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Some of these are a bit outdated for newer models.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.

Andon has dashboards up for past experiments where you can see how well they're doing:

https://andonlabs.com/market - $25,098 all-time revenue

https://andonlabs.com/cafe - $14,343 all-time revenue

The shops aren't doing particularly well (i.e. they don't seem to be turning a profit over time), but it's an interesting trial and benchmark.

> Our annualized revenue run rate is up more than 200% year over year, and net dollar retention's stayed above 200% since February. In August alone, the platform processed 1.9 trillion billable actions, up more than 350% year over year, while open source installs passed 43 million, up 134% since December 2025. We're now working with more than 4,300 paying customers, up 139% year over year, including OpenAI, Snap, NVIDIA, and JPMorgan Chase.

Congrats to Temporal! They'd just previously raised in February, https://temporal.io/blog/temporal-raises-usd300m-series-d-at...

> As part of the deal, Google signed a 22-year contract with Finnish utility Fortum to buy up to 50% of the output from the Loviisa nuclear power plant.

> Finland has emerged as an attractive location for data centres because of its cool climate, plentiful low-carbon electricity and relatively uncongested power grid.

Playing around with it tonight and I feel like a killer feature is its speed. Really fast response times.

> The Early Access Network is an invite-only group of senior technology leaders who want to see what's coming before it's on anyone's shortlist.

> Four times a year, we put a hand-picked group of YC's enterprise-AI founders in a room with buyers who can actually run a pilot.

Seems like a win-win.

“contributor” pricing at $0.10/$0.20 is crazy cheap if it’s measuring up to Sol.

Definitely shows how important a user data flywheel is for RL and model improvement.

> The rolodexes came from the now dispersed collection of former lab employee Ed Grothus, who operated a salvage company of lab cast-offs known as The Black Hole.

Too good.

> the True Rate of Unemployment tracks the percentage of the U.S. labor force that does not have a full-time job (35+ hours a week) but wants one, has no job, or does not earn a living wage, conservatively pegged at $26,000 (in 2025 dollars) annually before taxes

Seems like reasonable criteria. "Regular" unemployment is

# unemployed/# employed

where unemployed is they do not have a job, have actively looked for work in the prior four weeks, and are currently available to work. But not much criteria in terms of what work people are finding.

How well does it work in practice?

100% agreed. Small, cheap, and hosted models. Luna (and open weight models and others) is ridiculously cheap @ $0.2/$1.2, easily accessible, and more than good enough for basic use cases (e.g. summarization, simple tool calling, etc.).

I just set up Tailscale as I've been hosting more personal apps off a small Hetzner VM and wanted a really simple personal networking solution. Works great, would recommend.

Really enjoyed how thick this essay was.

> The slop-acalypse that so many of us fear, when people will stop looking at anything made by humans and only look at things made by machines—why hasn’t that happened yet?

Unfortunately, I'm pessimistic here. I don't think the slop-acalypse is held back by taste or an appreciation for "thickness". I think there's already very successful slop media in the world (e.g. see microdramas). In other words, it is happening, but like anything else, it's happening in a non-evenly distributed way.

> Although I’ve been saying “Claude”, I actually did most of the work in OpenCode with some of my favorite open weights models. Kimi K3 & K2.6, and DeepSeek v4 Pro & Flash.

Interesting, is Claude becoming the Kleenex of the agent world?

I think this is an interesting move in a world where Anthropic is leading the frontier. But I don't think we're in that world, at least anymore. The current vibe feels like OpenAI and Codex are leading the race.

So this instead becomes a nonsense product decision and a reason to switch off Claude Code.

> Notably, our worker did not have access to the web_search tool, but instead decided to use curl to access DuckDuckGo, Github, grep.app, and SourceGraph.

Could this be fixed with better harness restrictions/tool sandboxing?

> And as we grow, we will relentlessly aim to preserve the velocity, agility, efficiency, and talent density of the 90-person startup that we are today.

Echoes of WhatsApp. Huge congrats to the team!