Tools, Resources & Workbench ·
25 high quality websites to address your 99% AI anxiety
English translation of the Chinese original. This version is generated for international readers and may be refined over time.
English translation of the Chinese original. This version is generated for international readers and may be refined over time.
Date: 2026-04-27
Today, at the fast pace of AI, we feel that we cannot keep up with the pace, because of the problem of the source of information, because of the problem of time management, and because of the possibility that we can't keep up with what most people really think, but we can only keep moving on, and we have a couple of very good AI front sites that are indexed below, which can directly solve the problem of your source. You can read it on the subway, it takes you half an hour to get to work, or you can read it after dinner, or whatever you want on the AI site.
After all this time, it has become increasingly clear to me that AI's sources of information should be seen in layers: first, in official laboratories, to confirm what really happened to models, papers and products; second, in dissertations and code platforms, to determine whether technological trends are actually advancing; third, in industry media and Newsletter, to understand commercialization, corporate competition and product landings; and lastly, in a small number of Chinese authorities, to complement domestic ecological and local corporate dynamics.
One, look at the official lab first: The facts are from the source Clear.
1. OpenAI News
Recommended index: 🌟🌟🌟🌟🌟
OpenAI's official news page is the most direct entry point with ChatGPT, Codex, Agents, API and modeling. Many of the second-hand stories combine model capabilities, release times, prices, and security restrictions, and it would be better to come back here and see the original version of the story when it comes to the big OpenAI news.
The value of such official sources is not "many content per day", but rather the provision of factual anchorages at key nodes. For example, new models should go online, API price changes, system card releases, tool capacity updates, and all should look at the official pages first.
Suitable: People interested in ChatGPT, Codex, OpenAI API, Agents, Model Evaluation.
2. Anthropic Research / News
https://www.anthropic.com/research https://www.anthropic.com/news
Recommended index: 🌟🌟🌟🌟🌟
Anthropic's research page is worth keeping. It does not just publish Claude models, but often writes about security, interpretability, angent behavior, system cards, model alignment, etc. It is clearer from here why the Claude series emphasizes long context, code capability, careful output and secure boundaries.
If OpenAI's official sources are more like product and platform road maps, Anthropic's official sources are more like manuals of model behaviour and security concepts.
Fits: People who care about Claude, AI safety, context, code Agent, model system card.
3. Google DeepMind Blog
https://deepmind.google/discover/blog/
Recommended index: 🌟🌟🌟🌟🌟
DeepMind is still one of the most important sources of the AI front research. Gemini, AlphaFold, AlphaGometry, robotics, intensive learning, polymodulations, AI for Science, many of the real research-related advances are here.
It is not necessarily the best place to "look at hot" websites, but it is well placed to judge whether there are real technological increases in one direction.
Suitable: People who want to see a breakthrough in research, not just model publishing and product news.
4. Meta AI Blog
Recommended index: 🌟🌟🌟🌟
The value of Meta AI lies in open source ecology. Llama, open-source models, visual models, referral systems, AI infrastructure, research engineering, and Meta often give very informative content.
Its expression is less storyteller than that of some companies, but Meta AI is an important entry point for developers and researchers to understand open source large model routes.
Suitable: People concerned with Llama, open source models, AI Infra, visual models and recommended systems.
5. Microsoft Research AI
https://www.microsoft.com/en-us/research/research-area/artificial-intelligence/
Recommended index: 🌟🌟🌟🌟
The Microsoft Institute's AI content is more "to study how to get into real software." It covers models, office productivity, enterprise applications, AI for Science, the development of tools and platform capabilities.
If you care how AI gets into Office, Windows, Copilot, Corporate Collaboration and Scientific Computation, Microsoft Institute is a good observation window.
Suitable: persons interested in AI office, enterprise applications, productivity tools and scientific calculations.
6. NVIDIA Technical Blog
https://developer.nvidia.com/blog/
Recommended index: 🌟🌟🌟🌟
AI front lines are not only in model parameters, but also in computation, reasoning, deployment, memory, throughput and cost. NVIDIA technical blogs can add to many media reports that do not provide clear details of the project.
This source is very useful if you do model deployment, reasoning optimization, GPU selection, CUDA, TensorRT or data centre-related work.
Suitable: AI infra, engineer, model deployment, reasoning optimization and hardware-related readers.
Two, read the papers and the code: Don't just listen to the press
7. Hugging Face Papers
Recommended index: 🌟🌟🌟🌟🌟
Hugging Face Papers is one of the most energy-efficient entry points for pursuing AI papers now. It brings together popular papers every day, often with community discussions, model links, data set links and clues.
It's better for a daily paper radar than arXiv: to sweep trends first, to pick out a few things worth reading.
Suitable: People who spend 10 minutes per day on the latest paper trends.
8. arXiv: cs. AI / cs. LG / cs. CL / cs. CV
https://arxiv.org/list/cs.AI/recent https://arxiv.org/list/cs.LG/recent https://arxiv.org/list/cs.CL/recent https://arxiv.org/list/cs.CV/recent
Recommended index: 🌟🌟🌟🌟🌟
ArXiv is the source of the paper and the information flood. Don't try to look at it all. A better way to do that would be to draw up a few relevant classifications, to sweep only titles and summaries, and to throw really worthwhile papers into models for initial screening.
Several commonly used classifications:
cs. AIArtificial intelligence synthesis.cs. LGMachine learning.cs. CL: Natural language processing.cs. CVComputer visualization.
Suitable: Researcher, engineer, person seeking direction from a hand-held paper.
9. Papers with Code
Recommended index: 🌟🌟🌟🌟🌟
Papers with Codes, data sets and benchmark. It would be easier to know what was going on in one direction, who was SOTA, whether or not it was open-sourced.
When I do the project selection, I will give priority to this: whether there is a replicable code, whether benchmark is active and whether the definition of the task is based on merit.
Suitable: person who reproduces the paper, works, tracks benchmark.
10. Stanford HAI / AI Index
Recommended index: 🌟🌟🌟🌟
Stanford HAI is not a daily source of information, but its AI Index Report is worth looking at every year. It brings together the dimensions of papers, investments, calculus, models, policies, education, social impact.
AI Index is more valuable than short news if you're writing industry analysis, making strategic judgements, or looking at AI changes over a longer period of time.
Fitness: industry analysis, strategic research, policy observations and long-term trend judgement.
11. Berkeley BAIR Blog
https://bair.berkeley.edu/blog/
Recommended index: 🌟🌟🌟🌟
Berkeley BAIR Blog has been a source of high-quality research interpretation. It does not follow hot spots like the media, but many articles make a research issue clear.
Robotics, enhanced learning, polymodels, smarts, basic models, all deserve to be followed in the long term.
Fitness: those who want to understand the issue of research per se, rather than rely only on conclusions.
Three, look at industry media and Newsletter: understanding companies, products and commercialization
12. The Information
https://www.theinformation.com/
Recommended index: 🌟🌟🌟🌟
The Information is a paid medium, but AI has strong intelligence. OpenAI, Anthropic, Google, Meta, finance, mergers and acquisitions, organizational change and internal dynamics are often earlier and deeper than the general technology media.
If you're concerned about competition between AI companies rather than just model parameters, this source is worth looking at.
Suitability: investors, entrepreneurs, industry researchers, people interested in business patterns.
13. TechCrunch AI
https://techcrunch.com/category/artificial-intelligence/
Recommended index: 🌟🌟🌟🌟
TechCrunch's advantage is fast and wide. AI has more timely coverage of start-ups, finance, product distribution, acquisitions, and policy changes.
The depth is not the strongest, but it fits well as a daily industry radar.
Suitable: People who quickly sweep the AI industry dynamics every day.
14. MIT Technology Review: AI
https://www.technologyreview.com/topic/artificial-intelligence/
Recommended index: 🌟🌟🌟🌟
MIT Technology Review reports more securely than general technology media. It is often associated with research, industry, risk and social impacts and is suitable for a more serious technical interpretation.
If you don't want to just look at "some model shock release," you want to know what this means, and it's worth subscription.
Suitable: To see AI reports with background, judgement and restraint.
15. The Verge: AI
https://www.theverge.com/ai-artificial-intelligence
Recommended index: 🌟🌟🌟🌟
The Verge is more product- and platform-oriented. ChatGPT, Gemini, Claude, Copilot, AI hardware, copyright disputes, consumer-level applications, all very fast.
It's not about dissertation details, it's about putting AI in the context of consumers, platform companies and technology products.
Suitable: People concerned with AI product experience, platform competition and consumer-level applications.
16. Ars Technica: AI
https://arstechnica.com/tag/artificial-intelligence/
Recommended index: 🌟🌟🌟🌟
Art Technica usually has more technical details than the general media. It's more solid with model publishing, open source models, AI disputes, engineering issues.
If you're technical, reading Ars would be better than reading pan-tech media.
Suitable: Engineers, technical readers, people who want to see a bit harder.
17. VentureBeat AI
https://venturebeat.com/category/ai/
Recommended index: 🌟🌟🌟
There are many stories about VentureBeat's businesses AI, start-ups, AI platforms, and model applications. It is appropriate to complement industry, particularly enterprise software and AI application layers.
Suitability: entrepreneurs, business AI practitioners, people interested in applications.
18. The Batch by DeepLearning. AI
https://www.deeplearning.ai/the-batch/
Recommended index: 🌟🌟🌟🌟🌟
The Batch is a Newsletter format that organizes AI research, industry and application progress on a weekly basis. It's good that the rhythm is stable, and you don't have to brush a bunch of stations every day.
If you just want to keep an AI touch instead of chasing hot spots all the time, that's the right thing.
Suitable: a person who wishes to update the AI information regularly every week.
19. Latent Space
Recommended index: 🌟🌟🌟🌟🌟
Latin Space is closer to the AI Engineering community. It focuses on models, application development, Agent, RAG, reasoning, productization and developers ecology.
It's not the best source for pure white, but it's very helpful to people who do AI applications.
Suitable: AI Application Developer, Independent Developer, Product Engineer.
20. SemiAnalysis
Recommended index: 🌟🌟🌟🌟🌟
If you want to understand AI algorithms, GPUs, data centres, chips, reasoning costs and large model arms races, SemiAnalysis is important.
Many articles were paid for, but the free portion was sufficient to see their value. AI is not just a model, but hardware, supply chain and cost structure.
Suitable: People interested in AI infrastructure, chips, arithmetical economics.
Four, domestic sources: a small amount of authority, high-trust noise.
21. The heart of the machine
Recommended index: 🌟🌟🌟🌟
The heart of the machine is the hard one in the national AI vertical media. Papers are read, technical reports, model releases, industry dynamics, and the whole is better suited to follow AI than the pan-tech media.
Appropriate: Technology to readers, researchers, engineers.
- Quantum bits
Recommended index: 🌟🌟🌟🌟
Quantum positions are dynamic with domestic AI, especially for large model products, domestic firms, business teams and industry hot spots.
It's sometimes more media-based, but it's valuable as an AI information radar in the country.
Suitable: People who quickly understand the dynamics of the AI industry in the country.
- The resource community
Recommended index: 🌟🌟🌟🌟
The resource community prefers research, reporting, activities and industry observations. It is more appropriate to look at research institutions ' perspectives and national AI ecological information than the media.
Suitable: persons concerned with large models of national production, studies, AI academic and industry integration.
- China Society of Artificial Intelligence
Recommended index: 🌟🌟🌟
The Chinese Society of Artificial Intelligence is not a daily source of news, but it is the official channel of the National Society of AI, suitable for meetings, academic events, policy-related developments and information on industry organization.
Suitable: People who follow the dynamics of academic conferences, industry organizations, policies and societies.
#25, Shinjimoto
Recommended index: 🌟🌟🌟
The new brain covers AI hotspots quickly, suitable for information radar. Its expression is more media-based, with recommendations cross-checked with official sources and dissertations.
Fit: Rapid sense hot spots, but not just one.
My recommended subscription group
If you want to follow up quickly every day:
- Hugging Face Papers.
- TechCrunch AI. The Verge AI
- The heart of the machine.
- Quantum bits.
If you prefer research and technology:
- arXiv
- Papers with Code
- Google DeepMind Blog
- Anthropic Research
- Berkeley BAIR Blog
- Hugging Face Papers
If you prefer industry and commerce:
The Information
- MIT Technology Review AI -Verture Beat AI. SemiAnalysis.
- Stanford HAI AI Index.
- Quantum bits.
If you do AI application development:
- OpenAI News
- Anthropic Research
- Hugging Face Papers
- Latent Space
- NVIDIA Technical Blog
- Papers with Code
And finally: the more information the better
This list is long, but when it really works, do not open it all.
My own rhythm is:
- Daily sweeps: Hugging Face Papers, TechCrunch AI, quantum bits.
- Weekly reading: The Batch, Latent Space, MIT Technology Review.
- Monthly CD-ROMs: Stanford Hai, Semianallysis, OpenAI/Anthropic/DeepMind official blog.
The real good source of information is to help you filter low-quality sources while you know more: what changes are worth spending time and what is just today's information noise.