Suryansh Tiwari
@Suryanshti777 • 35,885 subscribers
Exploring AI & SaaS trends early Sharing what’s actually useful Helping builders turn ideas → products → traction – 📩 Open to collabs
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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 Tiwari567,849 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 Tiwari320,040 görüntüleme • 1 ay önce
RAG might already be becoming obsolete. A month ago, Andrej Karpathy dropped a simple GitHub gist called “LLM Wiki.” Now the comments section looks like the birth of an entirely new AI category. 5000+ stars later, developers are rapidly building: • persistent AI memory systems • self-maintaining knowledge bases • multi-agent research environments • contradiction detection engines • AI-native company operating systems • local-first memory architectures • graph-based reasoning layers • evolving second brains And the craziest part? Most of them were built in DAYS. Because the core idea is insanely powerful: Instead of AI repeatedly retrieving raw chunks like traditional RAG… …the model continuously maintains a living knowledge system. Not temporary context. Persistent synthesis. The shift sounds subtle until you realize what it changes: RAG: retrieve → answer → forget LLM Wiki: ingest → synthesize → evolve That one architectural difference is causing an explosion of experimentation right now. People are already building: • agent memory operating systems • AI-maintained engineering documentation • self-healing knowledge graphs • persistent research environments • conversational memory architectures • contradiction-aware wikis • context compression engines • machine-readable company systems The comments section alone feels like watching an ecosystem form in real time. One developer built deterministic contradiction detection using sheaf cohomology Another built “sleep consolidation” for AI memory systems inspired by human memory formation Another created persistent multi-agent vault conversations Another turned entire repositories into continuously maintained AI wikis Another built local-first memory systems with audit trails, provenance, graph exports, and MCP integration This is the important part: Karpathy didn’t launch a product. He introduced a pattern. And patterns are what create ecosystems. The same way: • transformers created modern AI • RAG created AI retrieval startups • agents created orchestration frameworks LLM Wikis may create persistent AI memory infrastructure. That’s why this moment feels different. For years, AI systems have been stateless. Now developers are trying to build systems that actually accumulate understanding over time. And once knowledge compounds instead of resetting… …the entire interface layer of AI changes. (Link in comments)
Suryansh Tiwari140,516 görüntüleme • 28 gün 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 Tiwari132,001 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 • 21 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.