I was clearly wrong about Anthropic. They are obviously currently the leader in AI. No company has released a model as good as Mythos/Fable and they will undoubtedly have Mythos 2 ready soon.
And I would never cut them off in a way that hurt them badly, even as a competitor. That’s not my style.
Tesla open sourced its patents and we made the Supercharger network available to all competitors, even though we could have made it a walled garden.
SpaceX launches competing satellite systems with no increase in price or use of unfair terms.
Even my worst enemies can attack me on this platform.
…
White House demands that the computer nerds tell them, given the program's input, if any arbitrary computer program will finish running or continue to run forever
@SoundDobad I know this may sound petty, but I can’t stand it when people put photoshop a meth pipe in my mouth. A crack pipe doesn’t have that little bowl at the end. This is why we can’t trust AI. Please make the appropriate edit. Thank you for your attention to this matter.
In the last 6 months at @Ahrefs, we analyzed over 1 billion data points across 14 studies. Here's what we learned about AI search optimization:
1) "Best X" blog listicles are the single most prominent content format cited by AI chatbots. They make up 43.8% of all page types cited by ChatGPT specifically.
2) 67% of ChatGPT's top 1,000 citations come from sources marketers can't influence: Wikipedia (29.7%), homepages (23.8%), app stores (6.6%). Only 32.3% are influenceable content like educational pages, reviews, news, and blog posts.
3) 28.3% of ChatGPT's most-cited pages have zero Google organic visibility. These pages get cited repeatedly by ChatGPT despite not ranking in Google at all. A completely separate discovery layer.
4) ChatGPT only cites about 50% of the URLs it retrieves. It fetches dozens of pages per query but uses half as background context without attribution. This means that being retrieved and being cited are very different things.
5) Adding schema markup had zero meaningful impact on AI citations. AI Overviews actually dipped −4.6%, while AI Mode (+2.4%) and ChatGPT (+2.2%) showed changes indistinguishable from zero.
6) YouTube mentions have the highest correlation (0.737) with AI brand visibility out of all the factors we studied (including all the conventional SEO metrics like backlinks, page count, DR, etc). This held true for both Google-owned and OpenAI products.
7) AI Overviews reduce clicks to the #1 result by 58%. That’s up from 34.5% just 10 months earlier. The trend is accelerating.
8) 99.9% of AI Overviews appear on informational intent queries. Transactional, navigational, and local searches are almost entirely AIO-free. Shopping triggers AIOs just 3.2% of the time.
9) For a given search query, Google’s AI Mode and AI Overviews reach the same conclusions 86% of the time — but cite almost entirely different sources (only 13.7% citation overlap).
10) AI Overviews change every 2.15 days on average, with 70% of content differing between consecutive observations. But semantic similarity stays at 0.95. The words, sources, and entities constantly shuffle, but the actual meaning barely moves.
Map shows avg daily reserved passenger flow between districts for 🇮🇳 Indian railways.
And yet again, my first observation was that greenish line b/w Mumbai-Ahmedabad i.e. our first bullet train/HSR corridor 😅😅
You can visualise even the proposed HSR routes.
The future of devtools is dim because either:
1. Everything will be ingested upstream into LLM APIs
2. Anyone can just copy it (just look at comments on: https://t.co/EK8CUsgaqN)
Introducing Claude Managed Agents: everything you need to build and deploy agents at scale.
It pairs an agent harness tuned for performance with production infrastructure, so you can go from prototype to launch in days.
Now in public beta on the Claude Platform.
i trained Llama 2 on a $5 computer with less processing power than a thermostat.
it’s called 🥧 PiTorch: an ML library for Raspberry Pi Zeros.
i cover how to make it from scratch, from writing assembly GPU kernels to sending bytes over wires. (no hardware background needed!)
@oyster_brain@ChristosTzamos Neural Networks can be burned directly into chips, making it closer to a signal processor. The whole system much simpler, load weights and run the network in few clock cycles (GPU -> LPU -> Talas -> DSP). This detaches the "computer" from "hardware", it's a whole new paradigm!
AI nuked my billing!
Welp, a bug in directory management in my agent kernel just deleted the whole billing module to create one cron PR: https://t.co/jol3koL6NY
Man. But AI slop, AI fix!