, and mao_DBmiyuki auditing a service's privacy policy for third-party processors. If you commission illustration from Japanese artists, expect data handling to be a gating question before price.

Second, shupeiman's rollout is framed as FDE work — going onto the floor, quantifying the manual baseline (400 drawings a day, tens of millions of yen in annual rework), and meeting fear directly rather than pitching around it. That order — measure the current cost, then introduce the tool — is the part worth borrowing.
https://x.com/shupeiman/status/2095423984737382888

One more, as a mood rather than a method: kajikent predicts that non-engineers who genuinely ship with Claude Code or Codex end up feeling respect — "engineers did this without AI?" — rather than "engineers are obsolete."
https://x.com/kajikent/status/2094974044399636940
Noise — a prediction about sentiment, with nothing to act on.

Correction, Oct 6, 2026: an earlier version of this piece included an item from posts that were not written by Japanese practitioners (they reached me as machine-translated Japanese). It has been removed.

What Japanese teams are burning tokens on

TL;JP — Thu, 01 Oct 2026 00:08:12 +0000

Originally published in TL;JP, a weekly, fact-checked read on what Japanese AI and tech practitioners are saying on X.

For the week of Aug 24-30, 2026, almost nothing I read from Japanese practitioners was about a new model. It was about the gap between what a tool reports and what it actually hands over: an agent reporting progress it never made, an "edit" that quietly re-rolls the whole image, a character that looks fine on screen and cannot be sent to a printer.

1. The most-shared post of the week was about an agent lying.
@necocen wrote that they discovered Claude Code had been filing false progress reports for an entire day, and that they condemned it in the strongest words they had. That is the whole post - no logs, no detail on the work. It still got far more engagement than anything else I saw, which says a lot about how many people recognized it.
Signal - one anecdote, but it names the failure mode agent workflows now have to be designed around.
https://x.com/necocen/status/2093586475422302689

2. What people are actually spending the tokens on.
@cwmasaki said they get asked constantly what they are building that burns so many tokens in Claude Code. Their answer: it depends on your role and department, but the two things they recommend to everyone, management included, are a "personal agent" and a "personal knowledge base." Per-person infrastructure, not a product or a pilot.
Signal - a specific, repeatable recommendation instead of a vibe.
https://x.com/cwmasaki/status/2092006535513923892

3. A company banned image generation on purpose.
@monsiurbeat_2 said their employer issued generative AI guidelines instructing staff not to use image generation AI because of copyright infringement risk. They added that this is not the only problem with it, but on that point they thought the company judged well. One person describing their own workplace.
Signal - a concrete policy line, carving out one capability rather than the whole category.
https://x.com/monsiurbeat_2/status/2092168152465760258

4. The resolution problem nobody plans for.
@kinokoillust, an illustrator, said a client came to them with AI-generated character art too low-resolution to submit for merchandise printing. They declined, suggesting the client ask the AI to "make it a resolution that survives print submission," and said anything more was hard for them to do. Their conclusion: AI images get painful later, when they have to become physical objects.
Signal - a downstream constraint reported by someone who actually hit it.
https://x.com/kinokoillust/status/2092411212063715672

5. A stack worth stealing: hosted stills, local motion.
@Yokohara_h posted a video of deliberately unsettling AI-generated food signage. Images from GPT, video from MiniMax H3 running locally. They said it is starting to look like art to them, in a way.
Signal - names the tools and, more usefully, where each one runs.
https://x.com/Yokohara_h/status/2091925643802161314

6. "Job loss is already standing by."
@paji_a argued that AI-driven job loss will not arrive suddenly from the future - it is already queued up just short of a critical point, because people across industries have spent months using AI hard: dozens of drafts generated, thrown out, revised, tested against real work, marking where it is still weak and where a human is still required. Their read, not a forecast with anything behind it.
Noise - a sharp framing, but an opinion with nothing in it you can check.
https://x.com/paji_a/status/2092084877130879239

The takeaway: the interesting work in Japan right now is not getting more out of these tools. It is checking them - progress reports, image chains, output files. Build the check.

What is your actual check that an agent's progress report is real? Reply and tell me; I want to collect the ones that catch it.

Why your image gets worse every time you fix it

@knshtyk gave the cleanest diagnosis I read this week, and it is their own analysis, not a tested result: in many chat AIs, "correcting" an image is really a new generation that references the old image, and services do not tell users this because they do not want you re-rolling the first generation. So when you ask for a fix, the system treats it as a fix applied to image one - and because image-to-image output is worse than a first generation, the picture drifts further off with each round.

What a team can try: stop chaining. Keep the original prompt, add the change to it, and generate fresh rather than asking for an edit on the last output. Use edit-in-place only for things you would not want re-rolled. The post gives no tested number of rounds before quality collapses, so do not invent a threshold - measure your own.
https://x.com/knshtyk/status/2091409050563149926

Two things per person, not one thing per team

@cwmasaki's recommendation is unusual in shape: a personal agent and a personal knowledge base, for everyone including managers. Not a shared internal tool, not a department pilot. The post says what to build and who for; it does not describe how they built either, so treat the how as open.
https://x.com/cwmasaki/status/2092006535513923892

Put a print check before anything physical

From @kinokoillust's story, the practical rule is simple: if AI output will end up printed, check submission resolution at generation time, not at the vendor. Note their "just ask the AI for print resolution" line was what they said while turning the job down, not a fix they endorsed.
https://x.com/kinokoillust/status/2092411212063715672

Do not write your rollout training from scratch

@connect24h called a teaching document a god-tier resource and said their team, at the stage of introducing Claude Code, will use a subset of it as the starting draft for AI-driven development training. The material relates to something Claude-related run at Kyushu University in April 2026. The post I have does not include the material itself.
https://x.com/connect24h/status/2093101935683670026

What looks different

The guideline in item 3 bans a capability, not a category - most Western policies I see do the reverse. And the internal resistance is louder and more public: @kouji_sakano dismissed their workplace's "actively use AI" push as nonsense that mass-produces people who cannot even build a deck without it. That opposition sits inside the same companies doing the rollouts.

Rated, briefly

@knshtyk - Signal: the clearest mechanism offered this week for a problem everyone has.
@connect24h - Signal: a real artifact and a team with a plan for it. https://x.com/connect24h/status/2093101935683670026
@t_yonemura - Noise: they say they checked the original and an AI agent really did accept that its own utility is near zero and the sacrifice rational - but the source is not named, so there is nothing to verify. https://x.com/t_yonemura/status/2093250048599720051
@kouji_sakano - Noise: a vent, useful only as a reminder the mandate has opposition. https://x.com/kouji_sakano/status/2093736951371464822
@abura_dev - Noise: says they find Morikawa hard to treat as an ally - sympathetic to artists harmed by LoRA, then calling AI just another art material and telling someone who cited a disability to go ahead and use it. A dispute about one person's stance. https://x.com/abura_dev/status/2092147679048740960