[Talk] Text Diffusion — Google DeepMind's Brendan O’Donoghue

<p>Today Inception Labs dropped <strong>Mercury 3</strong>: a diffusion-based LLM with a 1M-token context window doing <strong>1,068 tokens/sec on NVIDIA GPUs</strong>, claiming

<!-- SC_OFF --><div class="md"><p>Spent the last week coding diffusion Gemma from scratch. Was super fun. Thought I'd share it here &lt;3</p> <p>If you are looking for just the

<table> <tr><td> <a href="https://www.reddit.com/r/LocalLLaMA/comments/1u26oyp/diffusiongemma_the_developer_guide_google/"> <img alt="DiffusionGemma: The Developer Guide- Google

<!-- SC_OFF --><div class="md"><p>Every &quot;AI agent&quot; that controls a computer today does this: generate text → parse it → execute → serialize result back to text → repeat.

&#32; submitted by &#32; <a href="https://www.reddit.com/user/nnxnnx"> /u/nnxnnx </a> <br /> <span><a href="https://introspective-diffusion.github.io/">[link]</a></span> &#32;

<!-- SC_OFF --><div class="md"><p>Peter Holderrieth and Ezra Erives just released their new MIT 2026 course on flow matching and diffusion models! It introduces the full stack of

<a href="https://news.ycombinator.com/item?id=49264583">Comments</a>

<p>Building a production-ready, fault-tolerant Retrieval-Augmented Generation system is an exercise in managing harsh tradeoffs. You want massive context, lightning-fast hybrid