From the source
You could chat with models and give them tasks, but not integrate AI into your own workflows, systems, and custom applications.
A few companies would have the capacity to run models on their own production servers; everyone else would be locked out of building with AI.
When Tinker launched one year ago, AI post-training was in a similar situation.
It required hiring an infrastructure team, sourcing your own GPUs, setting up software libraries, and debugging errors and failed runs.
There was no API that let anyone apply the full range of fine-tuning methods on their own data with just a few lines of code.
Our mission at Thinking Machines is to build AI that extends human will and judgment.
Post-training is a practical application of that mission: people training models using their own knowledge, evaluating them against their own standards, and evolving them as they learn.
We were convinced that the market for customizing models was small not because there was no value in fine-tuning, but because there was no post-training platform that was versatile, reliable, fast, and cheap enough to make it viable.
…
