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.

The schedule is up for the 2025 edition of the PyTorch conference, which is now at the Moscone West in San Francisco.

The Second Half – Shunyu Yao – 姚顺雨

Dao-AILab/quack: A Quirky Assortment of CuTe Kernels

Back in 2021, OpenAI nixed its robotics team, leading to comments on Hacker News like “Reinforcement learning itself is a dead-end on a road to AI”. Now, in 2025 we are surrounded by RL post-trained reasoning models and Mary Meeker is using the word “unprecedented” a lot. This kind of skepticism/hype overlap is very common right now, as Helen Toner breaks down in her excellent recent post/talk on unresolved questions in AI:

Statecraft recently re-ran an interview from 2023 with Jason Matheny, formerly of IARPA: https://www.statecraft.pub/p/how-to-predict-the-future-278

In May Nvidia shipped CuTe‑DSL, the Python library they teased at GTC earlier in the year that mirrors CUTLASS’s C++ tensor‑layout . Then, at the start of June, the ‑dev label disappeared (so presumably its production ready now). The pitch is simple: Write speed‑of‑light kernels from the comfort of Python.