Frontier: a stream, not a library

Sebastian Raschka (author of 《Build a Large Language Model (From Scratch)》)'s newsletter. Long posts, no fixed schedule. He either lines up the architectures of a batch of new models and compares them layer by layer, or walks through a month of papers by topic. His taste is the L2 teaching school: many diagrams, few formulas, and he has reproduced the work himself. Credibility: high. Look up when needed: come here when you want one readable comparison of an architecture change (MoE, attention variants, looped layers).

A "comment section on top of arXiv" built by Stanford students: one discussion page per paper, a trending list, and an auto-generated overview. The authors often answer questions in the comments. Since Papers with Code stopped being maintained, if you want to find "which papers got discussed this past week", this and Hugging Face Daily Papers are the two replacements. Credibility: the papers are primary sources. The comments and auto-generated overviews are secondary. Use them to locate questions, not as conclusions.

Three indexes: news covers models and products, engineering covers practical work on agent, tool calling, and Claude Code, and research covers interpretability, alignment, and model cards. The engineering blog is primary material for L4 (the pick in Agent：循环里带工具的模型 comes from here). Credibility: primary, but with a stance. For model cards and research, read the data. For product posts, read what they leave out. Look up when needed: on a new model's release day, check the eval table in the model card; when building an agent, check engineering.

An independent evaluation site. It runs models on the same set of benchmarks to produce an "intelligence index", then adds each API provider's throughput, first-token latency and price. It is the only place where I can see all three on one page when choosing a model and a provider. Credibility: higher than lab self-reports. The index is its own weighted definition, so scores are not comparable across version changes. Look up when needed: check the price and speed pages when choosing a model or provider; on release day, compare against the numbers the lab reports itself.

The original source for preprints. Several hundred papers a day, no filtering. Don't subscribe to the daily feed (it will drown you). Look up when needed: if you know the paper ID or title, go straight to the abs page. If you want to see what appeared in one area over the past week, use the listing page plus a keyword search. Credibility: the paper itself is a primary source, but it has not been peer reviewed. Before trusting a conclusion, check whether its eval setup and baselines are fair.

The official outlet for Gemini / Gemma releases, world models (Genie), scientific applications (the AlphaFold line), and reinforcement learning. Compared with other labs, it has more science and RL content. Historical milestones (AlphaGo, AlphaFold) also have their original write-ups here. Credibility is the same as other lab blogs: first-hand, with a point of view. The technical reports are more trustworthy than the blog posts. Look up when needed: on a Gemini launch day, read the technical report; for RL and scientific applications, start from here to find the original papers.

A long-form interview podcast hosted by Dwarkesh Patel. Guests are lab founders, chief scientists and economists. Each episode runs two to three hours. The host does thorough prep and keeps pushing until he gets a real answer. It is the main source of the views in opinions: most dated judgments from key people come from here. When it is worth your time: each month, pick one episode tied to a layer I care about and listen to the full version. For the rest, read only the subheadings of the transcript. When I hear a claim with a date attached, I note down one opinion.

A nonprofit research institute, Epoch AI, maintains these datasets and charts: training compute, data volume, hardware price-performance and compute growth rates for models over the years, plus its own benchmarks such as FrontierMath. When you need numbers for "how fast has scaling actually gone," this is the most commonly cited primary compilation. Credibility: high. The methods are public and the data can be downloaded. One caution: it has benchmark partnerships with labs (the FrontierMath funding dispute with OpenAI). Look up when needed: check here before you write any judgment that carries an order of magnitude.

A daily paper board run by Hugging Face. The community submits the arXiv papers worth reading that day and upvotes them into a ranking. Each paper comes with a short summary written by the authors or by AK. The taste leans toward open-source models, training and inference methods, and multimodal work, not theory. When it is worth your time: scan the titles once a week (titles and upvote counts only, 5–10 minutes), and log the one or two papers you really want to read in the "one-line record" of weekly reads: the frontier feed (≤ 5). It replaces the now-dormant Papers with Code as my entry point for "what's hot lately".

Jack Clark (co-founder of Anthropic, formerly policy lead at OpenAI) has run this weekly newsletter since 2016. Each issue picks 5–8 papers or releases and writes a paragraph on each: why it matters. At the end there is a policy and safety angle and a piece of short fiction. His stance is "tech optimism, but serious about risk." He rarely hypes. When it is worth reading: read one issue every week. Read the bold conclusion of each paragraph first, then decide which originals to open. 20 minutes is enough.

A newsletter by Nathan Lambert (post-training lead at Ai2). It focuses on open models, post-training (RLHF / DPO / RL reasoning) and lab news. It is one of the few public analyses written by someone who does post-training at the front line. The taste leans technical and open-source, and he gives independent judgment on closed labs' launches. When to read it: one issue a week; when a major model ships (especially open weights), read his quick take, which is more useful than the press release.

A newsletter + podcast on "AI Engineer" run by swyx and Alessio, plus a daily AINews roundup. The taste is application layer: agent frameworks, evals, RAG, inference providers. Many guests build products. Credibility: medium. It reads the mood of the industry well, but its technical judgment sometimes runs hot. Look up when needed: when I want to know what people use now in some application-layer area (eval tools, agent frameworks), I search its back issues. The reading list from the start of the year is a good starting point for L4.

Lilian Weng's personal blog. She posts a few times a year. Each post is a survey of one area: agent, the Transformer family, diffusion models, RL, hallucination. Reading one post is like reading the main line of papers in that area from the past several years. Credibility: high. The author does research on the front line. Look up when needed: before you enter a new area, check whether she has written a survey on it. Don't wait for updates. They are slow.

An anonymous, double-blind head-to-head leaderboard where people vote and the votes become Elo scores. It measures "which answer people like better," not the ceiling of a model's ability. Credibility: medium. It reflects preference, and preference can be gamed with formatting and a flattering tone. Llama 4's leaderboard gaming in 2025 and the public criticism in early 2026 both show it should be only one reference among several. Look up when needed: after a new model ships, watch how its position changes over the next week or two, and check the category boards (code, long text). Don't look at the rank on the overall board.

The official outlet for the Llama series and FAIR research (world models, JEPA, multimodal). It used to be the flag-bearer of open-weight models. Since Llama 4, Chinese models have taken a good share of that spot, so I read it more for the research line than for the leaderboards. Credibility: primary source, with a stake in the outcome. The model cards for open-weight models can be trusted. The comparison charts in launch posts need a third-party leaderboard next to them. Look up when needed: for a new Llama version, read the model card and the license terms. For progress on the JEPA line, find the original papers here.

The official outlet for model releases, system cards and research posts. Credibility: primary source, but marketing-facing. Read the curves in a launch post alongside third-party leaderboards (LMArena leaderboard (arena.ai), Artificial Analysis). The system card is more trustworthy than the launch post. Look up when needed: on launch day, check the system card and the pricing page. Research posts (reasoning, RL) are still the original source for that area.

A Reddit community for running local models. Within hours of a new open-weights release, there are quantized versions, VRAM numbers, and measurements of "how many token/s on my machine." It is also the first place where the exaggerations in a release post get called out. Credibility: low to medium. A single post is not trustworthy. The consensus across dozens of posts is. Look up when needed: when you want to run a model on your own hardware, or you doubt the numbers in a release, search the subreddit's posts from the past week.

Dylan Patel's semiconductor and AI infrastructure analysis: chips, interconnects, data center costs, and the economics of training and inference. Most of the in-depth content is paid. Credibility: his supply chain information is first-hand, but his model economics are estimates, and in late 2025 there were public questions about his conflicts of interest. Treat the numbers as references with error bars. Look up when needed: when you want to know what a particular chip or a particular lab's compute really costs, read the free summary first, then decide whether to pay for that piece.

Simon Willison (co-creator of Django, author of Datasette) writes a personal blog and updates it almost every day. On the day each new model ships, he runs it himself and posts the cost and the real results. His long-running coverage of prompt injection, tool calling, and agent security is a primary-source record for L4. His stance is a hands-on skeptic: try first, judge after. When to read: once a week, skim the titles of that week's posts. On a model release day, go straight to his hands-on tests. To check for pitfalls in an API, search his site first.

A weekly newsletter from DeepLearning.AI. Andrew Ng opens with a letter. Then come four or five news items, each with a "why it matters" line. It is aimed at beginners to intermediate readers. The tone is calm and it does not chase hype. Credibility: high but shallow. Look up when needed: when I have to explain this week's event to someone non-technical, its wording works best. I don't rely on it to keep up with the frontier.