The AI field publishes more every week than anyone can read. These are not the only good sources, but they are high signal: they update often, cover research and practice, and help you spot trends early. Use them as your default reading list — add RSS where available, or skim a fixed rotation so you are not stuck refreshing ten sites a day.
This post is a resource guide with links to original publishers. Articles stay on those sites; here you get what each feed is good for and how to combine them without overlap.
Frontier labs and product blogs
These are the usual first stops for model releases, safety notes, and big company narratives.
The Batch — DeepLearning.AI
Weekly AI news and commentary in plain language. Strong when you want a single digest of what moved the industry without reading every press release.
OpenAI
Product launches, research, and policy — high volume. Pair with one open-science source so your view is not only closed-stack vendors.
Google AI — Research Blog
Deep research across NLP, vision, and applied ML. Expect paper-length summaries and team announcements rather than hot takes.
Google DeepMind
Frontier research stories, Gemini-era updates, and science-heavy posts. Good when you care how capabilities are framed by a leading lab.
Anthropic — News
Claude updates, interpretability, and safety work from Anthropic’s perspective. Useful if you track agents, alignment, and product-grade assistants together.
Open models and open science
If you build on open weights or care about reproducible benchmarks, follow sources that ship code and weights alongside posts.
Allen Institute for AI — Blog
Open models (Olmo, Molmo, and related lines), datasets, and evaluation releases, often with full artifacts.
Ai2 Newsletters
Monthly archives with release notes style depth — ideal when you missed a drop and want the full context in one sitting.
Hugging Face — Blog
Ecosystem coverage: datasets, training, inference, and community benchmarks. Essential if your workflow touches the open stack daily.
Tutorials and applied ML
Machine Learning Mastery
Step-by-step tutorials and classical ML depth — increasingly overlapping with LLMs, agents, and practical evaluation. Good when you want to implement, not only read announcements.
Infrastructure and applied engineering at scale
NVIDIA — Technical Blog
Inference, agents, CUDA-era optimizations, and reference stacks. Especially relevant if you ship on GPUs or care about throughput and cost at production load.
Curated roundups (not a single author blog)
Zero To Mastery — AI and machine learning monthly
Long monthly posts that link out to tools, papers, and community threads. Use when you want someone else to filter the firehose into a single reading session.
A simple subscription recipe
You do not need all of these in your inbox.
- One weekly digest: The Batch.
- One lab feed: Pick OpenAI, DeepMind, or Anthropic based on which stack you build on.
- One open stack source: Hugging Face or Ai2.
- One “how to build” source: Machine Learning Mastery or NVIDIA, depending on whether you bias toward modeling or infra.
That is enough to stay current; add the rest only when a topic branches (safety, open weights only, or GPU-heavy deployment).
Note on republishing
Individual articles from these sites are copyrighted. This post only points to them. For your own site, prefer summaries in your words, short quotes with attribution, or original analysis — not full copies of someone else’s text.
If you want this list updated on a schedule, treat it like shared bookmark metadata: revisit twice a year, drop feeds that went quiet, and add one niche source (robotics, evals, EU policy) that matches what you are building next.