Cody Blakeney
20.2K posts

Cody Blakeney
@code_star
Data Dawg @datologyai | Formerly Data Research Lead @DbrxMosaicAI | Visiting Researcher @ Facebook | Ph.D | #TXSTFOOTBALL fan | https://t.co/4G6Jf3at5w



Your evals, your environments, your data, your model. You need to scale the ladder Using foundation models to build a good harness and getting eval coverage is a great way to start building business value. Building environments to improve on your use cases is something that is often something your domain experts at your company are uniquely positioned to do. CPT on open models once you have done all this leg work is a great, tight feedback loop to insure you have data of sufficient quantity and quality to even consider pretraining. Scale the ladder

@code_star 💯💯 every company should be fine-tuning an open source model with rl to adapt to their own ecosystem and workflows. a generic model will never get you the same output as one trained on your own harness.


@code_star Shall we open source TexTorch?

shocking: ai researchers write great papers using pytorch/jax but ask them to write their optimization loops in fortran and suddenly they collapse

We've evaluated a lot of base models on perplexity-based evals and Kimi k2.5 proved to be the strongest! After that, we do continued pre-training and high-compute RL (a 4x scale-up). The combination of the strong base, CPT and RL, and Fireworks' inference and RL samplers make Composer-2 frontier level. It was a miss to not mention the Kimi base in our blog from the start. We'll fix that for the next model.

Congrats to the @cursor_ai team on the launch of Composer 2! We are proud to see Kimi-k2.5 provide the foundation. Seeing our model integrated effectively through Cursor's continued pretraining & high-compute RL training is the open model ecosystem we love to support. Note: Cursor accesses Kimi-k2.5 via @FireworksAI_HQ ' hosted RL and inference platform as part of an authorized commercial partnership.

the model is the product

Looks like it’s confirmed Cursor’s new model is based on Kimi! It reinforces a couple of things: - open-source keeps being the greatest competition enabler - another validation for chinese open-source that is now the biggest force shaping the global AI stack - the frontier is no longer just about who trains from scratch, but who adapts, fine-tunes, and productizes fastest (seeing the same thing with OpenClaw for example).

For students or people looking to break into careers in AI this exists to be a talent pipeline tool. Making visible and meaningful entries here is probably one of the highest ROI ways to demonstrate your skills and break in without getting a PhD or publishing.


Contrarian 2026 AI take: finetuning OSS LLMs becomes the enterprise differentiator. OSS is close enough to SOTA, and the tooling is finally usable, so proprietary data will convert into real domain accuracy gains. Evidence? my team @nvidia + @CrowdStrike hit SOTA on CQL. crowdstrike.com/en-us/blog/cro…



