karan

@elonmusk @ericmetaxas Remember the dialogue from his film : “ You either die a hero or live long enough to become a villain”.

@ericmetaxas Chris Nolan has shown total contempt for the Greek people

The Odyssey is one of the greatest works of literature in history. As a lover of Western Civilization -- and as the proud son of a Greek immigrant -- I denounce Nolan's sacrilege in perverting this great epic with DEI & trans lunacy. Shame on him & shame on Hollywood.

@mmonis Agreed ! What do you think $NOK is it in your LT account?I know low adr but recently has got traction!

Too many opportunities for Quick 5%. If you can size up, you can move the account very fast.

Anthropic Head of Product just dropped a 28-minute masterclass on how to put agents into production with real-world use cases. 28 minutes. free. by the person who built it. prompt caching → tool search →programmatic tool calling → сompaction → advisor strategy. this will replace you 100 YouTube videos on agent building.

If you want to master RAG in 2026. Build these projects. Project 1: PDF Chat Application Upload PDFs, ask questions, retrieve context-aware answers, citations, semantic search. Project 2: AI Research Assistant Search across multiple documents, summarize findings, generate references, compare sources. Project 3: YouTube Q&A Engine Convert videos into searchable knowledge bases with transcript retrieval and timestamp citations. Project 4: AI Customer Support Bot Connect company docs, FAQs and support history with contextual responses and memory. Project 5: GitHub Codebase Assistant Ask questions across repositories, retrieve relevant code snippets, explain architecture. Project 6: Multi-Modal RAG System Combine PDFs, images, audio and databases into one unified retrieval pipeline. Project 7: Personal Knowledge OS Build your own second brain with notes, bookmarks, search and AI memory retrieval. Project 8: Agentic RAG Workflow Create autonomous agents that search, retrieve, reason and execute multi-step tasks. Project 9: Enterprise Search Engine Slack, Notion, Google Drive, Confluence retrieval with permission-aware search systems. Project 10: Medical/Legal RAG Assistant Domain-specific retrieval with hallucination reduction, citations and verification pipelines. Most people consume AI content. Builders ship systems.

Here are 10 websites that print money while you sleep. 1. Carrd.co Build a one-page website in 20 minutes. Sell it on Flippa for $500-$2000. People pay this much because they cannot do it themselves. 2. Gumroad.com Upload one PDF. One Notion template. One prompt pack. It sells at 3am while you are dreaming. Zero inventory. Zero shipping. 3. Systeme.io Free funnel builder. Build it once. Run ads to it. Wake up to Stripe notifications. The funnel does not sleep. 4. Payhip.com Sells digital products in 190 countries. Handles VAT automatically. You upload. They handle the tax nightmare. You keep the money. 5. Ko-fi.com 0% platform fee on tips and memberships. Creators are pulling $3-8k/month here while everyone else fights for scraps on Patreon. 6. Sellfy.com Print on demand + digital products in the same dashboard. Design a t-shirt at midnight. It ships itself for the next 5 years. 7. Teachable.com Record one course. Sell it 10,000 times. One creator I follow made $47k from a course she recorded in a weekend two years ago. 8. Beehiiv.com Newsletter platform with a built-in ad network. They place sponsors INTO your newsletter automatically. You write. They sell. You cash out. 9. Lemonsqueezy.com Stripe + Paddle had a baby. Handles global tax compliance so you can sell SaaS to anyone on earth without touching a lawyer. 10. Whop.com Sell access to a Discord, a Telegram, a course, a community. Recurring revenue. The kind that hits your account before your alarm goes off. The difference between people who make money online and people who don't is not talent. It is picking one of these and shipping this week. Save this. Share it with someone who needs it.

You will be a world-class backend engineer once you finish watching all these courses:

It is dangerously easy to build a neural network today without actually understanding how it works. We live in an era of 'import torch'. You can train a model in three lines of code, but the moment you need to debug a collapsing loss function or a vanishing gradient, syntax won't save you. You need first principles. I recently went through this notebook collection by Simon J.D. Prince, and it is the antidote to tutorial hell. Instead of just showing you the code, it forces you to visualize the mechanics: 1./ The Math => It builds the intuition for shallow networks and regions before adding complexity. 2./ The Optimization => It doesn't just use an optimizer; it compares Line Search, SGD, and Adam so you see why they behave differently. 3./ The Modern Stack => It connects the dots from basic backpropagation all the way to Self-Attention and Graph Neural Networks. Move from running code to engineering systems => this is a goldmine.

@mmonis @jtsla4 Do you use blademap did it help?

@jtsla4 You can go to college, have fun, and still use BladeMap. College days create memories; BladeMap may help create wealth. Not interchangeable.. but I get the context

Why spend $100k and 4 years in college then struggle to get a job when you can spend $1,500 on Blademap, learn it in 4 days, and have your skills pay you more than any job ever could?

@NoahKingJr I can sing,dance ,listen with empathy!

TELL ME SOMETHING YOU CAN DO THAT CLAUDE CANNOT

@8eenpoint5 I really hate when idiots with no self achievement try to insult legends who worked their ass off to be at level they are now!

I’m open sourcing JustHireMe 🚀 A local-first Agentic AI desktop app I’ve been building to make job searching more intelligent, transparent, and user-controlled. GitHub: github.com/vasu-devs/just… The current job search process is broken. Candidates spend hours scrolling through: stale job posts irrelevant roles spammy listings senior-only positions repeated listings across platforms jobs with almost no useful context And most AI job tools either scrape too broadly, rank opportunities like a black box, or try to automate applications without giving the user enough control. I wanted to build something different. JustHireMe is designed as a personal job intelligence workbench. Instead of blindly applying everywhere, it helps users discover better opportunities, evaluate them against their real profile, and generate tailored application materials while keeping sensitive career data local. What it can do: Ingest resume/profile data Build a local professional profile graph Discover job leads from multiple sources Filter out low-quality or irrelevant postings Score roles based on explainable fit Match jobs using graph + vector search Generate tailored resumes Generate cover letters Draft cold emails Draft LinkedIn outreach messages Track leads in a local CRM-style pipeline Keep the user in control through a human-in-the-loop workflow The main principle behind the project is: More signal. More explanation. More local control. Less blind automation. The tech stack: Tauri for the desktop shell React + TypeScript for the frontend Python + FastAPI for the backend sidecar SQLite for local lead tracking KuzuDB for graph-based profile modeling LanceDB for vector search and semantic matching Playwright for experimental browser automation One of the biggest goals is privacy. Your resume, career history, generated documents, job leads, application notes, and API keys should not have to live on someone else’s server by default. JustHireMe is built around a local-first architecture so users can keep ownership of their data while still benefiting from modern AI workflows. Another major goal is explainability. I don’t want an AI system that just says: “This job is a good match.” I want it to explain: which skills matched which projects support the application what gaps exist why a role was filtered out why a role deserves attention what to highlight in the resume or cover letter That matters because job search is not just a productivity problem. It is personal. It affects confidence. It affects opportunity. It affects people’s careers. The project is currently in alpha, but the foundation is in place. I’m looking for contributors interested in: Agentic AI AI agents workflow automation job source adapters web scraping ranking algorithms GraphRAG vector databases semantic search resume parsing document generation local-first software privacy-first AI UI/UX testing and documentation If you’re a developer, designer, AI engineer, student, or someone who has felt the pain of modern job searching, I’d love your feedback, ideas, issues, PRs, or even just a star ⭐ Repo: github.com/vasu-devs/just… Let’s build a better, more transparent job search system together. #OpenSource #AgenticAI #AIAgents #RAG #GraphRAG #Python #FastAPI #ReactJS #TypeScript #Tauri #VectorDatabases #JobSearch #CareerTech #Automation #PrivacyFirst

It took 1000’s of years to bring women out of oppression and gain respect and dignity now some women are losing it again for few likes and some money….remember even if you have millions, your kids and family members are going to get bullied and will suffer and live a undignified,shameful life because of your choices!

I bought Instagram exclusive content subscriptions of more than 45 Instagram female influencers and what I found common in each and every one’s content is that, in the name of “exclusive,” they are just sharing their nudes and semi-nudes, sexualizing their boobs, ass, and genital areas. What I actually figured out is that the objectification of women’s bodies isn’t something what men do. It’s something women themselves do if they are getting paid for it. And it has nothing to do with men.

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 @iansh04_ for more!! 👇 Comment “AI” for more resources Repost for your network ♻️ Bookmark for future.

ANTHROPIC PAYS $750,000 A YEAR FOR ENGINEERS WHO CAN BUILD LLM ARCHITECTURES FROM SCRATCH. Stanford taught the entire subject in one hour. And put it on YouTube for free. The engineers at Anthropic, OpenAI, and Google DeepMind who design the models powering every AI product you use did not learn this from a bootcamp or a YouTube influencer. They learned it from university lectures exactly like this one. Here is what makes this Stanford lecture different from every other AI explanation on the internet. Most content teaches you how to USE large language models. This teaches you how they are actually BUILT. The transformer architecture from first principles. Why attention mechanisms work and what they are mathematically doing. How tokenization shapes everything that comes out the other side. Why scale changes model behavior in ways nobody fully predicted. The training dynamics that determine whether a model becomes capable or collapses. Every concept powering GPT-5, Claude, and Gemini is in this lecture. Not at a surface level. At the level Anthropic interviews for when they offer $750,000 packages. The people who watch this tonight will understand AI at a depth that most people with AI job titles do not have. The people who skip it will keep using AI without understanding why it works. One hour. Free. From Stanford. Bookmark this before they realize they are giving away a $750,000 education for nothing. Follow @cyrilXBT for more elite resources that build real depth the moment they drop.

It took centuries to build a culture of dignity and respect, and now every other person in India is exposing their bodies for a few views and clicks in the name of modernization. #saveculture #Respect

This 9-minute lecture by Nassim Taleb on "Probability Distribution" will teach you more about prediction trading than 2 months as a Quant intern at Jane Street. Bookmark it & give it 9-minutes today. It’ll be the most productive start for your week. Then read article below.

JP Morgan's investment research team just shared exactly how they built their multi-agent system "Ask David", and it's the same architecture pattern showing up everywhere: - supervisor agent orchestrates - specialized subagents handle retrieval, structured data, analytics - LLM-as-judge reflection node before the answer ships - human-in-the-loop for the last accuracy gap worth watching for anyone building:

There are 2 career paths in AI right now: The API Caller: Knows how to use an API. (Low leverage, first to be automated, $150k salary). The Architect: Knows how to build the API. (High leverage, builds the tools, $500k+ salary). Bootcamps train you to be an API Caller. This free 17-video Stanford course trains you to be an Architect. It's CS336: Language Modeling from Scratch. The syllabus is pure signal, no noise:  ➡️ Data Collection & Curation (Lec 13-14) ➡️ Building Transformers & MoE (Lec 3-4) ➡️ Making it fast (Lec 5-8: GPUs, Kernels, Parallelism) ➡️ Making it work (Lec 10: Inference) ➡️ Making it smart (Lec 15-17: Alignment & RL) Choose your path. (I will put the playlist in the comments.) ♻️ Repost to save someone $$$ and a lot of confusion. ✔️ You can follow @techNmak, for more insights.