This page collects what the sources on blogs to watch have published recently and groups them by subject rather than by publisher. It is rebuilt every hour, so a post usually appears here within an hour of going up. Nothing on this page is required reading.
Grouping by subject is the point. A reader gives you 45 separate streams and leaves you to notice that four companies wrote about the same scheduling problem this week; this page puts those four posts under one heading. Topics come from the filter terms already listed on the blogs-to-watch page, so the vocabulary is the course's.
How a post is filed. Each post is scored by keyword against every topic, using its title and its summary, and it is filed under the topic it scores highest against. A term that names a subject on its own counts for more than one that merely co-occurs with it, and a match in the title counts for three times a match in the summary. Any second topic a post also matches is shown as a label beside it. The method is keyword matching rather than a model, which makes it predictable, cheap, and occasionally wrong.
You are reading one topic, training and rl infrastructure, over the last 120 days. Show every topic.
The other half of the fleet: pre-training, post-training, and RL loops.
A guide to fine-tuning Qwen3-TTS for high-quality voice cloning, comparing ICL, speaker-embedding-only, and fine-tuning approaches with a full training recipe.
When Pythian rolled out Google Cloud’s Gemini Enterprise across our 500-person company in 27 countries, the goal was simple: use our own company as a proving ground to discover how enterprise AI actually delivers ROI. What we found…
A randomized study of more than 1,000 students examines ChatGPT, critical thinking, originality, and student performance on a real-world university assignment.
The advanced side of supervised fine-tuning data prep. This second post in a two-part series covers evaluating data readiness with learning curves, selecting high-value data subsets, augmenting data with synthetic and distilled…
Data preparation determines the ceiling of any supervised fine-tuning project. This first post in a two-part series covers the foundations of SFT data prep: quality checks, conversational (JSONL) formatting, reasoning and tool-calling…
Amazon SageMaker HyperPod now offers managed Ray support on Amazon EKS. Create and monitor Ray clusters, connect JupyterLab and Code Editor notebooks to live clusters, get out-of-the-box observability, and run resilient distributed…
MTIA 300 is the first of Meta’s family of in-house training and inference accelerators optimized for training ranking and recommendation models. We’re sharing how MTIA 300’s built-in NIC chiplets allow it to meet the communication needs…
Every lab CEO is on X now
A release focused on higher-throughput diffusion rollout, reusable omni adapters, and broader recipe coverage.
Reinforcement learning for large language models combines two very different workloads: rollout generation and model training. During rollout, inference workers run the current policy on a set of pro...
We present Miles v0.1, a full-stack production-ready system for frontier post-training, the successor to our first Miles release. Building upon slime's clean design, Miles optimizes every stage in the...
Teams customize their models to hit their targets for latency, speed, memory, and compute. With the open NVIDIA Nemotron family of models, developers can find...
We Tracked a Shipment of Rare Books. It Ended at an Amazon AI Training Facility Excellent piece of reporting from 404 Media. For a while now there have been stories of book dealers receiving orders for large volumes of books from…
Large-scale training spends a surprising share of its wall-clock time waiting instead of computing. Under Fully Sharded Data Parallel (FSDP), every layer ends in a collective all-gather or reduce-scatter, and every collective is a…
After a few long years of finding time to document my lessons from training open models, my post-training book is done!
Nearly every major AI lab uses Google Cloud infrastructure, including for training of models, inference for agents, and new frontier research. Google Cloud also continues to be the platform of choice for new, high-growth AI startups who…
Together AI partners with Moonshot AI to natively serve Kimi models, starting with the 2.8T parameter Kimi K3, with day zero access and post-training.
A podcast with Florian Brand.
Announcing ROCm support for vime, now running end-to-end on AMD Instinct MI355X GPUs with prebuilt container.
Introducing Co-operative Time-Slicing to eliminate idle accelerators in distributed RL post-training loops.
"Interview" #18
What we've seen helping teams run Reinforcement Learning at scale on Modal. Plus an open-source library to skip the scaffolding.
How Applied Compute trains custom agents with Reinforcement Learning for enterprises like DoorDash, Cognition, and Mercor on Modal.
The index covers 45 sources from the watchlist. 37 publish a feed and are read from it; the other 8 publish none, so their index pages are scraped and each new post's own page supplies the title and date its card omits.
Last run finished 28 Aug 2026 at 01:08 UTC. The index holds 711 posts, keeps them for 120 days, and shows 26 posts on this page.
2 sources failed on the last run. Netflix TechBlog (HTTP 429); Replit (HTTP 403)
The last run read 833 posts across every source and added 1 post.
4 sources on the watchlist are not aggregated here, so check them by hand.
A date shown as "first seen" is not a publication date. Some sources publish no date at all, so the page records when the post entered the index instead of guessing.