One year of Tinker: Thinking Machines' API for owning your fine-tuned models
thinkymachines · x · 2026-10-16
Thinking Machines Lab marks one year of Tinker, its post-training platform. Before launch, fine-tuning required hiring infra teams, sourcing GPUs and debugging failed runs — there was no API for applying the full range of fine-tuning methods to your own data in a few lines of code.
Tinker exposes the kind of post-training API frontier labs use internally: your data and training loop stay under your control, while four API functions (sample, forwardbackward, optimstep, savestate) connect to Tinker's managed GPU and checkpoint infrastructure. The team argues the custom-model market stayed small not for lack of value in fine-tuning but for lack of a versatile, reliable, fast, cheap platform.
The retrospective covers a year of lowering the barrier so customers can turn their expertise into models they own.
Related event: Thinking Machines Marks One Year of Tinker with Cheaper Model Fine-Tuning(2 posts)→
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