Thinking Machines Lab has moved its Tinker training API into general availability and added 3 major capabilities, support for the Kimi K2 Thinking reasoning model, OpenAI compatible sampling, and image…
Catalyzing breakthroughs in science By proving it could navigate the massive search space of a Go board, AlphaGo demonstrated the potential for AI to help us better understand the vast…
In this tutorial, we walk step by step through using Hugging Face’s LeRobot library to train and evaluate a behavior-cloning policy on the PushT dataset. We begin by setting up…
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# Introduction
Python decorators are tailor-made solutions that are designed to help simplify complex software logic in a variety of applications, including LLM-based ones. Dealing…
In this tutorial, we explore how we use Daft as a high-performance, Python-native data engine to build an end-to-end analytical pipeline. We start by loading a real-world MNIST dataset, then…
Today, we're introducing Gemini 3.1 Flash-Lite, our fastest and most cost-efficient Gemini 3 series model. Built for high-volume developer workloads at scale, 3.1 Flash-Lite delivers high quality for its price…
Current end-to-end robotic policies, specifically Vision-Language-Action (VLA) models, typically operate on a single observation or a very short history. This ‘lack of memory’ makes long-horizon tasks, such as cleaning a…
