ANTHROPIC : A new internal research has been published, highlighting an accelerated AI development and a potential path to recursive self-improvement. > Claude Mythos Preview could work for “at least” 16 hours and was “at the upper end of [METR] can measure.” > Today,
LLMS
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Anthropic: Claude writes more than 80% of merged code
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We have just published internal data on the share of Claude's development that is already done by Claude: – More than 80% of all merged code in our codebase is now written by Claude – It has been months that many researchers at Anthropic
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Anthropic’s codebase 80% authored by Claude AI in 2026
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"As of May 2026, more than 80% of the code we merge into Anthropic’s codebase was authored by Claude." Matches independent measures. There really is no sign this is slowing down (which doesn't mean there aren't organizational challenges to absorbing this much productivity gain)
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AI helps Anthropic research and train better AI, closing the loop
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Very interesting article from Anthropic about how, thanks to AI, they're able to streamline their process of researching and training better AI. Or as it's colloquially known: closing the loop
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New course on serving LLMs efficiently with Red Hat
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New course on serving LLMs efficiently — how do you serve models to many concurrent users at low latency and reasonable cost? This short course is built with @RedHat and taught by @cedricclyburn.
— Andrew Ng (@AndrewYNg) 4 juin 2026
Efficient LLM serving requires efficient memory management. A 70B-parameter model… pic.twitter.com/KeKveT2IicNew course on serving LLMs efficiently — how do you serve models to many concurrent users at low latency and reasonable cost? This short course is built with @RedHat and taught by @cedricclyburn
. Efficient LLM serving requires efficient memory management. A 70B-parameter model -
Nemotron 3 Ultra open-weight release boasts impressive efficiency ratio
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And another open-weight release. Nemotron 3 Ultra has an ultra impressive capability:efficiency ratio! Design-wise, it carries forward the Mamba-2-attention hybrid stack and LatentMoE introduced in the previous Super variant. But everything is a bit bigger.
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OpenAI vs. Anthropic IPO: Where would you invest 100k?
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if you had 100k to invest in OpenAI and/or Anthropic IPOs which would you go for?
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AI Research: Claude’s Improved Decision-Making Outperforms Humans
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AI research is a series of next-step decisions. We looked at sessions where a human researcher took a wrong turn, showed Claude the session up to that point, and asked it what to do next. Mythos Preview improved on humans 64% of the time—up from 22% in 2024.
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Claude’s coding success jumps 50 points, rivaling human quality
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The speedup isn’t just in volume. On open-ended coding problems where answers are unclear, Claude’s success rate is now 76%—a 50 point jump in just 6 months. Many engineers also say Claude’s code quality is now on par with human code; we expect it to be better within the year.
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Anthropic’s AI models show massive speedup in code training tasks
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Each time we release a model, we run the same test: give it code that trains a small AI model, ask the new model to speed it up. It takes a skilled human 4-8 hours to reach 4x faster. In May 2024, Claude Opus 4 averaged a ~3x speedup. This April, Mythos Preview achieved ~52x.