[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"article-daily-huggingface-ai-papers-research-updates-en":3,"article-related-daily-huggingface-ai-papers-research-updates-en":31,"series-industry-60bda9b1-b32c-42cd-ba70-3ed9a634d8a5":76},{"id":4,"slug":5,"title":6,"content":7,"summary":8,"source":9,"source_url":10,"author":11,"image_url":12,"cover_image":12,"category":13,"language":14,"translated_content":11,"related_article_id":15,"keywords":16,"key_takeaways":23,"views":27,"created_at":28,"published_at":29,"topic_cluster_id":30},"60bda9b1-b32c-42cd-ba70-3ed9a634d8a5","daily-huggingface-ai-papers-research-updates-en","Daily HuggingFace AI Papers keeps research moving","\u003Cp>Want a daily feed of trending HuggingFace AI papers without manual searching?\u003C\u002Fp>\u003Cp data-speakable=\"summary\">This repo auto-updates trending AI papers from HuggingFace every day.\u003C\u002Fp>\u003Ctable>\u003Cthead>\u003Ctr>\u003Cth>Item\u003C\u002Fth>\u003Cth>What it gives you\u003C\u002Fth>\u003Cth>Signal\u003C\u002Fth>\u003C\u002Ftr>\u003C\u002Fthead>\u003Ctbody>\u003Ctr>\u003Ctd>Daily HuggingFace AI Papers\u003C\u002Ftd>\u003Ctd>Auto-updated paper list\u003C\u002Ftd>\u003Ctd>27 papers today\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>Archive\u003C\u002Ftd>\u003Ctd>Historical snapshots\u003C\u002Ftd>\u003Ctd>5,102+ papers total\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>Update cadence\u003C\u002Ftd>\u003Ctd>GitHub Actions refresh\u003C\u002Ftd>\u003Ctd>00:00 UTC daily\u003C\u002Ftd>\u003C\u002Ftr>\u003C\u002Ftbody>\u003C\u002Ftable>\u003Ch2>1. Daily trending paper feed\u003C\u002Fh2>\u003Cp>The main draw is the fresh paper list: a daily snapshot of what is trending on HuggingFace Papers. That means you can open one repo and see what the community is paying attention to right now, instead of checking multiple sources.\u003C\u002Fp>\n\u003Cfigure class=\"my-6\">\u003Cimg src=\"https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1783301567170-ezfg.png\" alt=\"Daily HuggingFace AI Papers keeps research moving\" class=\"rounded-xl w-full\" loading=\"lazy\" \u002F>\u003C\u002Ffigure>\n\u003Cp>In the current snapshot, the repo shows 27 papers for today, with direct links to HuggingFace, arXiv, PDFs, and code when available. A few examples from the latest batch include \u003Ca href=\"https:\u002F\u002Fhuggingface.co\u002Fpapers\u002F2607.02512\">Program-as-Weights\u003C\u002Fa>, \u003Ca href=\"https:\u002F\u002Fhuggingface.co\u002Fpapers\u002F2607.02255\">AgenticSTS\u003C\u002Fa>, and \u003Ca href=\"https:\u002F\u002Fhuggingface.co\u002Fpapers\u002F2607.01642\">Multi-Resolution Flow Matching\u003C\u002Fa>.\u003C\u002Fp>\u003Cul>\u003Cli>Daily refresh at 00:00 UTC\u003C\u002Fli>\u003Cli>Latest papers stored in \u003Ccode>data\u002Flatest.json\u003C\u002Fcode>\u003C\u002Fli>\u003Cli>Links out to arXiv and PDFs\u003C\u002Fli>\u003C\u002Ful>\u003Ch2>2. Historical archives you can actually search\u003C\u002Fh2>\u003Cp>This is more than a daily feed. The repo keeps daily, weekly, and monthly archives, so you can compare what was hot last week against what is trending now. That makes it useful for spotting paper cycles, recurring topics, and short-lived spikes.\u003C\u002Fp>\u003Cp>The archive count is already 5,102+ papers, which gives you enough history to track patterns over time. If you write newsletters, do research scouting, or build internal AI briefings, the archive is the part that turns a feed into a reference tool.\u003C\u002Fp>\u003Cul>\u003Cli>Daily, weekly, and monthly snapshots\u003C\u002Fli>\u003Cli>Archived data under \u003Ccode>data\u002F\u003C\u002Fcode>\u003C\u002Fli>\u003Cli>Good for trend tracking and topic review\u003C\u002Fli>\u003C\u002Ful>\u003Ch2>3. Machine-readable JSON for automation\u003C\u002Fh2>\u003Cp>If you want to plug paper data into a script, dashboard, or internal tool, the repo is already formatted for it. The JSON output means you do not need to scrape HTML or clean up a messy page before analysis.\u003C\u002Fp>\n\u003Cfigure class=\"my-6\">\u003Cimg src=\"https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1783301568257-p0s9.png\" alt=\"Daily HuggingFace AI Papers keeps research moving\" class=\"rounded-xl w-full\" loading=\"lazy\" \u002F>\u003C\u002Ffigure>\n\u003Cp>The README shows examples in cURL, Python, and JavaScript, so you can ingest the feed with a few lines of code. That makes it a fit for analysts who want to rank papers by stars, filter by topic, or push daily updates into a Slack bot.\u003C\u002Fp>\u003Ccode>curl https:\u002F\u002Fraw.githubusercontent.com\u002FAtharvaDomale\u002FDaily-HuggingFace-AI-Papers\u002Fmain\u002Fdata\u002Flatest.json\u003C\u002Fcode>\u003Ch2>4. A fast way to find papers worth reading\u003C\u002Fh2>\u003Cp>The repo helps you sort signal from noise by surfacing star counts and direct source links. Instead of opening random paper pages and guessing what matters, you can filter the list and focus on items with stronger community attention.\u003C\u002Fp>\u003Cp>That is especially handy when you only have a few minutes a day. The README says the automation saves 30+ minutes of paper hunting, which is believable once you compare the repo’s one-stop view with manual browsing across HuggingFace, arXiv, and \u003Ca href=\"\u002Ftag\u002Fgithub\">GitHub\u003C\u002Fa>.\u003C\u002Fp>\u003Cul>\u003Cli>Star counts make quick filtering possible\u003C\u002Fli>\u003Cli>Code links help you judge reproducibility\u003C\u002Fli>\u003Cli>Useful for researchers, ML engineers, and students\u003C\u002Fli>\u003C\u002Ful>\u003Ch2>5. A lightweight pipeline built in Python\u003C\u002Fh2>\u003Cp>The project itself is simple in the best way: it is written in Python and updated through GitHub Actions. That keeps the repo easy to inspect, easy to extend, and easy to fork if you want your own daily paper tracker.\u003C\u002Fp>\u003Cp>Because the workflow is automated, the maintenance burden stays low. You get a practical example of a scheduled data pipeline that gathers, archives, and republishes research metadata without requiring constant manual edits.\u003C\u002Fp>\u003Ccode>python -c \"import requests; print(requests.get('https:\u002F\u002Fraw.githubusercontent.com\u002FAtharvaDomale\u002FDaily-HuggingFace-AI-Papers\u002Fmain\u002Fdata\u002Flatest.json').status_code)\"\u003C\u002Fcode>\u003Ch2>How to decide\u003C\u002Fh2>\u003Cp>Pick this repo if you want one daily source for current HuggingFace papers, especially if you care about trends, archives, and easy machine access. It fits researchers who want a reading queue, and it fits builders who want structured JSON they can reuse.\u003C\u002Fp>\u003Cp>If you only need a single paper once in a while, the archive may be more than you need. But if you want a repeatable way to monitor AI research with minimal effort, this repo gives you that in a tidy, automated package.\u003C\u002Fp>","6 ways this daily HuggingFace papers repo saves time, tracks 5,102+ papers, and feeds your AI reading list.","github.com","https:\u002F\u002Fgithub.com\u002FAtharvaDomale\u002FDaily-HuggingFace-AI-Papers",null,"https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1783301567170-ezfg.png","industry","en","33d1bb43-0d47-42d6-878f-4283fefc5aa1",[17,18,19,20,21,22],"HuggingFace","AI papers","research tracker","GitHub Actions","JSON feed","paper archives",[24,25,26],"It updates daily and shows what is trending on HuggingFace Papers.","It includes 5,102+ archived papers for historical comparison.","It is easy to automate because the data is exposed as JSON.",1,"2026-07-06T01:32:21.757509+00:00","2026-07-06T01:32:21.75+00:00","d0f03097-d4ea-4543-b4e2-9ac6534ae04b",{"tags":32,"relatedLang":35,"relatedPosts":39},[33],{"name":20,"slug":34},"github-actions",{"id":15,"slug":36,"title":37,"language":38},"daily-huggingface-ai-papers-research-updates-zh","5 個功能，讓 HuggingFace 論文每天自動到位","zh",[40,46,52,58,64,70],{"id":41,"slug":42,"title":43,"cover_image":44,"image_url":44,"created_at":45,"category":13},"5444f5dd-df7e-462d-97da-aa4dc019d905","ai-weekly-2026-w28-en","AI Weekly: 2026-06-29 ~ 2026-07-06","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1783311624710-lsrj.png","2026-07-06T04:00:29.632651+00:00",{"id":47,"slug":48,"title":49,"cover_image":50,"image_url":50,"created_at":51,"category":13},"53fee7f6-0100-44f6-b8c8-58bdb5d66fea","ai-companion-rules-app-rollbacks-explained-en","AI Companion Rules and App Rollbacks Explained","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1783299903391-qh1b.png","2026-07-06T01:02:37.588693+00:00",{"id":53,"slug":54,"title":55,"cover_image":56,"image_url":56,"created_at":57,"category":13},"f4b84c44-2607-43b8-bae5-0533122d7121","meta-ai-infrastructure-bet-compute-sales-en","Meta’s $182.9B AI bet may need compute sales","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1783297980883-32ib.png","2026-07-06T00:32:32.225736+00:00",{"id":59,"slug":60,"title":61,"cover_image":62,"image_url":62,"created_at":63,"category":13},"7ff80d2c-c391-46cc-af75-ddeeae048921","dspark-vs-mtp-methods-comparison-en","DSpark vs MTP methods in one clear comparison","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1783296174151-ppau.png","2026-07-06T00:02:31.811764+00:00",{"id":65,"slug":66,"title":67,"cover_image":68,"image_url":68,"created_at":69,"category":13},"daaf6f31-ed36-4a61-9890-03d4c771dd6f","what-china-ai-unicorns-are-saying-2026-en","What China’s AI unicorns are saying in 2026","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1783274625748-blmc.png","2026-07-05T18:03:16.279057+00:00",{"id":71,"slug":72,"title":73,"cover_image":74,"image_url":74,"created_at":75,"category":13},"d94eea0f-e6cf-4260-b291-3f2932767df0","pinecone-milvus-and-3-rivals-power-ai-search-en","Pinecone, Milvus, and 3 rivals that power AI 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