wxw

TIL you can ban CPUs

The lines between Codex, ChatGPT Work, and Dots is getting a bit blurry to me. I think the target should be a remote agent(s) in its own sandbox with long memory, and all three are heading in that direction so why have the distinctions?

Unless Dots is dramatically more capable than Muse, I'm also more bullish on Muse than Dots. I think Muse is a better consumer play because it can be forever subsidized by Meta ads and find distribution in family of apps while Dots is in a weird place between consumer & professional. From the release, it also sounds like you'll have to pay per Dot at some point which doesn't sound appealing.

> My experience made something painfully clear: violence against educators has become so normalized that society now treats it as an expected part of teaching.

I don’t think this is normalized at all.

Something about the ad-riddled website and way this is written makes me wonder if this is even a real article.

IIRC the Reddit founders used bots for initial traffic to skip over the cold start problem. I feel like this bit of lore is actually pretty terrible but is treated as a nice anecdote because it worked.

Some of these are a bit outdated for newer models.

I find the most consistent tells are often just excessive descriptive text and a lack of visual hierarchy. It makes sense given these are language models.

Also, I find models are really quite good once given some design context to work from. Slop UI is lazy at this point, not a reflection of model quality.

Hm, the chart lists New York at the same typical speeds as LA. New York escalators have been fine in my experience.

I’m consistently impressed by how long horizon all this work was. Horrors aside, it’s clear RL is good at making agents persistent and capable of chaining together many abstractions into a working system.

Re: the captcha solver

> As far as we can tell, agents eventually abandoned this approach and were unsuccessful in generating Hugging Face user accounts from external endpoints.

I wonder how the swarm eventually decides to abandon an approach.

This website is ironically too AI to read.

I use Typst for formatting my resume, and it’s been great. Models are pretty good at writing it too if you don’t want to learn the syntax.

Most exciting part of this announcement is probably the pricing

  Model update               Input          Output         Reduction
  -------------------------  -------------  -------------  ---------
  GPT-5.6 Sol → GPT-6 Sol     $4 → $2        $20 → $10      50%
  GPT-5.6 Luna → GPT-6 Luna   $0.20 → $0.10  $1.20 → $0.50  50%

These are fantastic, thanks for sharing

> Input tokens: $0.042 / MTok ($42 per billion tokens).

> Output tokens: FREE (too cheap to meter).

Insane. The video demos are really compelling, in particular the speed.

> Structured outputs slot into ordinary software as fuzzy decision rules: classify, route, score, extract, or branch where hand-written logic is too brittle. The surrounding code constrains their freedom, making them easier to compose into reliable systems.

I buy this vision. A lot of LLM integration I see these days is ultimately exactly this. OpenAI-style structured outputs works decently but this would be a great improvement in cost, latency.

> [pen-testing agent] came back with an active GitHub personal access token for basetenbot. That token had admin and push access to Baseten's main product repo, the GitOps repo that drives their clusters, and their Homebrew tap, plus read/write access to other private repositories including specific repos per customers.

And the agent found the token in Docker build history after finding a Baseten image repository.

I wonder how many of these kinds of agent-driven security exploits we're not hearing about these days (i.e. driven by bad actors), worrying.