Suryansh Tiwari
@Suryanshti777 • 44,279 subscribers
GTM | Growth 📩 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 Tiwari574,733 просмотров • 5 месяцев назад
I spent two years telling myself our CRM was "mostly fine." It wasn't. Duplicate contacts, half-empty fields, and deals that went dark 90 days ago and never got touched again. The thing that finally handles it is called Nex.ai (YC S26) It builds and runs the high-volume GTM workflows that general-purpose agents choke on. Not another chat window you have to babysit. The three that matter: - Lead generation. Sourcing and building lists at real volume, not fifty rows at a time. - Qualification. Running thousands of leads against your actual criteria instead of whoever replied fastest. - CRM Hygiene. Duplicates, missing critical fields, stale records. Every change waits for your approval. Est. $200 to $500/mo saved. One example the founders shared: on a single onboarding call, Nex audited thousands of records in a customer's HubSpot and started fixing the bad data before the call was over. You connect HubSpot, Gmail, Slack, Calendar. Two minutes. Then it maps how your team actually works, the follow-up cadence and escalation triggers nobody ever wrote down, and builds agents around that. 100+ tools. SOC 2 audited. Built by two ex-HubSpotters who ran its three largest customer platforms. Francisco was the 4th person on the team that built HubSpot's CRM. Dharmesh Shah backs them. So does Girish Mathrubootham from Freshworks. YC too. One honest limitation. Annex, the browser its agents drive, is macOS only right now. Windows and Linux are listed as coming soon. They're live on Product Hunt today and both founders are in the comments all day. Congrats on the launch, najmuzzaman . Long overdue. (Link in the comments)
Suryansh Tiwari102,804 просмотров • 1 месяц назад
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 Tiwari322,619 просмотров • 5 месяцев назад
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 Tiwari142,314 просмотров • 5 месяцев назад
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 Tiwari132,906 просмотров • 6 месяцев назад
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 Tiwari20,679 просмотров • 4 месяцев назад
Stop editing videos like it's 2015 Scrub → Cut → Keyframe → Export → Notice a mistake → Repeat This is NOT how video should work. Meet ChatCut The AI editor that edits WITH you, not after you. Imagine this: You upload raw footage… and instead of scrubbing a timeline for hours — ChatCut already understands every clip. You just type… "cut the filler words, find the highlights, add captions" — and it's done. No timelines to fight. No After Effects. Just a finished cut. This changes everything: → YouTubers publish faster → Founders launch quicker → Teachers reach further Basically… you describe it. ChatCut edits it. Text-based editing. AI motion graphics. Captions in 100+ languages. Generated B-roll and music. All in one place. This is the future of video. Support & explore the launch here: If you've ever edited a video before… you NEED to see this. #AI #VideoEditing #ContentCreation #Startups #BuildInPublic #ProductHunt
Suryansh Tiwari12,918 просмотров • 3 месяцев назад
Everyone’s chasing NVIDIA, Gemini, and DeepSeek… but the real leverage just dropped quietly. Someone open-sourced a tool that turns Google Maps into a full business database. One search → names, phones, websites, reviews, hours… all exported to CSV. No scraping skills. No manual work. Just raw data at scale. This isn’t a tool. It’s a lead generation engine hiding in plain sight. Agencies will use it. Founders will build on it. Everyone else will scroll past it. 100% Open Source.
Suryansh Tiwari16,732 просмотров • 5 месяцев назад
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 Tiwari14,627 просмотров • 6 месяцев назад
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