posts · 2026-05-15

This hit both Hacker News front page and r/LocalLLaMA today, which tells you the community is hungry for inference speedups. Orthrus freezes Qwen3-8B's backbone and uses dual-view diffusion decoding to generate multiple tokens per forward pass instead of one-at-a-time autoregression. The 7.8× claim comes from that batching effect, and the output distribution is theoretically identical to the original model. I'd discount this on two fronts. One, we only have a GitHub repo and community chatter — no paper or technical report yet, so the method's edge cases are unknown. Does it hold up on long sequences? What's the memory cost? Two, both sources use nearly identical headlines pulled straight from the README, with no independent benchmarking. If the numbers check out, the real win is no retraining and no quality loss, which matters a lot for local inference. I'm waiting for someone to reproduce it before taking the 7.8× at face value.

LeCun’s sharpest move is not another anti-LLM rant; it is tying that critique to AMI’s world-model bet and telling PhD students to stop working on LLMs. The snippet gives hooks: a 2027 forecast, leaving Meta, disputes with Hinton and Bengio, and comparing OpenAI and Anthropic to Sun Microsystems. It gives no architecture, funding, benchmark, or reproducible result. I don’t buy the clean “stop doing LLMs” line. The 2025–2026 gains practitioners felt came from the LLM perimeter: tool use, code execution, long context, agent evals, synthetic data loops. LeCun is right that physical world modeling and robotics need something beyond next-token training. But until AMI shows a repeatable experiment, this is a route declaration, not a death certificate for LLM research.

Eric Jang walked through his from-scratch AlphaGo rebuild on Dwarkesh's podcast. Both sources are Dwarkesh's own content (article plus YouTube), so there's no independent angle here — but the material is Jang's firsthand technical explanation, not a secondhand summary. His core comparison is sharp: AlphaGo uses Monte Carlo Tree Search for self-play, where every move gets a clear "this is better than that" training signal. LLM RL training, by contrast, has to deal with trajectories of 100k+ tokens, and the model has to guess which specific action earned the reward. That's the credit assignment problem, and Jang argues human learning looks more like the former. Current LLM RL is stuck with the latter's inefficiency. He also touched on using LLMs for automated AI research — implementing experiments and tuning hyperparameters works decently, but picking the right research question and escaping dead ends still doesn't. That connects directly to the intelligence explosion debate. I'd treat the automation section as personal experience rather than a systematic evaluation, since he only ran this on one project.

Three outlets are tracking the Apple-OpenAI split, with aligned headlines but thin disclosed facts. The available body is only a Bloomberg scrape fragment, so legal claims, contract terms, and damages are not disclosed; FT frames legal action, while TechCrunch frames Apple burning another partner. I read this less as a lawsuit story and more as OpenAI discovering the cost of renting the iPhone AI surface. Apple Intelligence put ChatGPT inside Siri as a distribution win, but the moment Apple can negotiate with Google, Anthropic, or its own models, OpenAI becomes a replaceable backend. For model companies, default placement on-device is harsher than a benchmark loss.