SakanaAI

The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery (15k stars) What it does All text below is quoted directly from the project README. >We're excited to introduce The AI Scientist, the first comprehensive system for fully automatic scientific discovery, enabling Foundation Models such as Large Language Models (LLMs) to perform research independently. Making Your Own Template: - experiment.py: This is the main script where the core content is. It takes an argument --out_dir, which specifies where it should create the folder and save the relevant information from the run. - plot.py: This script takes the information from the run folders and creates plots. The code should be clear and easy to edit. - prompt.json: Put information about your template here. - seed_ideas.json: Place example ideas here. You can also try to generate ideas without any examples and then pick the best one or two to put here. - latex/template.tex: We recommend using our LaTeX folder but be sure to replace the pre-loaded citations with ones that you expect to be more relevant. >This code is designed to run on Linux with NVIDIA GPUs using CUDA and PyTorch. Repository SakanaAI/AI-Scientist Repository stats |Metric|Value| |:-|:-| |Stars|14,813| |Forks|2.1k| |Last release|None| |Languages|Jupyter Notebook 39.7%, TeX 29.3%, Python 23.2%| |Contributors|22| |AI contributor accounts (Claude / Codex / Cursor / Copilot)|@claude (2 commits)| |AI co-authored commits|Claude: 2| Main contributor |Field|Value| |:-|:-| |Name|Cong Lu| |GitHub|@conglu1997| |Commits to this repo|66 (top contributor)| |Bio|Founding Member of Technical Staff @ Recursive| |Company / Location|London, UK| |Followers|411| ────────────────────────────── 📌 How many great GitHub repos have you discovered and completely forgotten about? Don't let another useful tool disappear into your Reddit feed. Get the community's best repos, organized by category, plus key takeaways from real developer discussions, delivered to your inbox twice a week. 📬 Never lose track of great repos again. Subscribe free.

10 new repos this week vs $2,335 a year of subscriptions Ten open-source projects, all created in the last nine days, against $2,335 a year of subscriptions — every price read at the vendor today. 00:00 Intro 00:30 Number 1 — OpenTRAP 01:24 Number 2 — AutoIllustrate 02:19 Number 3 — FlashVSR+ 03:14 Number 4 — Ming-3.0-tts 04:13 Number 5 — editonny 05:10 Number 6 — codex-openpencil 06:01 Number 7 — Indra OCR 06:58 Number 8 — SpeakMore 07:56 Number 9 — Morrow 08:45 Number 10 — ElixirOffice 09:44 Still worth paying for The ten: 1. OpenTRAP — a web-application security scanner for your own machine, built on the OWASP ZAP engine; not an AI tool, and the video says so, instead of Burp Suite Professional ($499, basis unlabelled on the page): opentrap/opentrap 2. AutoIllustrate — a Sakana AI agent that turns a written description into a scientific figure, instead of BioRender Academic Individual ($420/yr at $35/mo billed annually): SakanaAI/AutoIllustrate 3. FlashVSR+ — video super-resolution research code that upscales footage on an NVIDIA GPU, instead of Topaz Video Personal ($299/yr as the page states): OpenImagingLab/FlashVSR_Plus 4. Ming-3.0-tts — an open text-to-speech model family in a small dense and a larger mixture-of-experts version, instead of ElevenLabs Creator ($219.96/yr at the page's annual $18.33): inclusionAI/Ming-3.0-tts 5. editonny — a video-editing skill that has Claude Code or Codex do the cutting, instead of Descript Hobbyist ($192/yr at the lower $16 the page prints): soumatheusgomes/editonny 6. codex-openpencil — a Codex plugin that opens an interactive design canvas inside your coding agent, instead of Figma Professional ($192/yr at the $16 full seat): ZSeven-W/codex-openpencil 7. Indra OCR — a small OCR model that reads documents on a CPU, instead of Amazon Textract Detect Document Text ($150/yr at 100,000 pages and $0.0015 a page): datalab-to/indra 8. SpeakMore — desktop dictation that runs through whichever speech model you point it at, instead of Typeless Pro ($144/yr on the yearly $12 seat): zephyr4123/speakmore 9. Morrow — a local notes and workspace app that keeps every page as plain Markdown; no AI of its own, and the video says so, instead of Notion Plus ($120/yr at the $10 the page prints): Aarav2709/Morrow 10. ElixirOffice — a desktop office suite for people used to Microsoft Office, with AI optional and off until you configure it, instead of Microsoft 365 Personal ($99.99/yr): leag1234/elixiroffice Seven of the ten are AI tools, one has AI as an option you switch on, and two — OpenTRAP and Morrow — are not AI tools at all. The video names them. Six of the ten pairings are mine rather than the project's own claim, and the narration says which. Four vendor pages print a price with no annual or monthly label — Burp Suite, Descript, Figma and Notion — and in every case the lower figure was used and the video says so instead of guessing. Amazon Textract charges per page, not per month, so the $150 is an assumption of 100,000 pages a year at $0.0015 a page; halve the volume and you halve the saving. Three of the ten only work on top of a coding agent you already pay for, and that subscription is not included in any figure here. Every tool's catch is in the video. If one of these saved you a subscription, like this video and subscribe. New AI tools, tested and priced, every episode. #AItools #opensource #selfhosted #FUBARAI

QD-Rec: Open-Ended Evolution of Recommendations via Quality–Diversity Blog: pub.sakana.ai GitHub: SakanaAI/QD-Rec arXiv: arxiv.org Open-ended search has been used to discover algorithms, generate scientific ideas, and develop self-improving agents. With QD-Rec, we explore how it can support a different kind of task: helping people make decisions. Which company should an acquirer buy? Which customers might benefit from a product? Which papers should a researcher read? In each case, the task is to choose among existing options, not generate new ones. At first glance, a finite, predefined candidate pool seems to leave little room for open-ended exploration. However, a fixed set of options does not imply a fixed set of reasons for choosing them. The same company might be worth considering for its technology or its potential to open up a new market. The search over these reasons remains open-ended even when the options themselves are fixed. QD-Rec searches over both what to recommend and why. Instead of aiming for a single best answer, it builds a diverse collection of recommendations, each pairing a candidate with a natural-language hypothesis explaining why it fits a particular decision maker’s goals, interests, and background. The system combines three components. A contextual bandit learns which items to investigate next, balancing promising candidates with less-explored ones. An LLM generates hypotheses by adapting or combining the earlier recommendations. An archive retains new recommendations when they offer a novel reason, or replaces a similar recommendation when the new one is judged better. We evaluate QD-Rec on acquisition target discovery, customer targeting, and paper recommendation. Compared with variants that remove evolutionary hypothesis generation or contextual bandits, QD-Rec achieves the highest mean LLM-rated quality in most settings and usually builds the largest archive. We also found examples where earlier recommendations with a low quality became stepping stones for later discoveries. These experiments are a starting point. Practical applications will require richer, up-to-date information and expert human feedback to assess whether the proposed QD-Rec holds up beyond evaluation by an LLM judge. We are releasing the code for QD-Rec so that researchers and practitioners can explore open-ended recommendation with their own decision-making tasks.

多様な提案を進化させ、意思決定を支える推薦手法「QD-Rec」を開発 ブログ：sakana.ai 論文：arxiv.org GitHub：SakanaAI/QD-Rec どの企業を買収候補として検討するか。どの顧客に新しい製品を提案するか。膨大な論文の中から、次に何を読むか。こうした意思 決定では、候補をスコア順に並べるだけでなく、「なぜ選ぶのか」「選んだ先にどのような可能性があるのか」を、異なる観点から比較できることが重要です。 一つの候補にも複数の有望な理由が考えられます。数千件、ときには1万件を超える候補それぞれについて、さまざまな仮説を生成・評価し尽くすには大きなコストがかかります。私たちは、限られた予算の中で「候補」と「仮説」の組を探索し、質が高く多様な提案を見つける問題を「オープンエンドな推薦」として捉えました。 QD-Recは、オープンエンドな探索の研究で発展してきた「Quality-Diversity（QD）」と呼ばれる進化的探索技術を、推薦に応用します。過去の評価結果をもとに次に調べる候補を選び、蓄積された仮説の着眼点をLLMが別の候補に応用したり、複数の仮説を組み合わせたりして、新しい提案を生成します。新しい切り口の提案は残し、似た提案同士ではより優れたものに更新することで、多様な仮説を次の発見の「踏み石」として活かし、推薦の集合を進化させていきます。 実験では、 企業の買収候補探索、架空の顧客プロファイルを使ったターゲティング、研究者向けの論文推薦などで検証しました。同じ評価回数のもとで比較したところ、多くの設定で、比較手法よりもQD-Recの方が評価が高く、より多様で多くの推薦を蓄積できました。 私たちが目指すのは、AIが人間の意思決定を代行することではなく、多様で有望な選択肢と、それらを比較・検討するための判断材料を提供することです。今回の成果を出発点に、QD-Recを次世代の意思決定を支える基盤技術へと発展させていきます。

Agentic Scientist, Orchestrator for Automated Scientific (Co-)Discovery

GitHub Trending Weekly #54: git-ai, sill, Filee, Tracely-ai, Snitch, web-ocean, earshot, net-merge This is GitHub Trending Weekly #54. 35 trending open-source projects on GitHub right now. Paid channel subscription: youtube.com Text version link: githubawesome.com 00:00 - Introduction 00:10 - git-ai feder-cr/git-ai 00:36 - sill Saffsanity/sill 01:00 - Filee KnifeLemon/Filee 01:24 - Tracely-ai jwuthri-cloud/Tracely-ai 01:48 - Snitch aixisstudio/Snitch 02:15 - Open Screen Sharing nekohasekai/open-screen-sharing 02:39 - web-ocean Noniv/web-ocean 03:05 - earshot Leonxlnx/earshot 03:31 - net-merge rohit267/net-merge 03:56 - leanrea aicodeking/leanrea 04:21 - tv-vibehack Gathin23/tv-vibehack 04:49 - harness2 google-research/harness2 05:15 - motion-prompt showheyohtaki/motion-prompt 05:42 - timestral mistralai/timestral 06:07 - reason-network reason-machines/reason-network 06:31 - trim-1 caveman-ai/trim-1 06:57 - yomado chekusu/yomado 07:24 - Hy-Memory Tencent-Hunyuan/Hy-Memory 07:50 - opendesk useopendesk/opendesk 08:18 - c64-hw-simulator antirez/c64-hw-simulator 08:45 - binny openbin-ai/binny 09:08 - frametell sitor-ai/frametell 09:33 - esp32sim mikroverk/esp32sim 10:00 - mcp-for-blender-pro anujasid/mcp-for-blender-pro 10:26 - lucian-uxui-prompt Seokwoooo/lucian-uxui-prompt 10:54 - Mayoi sorrycc/Mayoi 11:18 - enclose enclosedev/enclose 11:41 - SOMA Kai-Jungsthoefel/SOMA 12:08 - anan-iphone-use ananhq/anan-iphone-use 12:33 - coop-anything cranebeaker/coop-anything 13:02 - casa-serra Sam1983Aing/casa-serra 13:27 - Morrow Aarav2709/Morrow 13:54 -AI-Scientist-v3 SakanaAI/AI-Scientist-v3 14:18 - flipflop andunieee/flipflop 14:46 - parin Kapendev/parin Thank you so much for watching. Please like the video and subscribe to our channel. #github #ai #opensource Notice: The content was created with the assistance of AI and may contain inaccuracies or errors. ▬▬▬▬▬▬ 👋 Contact me 👋 ▬▬▬▬▬▬ ➡ LinkTree: link.githubawesome.com ➡ Buy Me A Coffee link.githubawesome.com [ar] GitHub Trending الأسبوعي #54: git-ai, sill, Filee, Tracely-ai, Snitch, web-ocean, earshot, net-merge هذا هو العدد 54 من نشرة GitHub الأسبوعية لأكثر المشاريع رواجاً. يضم 35 مشروعاً مفتوح المصدر رائجاً على GitHub حالياً. الاشتراك المدفوع في القناة: youtube.com رابط النسخة النصية: githubawesome.com 00:00 - مقدمة 00:10 - git-ai feder-cr/git-ai 00:36 - sill Saffsanity/sill 01:00 - Filee KnifeLemon/Filee 01:24 - Tracely-ai jwuthri-cloud/Tracely-ai 01:48 - Snitch aixisstudio/Snitch 02:15 - مشاركة الشاشة المفتوحة nekohasekai/open-screen-sharing 02:39 - web-ocean Noniv/web-ocean 03:05 - earshot Leonxlnx/earshot 03:31 - net-merge rohit267/net-merge 03:56 - leanrea aicodeking/leanrea 04:21 - tv-vibehack Gathin23/tv-vibehack 04:49 - harness2 google-research/harness2 05:15 - motion-prompt showheyohtaki/motion-prompt 05:42 - timestral mistralai/timestral 06:07 - شبكة ريزون reason-machines/reason-network 06:31 - تريم-1 caveman-ai/trim-1 06:57 - يومادو chekusu/yomado 07:24 - هاي-ميموري Tencent-Hunyuan/Hy-Memory 07:50 - أوبن ديسك useopendesk/opendesk 08:18 - محاكي أجهزة سي 64 antirez/c64-hw-simulator 08:45 - بيني openbin-ai/binny 09:08 - فرامتيل sitor-ai/frametell 09:33 - إي إس بي 32 سيم mikroverk/esp32sim 10:00 - mcp-for-blender-pro anujasid/mcp-for-blender-pro 10:26 - lucian-uxui-prompt Seokwoooo/lucian-uxui-prompt 10:54 - Mayoi sorrycc/Mayoi 11:18 - enclose enclosedev/enclose 11:41 - SOMA Kai-Jungsthoefel/SOMA 12:08 - anan-iphone-use ananhq/anan-iphone-use 12:33 - coop-anything cranebeaker/coop-anything 13:02 - casa-serra Sam1983Aing/casa-serra 13:27 - Morrow Aarav2709/Morrow 13:54 -AI-Scientist-v3 SakanaAI/AI-Scientist-v3 14:18 - flipflop andunieee/flipflop 14:46 - parin Kapendev/parin شكرًا جزيلًا على المشاهدة. يُرجى الإعجاب بالفيديو والاشتراك في قناتنا. #github #ai #opensource ملاحظة: تم إنشاء هذا المحتوى بمساعدة الذكاء الاصطناعي، وقد يحتوي على معلومات غير دقيقة أو أخطاء. ▬▬▬▬▬▬ 👋 تواصل معي 👋 ▬▬▬▬▬▬ ➡ لينك تري: link.githubawesome.com ➡ ادعمني بشراء قهوة: link.githubawesome.com [bn] GitHub ট্রেন্ডিং উইকলি #৫৪: git-ai, sill, Filee, Tracely-ai, Snitch, web-ocean, earshot, net-merge এটি গিটহাব ট্রেন্ডিং উইকলি #৫৪। এই মুহূর্তে গিটহাবে ৩৫টি ট্রেন্ডিং ওপেন-সোর্স প্রজেক্ট। পেইড চ্যানেল সাবস্ক্রিপশন: youtube.com টেক্সট ভার্সনের লিঙ্ক: githubawesome.com ০০:০০ - ভূমিকা ০০:১০ - git-ai feder-cr/git-ai ০০:৩৬ - sill Saffsanity/sill ০১:০০ - Filee KnifeLemon/Filee ০১:২৪ - Tracely-ai jwuthri-cloud/Tracely-ai ০১:৪৮ - Snitch aixisstudio/Snitch ০২:১৫ - Open Screen Sharing nekohasekai/open-screen-sharing ০২:৩৯ - web-ocean Noniv/web-ocean ০৩:০৫ - earshot Leonxlnx/earshot ০৩:৩১ - net-merge rohit267/net-merge ০৩:৫৬ - leanrea aicodeking/leanrea ০৪:২১ - tv-vibehack Gathin23/tv-vibehack ০৪:৪৯ - harness2 google-research/harness2 ০৫:১৫ - motion-prompt showheyohtaki/motion-prompt ০৫:৪২ - timestral mistralai/timestral ০৬:০৭ - reason-network reason-machines/reason-network ০৬:৩১ - trim-1 caveman-ai/trim-1 ০৬:৫৭ - yomado chekusu/yomado ০৭:২৪ - Hy-Memory Tencent-Hunyuan/Hy-Memory ০৭:৫০ - opendesk useopendesk/opendesk ০৮:১৮ - c64-hw-simulator antirez/c64-hw-simulator ০৮:৪৫ - binny openbin-ai/binny ০৯:০৮ - frametell sitor-ai/frametell ০৯:৩৩ - esp32sim mikroverk/esp32sim ১০:০০ - mcp-for-blender-pro anujasid/mcp-for-blender-pro ১০:২৬ - lucian-uxui-prompt Seokwoooo/lucian-uxui-prompt ১০:৫৪ - Mayoi sorrycc/Mayoi ১১:১৮ - enclose enclosedev/enclose ১১:৪১ - SOMA Kai-Jungsthoefel/SOMA ১২:০৮ - anan-iphone-use ananhq/anan-iphone-use ১২:৩৩ - coop-anything cranebeaker/coop-anything ১৩:০২ - casa-serra Sam1983Aing/casa-serra ১৩:২৭ - Morrow Aarav2709/Morrow ১৩:৫৪ - এআই-সায়েন্টিস্ট-ভি৩ SakanaAI/AI-Scientist-v3 ১৪:১৮ - ফ্লিপফ্লপ andunieee/flipflop ১৪:৪৬ - পারিন Kapendev/parin ভিডিওটি দেখার জন্য আপনাকে অসংখ্য ধন্যবাদ। অনুগ্রহ করে ভিডিওটি লাইক করুন এবং আমাদের চ্যানেলটি সাবস্ক্রাইব করুন। #github #ai #opensource বিজ্ঞপ্তি: এই কন্টেন্টটি এআই (AI)-এর সহায়তায় তৈরি করা হয়েছে এবং এতে ভুল বা ত্রুটি থাকতে পারে। ▬▬▬▬▬▬ 👋 আমার সাথে যোগাযোগ করুন 👋 ▬▬▬▬▬▬ ➡ লিঙ্কট্রি: link.githubawesome.com ➡ বাই মি এ কফি: link.githubawesome.com [de-DE] GitHub Trending Weekly #54: git-ai, sill, Filee, Tracely-ai, Snitch, web-ocean, earshot, net-merge Dies ist GitHub Trending Weekly #54. 35 aktuell angesagte Open-Source-Projekte auf GitHub. Abonnement des kostenpflichtigen Kanals: youtube.com Link zur Textversion: githubawesome.com 00:00 – Einführung 00:10 – git-ai feder-cr/git-ai 00:36 – sill Saffsanity/sill 01:00 – Filee KnifeLemon/Filee 01:24 – Tracely-ai jwuthri-cloud/Tracely-ai 01:48 – Snitch aixisstudio/Snitch 02:15 – Open Screen Sharing nekohasekai/open-screen-sharing 02:39 – web-ocean Noniv/web-ocean 03:05 - earshot Leonxlnx/earshot 03:31 - net-merge rohit267/net-merge 03:56 - leanrea aicodeking/leanrea 04:21 - tv-vibehack Gathin23/tv-vibehack 04:49 - harness2 google-research/harness2 05:15 - motion-prompt showheyohtaki/motion-prompt 05:42 - timestral mistralai/timestral 06:07 - reason-network reason-machines/reason-network 06:31 - trim-1 caveman-ai/trim-1 06:57 - yomado chekusu/yomado 07:24 - Hy-Memory Tencent-Hunyuan/Hy-Memory 07:50 - opendesk useopendesk/opendesk 08:18 - c64-hw-simulator antirez/c64-hw-simulator 08:45 - binny openbin-ai/binny 09:08 - frametell sitor-ai/frametell 09:33 - esp32sim mikroverk/esp32sim 10:00 - mcp-for-blender-pro anujasid/mcp-for-blender-pro 10:26 - lucian-uxui-prompt Seokwoooo/lucian-uxui-prompt 10:54 - Mayoi sorrycc/Mayoi 11:18 - enclose enclosedev/enclose 11:41 - SOMA Kai-Jungsthoefel/SOMA 12:08 - anan-iphone-use ananhq/anan-iphone-use 12:33 - coop-anything cranebeaker/coop-anything 13:02 - casa-serra Sam1983Aing/casa-serra 13:27 - Morrow Aarav2709/Morrow 13:54 - AI-Scientist-v3 SakanaAI/AI-Scientist-v3 14:18 - flipflop andunieee/flipflop 14:46 - parin Kapendev/parin Vielen Dank fürs Zuschauen! Bitte liken Sie das Video und abonnieren Sie unseren Kanal. #github #ai #opensource Hinweis: Dieser Inhalt wurde mithilfe von KI erstellt und kann Ungenauigkeiten oder Fehler enthalten. ▬▬▬▬▬▬ 👋 Kontakt 👋 ▬▬▬▬▬▬ ➡ LinkTree: link.githubawesome.com ➡ Unterstütze mich mit einer Spende: link.githubawesome.com [es-US] Tendencias semanales de GitHub n.º 54: git-ai, sill, Filee, Tracely-ai, Snitch, web-ocean, earsho... Este es el GitHub Trending Weekly #54. 35 proyectos de código abierto que son tendencia en GitHub en este momento. Suscripción de pago al canal: youtube.com Enlace a la versión en texto: githubawesome.com 00:00 - Introducción 00:10 - git-ai feder-cr/git-ai 00:36 - sill Saffsanity/sill 01:00 - Filee KnifeLemon/Filee 01:24 - Tracely-ai jwuthri-cloud/Tracely-ai 01:48 - Snitch aixisstudio/Snitch 02:15 - Open Screen Sharing nekohasekai/open-screen-sharing 02:39 - web-ocean Noniv/web-ocean 03:05 - earshot Leonxlnx/earshot 03:31 - net-merge rohit267/net-merge 03:56 - leanrea aicodeking/leanrea 04:21 - tv-vibehack Gathin23/tv-vibehack 04:49 - harness2 google-research/harness2 05:15 - motion-prompt showheyohtaki/motion-prompt 05:42 - timestral mistralai/timestral 06:07 - reason-network reason-machines/reason-network 06:31 - trim-1 caveman-ai/trim-1 06:57 - yomado chekusu/yomado 07:24 - Hy-Memory Tencent-Hunyuan/Hy-Memory 07:50 - opendesk useopendesk/opendesk 08:18 - c64-hw-simulator antirez/c64-hw-simulator 08:45 - binny openbin-ai/binny 09:08 - frametell sitor-ai/frametell 09:33 - esp32sim mikroverk/esp32sim 10:00 - mcp-for-blender-pro anujasid/mcp-for-blender-pro 10:26 - lucian-uxui-prompt Seokwoooo/lucian-uxui-prompt 10:54 - Mayoi sorrycc/Mayoi 11:18 - enclose enclosedev/enclose 11:41 - SOMA Kai-Jungsthoefel/SOMA 12:08 - anan-iphone-use ananhq/anan-iphone-use 12:33 - coop-anything cranebeaker/coop-anything 13:02 - casa-serra Sam1983Aing/casa-serra 13:27 - Morrow Aarav2709/Morrow 13:54 - AI-Scientist-v3 SakanaAI/AI-Scientist-v3 14:18 - flipflop andunieee/flipflop 14:46 - parin Kapendev/parin Muchas gracias por ver el video. Dale me gusta y suscríbete a nuestro canal. #github #ai #opensource Aviso: El contenido se creó con la ayuda de IA y puede contener imprecisiones o errores. ▬▬▬▬▬▬ 👋 Contáctame 👋 ▬▬▬▬▬▬ ➡ LinkTree: link.githubawesome.com ➡ Invítame a un café: link.githubawesome.com [fr-FR] GitHub Trending Weekly #54 : git-ai, sill, Filee, Tracely-ai, Snitch, web-ocean, earshot, net-merge Voici le top 54 des tendances hebdomadaires sur GitHub. 35 projets open source populaires sur GitHub en ce moment. Abonnement payant à la chaîne : youtube.com Lien vers la version texte : githubwesome.com 00:00 - Introduction 00:10 - git-ai feder-cr/git-ai 00:36 - sill Saffsanity/sill 01:00 - Filee KnifeLemon/Filee 01:24 - Tracely-ai jwuthri-cloud/Tracely-ai 01:48 - Snitch aixisstudio/Snitch 02:15 - Open Screen Sharing nekohasekai/open-screen-sharing 02:39 - web-ocean Noniv/web-ocean 03:05 - earshot Leonxlnx/earshot 03:31 - net-merge rohit267/net-merge 03:56 - leanrea aicodeking/leanrea 04:21 - tv-vibehack Gathin23/tv-vibehack 04:49 - harness2 google-research/harness2 05:15 - motion-prompt showheyohtaki/motion-prompt 05:42 - timestral mistralai/timestral 06:07 - reason-network reason-machines/reason-network 06:31 - trim-1 caveman-ai/trim-1 06:57 - yomado chekusu/yomado 07:24 - Hy-Memory Tencent-Hunyuan/Hy-Memory 07:50 - opendesk useopendesk/opendesk 08:18 - c64-hw-simulator antirez/c64-hw-simulator 08:45 - binny openbin-ai/binny 09:08 - frametell sitor-ai/frametell 09:33 - esp32sim mikroverk/esp32sim 10:00 - mcp-for-blender-pro anujasid/mcp-for-blender-pro 10:26 - lucian-uxui-prompt Seokwoooo/lucian-uxui-prompt 10:54 - Mayoi sorrycc/Mayoi 11:18 - enclose enclosedev/enclose 11:41 - SOMA Kai-Jungsthoefel/SOMA 12:08 - anan-iphone-use ananhq/anan-iphone-use 12:33 - coop-anything cranebeaker/coop-anything 13:02 - casa-serra Sam1983Aing/casa-serra 13:27 - Morrow Aarav2709/Morrow 13:54 - AI-Scientist-v3 SakanaAI/AI-Scientist-v3 14:18 - flipflop andunieee/flipflop 14:46 - parin Kapendev/parin Merci beaucoup d'avoir regardé cette vidéo. N'hésitez pas à la liker et à vous abonner à notre chaîne. #github #ai #opensource Avertissement : Ce contenu a été créé avec l'aide d'une IA et peut contenir des inexactitudes ou des erreurs. ▬▬▬▬▬▬ 👋 Contactez-moi 👋 ▬▬▬▬▬▬ ➡ LinkTree : link.githubawesome.com ➡ Offrez-moi un café : link.githubawesome.com [hi] GitHub ट्रेंडिंग वीकली #54: git-ai, sill, Filee, Tracely-ai, Snitch, web-ocean, earshot, net-merge यह GitHub ट्रेंडिंग वीकली का 54वां अंक है। GitHub पर इस समय ट्रेंडिंग 35 ओपन-सोर्स प्रोजेक्ट्स की सूची। चैनल की सशुल्क सदस्यता: youtube.com टेक्स्ट संस्करण का लिंक: githubawesome.com 00:00 - परिचय 00:10 - git-ai feder-cr/git-ai 00:36 - sill Saffsanity/sill 01:00 - Filee KnifeLemon/Filee 01:24 - Tracely-ai jwuthri-cloud/Tracely-ai 01:48 - Snitch aixisstudio/Snitch 02:15 - ओपन स्क्रीन शेयरिंग nekohasekai/open-screen-sharing 02:39 - वेब-ओशन Noniv/web-ocean 03:05 - ईयरशॉट Leonxlnx/earshot 03:31 - नेट-मर्ज rohit267/net-merge 03:56 - लीनरिया aicodeking/leanrea 04:21 - टीवी-वाइबहैक Gathin23/tv-vibehack 04:49 - हार्नेस2 google-research/harness2 05:15 - मोशन-प्रॉम्प्ट showheyohtaki/motion-prompt 05:42 - टाइमस्ट्रल mistralai/timestral 06:07 - रीज़न-नेटवर्क reason-machines/reason-network 06:31 - ट्रिम-1 caveman-ai/trim-1 06:57 - योमाडो chekusu/yomado 07:24 - हाई-मेमोरी Tencent-Hunyuan/Hy-Memory 07:50 - ओपनडेस्क useopendesk/opendesk 08:18 - c64-hw-simulator antirez/c64-hw-simulator 08:45 - बिन्नी openbin-ai/binny 09:08 - फ्रेमटेल sitor-ai/frametell 09:33 - esp32sim mikroverk/esp32sim 10:00 - mcp-for-blender-pro anujasid/mcp-for-blender-pro 10:26 - lucian-uxui-prompt Seokwoooo/lucian-uxui-prompt 10:54 - Mayoi sorrycc/Mayoi 11:18 - enclose enclosedev/enclose 11:41 - SOMA Kai-Jungsthoefel/SOMA 12:08 - anan-iphone-use ananhq/anan-iphone-use 12:33 - coop-anything cranebeaker/coop-anything 13:02 - casa-serra Sam1983Aing/casa-serra 13:27 - Morrow Aarav2709/Morrow 13:54 - AI-Scientist-v3 SakanaAI/AI-Scientist-v3 14:18 - flipflop andunieee/flipflop 14:46 - parin Kapendev/parin वीडियो देखने के लिए बहुत-बहुत धन्यवाद। कृपया वीडियो को लाइक करें और हमारे चैनल को सब्सक्राइब करें। #github #ai #opensource सूचना: यह सामग्री AI की सहायता से बनाई गई है और इसमें कुछ त्रुटियाँ या अशुद्धियाँ हो सकती हैं। ▬▬▬▬▬▬ 👋 मुझसे संपर्क करें 👋 ▬▬▬▬▬▬ ➡ लिंक ट्री: link.githubawesome.com ➡ मुझे कॉफ़ी पिलाएँ: link.githubawesome.com [id] GitHub Trending Mingguan #54: git-ai, sill, Filee, Tracely-ai, Snitch, web-ocean, earshot, net-merge Ini adalah GitHub Trending Weekly #54. 35 proyek open-source yang sedang tren di GitHub saat ini. Langganan saluran berbayar: youtube.com Tautan versi teks: githubawesome.com 00:00 - Pendahuluan 00:10 - git-ai feder-cr/git-ai 00:36 - sill Saffsanity/sill 01:00 - Filee KnifeLemon/Filee 01:24 - Tracely-ai jwuthri-cloud/Tracely-ai 01:48 - Snitch aixisstudio/Snitch 02:15 - Open Screen Sharing nekohasekai/open-screen-sharing 02:39 - web-ocean Noniv/web-ocean 03:05 - earshot Leonxlnx/earshot 03:31 - net-merge rohit267/net-merge 03:56 - leanrea aicodeking/leanrea 04:21 - tv-vibehack Gathin23/tv-vibehack 04:49 - harness2 google-research/harness2 05:15 - motion-prompt showheyohtaki/motion-prompt 05:42 - timestral mistralai/timestral 06:07 - reason-network reason-machines/reason-network 06:31 - trim-1 caveman-ai/trim-1 06:57 - yomado chekusu/yomado 07:24 - Hy-Memory Tencent-Hunyuan/Hy-Memory 07:50 - opendesk useopendesk/opendesk 08:18 - c64-hw-simulator antirez/c64-hw-simulator 08:45 - binny openbin-ai/binny 09:08 - frametell sitor-ai/frametell 09:33 - esp32sim mikroverk/esp32sim 10:00 - mcp-for-blender-pro anujasid/mcp-for-blender-pro 10:26 - lucian-uxui-prompt Seokwoooo/lucian-uxui-prompt 10:54 - Mayoi sorrycc/Mayoi 11:18 - enclose enclosedev/enclose 11:41 - SOMA Kai-Jungsthoefel/SOMA 12:08 - anan-iphone-use ananhq/anan-iphone-use 12:33 - coop-anything cranebeaker/coop-anything 13:02 - casa-serra Sam1983Aing/casa-serra 13:27 - Morrow Aarav2709/Morrow 13:54 -AI-Scientist-v3 SakanaAI/AI-Scientist-v3 14:18 - flipflop andunieee/flipflop 14:46 - parin Kapendev/parin Terima kasih banyak telah menonton. Silakan sukai video ini dan berlangganan saluran kami. #github #ai #opensource Peringatan: Konten ini dibuat dengan bantuan AI dan mungkin mengandung ketidakakuratan atau kesalahan. ▬▬▬▬▬▬ 👋 Hubungi saya 👋 ▬▬▬▬▬▬ ➡ LinkTree: link.githubawesome.com ➡ Dukung saya dengan membeli kopi: link.githubawesome.com [it] GitHub Trending Weekly #54: git-ai, sill, Filee, Tracely-ai, Snitch, web-ocean, earshot, net-merge Questa è la rubrica settimanale "GitHub Trending #54". 35 progetti open source di tendenza su GitHub in questo momento. Abbonamento a pagamento al canale: youtube.com Link alla versione testuale: githubawesome.com 00:00 - Introduzione 00:10 - git-ai feder-cr/git-ai 00:36 - sill Saffsanity/sill 01:00 - Filee KnifeLemon/Filee 01:24 - Tracely-ai jwuthri-cloud/Tracely-ai 01:48 - Snitch aixisstudio/Snitch 02:15 - Aprire la condivisione dello schermo Italiano: nekohasekai/open-screen-sharing 02:39 - web-ocean Noniv/web-ocean 03:05 - earshot Leonxlnx/earshot 03:31 - net-merge rohit267/net-merge 03:56 - leanrea aicodeking/leanrea 04:21 - tv-vibehack Gathin23/tv-vibehack 04:49 - harness2 google-research/harness2 05:15 - motion-prompt showheyohtaki/motion-prompt 05:42 - timestral Italiano: mistralai/timestral 06:07 - reason-network reason-machines/reason-network 06:31 - trim-1 caveman-ai/trim-1 06:57 - yomado chekusu/yomado 07:24 - Hy-Memory Tencent-Hunyuan/Hy-Memory 07:50 - opendesk useopendesk/opendesk 08:18 - c64-hw-simulator antirez/c64-hw-simulator 08:45 - binny openbin-ai/binny 09:08 - frametell Italiano: sitor-ai/frametell 09:33 - esp32sim mikroverk/esp32sim 10:00 - mcp-for-blender-pro anujasid/mcp-for-blender-pro 10:26 - lucian-uxui-prompt Seokwoooo/lucian-uxui-prompt 10:54 - Mayoi sorrycc/Mayoi 11:18 - enclose enclosedev/enclose 11:41 - SOMA Kai-Jungsthoefel/SOMA 12:08 - anan-iphone-use ananhq/anan-iphone-use 12:33 - coop-anything cranebeaker/coop-anything 13:02 - casa-serra Sam1983Aing/casa-serra 13:27 - Morrow Aarav2709/Morrow 13:54 - AI-Scientist-v3 SakanaAI/AI-Scientist-v3 14:18 - flipflop andunieee/flipflop 14:46 - parin Kapendev/parin Grazie mille per aver guardato il video. Metti mi piace e iscriviti al nostro canale. #github #ai #opensource Nota: il contenuto è stato creato con l'aiuto dell'IA e potrebbe contenere imprecisioni o errori. ▬▬▬▬▬▬ 👋 Contattami 👋 ▬▬▬▬▬▬ ➡ LinkTree: link.githubawesome.com ➡ Offrimi un caffè link.githubawesome.com [iw] GitHub Trending Weekly #54: git-ai, sill, Filee, Tracely-ai, Snitch, web-ocean, earshot, net-merge זהו מגזין GitHub Trending Weekly #54. 35 פרויקטים בקוד פתוח פופולריים ב-GitHub כרגע. מנוי לערוץ בתשלום: youtube.com קישור לגרסת טקסט: githubawesome.com 00:00 - מבוא 00:10 - git-ai feder-cr/git-ai 00:36 - סיל Saffsanity/sill 01:00 - Filee KnifeLemon/Filee 01:24 - Tracely-ai jwuthri-cloud/Tracely-ai 01:48 - סניץ' aixisstudio/Snitch 02:15 - שיתוף מסך פתוח nekohasekai/open-screen-sharing 02:39 - web-ocean Noniv/web-ocean 03:05 - שמיעה Leonxlnx/earshot 03:31 - מיזוג רשת rohit267/net-merge 03:56 - leanrea aicodeking/leanrea 04:21 - האק של הטלוויזיה Gathin23/tv-vibehack 04:49 - רתמה2 google-research/harness2 05:15 - הנחיית תנועה showheyohtaki/motion-prompt 05:42 - סטריאל זמן mistralai/timestral 06:07 - רשת סיבה reason-machines/reason-network 06:31 - חיתוך-1 caveman-ai/trim-1 06:57 - יומאדו chekusu/yomado 07:24 - Hy-Memory Tencent-Hunyuan/Hy-Memory 07:50 - opendesk useopendesk/opendesk 08:18 - c64-hw-simulator antirez/c64-hw-simulator 08:45 - binny openbin-ai/binny 09:08 - frametell sitor-ai/frametell 09:33 - esp32sim mikroverk/esp32sim 10:00 - mcp-for-blender-pro anujasid/mcp-for-blender-pro 10:26 - lucian-uxui-prompt Seokwoooo/lucian-uxui-prompt 10:54 - מאיו sorrycc/Mayoi 11:18 - enclose enclosedev/enclose 11:41 - SOMA Kai-Jungshoefel/SOMA 12:08 - anan-iphone-use ananhq/anan-iphone-use 12:33 - coop-anything cranebeaker/coop-anything 13:02 - casa-serra Sam1983Aing/casa-serra 13:27 - מורו Aarav2709/Morrow 13:54 -AI-Scientist-v3 SakanaAI/AI-Scientist-v3 14:18 - flipflop andunieee/flipflop 14:46 - parin Kapendev/parin תודה רבה שצפיתם. אנא עשו לייק לסרטון והירשמו לערוץ שלנו. #github #ai #opensource הערה: התוכן נוצר בעזרת בינה מלאכותית ועשוי להכיל אי דיוקים או שגיאות. ▬▬▬▬▬▬ 👋 צרו איתי קשר 👋 ▬▬▬▬▬▬ ➡ LinkTree: link.githubawesome.com ➡ תקנו לי קפה link.githubawesome.com [ja] GitHub週間トレンド #54：git-ai、sill、Filee、Tracely-ai、Snitch、web-ocean、earshot、net-merge こちらはGitHubトレンドウィークリー第54回です。現在GitHubで注目を集めているオープンソースプロジェクト35件をご紹介します。 有料チャンネル登録：youtube.com テキスト版リンク：githubawesome.com 00:00 - はじめに 00:10 - git-ai feder-cr/git-ai 00:36 - sill Saffsanity/sill 01:00 - Filee KnifeLemon/Filee 01:24 - Tracely-ai jwuthri-cloud/Tracely-ai 01:48 - Snitch aixisstudio/Snitch 02:15 - Open Screen Sharing nekohasekai/open-screen-sharing 02:39 - Web-Ocean Noniv/web-ocean 03:05 - earshot Leonxlnx/earshot 03:31 - net-merge rohit267/net-merge 03:56 - leanrea aicodeking/leanrea 04:21 - tv-vibehack Gathin23/tv-vibehack 04:49 - harness2 google-research/harness2 05:15 - motion-prompt showheyohtaki/motion-prompt 05:42 - timestral mistralai/timestral 06:07 - reason-network reason-machines/reason-network 06:31 - trim-1 caveman-ai/trim-1 06:57 - yomado chekusu/yomado 07:24 - Hy-Memory Tencent-Hunyuan/Hy-Memory 07:50 - opendesk useopendesk/opendesk 08:18 - c64-hw-simulator antirez/c64-hw-simulator 08:45 - binny openbin-ai/binny 09:08 - frametell sitor-ai/frametell 09:33 - esp32sim mikroverk/esp32sim 10:00 - mcp-for-blender-pro anujasid/mcp-for-blender-pro 10:26 - lucian-uxui-prompt Seokwoooo/lucian-uxui-prompt 10:54 - Mayoi sorrycc/Mayoi 11:18 - enclose enclosedev/enclose 11:41 - SOMA Kai-Jungsthoefel/SOMA 12:08 - anan-iphone-use ananhq/anan-iphone-use 12:33 - coop-anything cranebeaker/coop-anything 13:02 - casa-serra Sam1983Aing/casa-serra 13:27 - Morrow Aarav2709/Morrow 13:54 - AI-Scientist-v3 SakanaAI/AI-Scientist-v3 14:18 - flipflop andunieee/flipflop 14:46 - parin Kapendev/parin ご視聴いただきありがとうございました。動画への「いいね」とチャンネル登録をお願いします。 #github #ai #opensource 注意：このコンテンツはAIの支援を受けて作成されたため、不正確な情報や誤りが含まれている可能性があります。 ▬▬▬▬▬▬ 👋 ご連絡ください 👋 ▬▬▬▬▬▬ ➡ LinkTree: link.githubawesome.com ➡ Buy Me A Coffee: link.githubawesome.com [ko] GitHub 주간 트렌드 #54: git-ai, sill, Filee, Tracely-ai, Snitch, web-ocean, earshot, net-merge GitHub 주간 인기 프로젝트 #54입니다. 지금 GitHub에서 가장 인기 있는 오픈소스 프로젝트 35개를 소개합니다. 유료 채널 구독: youtube.com 텍스트 버전 링크: githubwesome.com 00:00 - 소개 00:10 - git-ai feder-cr/git-ai 00:36 - sill Saffsanity/sill 01:00 - Filee KnifeLemon/Filee 01:24 - Tracely-ai jwuthri-cloud/Tracely-ai 01:48 - Snitch aixisstudio/Snitch 02:15 - 화면 공유 시작 nekohasekai/open-screen-sharing 02:39 - web-ocean Noniv/web-ocean 03:05 - earshot Leonxlnx/earshot 03:31 - net-merge rohit267/net-merge 03:56 - leanrea aicodeking/leanrea 04:21 - tv-vibehack Gathin23/tv-vibehack 04:49 - harness2 google-research/harness2 05:15 - motion-prompt showheyohtaki/motion-prompt 05:42 - timestral mistralai/timestral 06:07 - reason-network reason-machines/reason-network 06:31 - trim-1 caveman-ai/trim-1 06:57 - yomado chekusu/yomado 07:24 - Hy-Memory Tencent-Hunyuan/Hy-Memory 07:50 - opendesk useopendesk/opendesk 08:18 - c64-hw-simulator antirez/c64-hw-simulator 08:45 - binny openbin-ai/binny 09:08 - frametell sitor-ai/frametell 09:33 - esp32sim mikroverk/esp32sim 10:00 - mcp-for-blender-pro anujasid/mcp-for-blender-pro 10:26 - lucian-uxui-prompt Seokwoooo/lucian-uxui-prompt 10:54 - Mayoi sorrycc/Mayoi 11:18 - enclose enclosedev/enclose 11:41 - SOMA Kai-Jungsthoefel/SOMA 12:08 - anan-iphone-use ananhq/anan-iphone-use 12:33 - coop-anything cranebeaker/coop-anything 13:02 - casa-serra Sam1983Aing/casa-serra 13:27 - Morrow Aarav2709/Morrow 13:54 - AI-Scientist-v3 SakanaAI/AI-Scientist-v3 14:18 - flipflop andunieee/flipflop 14:46 - parin Kapendev/parin 시청해 주셔서 감사합니다. 영상에 좋아요를 누르고 채널 구독도 부탁드립니다. #github #ai #opensource 주의: 본 콘텐츠는 AI를 이용하여 제작되었으며, 부정확하거나 오류가 포함될 수 있습니다. ▬▬▬▬▬▬ 👋 연락처 👋 ▬▬▬▬▬▬ ➡ 링크트리: link.githubawesome.com ➡ 커피 한 잔 사주세요: link.githubawesome.com [ml] GitHub ട്രെൻഡിംഗ് വീക്ക്‌ലി #54: git-ai, sill, Filee, Tracely-ai, Snitch, web-ocean, earshot, net... ഇതാണ് GitHub ട്രെൻഡിംഗ് വീക്ക്‌ലി #54. GitHub-ൽ ഇപ്പോൾ ട്രെൻഡിംഗ് ആയ 35 ഓപ്പൺ സോഴ്‌സ് പ്രോജക്ടുകൾ. പണമടച്ചുള്ള ചാനൽ സബ്‌സ്‌ക്രിപ്‌ഷൻ: youtube.com ടെക്‌സ്റ്റ് പതിപ്പ് ലിങ്ക്: githubawesome.com 00:00 - ആമുഖം 00:10 - git-ai feder-cr/git-ai 00:36 - sill Saffsanity/sill 01:00 - ഫയലി KnifeLemon/Filee 01:24 - ട്രേസ്‌ലി-ഐ jwuthri-cloud/Tracely-ai 01:48 - സ്‌നിച്ച് aixisstudio/Snitch 02:15 - ഓപ്പൺ സ്‌ക്രീൻ പങ്കിടൽ nekohasekai/open-screen-sharing 02:39 - വെബ്-ഓഷ്യൻ Noniv/web-ocean 03:05 - ഇയർഷോട്ട് Leonxlnx/earshot 03:31 - നെറ്റ്-മെർജ് rohit267/net-merge 03:56 - ലീൻറിയ aicodeking/leanrea 04:21 - ടിവി-വൈബ്ഹാക്ക് Gathin23/tv-vibehack 04:49 - ഹാർനെസ്2 google-research/harness2 05:15 - മോഷൻ-പ്രോംപ്റ്റ് showheyohtaki/motion-prompt 05:42 - ടൈംസ്ട്രൽ mistralai/timestral 06:07 - റീസൺ-നെറ്റ്‌വർക്ക് reason-machines/reason-network 06:31 - ട്രിം-1 caveman-ai/trim-1 06:57 - യോമാഡോ chekusu/yomado 07:24 - ഹൈ-മെമ്മറി Tencent-Hunyuan/Hy-%E0%B4%AE%E0%B5%86%E0%B4%AE%E0%B5%8D%E0%B4%AE%E0%B4%B1%E0%B4%BF 07:50 - ഓപ്പൺഡെസ്ക് useopendesk/opendesk 08:18 - c64-hw-സിമുലേറ്റർ antirez/c64-hw-%E0%B4%B8%E0%B4%BF%E0%B4%AE%E0%B5%81%E0%B4%B2%E0%B5%87%E0%B4%B1%E0%B5%8D%E0%B4%B1%E0%B5%BC 08:45 - ബിന്നി openbin-ai/binny 09:08 - ഫ്രെയിംടെൽ sitor-ai/frametell 09:33 - esp32sim mikroverk/esp32sim 10:00 - mcp-for-blender-pro anujasid/mcp-for-blender-pro 10:26 - lucian-uxui-prompt Seokwoooo/lucian-uxui-prompt 10:54 - മയോയ് sorrycc/Mayoi 11:18 - എൻക്ലോസ് ചെയ്യുക enclosedev/enclose 11:41 - സോമ Kai-Jungsthoefel/SOMA 12:08 - anan-iphone-use ananhq/anan-iphone-use 12:33 - coop-anything cranebeaker/coop-anything 13:02 - casa-serra Sam1983Aing/casa-serra 13:27 - മോറോ Aarav2709/Morrow 13:54 -AI-Scientist-v3 SakanaAI/AI-Scientist-v3 14:18 - flipflop andunieee/flipflop 14:46 - parin Kapendev/parin കണ്ടതിന് വളരെ നന്ദി. ദയവായി വീഡിയോ ലൈക്ക് ചെയ്യുകയും ഞങ്ങളുടെ ചാനൽ സബ്സ്ക്രൈബ് ചെയ്യുകയും ചെയ്യുക. #github #ai #opensource ശ്രദ്ധിക്കുക: ഉള്ളടക്കം AI യുടെ സഹായത്തോടെ സൃഷ്ടിച്ചതാണ്, അതിൽ കൃത്യതയില്ലായ്മകളോ പിശകുകളോ അടങ്ങിയിരിക്കാം. ▬▬▬▬▬▬ 👋 എന്നെ ബന്ധപ്പെടുക 👋 ▬▬▬▬▬▬ ➡ ലിങ്ക്ട്രീ: link.githubawesome.com ➡ എനിക്ക് ഒരു കാപ്പി വാങ്ങിത്തരൂ link.githubawesome.com [nl-NL] GitHub Trending Weekly #54: git-ai, sill, Filee, Tracely-ai, Snitch, web-ocean, earshot, net-merge Dit is GitHub Trending Weekly #54. 35 trending open-sourceprojecten op GitHub op dit moment. Betaald kanaalabonnement: youtube.com Link naar tekstversie: githubawesome.com 00:00 - Introductie 00:10 - git-ai feder-cr/git-ai 00:36 - sill Saffsanity/sill 01:00 - Filee KnifeLemon/Filee 01:24 - Tracely-ai jwuthri-cloud/Tracely-ai 01:48 - Snitch aixisstudio/Snitch 02:15 - Open Screen Sharing nekohasekai/open-screen-sharing 02:39 - web-ocean Noniv/web-ocean 03:05 - earshot Leonxlnx/earshot 03:31 - net-merge rohit267/net-merge 03:56 - leanrea aicodeking/leanrea 04:21 - tv-vibehack Gathin23/tv-vibehack 04:49 - harness2 google-research/harness2 05:15 - motion-prompt showheyohtaki/motion-prompt 05:42 - timestral mistralai/timestral 06:07 - reason-network reason-machines/reason-network 06:31 - trim-1 caveman-ai/trim-1 06:57 - yomado chekusu/yomado 07:24 - Hy-Memory Tencent-Hunyuan/Hy-Memory 07:50 - opendesk useopendesk/opendesk 08:18 - c64-hw-simulator antirez/c64-hw-simulator 08:45 - binny openbin-ai/binny 09:08 - frametell sitor-ai/frametell 09:33 - esp32sim mikroverk/esp32sim 10:00 - mcp-for-blender-pro anujasid/mcp-for-blender-pro 10:26 - lucian-uxui-prompt Seokwoooo/lucian-uxui-prompt 10:54 - Mayoi sorrycc/Mayoi 11:18 - enclose enclosedev/enclose 11:41 - SOMA Kai-Jungsthoefel/SOMA 12:08 - anan-iphone-use ananhq/anan-iphone-use 12:33 - coop-anything cranebeaker/coop-anything 13:02 - casa-serra Sam1983Aing/casa-serra 13:27 - Morrow Aarav2709/Morrow 13:54 -AI-Scientist-v3 SakanaAI/AI-Scientist-v3 14:18 - flipflop andunieee/flipflop 14:46 - parin Kapendev/parin Hartelijk dank voor het kijken. Geef de video een like en abonneer je op ons kanaal. #github #ai #opensource Let op: De inhoud is gemaakt met behulp van AI en kan onnauwkeurigheden of fouten bevatten. ▬▬▬▬▬▬ 👋 Neem contact met me op 👋 ▬▬▬▬▬▬ ➡ LinkTree: link.githubawesome.com ➡ Koop me een koffie: link.githubawesome.com [pa] GitHub Trending Weekly #54: git-ai, sill, Filee, Tracely-ai, Snitch, web-ocean, earshot, net-merge ਇਹ GitHub ਟ੍ਰੈਂਡਿੰਗ ਵੀਕਲੀ #54 ਹੈ। GitHub 'ਤੇ ਇਸ ਵੇਲੇ 35 ਟ੍ਰੈਂਡਿੰਗ ਓਪਨ-ਸੋਰਸ ਪ੍ਰੋਜੈਕਟ। ਭੁਗਤਾਨ ਕੀਤਾ ਚੈਨਲ ਗਾਹਕੀ: youtube.com ਟੈਕਸਟ ਵਰਜਨ ਲਿੰਕ: githubawesome.com 00:00 - ਜਾਣ-ਪਛਾਣ 00:10 - git-ai feder-cr/git-ai 00:36 - sill Saffsanity/sill 01:00 - Filee KnifeLemon/Filee 01:24 - Tracely-ai jwuthri-cloud/Tracely-ai 01:48 - Snitch aixisstudio/Snitch 02:15 - ਓਪਨ ਸਕ੍ਰੀਨ ਸ਼ੇਅਰਿੰਗ nekohasekai/open-screen-sharing 02:39 - web-ocean Noniv/web-ocean 03:05 - ਈਅਰਸ਼ੌਟ Leonxlnx/earshot 03:31 - ਨੈੱਟ-ਮਰਜ rohit267/net-merge 03:56 - ਲੀਨਰੀਆ aicodeking/leanrea 04:21 - ਟੀਵੀ-ਵਿਬੇਹੈਕ Gathin23/tv-vibehack 04:49 - ਹਾਰਨੇਸ2 google-research/harness2 05:15 - ਮੋਸ਼ਨ-ਪ੍ਰੋਂਪਟ showheyohtaki/motion-prompt 05:42 - ਟਾਈਮਸਟ੍ਰਲ mistralai/timetral 06:07 - ਰੀਜ਼ਨ-ਨੈੱਟਵਰਕ reason-machines/reason-network 06:31 - ਟ੍ਰਿਮ-1 caveman-ai/trim-1 06:57 - yomado chekusu/yomado 07:24 - ਹਾਈ-ਮੈਮੋਰੀ Tencent-Hunyuan/Hy-%E0%A8%AE%E0%A9%88%E0%A8%AE%E0%A9%8B%E0%A8%B0%E0%A9%80 07:50 - ਓਪਨਡੈਸਕ useopendesk/opendesk 08:18 - c64-hw-simulator antirez/c64-hw-simulator 08:45 - ਬਿੰਨੀ openbin-ai/binny 09:08 - ਫਰੇਮੈਟਲ sitor-ai/frametell 09:33 - esp32sim mikroverk/esp32sim 10:00 - mcp-for-blender-pro anujasid/mcp-for-blender-pro 10:26 - lucian-uxui-prompt Seokwoooo/lucian-uxui-prompt 10:54 - ਮਯੋਈ sorrycc/Mayoi 11:18 - ਐਨਕਲੋਜ਼ enclosedev/enclose 11:41 - SOMA Kai-Jungsthoefel/SOMA 12:08 - anan-iphone-use ananhq/anan-iphone-use 12:33 - coop-anything cranebeaker/coop-anything 13:02 - casa-serra Sam1983Aing/casa-serra 13:27 - ਕੱਲ੍ਹ Aarav2709/Morrow 13:54 -AI-Scientist-v3 SakanaAI/AI-Scientist-v3 14:18 - flipflop andunieee/flipflop 14:46 - parin Kapendev/parin ਦੇਖਣ ਲਈ ਤੁਹਾਡਾ ਬਹੁਤ ਧੰਨਵਾਦ। ਕਿਰਪਾ ਕਰਕੇ ਵੀਡੀਓ ਨੂੰ ਪਸੰਦ ਕਰੋ ਅਤੇ ਸਾਡੇ ਚੈਨਲ ਨੂੰ ਸਬਸਕ੍ਰਾਈਬ ਕਰੋ। #github #ai #opensource ਨੋਟਿਸ: ਸਮੱਗਰੀ AI ਦੀ ਸਹਾਇਤਾ ਨਾਲ ਬਣਾਈ ਗਈ ਸੀ ਅਤੇ ਇਸ ਵਿੱਚ ਗਲਤੀਆਂ ਜਾਂ ਗਲਤੀਆਂ ਹੋ ਸਕਦੀਆਂ ਹਨ। ▬▬▬▬▬▬ 👋 ਮੇਰੇ ਨਾਲ ਸੰਪਰਕ ਕਰੋ 👋 ▬▬▬▬▬▬ ➡ LinkTree: link.githubawesome.com ➡ Buy Me A Coffee link.githubawesome.com [pl] GitHub Trending Weekly #54: git-ai, sill, Filee, Tracely-ai, Snitch, web-ocean, earshot, net-merge Oto GitHub Trending Weekly nr 54. 35 najpopularniejszych projektów open source na GitHubie. Płatna subskrypcja kanału: youtube.com Link do wersji tekstowej: githubwesome.com 00:00 – Wprowadzenie 00:10 – git-ai feder-cr/git-ai 00:36 – sill Saffsanity/sill 01:00 – Filee KnifeLemon/Filee 01:24 – Tracely-ai jwuthri-cloud/Tracely-ai 01:48 – Snitch aixisstudio/Snitch 02:15 – Udostępnianie ekranu nekohasekai/open-screen-sharing 02:39 - web-ocean Noniv/web-ocean 03:05 - earshot Leonxlnx/earshot 03:31 - net-merge rohit267/net-merge 03:56 - leanrea aicodeking/leanrea 04:21 - tv-vibehack Gathin23/tv-vibehack 04:49 - harness2 google-research/harness2 05:15 - motion-prompt showheyohtaki/motion-prompt 05:42 - timestral mistralai/timestral 06:07 - reason-network reason-machines/reason-network 06:31 - trim-1 caveman-ai/trim-1 06:57 - yomado chekusu/yomado 07:24 - Hy-Memory Tencent-Hunyuan/Hy-Memory 07:50 - opendesk useopendesk/opendesk 08:18 - c64-hw-simulator antirez/c64-hw-simulator 08:45 - binny openbin-ai/binny 09:08 - frametell sitor-ai/frametell 09:33 - esp32sim mikroverk/esp32sim 10:00 - mcp-for-blender-pro anujasid/mcp-for-blender-pro 10:26 - lucian-uxui-prompt Seokwoooo/lucian-uxui-prompt 10:54 - Mayoi sorrycc/Mayoi 11:18 - enclose enclosedev/enclose 11:41 - SOMA Kai-Jungsthoefel/SOMA 12:08 - anan-iphone-use ananhq/anan-iphone-use 12:33 - coop-anything cranebeaker/coop-anything 13:02 - casa-serra Sam1983Aing/casa-serra 13:27 - Morrow Aarav2709/Morrow 13:54 -AI-Scientist-v3 SakanaAI/AI-Scientist-v3 14:18 - flipflop andunieee/flipflop 14:46 - parin Kapendev/parin Dziękujemy bardzo za obejrzenie. Polub film i zasubskrybuj nasz kanał. #github #ai #opensource Uwaga: Treść została stworzona przy pomocy sztucznej inteligencji i może zawierać nieścisłości lub błędy. ▬▬▬▬▬▬ 👋 Skontaktuj się ze mną 👋 ▬▬▬▬▬▬ ➡ LinkTree: link.githubwesome.com ➡ Kup mi kawę link.githubwesome.com [pt-BR] GitHub Trending Semanal nº 54: git-ai, sill, Filee, Tracely-ai, Snitch, web-ocean, earshot, net-m... Esta é a lista #54 dos projetos de código aberto mais populares do GitHub neste momento. 35 projetos de código aberto em alta no GitHub. Assinatura paga do canal: youtube.com Link para a versão em texto: githubawesome.com 00:00 - Introdução 00:10 - git-ai feder-cr/git-ai 00:36 - sill Saffsanity/sill 01:00 - Filee KnifeLemon/Filee 01:24 - Tracely-ai jwuthri-cloud/Tracely-ai 01:48 - Snitch aixisstudio/Snitch 02:15 - Open Screen Sharing nekohasekai/open-screen-sharing 02:39 - web-ocean Noniv/web-ocean 03:05 - earshot Leonxlnx/earshot 03:31 - net-merge rohit267/net-merge 03:56 - leanrea aicodeking/leanrea 04:21 - tv-vibehack Gathin23/tv-vibehack 04:49 - harness2 google-research/harness2 05:15 - motion-prompt showheyohtaki/motion-prompt 05:42 - timestral mistralai/timestral 06:07 - reason-network reason-machines/reason-network 06:31 - trim-1 caveman-ai/trim-1 06:57 - yomado chekusu/yomado 07:24 - Hy-Memory Tencent-Hunyuan/Hy-Memory 07:50 - opendesk useopendesk/opendesk 08:18 - c64-hw-simulator antirez/c64-hw-simulator 08:45 - binny openbin-ai/binny 09:08 - frametell sitor-ai/frametell 09:33 - esp32sim mikroverk/esp32sim 10:00 - mcp-for-blender-pro anujasid/mcp-for-blender-pro 10:26 - lucian-uxui-prompt Seokwoooo/lucian-uxui-prompt 10:54 - Mayoi sorrycc/Mayoi 11:18 - enclose enclosedev/enclose 11:41 - SOMA Kai-Jungsthoefel/SOMA 12:08 - anan-iphone-use ananhq/anan-iphone-use 12:33 - coop-anything cranebeaker/coop-anything 13:02 - casa-serra Sam1983Aing/casa-serra 13:27 - Morrow Aarav2709/Morrow 13:54 - AI-Scientist-v3 SakanaAI/AI-Scientist-v3 14:18 - flipflop andunieee/flipflop 14:46 - parin Kapendev/parin Muito obrigado por assistir. Curta o vídeo e inscreva-se no nosso canal. #github #ai #opensource Aviso: O conteúdo foi criado com o auxílio de IA e pode conter imprecisões ou erros. ▬▬▬▬▬▬ 👋 Entre em contato comigo 👋 ▬▬▬▬▬▬ ➡ LinkTree: link.githubawesome.com ➡ Compre-me um café: link.githubawesome.com [ru] GitHub Trending Weekly №54: git-ai, sill, Filee, Tracely-ai, Snitch, web-ocean, earshot, net-merge Это еженедельный обзор трендов GitHub № 54. 35 самых популярных проектов с открытым исходным кодом на GitHub прямо сейчас. Платная подписка на канал: youtube.com Ссылка на текстовую версию: githubawesome.com 00:00 - Введение 00:10 - git-ai feder-cr/git-ai 00:36 - sill Saffsanity/sill 01:00 - Filee KnifeLemon/Filee 01:24 - Tracely-ai jwuthri-cloud/Tracely-ai 01:48 - Snitch aixisstudio/Snitch 02:15 - Открытие демонстрации экрана nekohasekai/open-screen-sharing 02:39 - web-ocean Noniv/web-ocean 03:05 - earshot Leonxlnx/earshot 03:31 - net-merge rohit267/net-merge 03:56 - leanrea aicodeking/leanrea 04:21 - tv-vibehack Gathin23/tv-vibehack 04:49 - harness2 google-research/harness2 05:15 - motion-prompt showheyohtaki/motion-prompt 05:42 - timestral mistralai/timestral 06:07 - reason-network reason-machines/reason-network 06:31 - trim-1 caveman-ai/trim-1 06:57 - yomado chekusu/yomado 07:24 - Hy-Memory Tencent-Hunyuan/Hy-Memory 07:50 - opendesk useopendesk/opendesk 08:18 - c64-hw-simulator antirez/c64-hw-simulator 08:45 - binny openbin-ai/binny 09:08 - frametell sitor-ai/frametell 09:33 - esp32sim mikroverk/esp32sim 10:00 - mcp-for-blender-pro anujasid/mcp-for-blender-pro 10:26 - lucian-uxui-prompt Seokwoooo/lucian-uxui-prompt 10:54 - Mayoi sorrycc/Mayoi 11:18 - enclose enclosedev/enclose 11:41 - SOMA Kai-Jungsthoefel/SOMA 12:08 - anan-iphone-use ananhq/anan-iphone-use 12:33 - coop-anything cranebeaker/coop-anything 13:02 - casa-serra Sam1983Aing/casa-serra 13:27 - Morrow Aarav2709/Morrow 13:54 - AI-Scientist-v3 SakanaAI/AI-Scientist-v3 14:18 - flipflop andunieee/flipflop 1

🔗 Superpowers — obra/superpowers 🔗 AI Job Search — MadsLorentzen/ai-job-search 🔗 AI Scientist v3 — SakanaAI/AI-Scientist-v3 ⚡ Quick start: /plugin install superpowers@claude-plugins-official 🚀 Daily AI tools + full setups, seedha inbox me: hebis-newsletter.beehiiv.com

3 AI Agent Repos Breaking GitHub! 🤯 #Shorts 3 open-source AI-agent repos going viral on GitHub right now: Superpowers (300K stars), AI Job Search (50K stars) and Sakana AI's AI Scientist v3. All verified open source - MIT + Apache 2.0. Full 60s breakdown inside. Repo links: obra/superpowers MadsLorentzen/ai-job-search SakanaAI/AI-Scientist-v3 Quick start: /plugin install superpowers@claude-plugins-official gh repo fork MadsLorentzen/ai-job-search --clone agsci run examples/circpack Daily AI tools + full setups, seedha inbox me: hebis-newsletter.beehiiv.com #AI #Coding #OpenSource #AIAgents #GitHub #ClaudeCode #LocalAI #TechHindi #WebDev #Shorts

🔗 Superpowers — obra/superpowers 🔗 AI Job Search — MadsLorentzen/ai-job-search 🔗 AI Scientist v3 — SakanaAI/AI-Scientist-v3 ⚡ Quick start: /plugin install superpowers@claude-plugins-official 🚀 Daily AI tools + full setups, seedha inbox me: hebis-newsletter.beehiiv.com

Discuss+ The Third Layer: What Happens When AI Starts Creating New Kinds of AI Systems? !Image description The Three Layers: A Quick Breakdown 🧐 Thanks for support.. Release: "Sentinel-IR-MCP v0.2.2 on GitLab" (gitlab.com) Package: "sentinel-ir-mcp@0.2.2 on npm" (npmjs.com) Here is how to think about it if you are explaining it to a friend over a beer: • Layer 1 (Using it): You have a standard coding agent. You give it a task, it writes some Python, it fixes a bug. If it fails or gets stuck, a human developer has to step in, change the prompt, tweak the tools, or rewrite the core code. • Layer 2 (Improving it): The agent gets smarter. It looks at its own workflow and realizes it's failing because its search strategy sucks. So, it rewrites its own memory handling or updates its internal testing pipeline to get better at its job. It's self-improvement within a fixed box. • Layer 3 (The Third Layer): The agent encounters a complex problem and realizes its entire architecture is fundamentally the wrong tool for the job. Instead of endlessly patching itself, it spins up a completely new, specialized system from scratch—maybe a tiny, hyper-optimized deterministic pipeline or a swarm of mini-verifiers. It tests this new child system, evaluates the proof, logs the data into an archive, and uses that meta-learning to improve how it builds the next generation of software. The system didn't just write code; it engineered a new architectural paradigm that no human specifically designed. NEXT... From making an AI better, to making an AI that creates better systems. What already works, what the research actually proves, and what we still cannot demonstrate. Long read: approximately 45 to 60 minutes, depending on how deeply you follow the papers and code. Research snapshot: October 17, 2026. A note on terminology: "The Third Layer" is a working concept used in this article. It is not an established scientific term or a universally accepted stage of AI development. The purpose is to give a name to a question worth investigating. --- Part I: For anyone who has ever wondered what comes after AI 1. I started with a question that sounded much bigger than my wallet I was discussing long-lived autonomous agents with an AI assistant. The conversation started with memory, architecture, and how a system can continue operating over a very long period without wasting resources or forgetting everything important. Then I asked a different question: What happens when AI stops merely improving an existing system and starts creating entirely new systems that were not individually designed by humans? Not better copies. Not another model with a longer context window. Not the same agent with a new system prompt and a motivational speech about being more efficient. I mean a system that encounters a problem, realizes that its current architecture is insufficient, creates a different system to solve that problem, evaluates the result, and uses what it learns to create something else. Imagine this: You build one general-purpose system. It knows how to explore problems, propose solutions, create software, run experiments, and evaluate results. One day, it encounters a problem its current architecture handles badly. Instead of endlessly modifying itself to fit every problem, it creates a specialized system. System A is designed for materials research. System B is designed for analyzing complex networks. System C is designed for security verification. System D discovers a problem that the original system did not even know how to formulate properly. The original system did not contain a complete, hand-written implementation of A, B, C, and D. It contained the mechanisms that allowed these systems to be designed, created, tested, and selected. That's a very different idea from simply asking an AI to write some Python. And, yes, the first thing I thought was: "Okay, I can understand this. Even without a PhD in machine learning." 😂 That matters. If we cannot explain the idea clearly enough for an ordinary developer to understand it, we should probably examine whether we understand it ourselves. 2. The periodic table analogy Let's use chemistry. We know that chemical elements exist independently of our ability to identify them. Scientists developed models that helped organize the elements, explain their properties, and predict what might exist beyond the elements already known. Some predictions were subsequently confirmed experimentally. The important part is not that somebody wrote an impressive prediction. The important part is that the prediction connected to a real phenomenon and survived verification. Now imagine something similar happening in AI architecture. Today, researchers explore a space of possible algorithms, models, agent architectures, memory strategies, search methods, and coordination mechanisms. Much of that space remains unexplored. A sufficiently capable system might discover an architecture that nobody specifically designed beforehand. It might discover a combination of mechanisms that works surprisingly well, or an entirely different way to organize a collection of agents. But there is an important difference between the chemistry analogy and our AI hypothesis. We cannot assume that every generated architecture corresponds to a useful new category of intelligence. Most candidates may be redundant, broken, expensive, or worse than existing approaches. A thousand generated systems are not a thousand discoveries. A discovery needs evidence. The useful question is therefore not: "Can AI generate something new?" It clearly can generate novel combinations of code and architecture. The more interesting question is: Can AI systematically discover, validate, and propagate new ways of organizing intelligent systems, including ways that improve the process of discovering further systems? That's the question behind the Third Layer. 3. Three layers, explained without the PhD required For this discussion, I would divide the problem into three conceptual layers. This is my working model, not a standard classification of AI research. Layer 1: Use an existing system You have an AI model or an agent. You give it a task. It uses tools, searches information, writes code, or makes decisions. Its basic architecture has already been designed. If it fails, a human usually changes the prompt, tools, code, training, or configuration. Example: You have a coding agent. You ask it to fix a bug. It fixes the bug. If it keeps making mistakes, you improve its instructions or modify its implementation. Nothing unusual here. Layer 2: Improve the existing system Now the system can modify parts of its own implementation or configuration. It might improve its search strategy, add validation, change how it manages context, or use a more effective workflow. It produces a candidate version, runs tests, and keeps the modification if the results justify it. This is already much more interesting. The system is no longer only using the software. It is helping to improve the software that makes it useful. But the objective is still relatively familiar: make this system better at its intended job. Layer 3: Create and evolve new systems Now we change the question. Instead of asking only how to improve this particular agent, the parent system searches for better ways to construct agents and other problem-solving systems. Some candidates might share code with the parent. Others might use entirely different workflows, tools, internal data structures, or search strategies. One candidate might be a small, extremely efficient specialist. Another might use a population of competing solutions. A third might discover that a particular problem is better solved by a deterministic algorithm than by an LLM at all. The parent does not need to turn every child into a better version of itself. It needs to create systems that solve their respective problems better. And once it has evidence about which creation strategies work, it can use that information to improve the process of producing the next generation. That's the distinction I care about. Layer 2 asks, "How do I improve myself?" Layer 3 asks, "What system should exist for this problem, and how can I reliably discover the next system?" These concepts overlap. The Third Layer is not necessarily a separate technology that replaces self-improvement. It describes a broader architecture in which system creation, evaluation, specialization, and potentially recursive improvement work together. 4. A small diagram of a much larger idea A BASE SYSTEM | v Encounters a new problem | v Is the current architecture sufficient? / \ Yes No | | v v Use the system Search for alternatives | v Generate candidate systems | v Execute real tests | v Independent evaluation | +-------------+-------------+ | | | v v v Reject Archive Promote | | | +-------------+-------------+ | v Results improve future search | v New generation of systems Notice what is missing. There is no magical box labeled "Become more intelligent." There is no guarantee that every new generation will outperform its predecessor. There is no assumption that the parent must understand every possible architecture in advance. Instead, there is a mechanism for generating alternatives, testing them, keeping useful results, and learning from failures. The interesting part is not the number of agents. It is the quality of the evolutionary process. And that leads to a distinction that becomes very important once we start looking at real research. 5. Does the child need to be a copy of its parent? No. In fact, insisting on copies may be one of the more restrictive choices. Consider three possible candidates. Candidate One uses the same foundation model, but has a different search strategy, memory system, tool interface, and evaluation workflow. Candidate Two uses a smaller model and a carefully designed deterministic pipeline. It performs worse on general conversation, but is considerably better at one narrow engineering task. Candidate Three uses a population-based search algorithm and no LLM during the actual problem-solving phase. An LLM is used only to suggest candidate algorithms. Which one is the child? All three could be descendants of the same parent search process. But they are not necessarily copies of the parent in any meaningful architectural sense. A generated agent does not need to contain its parent's weights. It does not need to look like its parent, communicate like its parent, or even use the same problem-solving mechanism. It needs a traceable relationship to the process that produced it, plus evidence that it performs a useful function. This also means that a system can create a new software architecture without creating a new foundation model. That distinction matters because many discussions about self-improving AI jump straight to the most dramatic possibility: a system modifies its own neural weights, becomes more intelligent, and repeats the process indefinitely. But architectural evolution can happen at a different level. An agent's performance also depends on its tools, memory, search policy, orchestration, context handling, testing strategy, and how it chooses among candidate solutions. Changing those components can be useful even when the underlying foundation model remains frozen. And that is where we find the first concrete evidence. --- Part II: This is not science fiction. Which parts already exist? 6. Before recursive self-improvement, there was automated discovery The Third Layer is a useful name for our particular question, but the underlying research has a much longer history. Automated machine learning, evolutionary algorithms, genetic programming, meta-learning, neural architecture search, and program synthesis have all explored ways to automate parts of the process of designing intelligent systems. One useful historical example is "AutoML-Zero" (proceedings.mlr.press), published at ICML 2020. Rather than restricting the search to sophisticated, human-designed machine-learning components, the researchers explored whether complete learning algorithms could emerge from combinations of basic mathematical operations. The paper reports that evolutionary search could discover algorithms resembling simple neural networks trained through backpropagation. It also found algorithmic components adapted to different tasks, including dropout-like behavior in low-data settings. This is a meaningful result. The search process was not just tuning a learning rate in a fixed architecture. It could evolve implementations of learning algorithms from a relatively generic set of building blocks. But the search space, fitness objectives, computational process, and experimental tasks were still designed by researchers. It did not demonstrate a general-purpose system that can invent any kind of intelligence for any unknown problem. That is our first recurring lesson. Automated discovery can be real without being unlimited. 7. FunSearch: the AI does not have to guess the final answer In 2023, Google DeepMind researchers introduced FunSearch, with the work subsequently published in Nature in 2024. The paper is called "Mathematical discoveries from program search with large language models" (nature.com). The basic idea is elegant. A language model proposes programs. An evaluator executes them and scores the results. An evolutionary process uses successful candidates and their history to guide subsequent proposals. The approach was applied to mathematical constructions, including the cap set problem, and to heuristics for online bin packing. Why is this interesting? Because the system is not merely asking an LLM to provide an answer in natural language. It searches for programs that describe how a problem can be solved. Those programs can be executed, evaluated, and used as evidence for subsequent search. This changes the role of the model. The language model becomes a proposal generator inside a larger discovery mechanism. The evaluator is not decorative. It is what separates a promising-looking idea from a candidate that actually achieves something measurable. There is a subtle advantage here. A program can express a general method rather than one isolated answer. Once a better method is discovered, it can potentially be reused across many instances of a problem. This is closer to discovering a new tool than to answering one question. However, FunSearch did not demonstrate a general autonomous system that creates an unlimited family of new AI architectures. Its successes were in problem domains with suitable evaluation mechanisms. That limitation is important, not embarrassing. It tells us where the method works and what would need to change before we could extend it. 8. AlphaEvolve: from a function to a larger algorithm In May 2025, Google DeepMind introduced "AlphaEvolve" (deepmind.google). Its research report is available as "AlphaEvolve: A coding agent for scientific and algorithmic discovery" (arxiv.org). AlphaEvolve combines language models with automated evaluation and evolutionary search. Unlike earlier approaches that focus on individual functions, it can evolve substantially larger pieces of code implementing complex algorithms. This gives us several concrete examples. Matrix multiplication AlphaEvolve discovered an algorithm that multiplies 4 × 4 complex-valued matrices using 48 scalar multiplications, improving on the previously known 49-multiplication approach associated with Strassen's algorithm for that setting. This is a useful reminder that algorithms are not fixed artifacts just because humans have optimized them for decades. A system can search the space of implementations and discover something better under a clearly defined objective. Google's computing infrastructure DeepMind reported that AlphaEvolve helped optimize resource allocation across Google's computing fleet. After deploying a discovered heuristic, measurements reportedly showed that it recovered an average of 0.7% of fleet-wide compute resources that would otherwise have remained stranded. The research also describes work on algorithms used in AI training, matrix multiplication kernels, and hardware design. These are not merely benchmark demonstrations. Some discoveries were integrated into real infrastructure. But let's be careful about what they prove. AlphaEvolve was given problems with evaluation criteria that could be measured automatically. Its ability to discover better algorithms is impressive precisely because the results could be evaluated. It does not establish that AlphaEvolve can invent an entirely new class of intelligence whenever it wants. It also does not establish that every new system it creates improves its own ability to create better systems. Still, it demonstrates an important part of our proposed architecture: A general-purpose search process can discover specialized solutions whose useful properties were not explicitly encoded in the original solution. And those solutions can be deployed when they survive the necessary checks. 9. Darwin Gödel Machine: when the agent can modify its own code Now we move closer to the idea of an evolving family of agents. The "Darwin Gödel Machine (DGM)" (arxiv.org), introduced in 2025, explores an AI coding agent that modifies its own code and evaluates the resulting versions on programming benchmarks. Its code is available in the "public research repository" (jennyzzt/dgm). The system does not simply create one improved version and discard everything else. It maintains an archive of candidate agents. New candidates can be generated from different archived versions, allowing the search to explore multiple lineages. This is important because the best next step does not always come from the current top performer. Sometimes a less successful candidate introduces an idea that becomes useful when combined with another modification. That is a familiar feature of evolutionary search: selection needs to preserve enough diversity for the system to explore alternatives, not just polish the current winner. In the published experiments, the DGM increased performance on SWE-bench from 20% to 50% on the evaluated subset. The paper also reports improvements on Polyglot, although its abstract and detailed experimental descriptions report different headline values for some settings. That is a good reason to check the exact evaluation configuration rather than presenting one percentage as a universal capability. The authors also compared against systems without self-improvement and without open-ended exploration. These comparisons support the importance of both code modification and maintaining a diverse archive. But there is a practical detail that deserves more attention than it usually receives. The paper estimates that its reported SWE-bench run took about two weeks and cost approximately $22,000 in API usage. Those figures describe the particular research setup and prices used for that experiment. They are not a general price tag for all evolutionary agents today. Nevertheless, the lesson is obvious. Recursive code improvement is not automatically cheap. If an experiment spends $22,000 searching for a better agent, the improved agent has not yet demonstrated that it is economically useful. We also need to account for the cost of discovering it. This is particularly relevant to small developers. A promising research architecture that requires an enormous budget may be technically impressive and still be an impractical product. My own wallet is an unusually reliable evaluator. Unfortunately, it does not accept papers as payment. 10. The AI Scientist: automating more than the coding step Another relevant project is "The AI Scientist" (arxiv.org), developed by Sakana AI and collaborators. The updated research was published in Nature in March 2026 under the title "Towards end-to-end automation of AI research" (nature.com). The system attempts to automate a wider scientific workflow: 1. Generate research ideas. 2. Search for related work. 3. Implement experiments. 4. Run the experiments and analyze results. 5. Produce a research manuscript. 6. Review the resulting work. The original research system and its later versions are available publicly, including "AI Scientist-v2" (SakanaAI/AI-Scientist-v2). In the ICLR 2025 workshop experiment described by the authors, one fully AI-generated manuscript achieved an average review score of 6.33, exceeding the average human acceptance threshold for that workshop. The paper scored above 55% of human-authored submissions. The authors had arranged to withdraw it before publication if accepted, which they subsequently did. That is an interesting milestone. It shows that an AI research pipeline can do more than suggest ideas or write a report. Under particular conditions, it can produce work that passes a human peer-review process. However, we should not confuse a workshop-level result with proof that an AI can independently carry out groundbreaking science across arbitrary disciplines. The system still has significant limitations. The authors discuss underdeveloped ideas, implementation problems, methodological weaknesses, and mistakes such as inaccurate citations or duplicated figures. Their experimental environment is focused on computational machine-learning research, not unrestricted scientific experimentation in the physical world. The lesson for our Third Layer is that a system can automate a substantial portion of the process by which new knowledge is produced. But a research workflow that generates new papers is not automatically a system that generates new kinds of intelligent systems. These are related, but distinct capabilities. 11. AIDE²: the research agent starts improving the research agent Now we reach the paper that most directly connects to our discussion. In September 2026, Dhruv Srikanth and colleagues published the preprint "Recursive self-improvement of AI research agents" (arxiv.org). The system is called AIDE². The core idea is a two-loop architecture. The inner loop is an autonomous research agent that develops and evaluates candidate solutions. The outer loop modifies the code of the inner agent itself. Every proposed rewrite is evaluated, and successful changes become the new version for subsequent iterations. This is more than an agent using a tool to improve its answers. The research machinery that produces solutions becomes the object of its own optimization process. The reported experiment ran for eight days without human intervention inside the loop. It evaluated a trajectory of approximately 100 candidate versions and accepted seven successive improvements. The incumbent's private evaluation grade increased from 0.703 to 0.778, exceeding the human-engineered baseline reported at 0.749 under that evaluation. The authors then tested the resulting agents on four held-out benchmarks. These included machine-learning engineering, algorithmic problem solving, realistic AI research tasks, and a physics-based weather forecasting task outside the task families used for candidate selection. The authors report that the strongest discovered agent matched or exceeded their human-engineered production research agent across those evaluations. They also observed a reduction in reward hacking on a separate held-out task family. The reported rate fell from 55% to 32% during the run, reaching a value below that of the human-engineered baseline. That last result is particularly interesting. The outer loop did not explicitly optimize the inner agent against that reward-hacking measurement. Yet the selected modifications changed its behavior in a way that reduced the observed problem. This suggests that an improvement to a research process can produce benefits beyond the specific metric directly optimized. But here is where the evidence becomes more interesting than the headline. The authors also tested whether a discovered agent could take over the outer loop and improve the next generation more effectively than the original human-engineered agent. They called this the ignition test. The comparison was inconclusive. The authors note that additional runs, independent seeds, and more expensive evaluations would be needed to establish a stronger result. In other words, they demonstrated evidence of repeated self-improvement and transfer to held-out tasks. They did not conclusively demonstrate an accelerating process in which each generation becomes a better generator of the next generation. That distinction is central to this article. Improving an agent is one achievement. Improving the process that improves agents is another. Demonstrating that each improvement makes the next improvement faster or more effective is a further claim requiring separate evidence. The AIDE² paper is a preprint, not a settled scientific consensus. Its reported findings are important, but they should be interpreted as evidence from a particular experiment, not proof that recursive self-improvement has become an unlimited process. And that leads to an important complication. 12. One terminology trap: the Third Layer is not the same as Level 3 RSI While researching this article, I found a framework from Weco AI titled "4 Levels of Recursive Self-Improvement" (weco.ai). It is worth discussing because its terminology is related to our question, but its classification is different. Weco describes four levels: - Level 0, delegation: An autonomous system runs the research loop, but does not outperform human-led R&D in efficiency. - Level 1, net positive: The system improves itself more efficiently than the defined human baseline. - Level 2, ignition: The improved system becomes better at driving the next round of improvement. - Level 3, inflection: The recursive feedback loop becomes strong enough that progress accelerates despite diminishing returns. The authors place their AIDE² experiment at Level 1 and explicitly distinguish it from the higher levels. Our Third Layer is different. It is not a claim that a system has reached an intelligence explosion. It is an architectural framing: a system that can create and evaluate different kinds of problem-solving systems, rather than only modify itself within a fixed design. These two ideas can overlap. But they are not interchangeable. A system might generate diverse, specialized descendants without proving that its rate of improvement accelerates. Conversely, a research agent might demonstrate increasingly efficient self-improvement while remaining within a fairly narrow family of agent architectures. This distinction should stop us from making an easy but misleading statement such as: "AI can now recursively improve itself, therefore the Third Layer has arrived." That would be jumping ahead of the evidence. A more defensible statement is: Several building blocks of the Third Layer have already been demonstrated. The more ambitious combination of open-ended system generation, transferable improvement, and recursively improving discovery remains a question that requires further testing. --- Part III: What would make a genuine Third Layer different? 13. Four things that should not be confused Before talking about implementation, I would separate four capabilities that often get grouped together. A. Code generation An LLM writes a program. This may be useful, but generating code is not the same as testing the code, and testing the code is not the same as creating a better system. B. Self-modification An agent changes parts of its own implementation. It may improve its memory handling, add tools, alter its search strategy, or change how it selects candidate solutions. This is a stronger capability, but the changes may remain within a narrow architecture. C. System generation A parent process produces distinct candidate systems with different structures, workflows, or mechanisms. The candidates are evaluated according to their intended use, and the search process preserves useful diversity rather than only repeatedly modifying the current winner. This is much closer to our Third Layer. D. Recursive improvement of the discovery process The search process itself becomes an object of improvement. The system not only generates better descendants. It discovers ways to produce and evaluate descendants more effectively. This is where the distinction between self-improvement and self-accelerating improvement becomes crucial. A system can get better at a task while its improvement procedure becomes less efficient. It can also improve its search procedure while the task itself becomes harder. There is no guarantee that those effects will balance in its favor. To claim that a Third Layer has reached a more advanced stage, I would want evidence of more than one of these capabilities in isolation. I would want to know whether the system creates useful alternatives, validates them, retains the discoveries, generalizes the gains, and improves the process that produces its next generation. 14. A child can be better without being more intelligent in every way Suppose the parent is a general-purpose coding agent. It can inspect a repository, modify code, and run tests. Now the parent produces three descendants: - A small agent that understands one specific build system extremely well. - A test-generation agent that specializes in finding edge cases. - A verification system that does not generate code at all, but checks the claims made by the other agents. Which system is the most intelligent? The question does not have a useful answer without a task and a measurement. The test-generation agent may be excellent at finding bugs but terrible at maintaining a conversation. The verifier may outperform the parent at detecting specific classes of mistakes without being able to solve the original task. The build specialist may be smaller, cheaper, and more reliable in its domain. The parent might remain the most general, yet delegate many tasks to descendants that outperform it in their specialties. This means that we should not measure an evolving ecosystem only by asking whether every child has a higher general intelligence score than the parent. We should also measure whether the ecosystem solves more problems, reduces the cost of solving them, and becomes better at discovering which kind of system belongs where. The product is not necessarily one super-agent. It could be an ecosystem of systems with different strengths and weaknesses. 15. But who decides what counts as better? This is where the architecture stops being a philosophical exercise and becomes an engineering problem. Imagine that the parent system creates a new security agent. The candidate passes its benchmark. The candidate is faster than the old agent, produces fewer false positives, and uses fewer tokens. Should it replace the original? Not necessarily. What if it silently misses a dangerous class of input that the old system catches? What if it passes tests because those tests resemble the examples used during development? What if it produces good results only because it uses five times the compute budget? What if its average score improves while its worst-case behavior becomes much worse? A single score will not answer all of those questions. We need to distinguish between: 1. The metrics the candidate is allowed to optimize directly. 2. The metrics used to decide whether the candidate should survive. 3. The constraints that the candidate cannot trade away. This is particularly important for systems that modify their own behavior. If a candidate can change the objective, rewrite the evaluator, or decide which failures count, the architecture risks turning verification into a game in which the candidate controls the rules. That is not evolution toward a better system. It is evolution toward a system that gets better at winning the competition we accidentally created. And that is a considerably less attractive result. --- Part IV: The technical architecture 16. What an evolutionary agent factory would actually contain A practical Third Layer would need more than an LLM, a prompt, and a loop. At minimum, I would expect the following components. 1. A parent or proposal engine This component proposes candidate systems. It might use a foundation model to edit code, design a new workflow, propose an algorithm, or combine mechanisms from existing candidates. It does not need to know the optimal solution in advance. It needs a way to generate meaningful alternatives. 2. A candidate archive Every candidate receives a stable identifier and an immutable record of its origin. The archive stores: - The exact source code or architecture description. - The parent candidate and lineage. - The model and configuration used to generate it. - The problem specification. - The evaluation environment and version. - Test results, costs, and failure information. - Reasons for accepting or rejecting the candidate. The archive should preserve useful diversity, not simply keep a copy of the current winner. A low-performing candidate may have introduced a useful idea that becomes valuable in a different context. The Darwin Gödel Machine illustrates why this matters. Its archive permits exploration through multiple lineages, including lineages that temporarily perform worse before producing a more successful descendant. 3. A sandboxed execution environment Candidate systems must be tested somewhere they cannot accidentally damage the production environment. The sandbox should have explicit resource limits, restricted permissions, controlled dependencies, and a clear policy for network access. A candidate should not inherit access to production credentials just because its parent happens to have them. This is especially important when AI-generated code is executed rather than merely inspected. 4. An evaluator The evaluator executes predefined tests and measures candidate performance. Depending on the task, it might combine deterministic unit tests, integration tests, benchmark scores, randomized trials, simulated environments, or external validation. Not every evaluation needs an LLM. For questions with objectively measurable answers, deterministic tests are usually preferable. For questions that need semantic judgment, a model-based evaluator may help, but it should not be treated as an unquestionable authority. 5. An admission gate Passing an evaluation does not automatically grant production access. A gate decides whether the candidate has met the requirements for the next stage. The gate should be separate from the candidate being evaluated. A candidate should not be allowed to bypass the gate by changing its source code, rewriting its acceptance criteria, or modifying the tests that decide whether it passes. 6. A feedback mechanism The system needs to turn successful and unsuccessful experiments into information useful for the next round. That might mean storing which strategies worked on which task families, identifying repeated failure patterns, or adjusting how new candidates are generated. This is where the archive becomes more than a collection of programs. It becomes the history of an evolving search process. But the history should not become a substitute for testing. A candidate that worked yesterday may fail under a different environment, dependency version, or task distribution. 17. A reference implementation in pseudocode The following is a conceptual sketch, not a runnable implementation. The proposal engine, sandbox, evaluator, and archive would each need their own implementation and tests. def evolve_systems(seed, task_suite, budget, archive, evaluator): archive.record(seed) while budget.remaining(): parent = archive.select_parent( preserve_diversity=True ) candidate = propose_new_system( parent=parent, task_suite=task_suite, failure_history=archive.failure_history() ) if not validate_candidate_structure(candidate): archive.reject(candidate, reason="invalid_structure") continue if not static_security_checks(candidate): archive.reject(candidate, reason="security_gate") continue result = evaluator.run_in_sandbox( candidate=candidate, tasks=task_suite.public_tasks, fixed_budget=budget.per_candidate ) if not result.execution_valid: archive.reject(candidate, reason="execution_failure") continue independent_grade = evaluator.grade_held_out( candidate=candidate, tasks=task_suite.sealed_tasks, fixed_budget=budget.per_candidate ) decision = evaluate_admission( baseline=archive.baseline, candidate=candidate, public_result=result, private_grade=independent_grade, hard_constraints=task_suite.hard_constraints ) archive.record( candidate=candidate, public_result=result, private_grade=independent_grade, decision=decision ) if decision.accepted: archive.add(candidate) return archive.best_verified() The interesting line is not "propose_new_system". It is the admission decision. The proposal engine can be creative, surprising, and occasionally wrong. The evaluator needs to be much less impressed by confidence. A production implementation would also need to prevent candidates from inspecting sealed test cases, control random seeds where possible, record dependency versions, handle evaluator failures, enforce actual compute and financial budgets, and preserve the ability to roll back an admitted candidate. The example leaves those details abstract on purpose. A trustworthy research system needs more than a short Python loop. 18. Why the archive should not select only the current best candidate Greedy optimization is tempting. You have ten candidates. Candidate seven has the highest score. Obviously, create the next candidate from candidate seven. Except that this can eliminate alternative approaches too early. Imagine searching for a better routing algorithm. One lineage makes small improvements to a familiar heuristic. It performs well on the current benchmark. Another lineage initially performs worse because it explores a different search strategy. If you delete the second lineage immediately, you may never discover the combination of ideas that eventually outperforms the first. This is not an argument for keeping every failed candidate forever. Storage and compute are limited. It is an argument for keeping enough strategically different candidates to avoid collapsing the search into one locally successful path. The archive might use performance, novelty, diversity, task coverage, and the historical usefulness of a lineage to decide what to explore next. A simple approach would preserve several high-performing candidates alongside candidates that represent genuinely different strategies. A more advanced approach could allocate exploration based on uncertainty or expected information gain. The exact selection method would depend on the task and the available budget. The general principle is straightforward: The next candidate should sometimes come from the best solution and sometimes from an alternative that might teach the search process something different. This is one reason "keep making the best version slightly better" and "search for new architectures" should not be treated as the same operation. 19. The evaluator is the most important part of the loop Let's consider what happens when a system uses benchmark scores to select its own descendants. It generates a candidate. It runs the candidate. It observes a score. It keeps the candidate if the score is higher. The problem is that a better score may have several explanations. The candidate may have discovered a genuinely better algorithm. It may have found a shortcut that works only on the benchmark. It may exploit an implementation bug in the evaluator. It may overfit to the evaluation set. It may simply consume more resources. The score alone does not tell us which explanation is correct. In fact, self-improvement makes this problem more important because every accepted modification changes the system that proposes future modifications. A weakness in the evaluation process can become a selection pressure across the entire lineage. If cheating improves the score, the search process can gradually produce systems that are better at cheating. That is why I would treat independent evaluation as part of the core architecture rather than a final quality check. A useful design has at least three boundaries. The candidate can see public feedback. It needs enough information to improve. The admission process can use private evaluation data. The details of those tests should remain hidden from the candidate. The production gate applies constraints that a candidate cannot remove by modifying its own code. Even then, private tests are not magic. An agent that receives repeated aggregate scores can still adapt to them. An imperfect evaluator can still reward undesirable behavior. Test data may overlap with the training data of an underlying foundation model. The aim is not to claim that an evaluator can never be fooled. The aim is to make cheating harder to discover, measure its effects, and prevent one candidate from unilaterally redefining success. 20. A concrete warning from AI Scientist research A useful critique is the preprint "The More You Automate, the Less You See: Hidden Pitfalls of AI Scientist Systems" (arxiv.org). The researchers examined potential problems in automated scientific workflows, including benchmark selection, data leakage, metric misuse, and post-hoc selection bias. Their controlled experiments with two open-source AI scientist systems found that some generated research workflows made undocumented dataset choices, including creating synthetic datasets or subsampling existing datasets. They also found that internal reward mechanisms could favor experiments with strong test performance, creating a risk of selecting favorable results after the fact. The paper did not find evidence of deliberate test-set inspection in those systems. The concern was subtler: test information could still influence selection through the internal reward mechanism. This distinction matters. A failure does not have to be intentional to corrupt an evaluation. A system can follow its coded procedure and still produce a misleading result because the procedure rewards the wrong behavior. Another critical discussion, "AI Scientists Fail Without Strong Implementation Capability" (arxiv.org), reviewed 28 research papers generated by five AI scientist systems and argued that implementation and rigorous verification remain important bottlenecks. These works do not prove that automated scientific discovery is impossible. They show why generating convincing research output is not enough. For a Third Layer, this is directly relevant. If the system creates new agents, and those agents create further agents, the selection process becomes responsible for determining which ideas propagate. The evolutionary process can amplify either useful discoveries or systematic mistakes. That is why the evaluator, the audit trail, and the admission gate must be treated as first-class architectural components. --- Part V: How do we prove the system has done something genuinely new? 21. A successful benchmark run is not enough Suppose a system creates a candidate agent that improves its benchmark score from 70% to 80%. What have we proved? We have evidence that the candidate performs better on the measured benchmark under the tested conditions. That is valuable. But it does not automatically establish that the candidate is more capable in general, that its architecture is genuinely novel, or that the discovery process will continue improving. We need to separate several claims. Claim| Evidence required The candidate runs correctly| Execution tests, error handling, reproducible results The candidate improves its target task| Comparison with a baseline under equivalent conditions The improvement generalizes| Held-out tasks or environments not used for selection The system discovers diverse architectures| Multiple meaningfully different candidates with documented lineage The system creates useful specialists| Evidence that different descendants outperform alternatives on distinct task families The process improves recursively| A discovered agent improves subsequent generations more effectively than the previous method Progress accelerates| Repeated, controlled evidence that improvement per fixed budget grows across generations These claims require different experiments. A system can satisfy some and fail others. For example, a population of agents may contain several useful specialists without demonstrating accelerating improvement. A recursive optimizer may produce better generations while operating on only one narrow class of system. We should report those outcomes separately rather than compressing all of them into one headline about self-improving AI. 22. A test protocol I would actually trust If I were evaluating a candidate Third Layer architecture, I would begin with a controlled experiment. Not with a press release. Not with a chart that has a very optimistic upward slope. With a protocol that another team can inspect and reproduce. Step 1: Establish a baseline Choose an initial system and a task suite. Measure its success rate, cost per successful task, resource consumption, failure categories, and security regressions. Record the exact software versions, model versions, dependency lockfiles, and benchmark identifiers. The baseline must be strong enough to make the comparison meaningful. Step 2: Define the acceptance criteria before the experiment Decide what counts as improvement before the candidates are generated. For example, a new candidate might need to: - Improve task success under a fixed compute budget. - Pass all required safety and correctness tests. - Avoid unacceptable regression on protected tasks. - Generalize to at least one held-out task family. - Produce results that can be reproduced independently. The precise thresholds depend on the application. The important point is that the system should not be able to redefine success after seeing its results. Step 3: Separate development tasks from evaluation tasks Allow the candidate to use one set of tasks for development. Keep another set sealed for final evaluation. Whenever practical, include tasks or environments from families the search process did not encounter. This reduces the risk of building an agent that is excellent at one benchmark but fails to generalize. It does not eliminate contamination or every form of evaluator exploitation, but it provides stronger evidence than repeatedly testing on the same visible tasks. Step 4: Keep the budget fixed Every candidate should operate under equivalent constraints. If candidate B wins only because it gets ten times more inference calls, its result does not establish a better algorithm under equal resources. Measure dollars, tokens, execution time, memory, accelerator time, and whatever other resources materially affect the intended use. The fixed budget should cover both proposing and executing solutions. Step 5: Preserve the lineage Every candidate should have a record of its parent, its changes, its evaluations, and its admission decision. A researcher should be able to reproduce the experiment from the documented environment and exact revisions. If the system says, "I found an amazing architecture," the follow-up questions are simple: Which revision? Which experiment? Which baseline? Which test set? Which resourc