Machine learning researchers may soon no longer need to manage complex GPU clusters, thanks to Tinker, a new innovation from Mira Murati’s Thinking Machines Lab. The recently launched Tinker API aims to simplify machine learning development by removing the burden of infrastructure management. It enables researchers to focus purely on experimentation, discovery, and innovation.
“For ML researchers who don’t care about infrastructure but only about the environment and data, this is excellent,” said Tyler Griggs, reinforcement learning researcher at UC Berkeley.
What makes Tinker stand out is its unique ability to merge training and inference into a single programmable interface, allowing for a smooth and continuous workflow. Tools such as SkyRL tx use Tinker’s backend to let researchers maintain full control over reinforcement learning logic while automating distributed computing.
“If the Tinker training API becomes as standard for training as OpenAI’s API is for inference, many will build their loops in Tinker,” added Griggs.
Early adopters are already experiencing major productivity gains. Sachin Dharashivkar, founder of Athena Agent, shared that projects which previously took five months now take only half that time. “Tinker keeps the fun part alive for deep learning engineers,” he said.
With Tinker, the process of AI experimentation has become faster, simpler, and more engaging. By combining automation with flexibility, it allows researchers to spend more time on innovation rather than system management — marking a major step forward for machine learning development.
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