Thinking Machines Lab’s Tinker abstracts away GPU clusters and distributed‑training plumbing so smaller teams can fine‑tune powerful models with full control over data and algorithms. This turns high‑end customization from a lab‑only task into something more like a managed workflow for researchers, startups, and even hobbyists.
— Lowering the cost and expertise needed to shape frontier models accelerates capability diffusion and forces policy to grapple with wider, decentralized access to high‑risk AI.
BeauHD
2025.10.02
100% relevant
Mira Murati and John Schulman describe Tinker as automating large‑scale fine‑tuning while exposing the training loop and keeping user control of data/algorithms.
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