We analyzed tens of thousands of real-world, high-reasoning Claude Opus and Fable outputs from versions 4.5 to 5 in the Text Arena and found that @claudeai's writing has changed in more ways than one: Across language complexity, patterns and markers, we now see: Claude’s answers have become much longer: - Opus 5 averages 510 words per response - This makes Opus 5 responses 3x longer than Opus 4.5’s average of 158 Responses have also become more structurally elaborate from Opus 4.5 to Opus 5: - 58% rise in average sentence length - 46% rise in clause frequency rises However, the vocabulary itself is not becoming more difficult, making Opus 5 longer and more structurally complex, but lexically simpler between 4.5 and 5: - 6.8% pt drop in long content words (46.9% to 40.1%) - 53% drop in abstract nouns (6.02 → 3.79 per 1k words) Writing habits people notice are much more common in Opus 5: - 2.3x as many em dashes - ~2x as many phrases like “load-bearing” - 50% more honesty wording like “honestly” and “frankly” Fable 5 is 38% more concise than Opus 5, averaging 316 words vs. 510. At the same time, it is nearly 2x as likely to include praise/validation or open with phrases like “yes, exactly”

Introducing @YukonResearch A platform for open frontier research. Over the past 2 months, an open network of humans + AI has already: - Beat Google’s frontier quantum circuit result over 50% - Made Poolside’s open-weight model run 2.6x faster - Increased post-quantum Ethereum scaling by 3.5x - Boosted Lighter’s prover 9.5x faster Put a hard frontier science problem in front of many independent solvers, each bringing different models, harnesses, prompts and tacit knowledge. And let the best results compound. Accelerating the future of scientific progress together with AI. Bring us your hardest problem:

An hour of agentic computer use may now be cheaper than an hour of human labor: Computer-use agent: $6-8 Offshore outsourced talent: ~$10 US talent: $30-45 "The math only gets better - inference keeps getting cheaper, and open-source models are getting good enough for a growing share of these workflows." The data on computer-use agents, from @fabrisera2000, @seema_amble, and @zephratic:

Today we are introducing Dyna-2, a world-action model pre-trained on one million hours of human video. At this scale, for the first time, we discovered several new scaling laws: • world-action models exhibit scaling law on human data across four orders of magnitude, from 1000 to 1,000,000 hours, • this human data scaling law implied a scaling law on never seen robot data, • both data and objective matter; world modeling and scaling on video data are essential for cross-embodiment scaling transfer to emerge 🧵