Fuck Fable I have just swapped the model I am using for rebuilding the godofprompt website saving 11x on the token costs!
GENERATIVE AI
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Introducing GLM-5.2 for Understanding Research Articles
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Introducing GLM-5.2 for understanding research papers 🚀
— alphaXiv (@askalphaxiv) 16 juin 2026
Highlight any section of a paper to ask questions and “@” other papers for quick context, comparisons, and benchmark references pic.twitter.com/84LDzhLjhhHighlight any section of an article to ask questions and '@' other articles for quick context, comparisons, and benchmark references.
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No moat when everyone builds the same thing, says Marcus
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Like I said in January 2024, with everybody building the same thing, there is no moat. That’s been catching to OpenAI ever since. The numbers don’t lie.
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GLM-5.2 open-source model matches Claude Opus 4.8
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@ZAI_ORG JUST DROPPED GLM-5.2, AND IT IS PUNCHING RIGHT AT THE LEVEL OF CLAUDE OPUS 4.8 The kicker? It’s a 753B parameter model with a true 1M-token context, released fully open-source under an MIT license What makes this release technically interesting: → IndexShare
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Meetup: ask Codex for startups, finances, groceries, games
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went to a meetup and people asked what should I be doing that I’m not doing rn just ask things want to build a startup – ask codex
want to sort your finances – asl codex want to order groceries – yes ask codex
want to build a game – indeed you should ask codex want to get a -
Analysis of ChatGPT conversations from opted-in users with anonymization and aggregate results
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For this research, we analyzed only ChatGPT conversations from users who allow their data to be used to improve models. Before analysis, we removed account-linked identifiers and identifiable information, and we report only aggregate findings.
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OpenAI analyzes opt-in ChatGPT conversations after anonymization
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For this research, we analyzed only ChatGPT conversations from users who allow their data to be used to improve models. Before analysis, we removed account-linked identifiers and identifiable information, and we report only aggregate findings.
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Average Claude Code task value grew 27% in six months
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The average task in Claude Code has grown more valuable. We compared the type of work done in each session to what that same task would cost on a freelance marketplace. From October to April, the monetary value of the average session grew 27%.
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Claude Code success rates: all fields close to software engineering
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We compared Claude Code success rates between occupations. On our toughest measure of success—requiring verifiable evidence that a goal was completed, like committed code—every field was within 7 percentage points of software engineering.
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Anthropic research tracks Claude Code usage, tasks, and expertise
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Our latest economic research introduces a framework for tracking Claude Code as it scales. Who is using Claude Code, and what are they using it for? How is the value of tasks changing? And how much does domain expertise shape whether a session succeeds?