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100 Claude Repos that will completely change your life: (save this) 1. Terminal AI coding agent github.com/anthropics/cla… 2. Ready-to-use starter apps github.com/anthropics/cla… 3. Official agent skills github.com/anthropics/ski… 4. Plugin marketplace github.com/anthropics/cla… 5. Full ecosystem github.com/orgs/anthropic… 6. Master list github.com/hesreallyhim/a… 7. 1000+ plugins github.com/quemsah/awesom… 8. Huge skills library github.com/sickn33/antigr… 9. Curated skills github.com/VoltAgent/awes… 10. Cross-platform skills github.com/alirezarezvani… 11. LLM pipelines github.com/langchain-ai/l… 12. Agent workflows github.com/langchain-ai/l… 13. Multi-agent systems github.com/microsoft/auto… 14. Team-based agents github.com/crewAIInc/crew… 15. AI dev team github.com/metaGPT/metaGPT 16. Code agents github.com/gpt-engineer-o… 17. Auto PR fixes github.com/sweepai/sweep 18. AI coding assistant github.com/continue-repl/… 19. Code search github.com/BloopAI/bloop 20. Agent standards github.com/agentprotocol/… 21. Productivity plugins github.com/anthropics/kno… 22. AI SDK github.com/vercel/ai 23. Memory layer github.com/upstash/contex… 24. Voice agents github.com/fixie-ai/ultra… 25. Deploy agents github.com/superagent-ai/… 26. Web agents github.com/xlang-ai/OpenA… 27. Reasoning agents github.com/ysymyth/ReAct 28. Long-term memory github.com/mem0ai/mem0 29. AI apps infra github.com/helixml/helix 30. API layer github.com/trpc/trpc 31. Clean UI github.com/ChatGPTNextWeb… 32. Self-hosted UI github.com/open-webui/ope… 33. Modern UI github.com/mckaywrigley/c… 34. Desktop app github.com/lencx/ChatGPT 35. Next.js clone github.com/Nutlope/chatGP… 36. Template github.com/vercel-labs/ai… 37. Claude-ready UI github.com/Yidadaa/ChatGP… 38. Minimal UI github.com/ivanfioravanti… 39. Lightweight UI github.com/louislam/ChatG… 40. Adaptable UI github.com/zk-ml/chatglm-… That's a wrap I hope you found this helpful Follow me @HeyZaraKhan for more....

Microsoft Senior AI developer just showed how they build AI agents with Claude at Microsoft. 34-minutes. free. By Microsoft team Opus 4.7 + 1,400+ pre-built MCP tools plug Claude into agent → give it tools → ship to production worth more than any $500 vibe-coding course.

A Claude Code skill bundle for bug hunting and external red-team work - 51 skills, 15 slash commands, 574+ disclosed-report patterns curated across 24 vulnerability classes, plus enterprise identity + infrastructure attack matrices. github.com/elementalsouls…

harden your github actions! - Use Static analysis for GHA: github.com/zizmorcore/ziz… - set locally: pnpm config set minimum-release-age 4320 # 3 days in minutes pnpm.io/supply-chain-s… for other package managers check: gist.github.com/mcollina/b294a… - add Socket Free Firewall when installing npm packages on CI #github-actions" target="_blank" rel="nofollow noopener">docs.socket.dev/docs/socket-fi…

We are investigating unauthorized access to GitHub’s internal repositories. While we currently have no evidence of impact to customer information stored outside of GitHub’s internal repositories (such as our customers’ enterprises, organizations, and repositories), we are closely monitoring our infrastructure for follow-on activity.

BAD NEWS: YOUR CHATGPT OR CLAUDE WRITING IS OBVIOUS Most people won't say it, But they spot it instantly. Here are 7 Claude Anti-AI Writing System Prompts that remove the AI fingerprints from your writing:

Cloudflare on their vulnerabilty discovery harness • Recon: Read the codebase, return an architecture doc • Hunt: ~50 agents look for bugs concurrently • Validate: Independent agents try to disprove findings • Gapfill: Areas that need a 2nd pass are flagged • Dedup: Findings with the same root cause combined • Trace: Confirms if attacker input reaches the bug • Feedback: If reachable, becomes new hunt tasks • Report: Write report with predefined schema blog.cloudflare.com/cyber-frontier…

Repo: github.com/FareedKhan-dev…

One of the most useful cybersecurity resource repositories I’ve seen lately. It covers everything from web security, bug bounty, Linux, networking, cloud, OSINT, reverse engineering, CTFs, tools, labs, and learning platforms, all organized properly in one place. A really good bookmark for anyone serious about learning cybersecurity or improving their skills. github.com/Striving-to-le… #CyberSecurity #BugBounty #EthicalHacking #InfoSec #Hacking #WebSecurity #OSINT #Linux #CTF #Programming

Stop wasting hours trying to learn AI. I have already done it for you. With one list. Zero confusion. And no fluff. 📹 Videos: 1. LLM Introduction: t.co/kyDon6qLrb 2. LLMs from Scratch: t.co/2hyMhuKoiI 3. Agentic AI Overview (Stanford): t.co/FXu6cAqITC 4. Building and Evaluating Agents: t.co/ZigR1tdOFL 5. Building Effective Agents: t.co/uYwfwO55mO 6. Building Agents with MCP: t.co/4arFTW1b3i 7. Building an Agent from Scratch: t.co/eOmveyM9Hz 8. Philo Agents: t.co/zLu7x1tx9m 🗂️ Repos 1. GenAI Agents: t.co/eXCl2YaRPv 2. Microsoft's AI Agents for Beginners: t.co/3CSW4zPAwf 3. Prompt Engineering Guide: t.co/GVzvxPYDVO 4. Hands-On Large Language Models: t.co/0rgDvhx3pI 5. AI Agents for Beginners: t.co/3CSW4zPAwf 6. GenAI Agents: lnkd.in/dEt72MEy 7. Made with ML: t.co/9z5KHF9DMe 8. Hands-On AI Engineering: t.co/dldAj5Xkr6 9. Awesome Generative AI Guide: t.co/U2WZhT4ERV 10. Designing Machine Learning Systems: t.co/sYAZX34YdQ 11. Machine Learning for Beginners from Microsoft: t.co/NjFxHbC9jZ 12. LLM Course: t.co/N34YTPu1OK 🗺️ Guides 1. Google's Agent Whitepaper: t.co/bW3Ov3vMW0 2. Google's Agent Companion: t.co/wredwWAbBA 3. Building Effective Agents by Anthropic: t.co/fxtE4alVrJ 4. Claude Code Best Agentic Coding practices: t.co/lLSwJ9pG7C 5. OpenAI's Practical Guide to Building Agents: t.co/xgkEIogGfh 📚 Books: 1. Understanding Deep Learning: t.co/CjcKpTemmV 2. Building an LLM from Scratch: t.co/DaWBxOx8o3 3. The LLM Engineering Handbook: t.co/ZA1n0N41Mf 4. AI Agents: The Definitive Guide - Nicole Koenigstein: t.co/boLkl1VlKb 5. Building Applications with AI Agents - Michael Albada: t.co/H1Xf5EkJLL 6. AI Agents with MCP - Kyle Stratis: t.co/JI3ELQZE6a 7. AI Engineering: t.co/Xk0JzMIf7o 📜 Papers 1. ReAct: t.co/QNqE4UU55w 2. Generative Agents: t.co/CwEpoJgY1U 3. Toolformer: t.co/5m9xZd5teZ 4. Chain-of-Thought Prompting: t.co/KjVlgdWi77 🧑🏫 Courses: 1. HuggingFace's Agent Course: t.co/7FSUYKxIdG 2. MCP with Anthropic: t.co/IkZGiWm2yS 3. Building Vector Databases with Pinecone: t.co/2YRoMfLdXd 4. Vector Databases from Embeddings to Apps: t.co/23A50ixbHJ 5. Agent Memory: t.co/uc3L9BrNF7 Follow @ajitcodes for more!! 👇 Comment “AI” for more resources Repost for your network ♻️ Bookmark for future.

The most complete list of LLM vulnerability research for cybersecurity I've found: github.com/tmylla/Awesome… It covers threat intelligence, NIDS rule labelling, vulnerability detection, code repair, and jailbreak defenses etc.

This is the most underrated AI course on the internet and its taught at Stanford and it costs zero dollars. It's called CS336 and Stanford is teaching students to build a language model from absolute scratch. Here's the actual curriculum: → Module 1: Tokenization, byte-pair encoding, and vocabulary design → Module 2: Transformer architecture written from raw PyTorch → Module 3: Distributed training and the pretraining loop → Module 4: Scaling laws and data curation → Module 5: Supervised fine-tuning and instruction following → Module 6: RLHF, DPO, and preference optimization → Module 7: Inference, KV caching, speculative decoding, and serving → Module 8: Evaluation, red teaming, and capability measurement Every lecture is on YouTube. Every slide deck is downloadable. Every assignment is open. Taught by Percy Liang and Tatsu Hashimoto. Two of the most cited researchers in the field of LLMs working today. cs336.stanford.edu/spring2025 / youtube.com/playlist?list=… If you finish this course, you understand how language models work at every layer from raw bytes to RLHF. That is the difference between an AI engineer and an AI tourist. Save this. Pick the first lecture tonight.

🚨 BREAKING: NotebookLM can now tutor you like a $150/hr private tutor from any top university. For free. Here are 8 prompts that replace hours of paid tutoring sessions: [Save this before it goes viral]

Simulates LLM attacks locally with 50+ vulnerabilities github.com/confident-ai/d…

Finding Zero-Days With Any Model Blog: provos.org/p/finding-zero… Author: Niels Provos

Introducing deepsec, an open source coding security harness. • CLI-first • Sandbox-based scaling • Pluggable coding agents • Designed for large-scale repos • Use AI Gateway or your own subscription After months of successful internal use, we put it to the test on some of the largest open source codebases. vercel.com/blog/introduci…

Learn Claude Code- youtube.com/playlist?list=…

An MIT professor taught the same math course for 62 years, and the day he retired, students from every country on earth showed up online to watch him give his final lecture. I opened the playlist at 2am and ended up watching three of them back to back. His name is Gilbert Strang. The course is MIT 18.06 Linear Algebra. Every machine learning engineer, every data scientist, every quant, every self-taught programmer who actually understands how AI works learned the math from this one man. Most of them never set foot on MIT's campus. They just opened a free playlist on YouTube and let him teach. Here's the story almost nobody tells you. Strang joined the MIT math faculty in 1962. He retired in 2023. That is 61 years of standing at the same chalkboard teaching the same subject to 18-year-olds. The interesting part is what he did when MIT launched OpenCourseWare in 2002. Most professors were skeptical. They worried that putting their lectures online would make their classrooms irrelevant. Strang did not hesitate. He said his life's mission was to open mathematics to students everywhere. He filmed every lecture and gave it away. The decision quietly changed how the world learns math. For decades linear algebra was taught the wrong way. Professors started with abstract vector spaces and proofs about field axioms. Students drowned in the abstraction. Most never recovered. They walked out believing they were bad at math when they had simply been taught in an order that nobody's brain is built to absorb. Strang inverted the entire curriculum. He started with matrix multiplication. Something you can write down on paper. Something you can compute by hand. Something you can see. Then he showed his students that everything else in linear algebra eigenvectors, singular value decomposition, orthogonality, the four fundamental subspaces was just a different lens for understanding what the matrix was actually doing under the hood. His rule was strict. If a student could not explain a concept using a concrete 3 by 3 example, that student did not actually understand the concept yet. The abstraction was supposed to come last, not first. The intuition was the foundation. The proofs were just confirmation that the intuition was correct. The second thing Strang changed was the classroom itself. He said please and thank you to his students. Every single lecture. He paused mid-derivation to ask "am I OK?" to check if anyone was lost. He never used the word "obviously" or "trivially" because he knew exactly what those words do to a student who is one step behind. He treated 19-year-olds learning math for the first time the way he treated his own colleagues. With patience. With respect. With the assumption that they belonged in the room. For 62 years. The result is something that has never happened in the history of education. A single math professor became the default teacher of his subject for the entire planet. Universities in India, China, Brazil, Nigeria, every country with a computer science department, started telling their own students to just watch Strang's lectures. The University of Illinois revised its linear algebra course to do almost no in-person lecturing. The reason was honest. The professor said they could not compete with the videos. His final lecture was in May 2023. The auditorium was packed with students who had never met him before. He walked to the chalkboard, taught for an hour, and at the end the entire room stood and applauded. He looked confused for a moment, like he genuinely did not understand why they were cheering. Then he smiled and waved them off and walked out. His written comment under the YouTube video of that final lecture was four sentences long. He said teaching had been a wonderful life. He said he was grateful to everyone who saw the importance of linear algebra. He said the movement of teaching it well would continue because it was right. That was it. No book promotion. No farewell speech. No legacy management. The man whose teaching is the foundation of modern AI just thanked the audience and went home. 20 million views. Zero ego. The entire engine of the AI revolution sits on top of math that millions of people learned for free from one quiet professor in Cambridge. The course is still on MIT OpenCourseWare. Every lecture, every problem set, every exam, every solution. Free. The most important math course of the 21st century is sitting one click away from you. Most people will never open it.

Interactive codebase knowledge graphs github.com/Lum1104/Unders…

People have been paying $500 private tutors. But Google's NotebookLM can replace them for free, and almost no student is using it right. Here are 6 prompts that turn it into your personal exam-crushing coach: 📌 Save this before finals week