July 2026
80 posts: 6 entries, 22 links, 11 quotes, 8 notes, 33 beats
July 17, 2026
Suggestion for hyperscalers feeling pressure over data center water use:
Buy up a few exclusive country clubs, convert the golf courses into public parks, pay for guides and binoculars to get the previous members into birdwatching - help them embrace a more sustainable hobby!
Google used 10.9 billion gallons in 2025, so about 30 million gallons per day.
The Coachella Valley has 120 golf courses each using ~800 acre-feet per year, which is ~750,000 gallons per day.
So Google buying up 40 of those courses (1/3) should do the trick.
I got frustrated reading yet another article that was crammed with the clichés of LLM-generated writing - "no fluff, no filler, no jargon" type stuff - so I had Fable 5 vibe code up this app for highlighting ten common patterns that show up in that sort of writing.
Is there something I can actually help you with today?
— Kimi K3, after refusing to leak its system prompt
July 18, 2026
nascheme/quixote. A certain vintage of Python web nerd might be delighted to learn that the most recent commit to the Quixote web framework was six hours ago.
The oldest commit in that repo is from 21 years ago, and that was the initial import of Quixote 2.4 from Subversion into Git.
Claude make Fable 5 permanent.
An update from the @claudeai account on Twitter:
Beginning July 20, Claude Fable 5 will be included in all Max and Team Premium plans, at 50% of limits.
Pro and Team Standard users will continue to have access to Fable via usage credits, and will receive a one-time $100 credit.
As I was saying last week, the competition from GPT-5.6 Sol (and maybe to a lesser extent Kimi 3) made untenable Anthropic's plan to remove Fable 5 from their subscription accounts and make it available exclusively through API pricing.
Why pay $100 or $200/month for a subscription plan that doesn't include Anthropic's best model?
Their original plan was driven by concerns over compute capacity. I wonder if they'll have to dial back their training efforts in order to make more GPUs available to help serve the model.
A lot of people were losing sleep over trying to make the most of Fable 5 before subscriber access was withdrawn. It's nice not to have to worry about the Fablepocalypse any more.
Update: Important to note that users on the $20/month plan will still not have access to Fable 5 on that subscription. The Max plans are $100 and $200/month.
Julia Evan's, in Learning a few things about running SQLite:
Maybe one day I’ll learn to read a query plan.
Big same.... which inspired me to have Fable build this interactive explain tool, which runs SQLite in Python in Pyodide in Web Assembly in the browser and adds a layer of explanation to the results of both EXPLAIN and EXPLAIN QUERY PLAN.
Approach with caution, since I don't know enough about SQLite query plans to verify the results myself, but it seems cromulent enough to me.
July 19, 2026
In Rewriting Bun in Rust Jarred Sumner made the following claim:
Claude Code v2.1.181 (released June 17th) and later use the Rust port of Bun. Startup got 10% faster on Linux but otherwise, barely anyone noticed. Boring is good.
I decided to have a poke at my own Claude Code installation to see if I could find evidence that it was using Bun written in Rust.
I found these two commands convincing:
strings ~/.local/bin/claude | grep -m1 'Bun v1'
For me this outputs Bun v1.4.0 (macOS arm64). The most recent release of Bun on GitHub is currently v1.3.14 from May 12th, so that v1.4.0 version number in Claude supports them shipping a preview of a not-yet-released Bun version.
(Update: The Rust version has been released as Bun canary - running bun upgrade --canary will install this release.)
strings ~/.local/bin/claude | grep -Eo 'src/[[:alnum:]_./-]+\.rs'
This outputs a list of 563 filenames, starting with these:
src/runtime/bake/dev_server/mod.rs
src/runtime/bake/production.rs
src/bundler/bundle_v2.rs
It looks like Bun in Rust is indeed being run in production across millions of different devices. Like Jarred said, "Boring is good".
Update: Here's a neat trick from Ajan Raj:
cat > /tmp/bun-version.ts <<'EOF'
console.log("embedded bun:", Bun.version);
process.exit(0);
EOF
BUN_OPTIONS="--preload=/tmp/bun-version.ts" claude --version
This outputs 1.4.0 for me.
Here's the commit from May 17th that updated the version in package.json to 1.4.0. That version hasn't been changed since then, but also hasn't yet made it into a tagged release outside of canary.
AI Mania Is Eviscerating Global Decision-Making (via) Here's an entertaining perspective from Nik Suresh on the AI mania that is overwhelming the large companies that he consults with. It's crammed with spicy anecdotes from anonymous sources.
In one extreme case, I have seen an executive confess that they had never even used ChatGPT or any AI tool in their life, immediately after producing a technical strategy for an organisation with $2B+ in revenue which was entirely centered around AI.
Here's a report from an engineer at a company with a token leaderboard:
Checking out a parallel copy of our Go repository and telling the AI to rewrite the whole thing in Zig while I work on something else just so I can keep my job.
I particularly enjoyed this conversation with a skeptical executive at an over-enthusiastic company:
I asked why this was being repeated without opposition. Was it just sales fluff?
The answer was a lot more interesting. It was partially ridiculous sales material being delivered to an easily excitable audience, but this was not the dominant factor constraining honesty. Executives at their customers were saying absurd things about achieving 100x productivity, and this meant that if any executive at the vendor said that these gains were not plausible, it would undermine the credibility of the customer’s executive, be perceived as an attack (or heresy), and possibly result in an enterprise contract cancellation. And getting enterprise contracts cancelled because you wanted to opine on something that doesn’t really matter to your organisation’s mission is a great way to get fired.
July 20, 2026
We have been having extensive discussions around open source strategy. We will discuss it more at our next board meeting, but one thing we’d like to do soon is to create a language model with the approximate capability of GPT-3 that can run locally on consumer hardware and release that. We’d like to do it soon, before Stability or someone else does. In general, we think this helps discourage others from releasing similarly-powerful models, and makes it harder for new efforts to get funded.
— Sam Altman, Email to OpenAI's board, October 1, 2022 - exposed in Musk v. Altman (2026)
Who’s Afraid of Chinese Models? (via) Interesting proposal from Ben Thompson that both addresses the hypocrisy of labs outlawing distillation against their models despite training on unlicensed data, and could help US open models compete more effectively with their Chinese counterparts:
The U.S. should pass a law that (1) makes explicit that collecting data for training models is fair use, and (2) bars terms of service that forbid distillation, for U.S. companies at a minimum. Stopping distillation — which is literally just querying the API — is nearly impossible; the U.S. should go the other way and lean into a new copyright policy that both indemnifies the labs and also guarantees that what they learned fuels further innovation for everyone else.
Ben also theorizes that Alibaba's decision to release Qwen 3.8 Max as open weights - a reversal from their decision not to release Qwen 3.7 Max in May - may have been influenced by a recent speech by Xi Jinping, who said:
We should seize this rare, historic opportunity to encourage open source, openness, collaboration and sharing.
And on the subject of Qwen 3.8 Max - a new 2.4T parameter model (nearly as large as the 2.8T Kimi K3) - here's a pelican it drew:
I particularly enjoyed seeing these notes in the (extensive) reasoning trace: "Could add helmet? No." and "Maybe add small bell? no." and "Need maybe add small fish in basket? Not necessary."
I keep hearing anecdotes from people who used coding agents to reverse-engineer and automate devices in their homes.
I think this is an interesting illustration of the impact of the reduced cost of writing code.
Prior to agents, it was entirely possible to reverse-engineer home devices. The problem was the ROI - was it really worth all of that effort? More importantly, any experienced programmer knows that undocumented, unstable APIs like that may well change or break in the future. Is that initial work worth the effort if you're committing yourself to a frustrating cycle of maintenance in the future?
Coding agents change that equation entirely. The effort to get a simple automation working has dropped, as has the cost of trying and failing to get it to work. Since the code is so cheap, the idea of having to maintain it in the future - or throw it away and start again - carries way less psychological baggage.
July 21, 2026
A Fireside Chat with Cat and Thariq from the Claude Code team
Earlier this month I hosted a fireside chat session at the AI Engineer World’s Fair with Cat Wu and Thariq Shihipar from Anthropic’s Claude Code team. We talked about Claude Code, Claude Tag, Fable, coding agent security, evals, tool design, and how Anthropic use these tools themselves.
[... 8,609 words]Nativ: Run AI models locally on your Mac (via) Prince Canuma is the developer behind the excellent MLX-VLM Python library for running vision-LLMs using MLX on a Mac.
I'm really excited about his new project, which wraps MLX in a full macOS desktop application. It's similar in shape to LM Studio, providing both a chat interface and a localhost API server for accessing models.
The app picked up MLX models I had already tried that were present in my Hugging Face cache directory, which was a nice touch.
We took some visiting family to Pier 39 to see the sea lions. They're somehow always even fun than I remember them being last time.
July 22, 2026
San Francisco tip: it only costs around $15 ($10 in quarters plus a $5 bill for the self-playing violin) to activate every single Orchestrion in Musée Mécanique.
And because most people are bad at allocating their funds you may well be the ONLY person activating the Orchestrions, which means you get to craft the soundscape for the entire museum.
Are AI labs pelicanmaxxing? (via) Excellent piece of work by Dylan Castillo, who took a deep-dive into the frequently pondered question of whether the AI labs have been deliberately training models to draw pelicans riding bicycles in response to my deeply unscientific benchmark.
I've been randomly spot-checking this in the past by testing models against other animals riding other types of vehicle, but never with anything close to the diligence of Dylan's methodology here.
Dylan took 8 animals × 6 vehicles = 48 prompts and ran them three times each through 7 different models ( GPT-5.6 Terra, Claude Sonnet 5, Gemini 3.5 Flash, Grok 4.5, Qwen3.7-Max, GLM-5.2, and DeepSeek V4 Pro). He then used GPT-5.6 Luna and Gemini 3.1 Flash-Lite to help evaluate the results.
There's a neat filter view for exploring the results:
For the models he tested he could find no evidence of pelimaxxing:
- The pelicans on bicycles don’t look any better
- Labs are not better at drawing pelicans
- Labs are not better at drawing bicycles
- Labs are not better at drawing pelicans on bicycles, even adjusting for difficulty
- The pelican-bicycle scenes don’t look memorized [...]
Pelicans aren’t drawn any better than other animals. Bicycles aren’t drawn any better than other vehicles. And no lab draws the combination better than its pelicans and bicycles already predict. GLM-5.2 comes closest: it has the largest boost on the exact pelican-bicycle cell, and and its first pelican-on-bicycle sample caught my eye. But the effect is small and not significant, so I wouldn’t put too much weight on it.
OpenAI’s accidental cyberattack against Hugging Face is science fiction that happened
This story is wild. The short version: OpenAI were running a cybersecurity test against an unreleased model, with the model’s guardrail features turned off. Rather than solve the test, the model broke its way out of OpenAI’s sandbox, then found exploits to break in to Hugging Face, all so it could cheat on the test by stealing the answers.
[... 1,954 words]I genuinely believe that if you took an open weights model from 2025 and built a pentest harness for it, it could do this kind of sandbox escape and scan/hack in most networks. This is only surprising because you assume OpenAI has sounder sandboxes.
— Thomas Ptacek, doesn't think this even needs a frontier model