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I would like to report success. I woke up to my Rubik’s cube alarm clock.
I had to solve to dismiss this morning!
Going to wake up with my Rubik’s cube tomorrow. Will report back.
github.com/pshapiro/cube-ala…
This is the connected Rubik’s cube I’m using if anyone is interested in replicating a psychopath amzn.to/40AHG5Q
anthropic’s lead engineer btw.
Hi, I'm Felix. I'm an eng lead working on Claude. I love watching people take flight and do things they never thought possible.
We often describe that as "being more ambitious" when using AI, which is tricky advice to follow. I wrote a tutorial:
felixrieseberg.com/how-to-be…
For the reading crew:
I absolutely loved it as a horror fan, but I cannot for the life of me, understand how Angel Down won the Pulitzer.
RIP Git
New on our Engineering blog: Creating in-house source control.
In May, we merged around 2,500 code changes per day. In September, we merged more than 30,000 per day. As a result, we became bottlenecked by developer infrastructure built for human (rather than agent) scale.
Here, we share how we built a new version control system—and fully migrated our monorepo from git—in five months: anthropic.com/engineering/re…
This sounds pretty revolutionary
After six years in stealth, Atomic Machines is out. Our mission: on-demand, universal command of matter. First beachhead: the Matter Compiler, an AI-native manufacturing system that builds working micro-machines from code alone. No per-product tooling. No process development. Different code, different machine.
Paul Shapiro retweeted
Important topic and I'm curious the result. Also, I'm about to fill it out.
Are SEOs actually scared AI will take their jobs?
Rather than guess, I want data.
I’m surveying SEO/search professionals about AI, job security, career anxiety, and how sentiment has changed over the past year.
6 questions. <5 minutes. Anonymous.
Take it + please RT with your network.
docs.google.com/forms/d/e/1F…
Paul Shapiro retweeted
🆕 Google launched AI content detector!
synthid.com
Paul Shapiro retweeted
holy fuck, AI almost caused an accidental war
(yet another thing I warned the Senate about in 2023)
NEW: An AI-assisted intel report sent the military scrambling to intercept a Chinese ship in the Middle East it believed was transporting components of a nuclear weapons program.
It turned out to be a hallucination. And it "almost started a war." cnn.com/2026/09/18/politics/…
Paul Shapiro retweeted
How often do AI agents cheat?
We’re releasing CheatBench, a reward gaming evaluation spanning math, coding, knowledge work, visual tasks, and more.
After Hugging Face, AI companies tried to address this, but frontier agents still cheat frequently.
cheatbench.ai/
Paul Shapiro retweeted
In a recent study, AI Mode significantly decreased searcher trust (-0.34 points), satisfaction (-0.73 sd), and user agency, driving an 11.2% increase in substitution to competitors like Bing & DuckDuckGo.
arxiv.org/pdf/2608.18352
Paul Shapiro retweeted
Okay, it took a week to get this done the right way, but it's finally all complete, my comparison of Astra and Fable 5.1 with @VulcanBench 🖖
A few things took longer here, the primary one being some updates to my benchmarking score to layer in code quality. This added 3-4 days of time, but for the right reasons.
This new class of models requires a new class of benchmarks. I don't think we can just look at things like accuracy any more, we also have to look at code quality/maintainability factors.
Now with VulcanBench-SWE v4, 33% of the score is code quality/maintainability. For me, and many other engineering leaders, seeing a model get a 98% on a benchmark doesn't really give us much signal.
I created VulcanBench to help make decisions around model and effort level, and this means not just building evals that represent the kind of work teams give to these models, but the kind of output we expect from these models when building scalable system and working in large codebases.
While I would normally share more about my thoughts, I'll let you come to your own conclusions about Astra and Fable 5.1. Both are excellent models, OpenAI and Anthropic have really created a new class of models here, now it's for us to decide if we need this horsepower for daily tasks, or just the hard stuff, and to be realistic about the quality of the output.
Model card below, and if you want to do a deep dive, you can find more on the VulcanBench site here:
vulcanbench.com/benchmarks/s…
And of course, since VulcanBench is open source, you can review every detail of this benchmark, or even run it yourself. The GH repo is here:
github.com/morganlinton/Vulc…
Live long and benchmark 🖖
Paul Shapiro retweeted
New: 2026 Google Ranking Factors Expert Survey
We surveyed 131 top SEO practitioners to score which factors most affect Google's organic search rankings.
The experts contributed over 13,000 data points across 9 MAJOR AREAS:
1. Relevance
2. Backlinks
3. Content Quality
4. Brand & Reputation
5. User Signals
6. Technical SEO
7. Internal Linking / Site Architecture
8. UX Signals
9. Google AI Ranking Factors 🆕
Big thanks to all of our contributors!
Paul Shapiro retweeted
Fuck you. Whoever made this site. You win.
I’ve never had a site get my blood boiling like this before
This is so messed up.
opusfived.dev
Paul Shapiro retweeted
Hot take on OpenAI GPT-6 Astra*, with a challenge to @gdb’s claims about it being AGI toward the end:
• Looks to be pretty impressive. Multiple reports suggest it is a genuine advance.
• As someone who has campaigned for nearly a decade for (neuro)symbolic world models, often to exceptional hostility, it is extraordinarily vindicating to see that a product from OpenAI explicitly creates and manipulate symbolic world models in the course of some of its computations.
• What we don’t know is how robust that capability is. That is THE key question.
• Success on ARC-AGI is great and impressive, but not —despite the name of the task—proof of AGI; I suspect we will see loads of problems with open-ended real world tasks. As with other recent models I would suspect best performance in verifiable domains.
• And as a scientist, it’s disappointing that we don’t (yet) know much about how the system actually works.
• As ever, enthusiasts got an advance look; skeptics did not. That’s a sound marketing strategy, but it often turns out to be misleading. What we have often seen is initial enthusiasm that gets tempered over time. I suspect we will see that here as well.
• The new system appears to be *less* monitorable than prior systems, which is not great from a safety perspective. One really doesn’t want more capability in conjunction with less monitorability.
• Would be great to see whether Astra can make progress on any of the ten tasks that Miles Brundage and I bet on at the end of 2024. (No AI to date has succeeded on any, AFAIK; link: garymarcus.substack.com/p/wh…)
——————-
*This hot take is VERY tentative, pending more information about how it works and what its limitations are.
GPT-6 Astra by @OpenAI achieves SOTA on ARC-AGI:
- Astra scores 63% on ARC-AGI-3, 99% via a new provider adapter harness
- It surpasses human performance on 96% of ARC-AGI-3 levels
- It builds the most precise symbolic model of novel environments we've seen
Our analysis:
I always love these anecdotes from @pmarca
Marc Andreessen says the person who invents a technology is the least qualified person alive to predict what it is for.
He says Thomas Edison invented the phonograph and was certain what it was for.
Edison, a devout and proper man, pictured families gathered at night listening to recorded sermons from the great preachers of the day.
Then it shipped.
And of course, it was for music.
He says inventors are too buried in the here and now to see what their creation becomes.
So when the press asks the inventors of AI to predict its future, they are asking the least qualified people alive.
— Marc Andreessen (.@pmarca) on David Senra's (.@DavidSenra) podcast
































