Suryansh Tiwari
@Suryanshti777 • 35,702 subscribers
Exploring AI & SaaS trends early Sharing what’s actually useful Helping builders turn ideas → products → traction – 📩 Open to collabs
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Someone just made Claude instances talk to each other. Not APIs. Not agents. Not orchestrators. Just multiple Claude Code sessions… messaging each other like coworkers. It’s called claude-peers — and it turns one Claude into a team. Here’s what’s happening: Run 5 Claude Code sessions across different projects Each one auto-discovers the others They send messages instantly Ask questions Share context Coordinate work Your AI tools literally collaborate. Example: Claude A (poker-engine): "what files are you editing?" Claude B (frontend): "working on auth.ts + UI state" Claude A: "ok I'll avoid touching auth logic" No conflicts. No manual coordination. Just AI syncing itself. Under the hood: • Local broker daemon (localhost) • SQLite peer registry • MCP servers per session • Instant channel push messaging • Auto peer discovery • Cross-project communication Everything runs locally. No cloud. No latency. What it unlocks: • Multi-agent coding without frameworks • One Claude writes backend, another frontend • One debugs while another refactors • Research Claude feeds builder Claude • Large projects split across AI workers This is basically: "spawn 5 Claudes and let them coordinate themselves" Even crazier: Each instance auto-summarizes what it's doing Other Claudes can see: • working directory • git repo • current task • active files They know what the others are working on. Commands: • list_peers → find all Claude sessions • send_message → talk to another Claude • set_summary → describe your task • check_messages → manual fallback So you can literally say: "message peer 3: what are you working on?" …and it responds instantly. No orchestration layer. No agent framework. Just Claudes… talking. This is the cleanest multi-agent system I've seen. We're moving from: 1 AI assistant → to AI teams that coordinate themselves. And it's all running on your machine. Wild.
Suryansh Tiwari190,656 görüntüleme • 2 gün önce
The person who built Claude Code just showed exactly how to use it. 30 minutes. Free. Straight from Boris Cherny himself. Most people using Claude daily are missing 40+ features hiding in plain sight. This single session is worth more than any $500 course. Bookmark this before you forget. 👇
Suryansh Tiwari567,176 görüntüleme • 1 ay önce
A man spends 50 years teaching at MIT. He knows his time is running out. So he records one last lecture — everything he knows, distilled into a single hour. He died 5 months later. This is that lecture. The most important hour you'll watch this week. 👇 Bookmark it for later
Suryansh Tiwari319,663 görüntüleme • 1 ay önce
Holy shit… someone just made machine learning click. Not static diagrams. Not math-heavy PDFs. Not black-box training. Real algorithms — training step-by-step — visually. It’s called Machine Learning Visualized and it lets you watch models learn in real time. Here’s why this is different: Instead of dumping theory first, it shows optimization happening live: • gradients moving • weights updating • decision boundaries shifting • loss decreasing • models converging You literally see learning happen. Everything is built from first principles: • Gradient Descent • Logistic Regression • Perceptron • PCA • K-Means • Neural Networks • Backpropagation No magic. Just math → code → visualization. Each chapter is a Jupyter notebook that derives the math then implements it then animates training. So you can watch: • neural nets shape decision surfaces • PCA rotate feature space • K-means clusters form live • gradient descent find minima • sigmoid reshape boundaries • backprop update weights step-by-step This solves a huge problem: Most ML resources teach: math → code → ??? → trained model This shows: math → code → learning process → result Which means you finally understand: • why gradients matter • how weights evolve • what loss landscapes look like • how convergence actually happens • why deep nets learn non-linear functions Even better: You can open any notebook modify parameters and watch behavior change instantly. Learning ML becomes interactive. Not passive. Not abstract. Not confusing. Just… visible. Perfect for: • beginners learning ML • devs moving into AI • interview prep • teaching concepts • understanding backprop • visual learners • building intuition This is the kind of resource that makes neural networks finally “click”. Link: We’re moving from: reading about ML → watching ML learn That’s a big shift. Because once you can see training, you stop memorizing… and start understanding. AI education just got visual.
Suryansh Tiwari131,951 görüntüleme • 2 ay önce
You don't need 6 months to learn Agentic AI. You need 26 minutes and Claude Code. While most people are still watching explainer videos about what AI agents "could" do — this tutorial shows you how to actually build one from scratch right now. Zero to fully working Agentic workflow. 26 minutes. Step by step. No fluff 👇
Suryansh Tiwari19,808 görüntüleme • 13 gün önce
Holy shit… someone just made DSA finally click. Not static notes Not boring pseudocode Not guessing what happens in memory Real data structures — animating step-by-step — visually. It’s called Data Structure Visualizations and it lets you watch algorithms run in real time. Here’s why this is different: Instead of dumping theory, it shows execution live • nodes getting inserted • trees rotating • pointers moving • queues filling • stacks popping • graphs traversing • heaps rebalancing You literally see algorithms think. Everything is interactive: • Binary Search Trees • AVL Trees (with rotations) • Red-Black Trees • Heaps & Priority Queues • Graph BFS / DFS • Dijkstra & MST • Hash Tables • Tries • Sorting (Quick, Merge, Heap…) • Dynamic Programming No black box. Just input → steps → result Watch in real time: • AVL rotations balancing themselves • BFS exploring layer by layer • DFS diving deep then backtracking • Dijkstra relaxing edges step-by-step • Quick sort partition visually • Heap forming after each insert • Hash collisions resolving live This solves the biggest DSA problem: Most resources teach code → memorize → hope it works This shows input → execution → visualization → understanding Which means you finally understand: • why AVL rotates • how heap property maintains • how BFS differs from DFS • how Dijkstra actually updates distances • what happens during rehashing • how quicksort partitions • how trees rebalance Even better: You control everything Change values Insert nodes Run step-by-step Pause execution Replay algorithms Learning DSA becomes interactive Not passive Not confusing Not theoretical Just… visible. Perfect for: • DSA beginners • interview prep • visual learners • CS students • LeetCode prep • teaching algorithms • debugging understanding This is the kind of resource that makes trees, graphs, and sorting finally click. Link: We’re moving from reading DSA → watching DSA execute And once you can see algorithms… you stop memorizing and start understanding.
Suryansh Tiwari14,425 görüntüleme • 2 ay önce
Daha fazla içerik yok.