agent artist @letta_ai | @LTIatCMU @bitspilaniindia
San Francisco, CA
Joined July 2015
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sota on chonkbench 🇫🇷
Meet Mistral Large 4, aka Le Chonk.
• 1T parameters, natively multimodal. 49B active.
It is the best open weights model from US or Europe on aggregated benchmarks.
• State-of-the-art on critical workloads, including cyber defense, manufacturing and finance and it surpasses closed frontier models on visual grounding.
• Forged in Europe end-to-end and is deployable from Europe via our own Mistral Cloud infrastructure.
• Available to all via API today. Working with cybersecurity partners privately.
Open weights release end of October.
Devansh Jain retweeted
Today we’re announcing Not Diamond Code, the world’s most powerful intelligent model router for long-horizon coding agents.
Not Diamond works with any gateway or harness, including Claude Code, to select the best model and reasoning effort for each step, reducing costs by 20-65% without impacting quality.
> maybe continual learning should just focus on loading the right information up for icl (ie into the context window)
13/ What I take away from all this is that all the training to create the model is engineered around crafting the best possible ICL mechanism. Further training degrades this mechanism, at least for knowledge acquisition, and maybe continual learning should just focus on loading the right information up for icl (ie into the context window)
So this provides an answer to the architecture question, at least for now: the channel worth engineering for memory is the one that comes with addresses ie the context.
Paper: arxiv.org/abs/2607.11020
Code and data to follow shortly
Done at @baseten.
Devansh Jain retweeted
Really nice research from @baseten on parametric learning vs in-context learning.
ICL is the 'bitter lesson' of continual learning. Each new generation of model will be better at ICL than the last. Mechanisms for CL through context will ride the waves of model improvements.
13/ What I take away from all this is that all the training to create the model is engineered around crafting the best possible ICL mechanism. Further training degrades this mechanism, at least for knowledge acquisition, and maybe continual learning should just focus on loading the right information up for icl (ie into the context window)
So this provides an answer to the architecture question, at least for now: the channel worth engineering for memory is the one that comes with addresses ie the context.
Paper: arxiv.org/abs/2607.11020
Code and data to follow shortly
Done at @baseten.