I built a free handbook where the entire field is laid out as an interactive map — ~25 algorithms grouped into branches (value-based, policy-based, model-based, planning), and clicking any node takes you to a full chapter with the intuition, math, and runnable code.

Site: rl-handbook.com
Code: github.com/lubludrova/rl-handbook

Would really appreciate feedback — especially where explanations are unclear or where you'd want more depth. What topics should I prioritize next?

All of this could have been avoided if they just declared Fable their first “Trump class” model in the first place. 

meme from my favourite free ai coding newsletter: ijustvibecodedthis.com

Ohio State University's NLP team released QUEST-35B, an open-source Deep Research agent trained using ~32 H100s and ~8K synthetic samples.

The team open-sourced the training recipe, code, weights and datasets. Benchmark results show competitive performance against several frontier Deep Research systems.

What do you think is the biggest remaining gap between open-source Deep Research agents and frontier closed systems?

Source: Professor Yusu

Been refreshing the hf page since Wednesday. GLM-5.2 was announced back on June 13, blog went up, api went live, but the actual weights just dropped this morning. First safetensor push was a few hours ago. This is the full-size model, not a distilled variant, under MIT.

Spent the afternoon reading the release material. The numbers are worth a look. Not because it "beats everything" but because for once the open-source line is actually touching the frontier, not just chasing it.

Coding head-to-heads against opus 4.8:

SWE-bench Pro: 62.1 vs 69.2. Seven-point gap. That is closer than any open-weight model has been to opus on anything real.

Terminal Bench 2.1: 81.0 vs 85.0.

FrontierSWE (long-horizon, June 16 run): 74.4 vs 75.1. 0.7 apart. Basically a tie on the marathon coding benchmark.

The headline everyone's already posting is aime 2026 at 99.2, beating gpt 5.5 and opus. Whatever. That is a poster number and every lab does it. On gpqa-diamond it is behind gemini 3.1 pro and tied with opus. The interesting story is the coding stack, not the reasoning poster.

For this sub the license matters more than any score. MIT, no commercial restriction, no "you may not compete with the api" rider. Read the full text myself. The weights are just... out.

Weights： https://huggingface.co/zai-org/GLM-5.2

Blog：https://z.ai/blog/glm-5.2

And here is the part that makes this less of a celebration than people want it to be.

744B parameters bf16. Safetensor list is 282 shards of ~5GB each. Fp8 helps a bit but the model still needs more VRAM than a single H100. The ollama library entry lists a 4.4B tag which is clearly not the full weights, probably a router stub. Did not see a real community GGUF yet. Best quant effort at INT4 still puts you north of 372 billion effective weights and 400GB+ download before you even boot a tokenizer. Nothing here is runnable on a home setup.

Total guesses about who can actually serve this: 8x H100 minimum for fp8. H100 is already three years old and the people running production inference on it are cloud providers, not hobbyists.

And nothing smaller exists. No mini, no 14B, no local-friendly version. The GLM-5 line has been full-weight only from the start. Without a compact version there is no way to tell whether the architecture scales down sensibly.

So what you have is a frontier-class model sitting on huggingface in a form factor that only a cluster can serve. Historically meaningful. Practically useless for most people here.