Where to Find AI Research Papers — A Practical Guide
June 29, 2026
If you want to follow AI research but don't know where to start, you're not alone. The field moves incredibly fast — papers drop daily across multiple platforms, and the signal-to-noise ratio can be brutal. Here's a practical breakdown of the top sources, ranked by how useful they actually are for someone who isn't a full-time researcher.
arXiv (cs.AI, cs.CL, cs.LG)
The mothership. Every significant AI paper lands here first — often weeks before it hits the news. Sub-categories cs.AI, cs.CL (computation & language), and cs.LG (machine learning) are the ones to watch. The catch: dozens of new papers every day, most of which you don't need to read.
HuggingFace Daily Papers
HuggingFace's community upvotes the most interesting papers each day. Think of it as peer-reviewed curation — if the ML community thinks a paper matters, it surfaces here. Also includes AI-generated summaries, GitHub star counts, and direct links to models and demos.
Hacker News (AI-related threads)
HN surfaces applied AI stories — launches, demos, and industry moves — that papers alone miss. The comment threads are often more valuable than the article itself, especially when practitioners dissect claims. Filter for threads with 50+ points for the highest signal.
Reddit r/MachineLearning
Less polished than HN but more academically focused. Good for catching discussion threads on papers that are generating debate. The weekly "What are you reading?" threads are surprisingly high-quality. Filter for posts with 20+ upvotes to skip the noise.
GitHub Trending (AI/LLM repos)
Open-source AI projects blow up on GitHub before they make the news. Trending repos in the AI/LLM space are a leading indicator of what tools people will be using next month. Star velocity is the metric — fast-growing repos matter more than high total stars.
Industry News (NewsAPI)
Company announcements, funding rounds, regulatory moves, and product launches from mainstream and tech outlets. Lower signal for technical depth but essential for understanding the broader AI landscape — who's building what, who's funding whom, and where regulation is heading.
The real problem: nobody has time to check all six
Here's the thing. Each of these sources is valuable in its own way. Papers on arXiv tell you what's possible. HuggingFace tells you what's interesting. HN and Reddit tell you what people are arguing about. GitHub tells you what's usable. Industry news tells you where the money is going.
But checking all six every day? That's 30-60 minutes minimum. And most of what you'll see isn't actually important — it's noise you have to sift through to find the signal.
This is exactly the problem AI Frontier Daily solves. Every morning at 8 AM, it scans all six sources — arXiv, HuggingFace, Hacker News, Reddit ML, GitHub Trending, and industry news — then uses DeepSeek AI to pick only the 5-8 items that genuinely matter. You open one email. You spend five minutes. You know what's happening across all of AI research.
No tabs. No scrolling. No FOMO.
All six sources. One email. Zero noise.
Stop checking six tabs every morning.
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