GPT4o’s image generation was a remarkable event, beyond the brief Ghiblification of all social media.GPT-4o offered significantly more steerability than earlier image generation models,, while offering image quality in the ball park of the best diffusion models. Qwen-Image gives a similar level of fidelity and accuracy and is an open-weights model with a pretty decent technical report: QwenLM/Qwen-Image.

There’s a chart making the rounds that caused Tim Lee over at Understanding AI to rewrite his recent (excellent!) article aboutthe impact of AI on jobs. MIT’s Erik Brynjolfsson and colleagues found¹ that young workers in AI-exposed jobs² have seen their employment drop by 13% since ChatGPT arrived. Meanwhile, their older colleagues in the same fields are doing just fine.

A Primer on LLM Post-Training – PyTorch

You could have invented CuTe hierarchical layout (but maybe not the rest of it?) : ezyang’s blog

Extending AFM-4.5B to 64k Context Length

Pre-training is about making AI correct, post-training is about making AI helpful¹. That helpfulness is (primarily) shaped by reinforcement learning. RL for LLMs really took off with RLHF (RL from Human Feedback), which trained based on the score from a reward model.

I recently read a few posts that helped connect the dots on why Python is a) so successful as the lingua franca of ML b) also seems likely to be successful in the future¹.

It has been hard to keep up with the flurry of strong agentic open-source models coming out of Chinese labs recently, including Moonshot’s Kimi K2, Z.ai’s GLM 4.5, and Qwen3-Coder¹.