Chinmaya Sahu

We asked GPT-6 Astra to edit Qwen3.5-0.8B weights to improve math. It directly changed only 0.016% of weights, fixing 0.8B's reasoning loops & improving math up to 6x and even instruction following 3x, without gradients or training. We call this Weight-Space Programming: frontier LLMs can now natively read/write neural network weights! 🧵 Blog: rikiyat.github.io/blog/weigh…

a year ago, an interviewer called me naive. all because i said customers would fine-tune open models instead of using frontier APIs. (true story) this week, we released LFM3 at @liquidai: small open models built to be specialized. as a demo, I fine-tuned our 2B vision model to spot defects in drone photos of wind turbines. before: LFM3-VL-2B and GPT-6 Luna named the defect correctly in under 10% of images. after: a few hours of fine-tuning on one GPU made it jump to 67%. frontier models are generalists. your domain-specific problem needs a specialist. how you can do it too: 1. pick your domain-specific, narrow task and evaluate if a frontier model is sufficient. If not: 2. choose the LFM3 model that fits your data. 3. curate your own examples from your own proprietary data 4. fine-tune in a few hours, even on a single GPU. 5. rerun the evals and deploy anywhere without having to call a cloud API. Fine-tuning notebooks: github.com/Liquid4All/cookbo… Wind turbine project: github.com/Liquid4All/cookbo… what task would you specialize a model for first?