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havent seen many people outside anthropic ultracode yet. this thing is scarily good at burning tokens but you need to set up your repo to parallelize properly to make use of the fanout that i think subagents are best at. basically the idea is "subroutines but intelligent". when
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btw insane amounts of alpha in telling claude code to "review my code for issues" on Fable rn while it is not pay per use be prepared to be in abject horror that you shipped anything to prod without a Fable Check™ first
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for those keeping track at home it was 34 days between signing this deal and launching Mythos-class model GA to the world. https://
x.com/leerob/status/
2052059466821198061?s=20
… building on @nvidia stack means you can just do things™.
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Mythos is live! so excited to have our FrontierCode recognized as the next frontier coding bench. on FC Diamond, BOTH Opus 4.8 and GPT 5.5 don't meaningfully scale with effort, which many of you caught yesterday. Mythos/Fable posttraining have really applied that test time
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i was very keen on publishing evals where we do not award ourselves gold medals, as an intellectual honesty thing and motivator for the team to hillclimb in ways we care about with public pressure
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It's finally out!!! @METR_Evals found that more than half of SWEBench results is unmergeable slop. FrontierCode represents over 1000+ hours of maintainer validated software engineering work most frontier models cannot yet solve, much less solve with high quality. Cog had IOI
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a smarter alternative to "always use plan mode": always frame your task as a question, so that the model is invited to push back and rate the quality of the idea/suggest alternatives, rather than blindly execute what you SAID to do (which is often not precisely what you
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tix and info here https://
ai.engineer/wf see you soon, it'll be ~bigger than every prior AIE combined.
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Finally! the first eval ship from cog!!!!!!!!!! To contextualize: @METR_Evals cap out at ~16 hours. Cog has private enterprise evals up to 100hrs, and is confident enough to put a financial guarantee on it METR dataset: ML eng, GPU kernels, cybersecurity > "METR (2026)