Thinking Machines Lab today officially open-sourced its first open-weight model, Inkling. It is a 975B-total / 41B-activated MoE Transformer, supporting 1M token context, pretrained on 450 trillion tokens of mixed text, image, audio, and video data, with native multimodal reasoning capability. Released alongside is a 12B-activated Inkling-Small preview, plus the Tinker customization platform. The company candidly admits that Inkling is not the current "strongest" open or closed model, but is betting on a "broad coverage + customizable" path: balanced training across text, Agent, code, instruction following, factuality, vision, and audio dimensions, aiming to let enterprises and researchers do low-cost post-training on Tinker. The most convincing demo at launch was making Inkling self-fine-tune a "no letter e" lipogram model — the Tinker pipeline ran end-to-end from data generation to weight update, completing a full self-iteration cycle in about 27 minutes. At the infrastructure level, Inkling is deeply integrated with NVIDIA, Hugging Face, vLLM, SGLang, llama.cpp, and Unsloth; full weights have been uploaded to Hugging Face, and an NVFP4-quantized version is provided for the Blackwell platform. In the Design Arena Agentic Web Dev blind evaluation, Inkling ranks slightly below GLM 5.2, above Claude Opus 4.6, and roughly on par with GPT-5.6 Sol. I think this is another watershed moment for the open-weight camp — at a time when the leading labs are all using "super-large + super-long RL" to climb leaderboards, Mira chose to return to the plain stance that "the model is the starting point of research, not the endpoint", handing post-training back to the community. If the Tinker platform's usability can really be as smooth as the launch demo suggests, Inkling is likely to become the preferred base for small and medium teams doing customized LLMs in the second half of the year; conversely, if the platform experience doesn't live up to the promise, it will be just another "open-source but hard-to-use" sample. Worth continuing to track subsequent evaluations and actual fine-tuning experience.