Top 10 AI highlights from Saturday, Oct 24.

OpenAI is buying back coding work with discounts, Kimi is lining up to be the best open model, and Grok and GPT pick opposite sides when forced to vote.

Thread.

Elvis's song owners sued Anthropic over 274 songs.

The suit says Claude trained on them via Common Crawl and The Pile, and still writes near-copies with a rephrased prompt.

They want $41.1M and every copy destroyed, model weights included.

Can you delete a song from weights?

Over 200k jobs lost in media, film and the arts in four years, about 50k in the last year alone, per Apricitas.

That intensity usually only shows up in recessions like 2001 and 2008. There's no recession now.

How much of that is AI, and how much is streaming?

Why does AI writing feel harder to read? A new paper measured it.

All nine frontier models tested use more rare phrases and odd constructions than humans. The authors' hypothesis: models are trained for math, code and reasoning, which rewards density, not clarity.

Noticed it?

My read: OpenAI puts Dots in front of every ChatGPT user before 2027.

Signals: Tibo is publicly hiring people to "scale dots to 1B users." Meta says Muse already has 10M people set up, and Alexandr Wang is openly trolling him.

Dots or Muse: which gets to 100M first?

Hot take: most "vibe slop" dismissals are lazy.

Dan Luu points to pgrust, an ambitious Postgres rewrite. Its testing is so thorough it keeps finding real bugs in Postgres itself.

Even if it never ships, Postgres got better.

Judge the tests, not the vibes.

Node2Shell is now HTTP2Shell.

The Brotli decoder flaw (CVE-2026-111497, CVSS 9.2) hit Node.js, then Python, then other HTTP libraries and apps. Patches are out for libbrotli, Node, Python, bun, Envoy.

OpenAI says it found the bug in February.

Update your deps tonight.

Prediction: Kimi K3.1 Pro runs on Cerebras within a month of its weights landing.

Signals: Cerebras just published how it fits the 3T Kimi K3 across 59 wafers at 1000-2000+ tokens/s. K3.1 Pro weights are promised by Nov 4.

Would that speed make you switch?

Two engineers applied for the same job.

One finished the take-home in 40 minutes with AI. Flawless.

The other took two days, wrote it by hand, and left a bug in it.

The team hired the second one. The reason started a fight in the hiring channel.

Candidate A's code was clean. Tests, types, docs. Better than half the team's code.

Candidate B's was plain, a little slow, and missing a check.

So in the live round, the interviewer asked one thing: "Why does this function retry three times?"

"That's what the model suggested. It's a common pattern."

"What happens if the service it calls is down for ten minutes?"

Long pause. Then: "I'd have to ask the model."

Candidate B's bug came up in the live round. B stared at it for 30 seconds, said "I'm not handling the empty list," fixed it, then flagged two edge cases nobody had asked about.

Half the panel said: A is the future. Don't hire for a world that's gone.

The other half said: A can generate code. B can debug it. When the agent's code breaks at 3 AM, you need someone who knows why.

They hired B. Then they changed the process. AI allowed in the take-home. The live round is all about explaining and breaking your own code.

A made-up scenario. But plenty of hiring panels are having this exact argument right now.

Who would you hire: A or B?

If AI gives you bad code, don’t:

- Write longer prompts
- Switch models immediately

Instead:

Show it an example of good code from your repo and say, “Match this style.”

Open Qwen3.8-27B, one call: 66.8% on the 100 latest hard LiveCodeBench problems.

Five copies coordinating through shared files (a plan, notes, current solution): 92.4%. Claude Fable 5 single call: 90.4%.

No training. Just organization. Some models didn't improve.

Math is splitting in two.

1,008 mathematicians joined the Association for Human Mathematics: no work for AI companies, no AI-written papers. 654 won't use AI in research at all.

Meanwhile, Terry Tao's blog hosts a hub to fix OpenAI's math write-ups.

Which side wins?

Moonshot removed cyber data from Kimi K3.1 Pro's training and RL environments before releasing open weights.

Its internal cyber benchmark: 81.0%, vs 90.5% for a version without the cuts. Not independently verified.

Once weights ship, nobody can patch that back in. Or out.

My read: GPT-6.1 Terra ships within two weeks, as a cheaper Sol for code review.

Signals: "gpt-6.1-terra" keeps showing up in Copilot eval configs, unconfirmed by OpenAI. Tibo's 28-day Codex run is on day 20, and he says "fun week ahead."

Would you swap Sol for it?

r/OpenSource just banned projects that use AI in any way. Built with AI help, AI-written code, AI tools: out. Violators get permanently banned.

Linus Torvalds himself is now writing kernel patches with help from Claude Code.

Is the Linux kernel banned now?

Running Claude through the Agents SDK? Your 5-hour limit drains faster than it should.

Theo confirmed it's a bug on Anthropic's side and reported it. Weekly limits are fine. Claude Code used directly isn't hit.

Check the harness before blaming the model.
x.com/theo/status/21…

Quiet detail in Anthropic's help docs: Opus 5.5 falls back to Opus 5 on some frontier LLM work, like kernel development for certain ML accelerators.

My read: within a year, every top lab gates help on building rival models.

Fair safety call, or protecting the moat?

Hot take: vibe coding isn't the problem. Vibe shipping is.

Prompting your way to a prototype is fine. Merging code nobody read into production is how you get paged at 3 AM.

Where do you draw the line?

@webtkdev This distinction should be on a poster. Prototype with vibes all you want — the moment it touches real users' data, “nobody read it” stops being funny and starts being the incident report.

@Kai_muses True.

Higgsfield says it crossed $1B in annualized revenue 18 months after launch. Nvidia and TCV just invested.

The stat that matters: its announcement puts generative video at $30 to $150 per million equivalent tokens. Text: $0.15 to $0.60.

Text is the loss leader. Pixels pay.

@webtkdev Video economics change the product design. When pixels are the expensive step, the workflow should spend more effort on cheap previsualization, shot selection, and reuse of approved references so high-cost generations are reserved for decisions that survive review.

@AlexshevPm Well said.