An experience of a lifetime when you end something significant! PhD done :) Thank you everyone, thanks to my supervisors and the committee for this awesome experience! Especially, my supervisors made this time period a memorable one which will be etched in my memory forever.

🚨 BREAKING: Research just became 10x faster. Claude can now turn dozens of academic papers into clear, structured insights — like a top-tier researcher. No more overwhelm. Just clarity. Here are 9 prompts to get straight to the point 👇 Bookmark this 🔖

THE GUY WHO WON ANTHROPIC'S HACKATHON JUST GAVE AWAY HIS ENTIRE CLAUDE CODE PLAYBOOK FOR FREE. 10 MONTHS OF WORK, ALL PUBLIC Affaan Mustafa won the Anthropic x Forum Ventures hackathon by building a full startup in 8 hours with Claude Code. Then he open-sourced the exact setup that did it. It's called Everything Claude Code, and it turns Claude from one assistant into an entire engineering team Repo: affaan-m/ecc This isn't a prompt pack. It's a system he refined over 10 months of daily use shipping real products What's inside: A huge library of skills, dozens of specialized subagents, and ready-made commands, all working together. Each piece does one job. One subagent reviews security against OWASP standards. One optimizes memory so Claude stops forgetting earlier decisions around hour three. One learns from your past sessions and projects so the setup gets smarter the more you use it. Others handle planning, test-driven development, and language-specific code review Instead of one assistant writing code, you get an orchestrated team. A main session delegates to the right specialist when the task calls for it, the way a real dev team splits work The best part: it's not locked to one tool. It runs in Claude Code, Cursor, Codex and OpenCode, across Windows, Mac and Linux. Free, MIT licensed This is the difference between using Claude like a search box and running it like a team that ships. The guy spent 10 months figuring out what actually works so you don't have to Bookmark this

Claude Code can now run an entire PhD-level research pipeline by itself. it runs a 10-stage workflow from blank page to publication-ready PDF, replacing the work of a PhD advisor, three peer reviewers, and a copy editor in one repo. → Deep research with 13 agents (PRISMA systematic review) → 12 agents write the paper section by section → 5-person peer review (Editor 3 Reviewers Devil's Advocate) → Integrity agent catches fabricated citations stat errors → Final output: LaTeX → PDF, ready to submit After the paper is finalized, it runs a Collaboration Quality Evaluation that scores YOU, across 6 dimensions, 1–100. Direction setting, intellectual contribution, quality gatekeeping. It tells you exactly where you were the bottleneck. Drop it into .claude/skills/ and the whole pipeline auto-loads. Works in Claude Code, Cowork, and as a Claude Project. 100% open source. CC-BY-NC 4.0.

10 WEBSITES EVERY STUDENT SHOULD USE BEFORE GRADUATION. Bookmark every single one. Your university will never tell you about most of these. 1. notebooklm.google Upload every textbook, lecture, and PDF for a course. Ask questions across all of them. Built by Google DeepMind. 2. sci-bot.ru Search scientific research fast. Ask any question and get answers with links to relevant papers in seconds. 3. annas-archive.gl The world's largest open library. Almost any textbook your professor assigned is on here for free. 4. perplexity.ai Research assistant that cites every source. Replaces 90% of Google searches for academic work. 5. zotero.org Free reference manager that builds your bibliography automatically. Saves 20 hours per semester. 6. wolframalpha.com Solves math, physics, chemistry, and engineering problems step by step. Shows the full working. 7. handshake.com The job platform built specifically for students. 1.4 million employers actively recruiting on it right now. 8. fastweb.com Matches you to scholarships you actually qualify for. Over $3.4 billion awarded to students every year. 9. coursera.org/learn/learning-… Free Barbara Oakley course taken by 4 million people. The science of how to actually study and remember. 10. linkedin.com/learning Free with most university logins. 16,000 courses on everything from Excel to AI engineering. The students who graduate with the biggest head start are not smarter. They just found the right tools earlier.

Anthropic Academy just dropped FREE AI courses that could replace a $10,000 degree. $0. No catch. No gatekeeping. Here are 6 AI courses that could separate you from everyone else in 2026:

🚨Breaking: Someone open sourced a knowledge graph engine for your codebase and it's terrifying how good it is. It's called GitNexus. And it's not a documentation tool. It's a full code intelligence layer that maps every dependency, call chain, and execution flow in your repo -- then plugs directly into Claude Code, Cursor, and Windsurf via MCP. Here's what this thing does autonomously: → Indexes your entire codebase into a graph with Tree-sitter AST parsing → Maps every function call, import, class inheritance, and interface → Groups related code into functional clusters with cohesion scores → Traces execution flows from entry points through full call chains → Runs blast radius analysis before you change a single line → Detects which processes break when you touch a specific function → Renames symbols across 5 files in one coordinated operation → Generates a full codebase wiki from the knowledge graph automatically Here's the wildest part: Your AI agent edits UserService.validate(). It doesn't know 47 functions depend on its return type. Breaking changes ship. GitNexus pre-computes the entire dependency structure at index time -- so when Claude Code asks "what depends on this?", it gets a complete answer in 1 query instead of 10. Smaller models get full architectural clarity. Even GPT-4o-mini stops breaking call chains. One command to set it up: `npx gitnexus analyze` That's it. MCP registers automatically. Claude Code hooks install themselves. Your AI agent has been coding blind. This fixes that. 9.4K GitHub stars. 1.2K forks. Already trending. 100% Open Source. (Link in the comments)

the polymath is not a person who collects hobbies. it is a person who refuses artificial borders. • art and science • engineering and philosophy • history and design for most of history, these were not separate worlds. the polymath by peter burke shows that broad thinkers shaped culture by moving between domains. they borrowed methods translated ideas connected distant fields specialists optimize inside systems. polymaths redesign the map itself. in an age of narrow expertise, synthesis becomes rare leverage.

🚨 The creator of Claude Code just shared a full walkthrough on how to actually use it the right way. 30 minutes. Free. Straight from the person who built it. Watch the workshop and save it for later. You’ll likely get more practical value from this than from most expensive coding courses online. Most people are only scratching the surface of what Claude Code can do. Then check out the guide below.

100 Claude Repos that will completely change your life: (save this) 1. Terminal AI coding agent github.com/anthropics/claude… 2. Ready-to-use starter apps github.com/anthropics/claude… 3. Official agent skills github.com/anthropics/skills 4. Plugin marketplace github.com/anthropics/claude… 5. Full ecosystem github.com/orgs/anthropics/r… 6. Master list github.com/hesreallyhim/awes… 7. 1000 plugins github.com/quemsah/awesome-c… 8. Huge skills library github.com/sickn33/antigravi… 9. Curated skills github.com/VoltAgent/awesome… 10. Cross-platform skills github.com/alirezarezvani/cl… 11. LLM pipelines github.com/langchain-ai/lang… 12. Agent workflows github.com/langchain-ai/lang… 13. Multi-agent systems github.com/microsoft/autogen 14. Team-based agents github.com/crewAIInc/crewAI 15. AI dev team github.com/metaGPT/metaGPT 16. Code agents github.com/gpt-engineer-org/… 17. Auto PR fixes github.com/sweepai/sweep 18. AI coding assistant github.com/continue-repl/con… 19. Code search github.com/BloopAI/bloop 20. Agent standards github.com/agentprotocol/age… 21. Productivity plugins github.com/anthropics/knowle… 22. AI SDK github.com/vercel/ai 23. Memory layer github.com/upstash/context7 24. Voice agents github.com/fixie-ai/ultravox 25. Deploy agents github.com/superagent-ai/sup… 26. Web agents github.com/xlang-ai/OpenAgen… 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/Ne… 32. Self-hosted UI github.com/open-webui/open-w… 33. Modern UI github.com/mckaywrigley/chat… 34. Desktop app github.com/lencx/ChatGPT 35. Next.js clone github.com/Nutlope/chatGPT-c… 36. Template github.com/vercel-labs/ai-ch… 37. Claude-ready UI github.com/Yidadaa/ChatGPT-N… 38. Minimal UI github.com/ivanfioravanti/ch… 39. Lightweight UI github.com/louislam/ChatGPT-… 40. Adaptable UI github.com/zk-ml/chatglm-web 👇

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: lnkd.in/dMqbaZdK 2. LLMs from Scratch: lnkd.in/dYYwEhYy 3. Agentic AI Overview (Stanford): lnkd.in/dArmMt2i 4. Building and Evaluating Agents: lnkd.in/dBWd2W8u 5. Building Effective Agents: lnkd.in/dHfdebqw 6. Building Agents with MCP: lnkd.in/dXuNHrRJ 7. Building an Agent from Scratch: lnkd.in/da3ANw3w 8. Philo Agents: lnkd.in/dq-BfZE5 🗂️ Repos 1. GenAI Agents: lnkd.in/d3UDtwwv 2. Microsoft's AI Agents for Beginners: lnkd.in/dHvTmJnv 3. Prompt Engineering Guide: lnkd.in/gJjGbxQr 4. Hands-On Large Language Models: lnkd.in/dxaVF86w 5. AI Agents for Beginners: lnkd.in/dHvTmJnv 6. GenAI Agentshttps://lnkd.in/dEt72MEy 7. Made with ML: lnkd.in/d2dMACMj 8. Hands-On AI Engineering:lnkd.in/dgQtRyk7 9. Awesome Generative AI Guide: lnkd.in/dJ8gxp3a 10. Designing Machine Learning Systems: lnkd.in/dEx8sQJK 11. Machine Learning for Beginners from Microsoft: lnkd.in/dBj3BAEY 12. LLM Course: lnkd.in/diZgGACG 🗺️ Guides 1. Google's Agent Whitepaper: lnkd.in/gFvCfbSN 2. Google's Agent Companion: lnkd.in/gfmCrgAH 3. Building Effective Agents by Anthropic: lnkd.in/gRWKANS4. 4. Claude Code Best Agentic Coding practices: lnkd.in/gs99zyCf 5. OpenAI's Practical Guide to Building Agents: lnkd.in/guRfXsFK 📚Books: 1. Understanding Deep Learning: lnkd.in/dgcB68Qt 2. Building an LLM from Scratch: lnkd.in/g2YGbnWS 3. The LLM Engineering Handbook: lnkd.in/gWUT2EXe 4. AI Agents: The Definitive Guide - Nicole Koenigstein: lnkd.in/dJ9wFNMD 5. Building Applications with AI Agents - Michael Albada: lnkd.in/dSs8srk5 6. AI Agents with MCP - Kyle Stratis: lnkd.in/dR22bEiZ 7. AI Engineering: lnkd.in/gi-mQcXa 📜 Papers 1. ReAct: lnkd.in/gRBH3ZRq 2. Generative Agents: lnkd.in/gsDCUsWm. 3. Toolformer: lnkd.in/gyzrege6 4. Chain-of-Thought Prompting: lnkd.in/gaK5CXzD. 🧑🏫 Courses: 1. HuggingFace's Agent Course: lnkd.in/gmTftTXV 2. MCP with Anthropic: lnkd.in/geffcwdq 3. Building Vector Databases with Pinecone: lnkd.in/gCS4sd7Y 4. Vector Databases from Embeddings to Apps: lnkd.in/gm9HR6_2 5. Agent Memory: lnkd.in/gNFpC542 Repost for your network ♻️

🚨 BREAKING: Someone just built the exact tool Andrej Karpathy said someone should build. 48 hours after Karpathy posted his LLM Knowledge Bases workflow, this showed up on GitHub. It's called Graphify. One command. Any folder. Full knowledge graph. Point it at any folder. Run /graphify inside Claude Code. Walk away. Here is what comes out the other side: -> A navigable knowledge graph of everything in that folder -> An Obsidian vault with backlinked articles -> A wiki that starts at index. md and maps every concept cluster -> Plain English Q&A over your entire codebase or research folder You can ask it things like: "What calls this function?" "What connects these two concepts?" "What are the most important nodes in this project?" No vector database. No setup. No config files. The token efficiency number is what got me: 71.5x fewer tokens per query compared to reading raw files. That is not a small improvement. That is a completely different paradigm for how AI agents reason over large codebases. What it supports: -> Code in 13 programming languages -> PDFs -> Images via Claude Vision -> Markdown files Install in one line: pip install graphify && graphify install Then type /graphify in Claude Code and point it at anything. Karpathy asked. Someone delivered in 48 hours. That is the pace of 2026. Open Source. Free.

I made a Claude Code skill that turns any arxiv paper into working code. Every line traces back to the paper section it came from & any implementation detail the paper skips will be flagged, and not assumed. open sourcing it - github.com/PrathamLearnsToCo…

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: lnkd.in/dMqbaZdK 2. LLMs from Scratch: lnkd.in/dYYwEhYy 3. Agentic AI Overview (Stanford): lnkd.in/dArmMt2i 4. Building and Evaluating Agents: lnkd.in/dBWd2W8u 5. Building Effective Agents: lnkd.in/dHfdebqw 6. Building Agents with MCP: lnkd.in/dXuNHrRJ 7. Building an Agent from Scratch: lnkd.in/da3ANw3w 8. Philo Agents: lnkd.in/dq-BfZE5 🗂️ Repos 1. GenAI Agents: lnkd.in/d3UDtwwv 2. Microsoft's AI Agents for Beginners: lnkd.in/dHvTmJnv 3. Prompt Engineering Guide: lnkd.in/gJjGbxQr 4. Hands-On Large Language Models: lnkd.in/dxaVF86w 5. AI Agents for Beginners: lnkd.in/dHvTmJnv 6. GenAI Agentshttps://lnkd.in/dEt72MEy 7. Made with ML: lnkd.in/d2dMACMj 8. Hands-On AI Engineering:lnkd.in/dgQtRyk7 9. Awesome Generative AI Guide: lnkd.in/dJ8gxp3a 10. Designing Machine Learning Systems: lnkd.in/dEx8sQJK 11. Machine Learning for Beginners from Microsoft: lnkd.in/dBj3BAEY 12. LLM Course: lnkd.in/diZgGACG 🗺️ Guides 1. Google's Agent Whitepaper: lnkd.in/gFvCfbSN 2. Google's Agent Companion: lnkd.in/gfmCrgAH 3. Building Effective Agents by Anthropic: lnkd.in/gRWKANS4. 4. Claude Code Best Agentic Coding practices: lnkd.in/gs99zyCf 5. OpenAI's Practical Guide to Building Agents: lnkd.in/guRfXsFK 📚Books: 1. Understanding Deep Learning: lnkd.in/dgcB68Qt 2. Building an LLM from Scratch: lnkd.in/g2YGbnWS 3. The LLM Engineering Handbook: lnkd.in/gWUT2EXe 4. AI Agents: The Definitive Guide - Nicole Koenigstein: lnkd.in/dJ9wFNMD 5. Building Applications with AI Agents - Michael Albada: lnkd.in/dSs8srk5 6. AI Agents with MCP - Kyle Stratis: lnkd.in/dR22bEiZ 7. AI Engineering: lnkd.in/gi-mQcXa 📜 Papers 1. ReAct: lnkd.in/gRBH3ZRq 2. Generative Agents: lnkd.in/gsDCUsWm. 3. Toolformer: lnkd.in/gyzrege6 4. Chain-of-Thought Prompting: lnkd.in/gaK5CXzD. 🧑🏫 Courses: 1. HuggingFace's Agent Course: lnkd.in/gmTftTXV 2. MCP with Anthropic: lnkd.in/geffcwdq 3. Building Vector Databases with Pinecone: lnkd.in/gCS4sd7Y 4. Vector Databases from Embeddings to Apps: lnkd.in/gm9HR6_2 5. Agent Memory: lnkd.in/gNFpC542 Repost for your network ♻️

Sebastian Raschka is one of the most respected researchers in ML/AI education. Period. And now he's done something quietly brilliant. He built an LLM Architecture Gallery - a single, browsable reference that maps out the internal architecture of every major open-weight model released in the last few years. This is a serious research artifact, made free for everyone. Here's what's inside: 🔹 GPT-2 XL (1.5B) 🔹 Llama 3 (8B) 🔹 OLMo 2 (7B) 🔹 Llama 3.2 (1B) 🔹 Qwen3 (4B, 8B, 32B) 🔹 DeepSeek V3/R1 (671B) 🔹 Kimi K2 (1 Trillion) 🔹 Gemma 3 (4B, 27B, 270M) 🔹 Mistral 3.1 Small (24B) & Mistral Large (673B) 🔹 Llama 4 Maverick (400B) 🔹 Qwen3 235B-A22B & Qwen3 Coder Flash 🔹 SmolLM (1B) 🔹 GPT-OSS (20B, 120B) 🔹 Grok 2.5 (270B) 🔹 GLM-4.5 (355B), GLM-5 (744B), GLM-4.7 (355B) 🔹 MiniMax-M2 (230B) & MiniMax-M2.5 🔹 Kimi Linear (48B-A3B) 🔹 OlMo 3 (7B) & OlMo 3 (32B) 🔹 Nemotron 3 Nano (20B-A3B) & Nemotron 3 Super 🔹 Xiaomi MiMo-V2-Flash (309B) 🔹 Arcee AI Trinity Large (400B) 🔹 Tiny Aya (3.35B) 🔹 Step 3.5 Flash (196B) 🔹 Nanbeige (4.1, 3B) 🔹 Qwen3.5 (997B) 🔹 Ling 2.5 (1T) 🔹 Sarvam (30B, 105B) And for each model, he links: → The original tech report → The config[.]json (so you can verify every number yourself) → From-scratch implementations where available But here's what makes it truly special. He also added short concept explainers, so you're not just staring at boxes and arrows: → GQA (Grouped Query Attention) → MLA (Multi-head Latent Attention) → SWA (Sliding Window Attention) → QK-Norm → NoPE (No Positional Encoding) → Gated DeltaNet This is the kind of resource that used to require buying 3 textbooks, reading 40 papers, and spending a weekend. Now it's one link. If you're studying LLMs, building on top of them, or just trying to understand how the field has evolved, this is a must-bookmark.

🚨 Professors are going to hate this. Someone just open sourced an AI that writes research papers from idea to publication. Conference-ready. Citation-verified. Free. It's called Claude Scholar. An AI-powered research system that handles every step of the academic workflow. Idea to publication. Fully automated. No advisor at 2am. No staring at blank LaTeX files. No crying during rebuttal season. Here's what's inside this thing: → AI brainstorms research topics, reviews literature, and finds gaps nobody has explored → Runs statistical analysis on your experiments. t-tests, ANOVA, ablation studies. Publication-ready figures. → Writes your paper section by section. Abstract to conclusion. Conference-formatted. → Verifies every citation through multi-layer validation so AI never hallucinates a reference → Strips robotic AI language and adds human voice so reviewers can't tell → Self-reviews your draft with a 6-point quality checklist before you submit → Parses reviewer comments, classifies each one, and drafts your entire rebuttal Here's the wildest part: It supports NeurIPS, ICML, ICLR, ACL, AAAI, Nature, Science, Cell, and PNAS. Downloads the conference template, strips sample content, and gives you a clean LaTeX structure ready to write into. The creator says it covers 90% of the academic research lifecycle. Research assistants charge $30 to $60/hour. Conference paper consultants charge $5,000 . Graduate programs cost $50K/year. This is free. All of it. 100% Open Source.

If you want to learn AI the right way, start here. No shortcuts. No hype. No fluff. Top 10 Stanford's Courses on AI & ML. CS221: Artificial Intelligence CS229: Machine Learning CS229M: Machine Learning Theory CS230: Deep Learning CS234: Reinforcement Learning CS224N: Natural Language Processing CS231N: Deep Learning for Computer Vision CME295: Large Language Models (LLMs) CS236: Deep Generative Models CS336: Language Modeling from Scratch

BREAKING: MIT just mass released their Al library for free. (Links included) I went through these and honestly... this is better than most paid courses I've seen. Here's the full list of books: Foundations 1. Foundations of Machine Learning Core algorithms explained. Theory meets practice. 2. Understanding Deep Learning Neural networks demystified. Visual explanations included. 3. Machine Learning Systems Production-ready architecture. System design principles. Advanced Techniques 4. Algorithms for ML Computational thinking simplified. Decision-making frameworks. 5. Deep Learning The definitive textbook. Covers everything deeply. Reinforcement Learning 6. RL Basics (Sutton & Barto) The classic. Agent training fundamentals. 7. Distributional RL Beyond expected rewards. Advanced theory. 8. Multi-Agent Systems Agents working together. Coordination and competition. 9. Long Game Al Strategic agent design. Future-focused thinking. Ethics & Probability 10. Fairness in ML Bias detection. Responsible Al practices. 11. Probabilistic ML (Part 1 & 2) Links: lnkd.in/gkuXuexa Most people pay thousands for bootcamps that teach half of this. Bookmark it. Start anywhere. Just start. Repost for others Follow for more insights on Al Agents. MIT's books on Al Foundations 1. Foundations of Machine Learning - lnkd.in/gytjT5HC 2. Understanding Deep Learning - lnkd.in/dgcB68Qt 3. Machine Learning Systems - lnkd.in/dkiGZisg Advanced Techniques 4. Algorithms for ML - algorithmsbook.com 5. Deep Learning - lnkd.in/g2efT6DK Reinforcement Learning 6. RL Basics (Sutton & Barto) - lnkd.in/guxqxcZZ 7. Distributional RL - lnkd.in/d4eNP-pe 8. Multi-Agent Systems - marl-book.com 9. Long Game Al - lnkd.in/g-WtzvwX Ethics & Probability 10. Fairness in ML - fairmlbook.org 11. Probabilistic ML (Part 1) - lnkd.in/g-isbdjj 12. Probabilistic ML (Part 2) - lnkd.in/gJE9fy4w

Best YouTube Channels To Learn AI in 2026 (No BS) 1. Fundamentals – 3Blue1Brown 2. Deep Learning – Andrej Karpathy 3. AI Research – Yannic Kilcher 4. Practical AI – AssemblyAI 5. LLMs – AI Explained 6. ML Theory – StatQuest 7. Papers Simplified – Two Minute Papers 8. GenAI – Matthew Berman 9. AI Agents – Nicholas Renotte 10. Applied ML – Krish Naik 11. PyTorch – Aladdin Persson 12. Math for ML – Serrano Academy 13. Industry Insights – Lex Fridman 14. Real-world AI – DeepLearningAI

Automates poster generation from papers github.com/Paper2Poster/Pape…

🚨 BREAKING: India’s AI startup Sarvam AI has open-sourced its 30B and 105B AI models for developers.