Official source is Anthropic, who say they received a directive from the US government directly www.anthropic.com/news/fable-m...
Gergely Orosz
Tracked through public AI activity and peer connections inside the directory.
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Articles & links
The amount of AI writing on OpenAI's docs makes me sick. Filler sentences for nothing. Mannerisms and phrases humans would not write. Has a human even read this? And all of this will change how other docs are written, and how we all talk-for the worse IMO. 🤮 developers.openai.…
Full article: techcrunch.com/2026/06/12/m... Been writing about exactly this this the last several weeks in paid @Pragmatic_Eng issues, in The Pulse, here btw newsletter.pragmaticengineer.com/s/the-pulse
- Meta reassigned roughly 6,500 engineers via surprise email to generate AI training data, with workers calling the experience 'literally the gulag.'
- Over 1,600 Meta employees signed a company-wide petition against keystroke and click monitoring for AI training data collection.
- Zuckerberg acknowledged in a Friday memo that the changes 'caused distress' and admitted the company made mistakes.
• Spotify: open.spotify.com/episode/7ahj... • Apple: podcasts.apple.com/us/podcast/c... Brought to you by: • Antithesis — verify your system’s correctness without human review or traditional integration tests – and avoid bugs or outages. antithesis.com/pragmatic
Last month in @pragmaticengineer.com about how Meta is destroying its engineering culture: newsletter.pragmaticengineer.com/p/why-is-met... Last week in Semi Analysis on how Meta's infra org needs a reset: newsletter.semianalysis.com/p/metas-infr...
- SemiAnalysis says Meta spent more than $2.5 billion on Rivos, then cut engineers in the parts of the company it never wanted.
- Meta's entire GB200 fleet reportedly uses a custom Ariel SKU whose TCO is 14% higher than the standard GB200 configuration.
- Stack-rank cycles cutting the bottom 10% to 15% each review round push engineers toward short-lived 'window washing' projects, the piece argues.
Knowing how LLM contexts work and how to work around context limitations – aka “context engineering” – is becoming more important for software engineers working with LLMs. Dex Horthy coined the term "context engineering," and shares practical details on it: YouTube: youtu.be/U…
• Spotify: open.spotify.com/episode/7ahj... • Apple: podcasts.apple.com/us/podcast/c... Brought to you by: • Antithesis — verify your system’s correctness without human review or traditional integration tests – and avoid bugs or outages. antithesis.com/pragmatic
Recent commentary
What I’m hearing: Instagram’s Trust and Safery org absolutely gutted the last few weeks. ~60% of the org gone - between layoffs and forced reassignments to data labelling. All while “AI maxxing” pushed a bunch of bugs to prod. And hence why today’s massive Instagram account takeover happening.
Well this is massive: - Every non-US company sees how they can be cut off from US vendors with a snap of a finger. Non-US vendors getting free advertisement - What does this mean for US AI labs’ offices and employees outside the US? Reads pretty draconian What a mess
Well, that's a first. An enterpreneur who I met at a cafe a while back and was a nice guy and said we'd follow up perhaps just sent me an AI-genereated pitch. This wiped out the personal connection I had in my mind with this person. How do people not realize AI does this?
So apparently after Meta leadership: - Force reassigned some of the best devs on teams to AI data labelling fulltime - Laid off another 10% - Started to record every dev’s screen in the US 24/7 They now realized that it has indeed started to destroy their eng culture. And now trying to walk back
Let me know if this is just me: Noticed someone I know who is very "AI-pilled" and uses agents 24/7 to... start to talk IRL noticeably more like these LLMs write. Eg more heavily using adjectives like "geniune", frequently terms like "the shape of" and many more examples
Seeing how SOTA models are evolving: becoming more restrictive in usage (decided by the company), less transparent (you cannot tell if the AI lab nerfed your model) + less private (your prompts are stored, no opt out) makes me much more interested in open models + local inference
Things I really dislike about Fable: 1. Anthropic collects my prompt history, stores it, and does whatever they want with it for 30 days. No opt-out 2. They can nerf their most expensive model without telling me, billing me the same amount, wasting my time. Whenever they want
Situation 1: dev A thinks approach X is correct, dev B thinks Y is the right way. They argue and try to convince each other. Situation 2: dev A thinks approach X is correct, tells the LLM to implement it. There is SO MUCH learning in Situation 1, lost when using LLMs....
Meta had a SEV-0 outage today… less than two weeks after Meta’s most embarrassing undetected-for-too-long account takeover (also an outage) It’s impossible to unsee Meta pushing AI for code + reviews and the end result being more massive outages vs before They are connected
Talked with a few folks inside of AI labs (OpenAI, Anthropic) about what they think of the future of software engineering. The “closer” to shipping production code engineers are, the less they believe software engineering will be “solved” fully by AI. The opposite true as well
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