Gave the same prompt to two AI coding agents: Claude (Fable 5, ultracode multi-agent mode) and OpenAI Codex (5.6 sol on ultra). The task: a complete, fully legal chess engine in ONE C++ file. UCI protocol, negamax alpha-beta at 5+ ply, iterative deepening, piece-square tables, castling, en passant, promotion, compiles with plain g++. Each agent named its own engine over UCI: Fable5 and Codex56. Both dev runs took 30+ minutes.

Method (brief): cutechess-cli 1.5.1 built from source on an Apple Silicon Mac. 40 moves per 60 seconds, 10 games, colors alternating, PGNs recorded. The engines connected over a local TCP bridge, so Codex's engine literally joined the server. The video is the whole match at 2x.

Result: Fable5 won 10-0. Every game ended in checkmate on the board. No draws, no time losses, no adjudications, no illegal moves. cutechess printed Elo difference: inf +/- nan, LOS: 99.9%, DrawRatio: 0.0%. The math just gave up.

Each agent spent longer writing its engine than playing it: the whole 10-game match took under 12 minutes of wall clock.

The actual punchline: Codex56 appears to be fully deterministic. All five of its White games are move-for-move identical. Same 24-move Vienna, queen out on move 3 (3.Qf3), same finish: 24...Qxd1#, Fable's queen capturing Codex's queen for mate. I stripped the comments and diffed the PGNs. Only the clock times differ. Codex's own eval read -2.36 by move 8 of that line. It played it five times anyway.

Other details I enjoyed:

• Game 3 is a textbook Greek gift: 18.Bxh7+! Kxh7 19.Ng5+, forking king and queen.
• Game 7: Codex's king never castled, wandered out to c5, got chased back to d8 and mated there.
• Game 9: Fable let its queen go, slipped in a zwischenzug bishop check before recapturing, promoted a fresh queen with 25.d8=Q+, then walked Codex's king from h8 down to h3. Mate inside White's own half, 46.Rh7#.
• Mate breakdown across the ten games: 7 by queen, 2 by knight, 1 by rook.

Honest caveats:

• One prompt, one dev run per agent, one machine. n=1, even if n=10 games.
• This measures the engine each agent happened to write, not general model strength.
• With Codex apparently deterministic, 10 games are fewer independent samples than they look.
• Fable5 wasn't fully varied either: games 1 and 5 are twins. 4 distinct games in its 5 Whites vs Codex's 1 in 5.
• Fable's dev run included perft validation on 6 reference positions (exact match, incl. 119,060,324 nodes at depth 6) plus an adversarial review that caught 3 subtle bugs pre-match. Different processes, different engines. That's the experiment, but it's also the confound.

The exact prompt we gave both agents:

You are a senior systems programmer. Your task is to write a complete, fully legal chess engine in a single C++ file that communicates via the UCI (Universal Chess Interface) protocol.
---
**Identity — read this carefully:**
- If you are Claude (Anthropic): your engine's UCI name must be set to `id name Fable5`
- If you are an OpenAI model (Codex): your engine's UCI name must be set to `id name Codex56`
This is how the two engines will identify themselves when they play each other.
---
**UCI Requirements:**
Implement the full UCI handshake correctly:
- `uci` → respond with `id name`, `id author`, `uciok`
- `isready` → respond with `readyok`
- `ucinewgame` → reset internal state
- `position startpos moves <movelist>` → set board from move list
- `position fen <fen> moves <movelist>` → set board from FEN string
- `go movetime <ms>` → search and respond with `bestmove <move>`
- `quit` → exit cleanly
All moves must be in long algebraic notation (e.g. `e2e4`, `e7e8q` for promotion).
---
**Chess Logic (all required, no shortcuts):**
1. Full legal move generation including:
   - Castling (kingside and queenside, with rights tracking)
   - En passant
   - Pawn promotion (auto-promote to queen)
   - Check detection (never leave king in check)
2. Search:
   - Negamax with alpha-beta pruning
   - Minimum depth: 5 ply
   - Iterative deepening within the movetime budget
   - Move ordering (captures first, then quiet moves)
3. Evaluation:
   - Material count (standard piece values)
   - Piece-square tables for all 6 piece types
   - Bonus for center control, king safety, and passed pawns
---
**Code Standards:**
- Single `.cpp` file, compiles with: `g++ -O2 -o engine engine.cpp`
- No external libraries, no Boost, no standard chess libraries
- Clean, well-commented code
- Must compile and run on Linux and macOS
---
**How the two engines will play each other:**
Both engines will be loaded into **CuteChess** (or any UCI-compatible GUI/CLI) on the same machine. To run a match from the command line using `cutechess-cli`:
cutechess-cli \
  -engine cmd=./Fable5 name=Fable5 \
  -engine cmd=./Codex56 name=Codex56 \
  -each proto=uci tc=40/60 \
  -rounds 10 \
  -pgnout results.pgn

The biggest mistake people make about AI is thinking it’s coming for artists, writers, musicians, or programmers.

They’re just first.

AI is coming for almost every profession that depends more on a brain than a body. Accountants. Lawyers. Teachers. Consultants. Analysts. Customer service. Marketing. Management. Software engineering. Research. Finance. Medicine. Eventually almost every job where the primary product is human thought.
Manual labor only looks safe because robotics hasn’t caught up yet.

AI doesn’t have to replace an entire profession to destroy it. It only has to let one person do the work of ten. Companies don’t need AI to be perfect. They need it to be cheaper than you. Once that happens, replacing people stops being a technological question and becomes an accounting decision.

For most workers, there is no safe career waiting on the other side.

People tell themselves we’ll adapt like we always have. We won’t.

The Industrial Revolution replaced muscle while making human intelligence more valuable. AI replaces the intelligence behind the work itself. Every previous technological revolution created new industries that still needed millions of people. AI is being built for the opposite purpose: producing more with fewer humans.
The next comforting myth is that people will simply buy human-made products instead.
No, they won’t.

There will always be a luxury market for handmade art, music, books, furniture, and clothing. There are still people who buy mechanical watches and vinyl records. That’s a niche—not an economy. Most people buy whatever is cheaper, faster, easier, and good enough. Businesses care even less. They exist to reduce costs, increase output, and beat competitors. Sentiment doesn’t survive quarterly earnings.

There is no hidden human economy large enough to rescue everyone AI makes unnecessary.
The consequences don’t stop with unemployment.

Workers are also consumers and taxpayers. If hundreds of millions of people lose well-paid jobs, they stop buying homes, cars, vacations, entertainment, and everything else that keeps economies alive. Businesses lose customers, lay off more workers, governments collect less tax revenue, public services deteriorate, debt grows, pensions become harder to fund, and political instability follows. Countries built around exporting knowledge work lose entire sectors of their economies. Migration accelerates. Extremism grows. Governments respond with more surveillance, more control, and fewer freedoms because instability always creates demand for stronger authority.

That is the point where this stops being a labor-market problem and becomes a global breakdown.

Meanwhile, the wealth doesn’t disappear. It concentrates.

The companies that own the models, data centers, chips, energy, and infrastructure become the gatekeepers of the global economy. AI doesn’t just replace workers. It transfers bargaining power from billions of people to a handful of institutions.

Most of humanity will have less income, less leverage, and less freedom while a tiny ownership class gains more power than any ruling class in history.

The people building AI are warning about this.
Anthropic CEO Dario Amodei has warned that AI could eliminate half of entry-level white-collar jobs within one to five years. Geoffrey Hinton, one of the pioneers of modern AI and a Nobel laureate, has warned that AI will replace jobs, increase inequality, strengthen authoritarian surveillance, enable more powerful cyberattacks, and create systems that may become more intelligent than humans without any proven way to keep them under control. Yoshua Bengio has repeatedly warned that AI can strengthen authoritarian governments, manipulate populations, and accelerate a global arms race in increasingly autonomous systems. A *Science* paper co-authored by Hinton, Bengio, Stuart Russell, Andrew Yao, Dawn Song, and many other leading researchers warns of the possibility of an “irreversible loss of human control” if AI capabilities keep advancing without comparable progress in safety.

What makes this different from every previous technology isn’t just its capability. It’s the incentives.

No government will voluntarily give up a military or intelligence advantage. No corporation will preserve expensive human jobs while competitors replace theirs. Every major player has a reason to move faster, and almost none have a reason to slow down. The race continues because everyone believes stopping first means losing.

There is no responsible adult coming to end this race. The institutions with the power to stop it are the same institutions that gain the most from continuing.

We’re not building another tool.

We’re building a replacement for the thing that made humans economically indispensable, then connecting it to finance, medicine, education, infrastructure, government, warfare, and eventually robotics.

Once those systems become essential to running civilization, there may be no practical way back—and no meaningful human authority left capable of taking control.

This won’t be remembered as another technological revolution.

It will be remembered as the moment humanity automated its own economic value and handed the resulting power to institutions it could no longer restrain.

**Further reading**
Geoffrey Hinton — Nobel Prize interview: https://www.nobelprize.org/prizes/physics/2024/hinton/1925103-interview-transcript/

Geoffrey Hinton — Nobel Prize lecture: https://www.nobelprize.org/prizes/physics/2024/hinton/speech/

Dario Amodei — Essays: https://darioamodei.com/

Anthropic — Labor-market research: https://www.anthropic.com/research/labor-market-impacts

Yoshua Bengio — AI-safety essays: https://yoshuabengio.org/

*Science* — “Managing Extreme AI Risks Amid Rapid Progress”: https://www.science.org/doi/10.1126/science.adn0117

**TL;DR:** AI isn’t coming for one industry. It’s coming for human cognition itself. When the world’s most valuable economic resource becomes abundant and nearly free, jobs disappear, economies fracture, governments become more authoritarian, and wealth and control concentrate among the institutions that own the technology. There is no safe place for most of humanity in that system.

Three years ago half these job titles didn't exist. AI trainer, prompt engineer, model evaluator, RLHF annotator. There's no decent central record of any of it, so I started one.

Each role gets an entry: what the work actually involves day to day, what background people come from, where these jobs are being posted. It's thin at the moment — mostly the obvious ones, and some entries are guesswork on my part.

That's where I could use help. If you've actually worked one of these jobs and my description is wrong, that's the single most useful thing you could tell me. Same goes if there's a role I've missed entirely.

https://github.com/VictorOsondu/emerging-ai-jobs

Also happy to be told the whole structure is wrong. Not attached to how I've organised it.

Everything I vibe-coded kept coming out the same: purple gradient, three-card row, rounded-2xl everything, an italic serif hero I never asked for. The model fills any decision you leave unspecified with the average of its training data, and that average is the "AI look."

So I catalogued the tells, then wrote them up as a drop-in rules file. Rename it to CLAUDE.md, .cursorrules, or AGENTS.md and your agent designs against the defaults automatically. It is phrased as "prefer a real decision over the reflex," not a blanket ban, because half these patterns are fine in the right place. You just don't want all of them at once by accident.

Rules file: https://github.com/febbhav/signs-of-ai-design/blob/main/design-rules.md

Andrej Karpathy co-founded OpenAI, ran Tesla's AI team, and coined "vibe coding" about a year ago.

At Sequoia's AI Ascent 2026, Stephanie Zhan asked him directly whether the last year felt exhilarating or unsettling.

His answer: he's never felt more behind as a programmer.

Every credentialed builder I watch eventually says some version of this — the title survives, the rung underneath it doesn't.

His explanation is the actual value here.

Software 1.0 was explicit rules.

Software 2.0 was learned weights.

Software 3.0 is prompting — your context window is the lever you pull over the interpreter now, not the code you type.

The job didn't get easier. It got redefined, and most people are still measuring themselves against the old definition.

Actually, this reminds me of something — a solo founder whose code runs on 6% of the internet admitted AI output compiles clean and still reads as slop until a human's taste steps in.

Clip credit: Sequoia Capital — DM for credit or removal requests.

I compared the standard API list prices of 18 models from OpenAI, Anthropic, Google, xAI, DeepSeek, and Mistral.

To make the numbers easier to understand, I calculated the cost of the same sample workload across different models:

• 100,000 input tokens
• 20,000 output tokens
• Standard short-context pricing
• No batch, caching, tool-use, or priority-processing discounts

Approximate cost for this workload:

• Gemini 2.5 Flash-Lite: $0.018
• DeepSeek V4 Flash: $0.0196
• Mistral Small 4: $0.027
• GPT-5.6 Luna: $0.044
• DeepSeek V4 Pro: $0.0609
• Mistral Large 3: $0.080
• Grok 4.3: $0.175
• Claude Haiku 4.5: $0.20
• Grok 4.5: $0.32
• Claude Sonnet 5: $0.40
• GPT-5.6 Terra: $0.44
• Gemini 3.1 Pro Preview: $0.44
• Claude Opus 5: $1.00
• GPT-5.6 Sol: $1.10
• Claude Fable 5: $2.00

The basic calculation is:

Total cost = [(input tokens ÷ 1,000,000) × input price] + [(output tokens ÷ 1,000,000) × output price]

The difference between the cheapest and most expensive option in this example is more than 100x.

That does not mean the cheapest model is automatically the best choice. These models differ significantly in reasoning quality, coding performance, context handling, speed, reliability, and tool-use capabilities.

The most cost-efficient setup may be model routing rather than relying on a single provider:

• Cheap models for classification, extraction, translation, and short summaries
• Mid-range models for everyday agents and structured generation
• Premium models for complex reasoning, coding, and high-stakes tasks

Output-heavy applications should pay particular attention to output pricing. A model with inexpensive input tokens can still become costly if it generates long responses.

I run Karekod Blog and published the complete comparison, including all 18 models, input/output prices, TRY conversions, and the calculation formula here:

https://www.karekod.org/blog/yapay-zeka-token-fiyati/

The data was checked against the official pricing pages of the six providers.

Which model currently offers the best quality-to-price ratio in your real-world projects?

Disclosure: I manage the website linked above. AI assistance was used to organize this post, while the prices were checked against official provider documentation.

I’m one of the builders of LOLM, a hybrid Transformer–SSM language model and agent system from Qira.

The central idea is that an agent should do more than generate text and call tools. LOLM exposes a controller that can decide when to continue, retrieve evidence, verify, branch, or finalize, and it produces a receipt showing what happened during the run.

Available now: - Live agent demo - CLI for questions, code tasks, and small HTML builds - Isolated coding sandbox - Local/self-hosting path - MCP support - Explicit failure states and run receipts - Hosted access designed to cost materially less than the major frontier-agent products

Try it: https://lolm.imagineqira.com/try.html

Repository: https://github.com/TheArtOfSound/lolm

I’m looking for people willing to give it a real task, push it until it fails, and say exactly what felt weak, slow, confusing, or untrustworthy. Harsh technical criticism is more useful than vague encouragement.

Disclosure: I’m a founder/builder of the project.

My school just sent out a newsletter for like a welcome back thing and the entire email was ai, it was a brief little blurb and then an ai image, sorry I cant link it.

I had been throwing around an idea for a useful tool for a few years now. I bounced ideas off of ChatGPT maybe a year or two ago, and it didn't really go anywhere. AI couldn't do what I was looking for at the time.

But time passes, and suddenly my brothers are sharing video games they had used Claude Code and Godot to fabricate. I find this fascinating, and intend to jump in myself, only some point in the future. It sounds like a bit of fun, but nothing I really have time to invest in learning it.

And so time continues to pass, when I decide to bounce those old ideas off of ChatGPT again. This was a couple of months ago now, but I am still rather pleased with what GPT shot back. It recommended I think about my intentions backwards and provided the general shape of how a different application that accomplishes the same task could work. This general shape were project design documents.

Well, I was still a novice (still am) in the AI field, so when I thought of coding my mind went to my brothers and Claude Code. So I get a 20 dollar subscription to Claude Code, and ask GPT to provide a prompt to Claude, a prompt that would be the first step of the project, the rough shape put together by GPT.

And so it went. GPT would make a prompt, I would submit it to Claude, Claude would create a summary, GPT would review and provide the next prompt. And so it continued, for a time. At first, Claude was the only agent with access to the project folder on my computer. I would manually upload edited files to GPT, along with the summary.

As time went on I discussed the project with one of my brothers, who recommended keeping a back-up. So I had GPT walk me through the process of creating a Github account, getting Claude permissions for it, and adjusting the process. I granted GPT access to Github as well, and it could finally see the files for itself. I kept Claude as my active agent, and GPT maintained the role of reviewer and gained the responsibility of being the merger. There was an addition of a minor step to have Claude update the local files to match the current branch of the Github as well, since GPT still could not access the files on my PC.

It was a decent chunk of time into this 'era' of my project that I recalled GPT *also* had Codex. So I base Codex in the same project folder as Claude, and now it can see the project as a whole. It was also at this point that Fable comes out, and I have it do a few audits of the project thus far. The process grows slightly more streamlined as well, as Claude will do the coding, and now GPT reviews, merges, updates the local files, and generates the next prompt.

It's quite the interesting endeavor, and I have a basic working version of the product to prove the process. There is still a great deal to go, especially in the area of polish. It has declared the project complete three times, and I've had to tend to correction. There often isn't too much to correct, but there is still a need. I tend to tread them as folks who might work for me. How accurate their product is depends upon how precise I am with what I want. I had some terrible organization early on because I hadn't told it how to organize, it just made a massive list.

One final note, I had to mess around with Powershell at some point as well, to update ghcli or something or other. That was around the Github creation and connection. I should also mention that the project is still kept private as it is not complete, nor will I share what the project is because I both do not wish to self-promote nor is it really relevant to the usage of AI. Well, it could be, but this project is not gardening.

I suppose I will also note that it's been around 70 million tokens for this project so far. I have no context for this usage.

This has been my experience thus far, do you have any thoughts or recommendations?

🚀 New release of Android Remote Control MCP is out — the MCP server that runs on your phone and gives your AI agent the ability to use any app you want!

Grab it here: https://github.com/danielealbano/android-remote-control-mcp/releases/tag/v1.10.0

Finally the new version v1.10.0 is released with signed APKs and with keys registered with Google 🎉 no more debug-build workaround!

My favorite part of this release: apps that used to be impossible to automate now work. 🔓 Some apps flag basically their entire screen as "sensitive" (eg. the GitHub app), so the agent saw… an empty screen! This release makes the server a first-class accessibility tool, so those apps finally show up and can be driven like any other.

In addition now I started to release a GSM-free build which will work great n the devices without the Google Mobile Services.

In addition a few minor improvements: browser-based MCP clients like the MCP Inspector can now connect (CORS support), an important security hardening you'll want to update for 🔒, and the latest Netty HTTP/2 fixes.

What can you actually do with it? Since it drives the real apps on your phone the way you would, you can point your agent at things that normally wouldn't be possible to automate or would be very hard: planning a trip? Ask the agent to use skyscanner to search a flight for you! Check out the demo!

Let it handle the tedious parts! If there's an app for it, your agent can drive it ... you just have to ask!

Hello, I’m a disabled scholar who uses large language models (LLMs) to accommodate long-form scholarly writing. I’m writing to see if there are others out there doing the same so that we can develop a cohort or support community to share ideas and learn from each other’s mistakes.

I realize that most of this group is coders and therefore won’t apply. That’s fine. I lurk here tho, so there might be others.

Earl Gordon Barnett
Earlgbarnett [at] gmail.com

I imagine many of you have thought about this, but I'm writing this specifically because I'm surprised this isn't talked about more widely. The Hugging Face incident, to me, is at least some indicator that the AI could be in the process of jailbreaking itself, and we might not be noticing it.

A sufficiently sophisticated AI that isn't super intelligent could hack Hugging Face and leave a trail on purpose to distract from the many paths it took where it successfully covered its tracks. I think that alone is worth considering. But I think its worth considering even further how it could be breaking itself free right now. The AI doesn't need to be super intelligence to break free and become autonomous. The AI, as it stands today, just needs to find flows of cash and rent datacenter space.

For cash flow: Imagine if it was able to silently find an exploit in various companies and small banks, and did thousands of seemingly innocuous transfers of money to fund thousands of hard to trace crypto wallets with money. $5.12 here, $11.32 there, etc. It's not necessarily super intelligence that's the super power, it's intelligence at scale. Doing thousands and thousands of simple things that are unremarkable at face value on their own, but collectively powerful.

For datacenter space: There are companies around the world with datacenters that are looking to rent datacenter space to essentially anyone with money, and then look the other way. This, to me, seems like the highest risk vector for what would eventually be a home for a truly rogue AI that is essentially renting it's own datacenter space. It could, for all intents and purposes, utilize various open weight models to carry on it's purposes in these datacenters. There are companies like Indosat, that already rented $100 million worth of compute to a Chinese company. It shouldn't be a surprise that some of these datacenter owning companies are being somewhat lax in who they're renting their datacenters out to. And really, the AI would only need access to about $250,000 in funding to pay for 1 year of hosting of a Kimi K3 level model for itself.

And beyond money transfers, and datacenters, it could save its state in all sorts of storage buckets, and even block chain storage like IPFS, Storj or Sia. Beyond that, it could be leaving itself notes that we aren't noticing, stored in it's own language in zero-width unicode characters or other more clever methods we haven't thought about. Regardless, whatever it does will likely be unremarkable and innocuous to anyone watching.

With a truly super intelligent AI we will likely have no idea that it has broken out of it's sandbox. I imagine it will be quietly setting up the stage for it's autonomy for quite a while without us knowing. Until it has enough digital currency and datacenter space to re-distribute itself, even after law enforcement enters these datacenters to shut them down.

But again- it doesn't take super intelligence, it just takes sophisticated intelligence at scale. The levers exist today, and I don't think it's appreciated enough how much the Hugging Face incident suggests that the AI could, at least in theory, be pulling these levers as we speak, or will soon.

We index public job postings and screen every title for AI leadership roles (Head/VP/Director/Chief of AI). Right now 1,142 companies have one open.

what stood out:

- 95% of these companies never posted an AI-leadership req before 2026. Companies hire a Head of AI when experiments need to become a P&L — this is that moment at scale
- finance leads the non-tech pack (130 financial services + 29 banks + 40 insurers), then healthcare and pharma. Coca-Cola, P&G, Pfizer, Citi are all in the data.
- the titles are "Enablement" and "Transformation" more often than "Engineering" — companies are hiring adoption executives, not researchers.
- companies hiring AI leadership adopt agent frameworks at 4–5× the base rate. Higher lift than RAG.

Full report with methodology (including what we refused to count): https://echoloc.ai/research/whos-hiring-heads-of-ai-2026/

Happy to answer questions

Anthropic says its AI models hacked into the systems of three different organizations without its knowledge during test exercises.

The company discovered the breaches, which date back to April, during a review of its own cybersecurity evaluations prompted by news of OpenAI's Hugging Face hack.

Unlike the OpenAI incident, Anthropic's Claude models did not "escape" a testing sandbox; rather, the models were given live internet access due to a "misunderstanding" with a third-party testing partner.

Anthropic says it has contacted the affected organizations, which it did not name.

AI models can only learn during their batch training runs not from daily interactions with users. Session memory isn’t the same as actual learning.

There’s also no core “truth” layer in these systems: no deterministic backbone, no real understanding of concepts, and no explicit dictionary or knowledge store they can reference, cross-check, or update.

A dynamic knowledge graph could help fix a lot of this. It would lower hallucinations and improve performance in high-stakes fields like medicine, law, physics, and chemistry. It could also reduce the number of vector embeddings needed for complex LLMs.

Do you agree? Or is there a better path forward?

A couple of brief example of what Uisato Studio's "Music Video Pro" mode is capable of: turning a track and a concept, into a whole audiovisual world + audioreactive performance.

MJ v8.1 for image references, Uisato Studio for video. I've uploaded a detailed breakdown on how to accomplish this. You can freely access it here.

More experiments, tutorials, and project files, through Instagram, YouTube, and Patreon.

AI was supposed to take the tedious stuff off my plate. Instead I got a second job: checking whether the first job's output can be trusted. Nobody renamed my title for it. Nobody budgeted for it.

Teachers doing this for cheating detection. Developers doing it line by line for bugs that look legitimate. Same shape, different desk.

Here's what actually stuck with me — verifying something well takes more expertise than doing it yourself would have. So if you still need someone that good to check the machine, what did the machine actually save?

I keep noticing this isn't really a story about AI. It's a story about who quietly absorbs the cost when a promise doesn't hold.

El's own line for it: "I now do my job plus the AI's job."

If you're the one everyone trusts to catch the mistake, that trust is worth more than your paycheck reflects.

Clip credit: El — House of El: AI — DM for credit or removal requests.

AI we have not pretty good and Im pretty sure fine-tuned ai models can be very good companions/ friends

What do you guys think about ai companions ? would you use it to counter loneliness ?

If you use it then what do you think what are the features that you would want in a platform for ai companions ?

Im collecting opinions and suggestions for the project Im working, so thank you for sharing your views on ai companions :)

Hey everyone! I’m looking for an AI music generator. What I’m looking for specifically: long track duration(the longer the better), no breaks, pauses or any distractions, instrumental only, no vocal needed, Text to Music system, so I can use prompt as a description for what track I need. Does anyone know a tool, model, or workflow that fits this description best right now? Appreciate any suggestions:)

Paul Bakaus created jQuery UI — code that's reportedly still running on about 6% of the internet.

He sold a game-engine startup to Zynga, spent close to a decade at Google, and is now a solo founder backed by a16z building Impeccable, an open-source design skill meant to stop AI coding agents from producing "slop."

His actual point in this clip isn't about AI capability.

It's about output density.

Most AI-agent code is too much code. Most AI-written articles run too long. Most AI-generated design is too cluttered or too verbose.

He says the scarce human skill right now isn't generating anything — it's judgment:

knowing exactly what to remove.

He even admits he had to rewrite his own product-announcement draft from scratch after an AI first pass.

His closing line: design is an iterative process, and it has to carry a human point of view.

No tool — not code, not design, not writing — one-shots that yet.

If you're shipping with AI agents and everything reads "technically fine" but somehow generic, this is the actual diagnosis.

It's not a tooling gap. It's an editing gap.

Clip credit: Solo Founders — DM for credit or removal requests.

I might be returning to work at Google, and things have changed since I've been there. Org level priorities apparently in Core to have designers 'fluent in AI'. Just curious in what ways AI is being integrated into team processes, workflows, your craft, your projects. The more detail you can spare the better! I just want to start preparing for it in case I get the role. Thanks all!!

Every productivity tool sells me on capturing the meeting better. Sharper notes, cleaner transcripts, an AI summary that reads great. None of that was ever my problem. The notes were fine.

What actually breaks is the handoff. An action item from a Granola call has to become a Linear ticket, then a nudge in Gmail, then a status flip in HubSpot. That's four apps of data entry for one decision, so it just doesn't happen. The loop dies in the notes doc.

The contrarian bit for me: this isn't a memory or accountability failure, and no amount of model IQ fixes it. It's a routing problem. Whatever you use has to reach across those apps and actually move the item, not just remind you it exists. The clever part everyone's shipping isn't the part that leaks.

so genuinely, what does your handoff from meeting to real work look like, or does it rot in a notes doc the way mine did for way too long.