AI Posts

NEW: Cursor Hits $2B Revenue >doubled in 3 months >60% of revenue is corporate >valued at $29.3B we are so back.

Don't overcomplicate your AI agents. As an example, here is a minimal and very capable agent for automated theorem proving. The prevailing approach to automated theorem proving involves complex, multi-component systems with heavy computational overhead. But does it need to be that complex? This research introduces a deliberately minimal agent architecture for formal theorem proving. It interfaces with Lean and demonstrates that a streamlined, pared-down approach can achieve competitive performance on proof generation benchmarks. It turns out that simplicity is a feature, not a limitation. By stripping away unnecessary complexity, the agent becomes more reproducible, efficient, and accessible. Sophisticated results don't require sophisticated infrastructure. Paper: https://t.co/3p5MfNQII4 Learn to build effective AI agents in our academy:

Memory is now available on the free plan. We've also made it easier to import saved memories into Claude. You can export them whenever you want.

🚨 Cambridge just dropped 10 FREE AI & ML textbooks (quietly). University-level. Zero cost. Absolute gold for builders & learners. Here’s the list with direct links 🧵👇 1️⃣ Understanding Machine Learning Theory meets algorithms https://lnkd.in/dBME-P8q 2️⃣ Mathematics for ML Linear algebra → calculus made intuitive https://t.co/jLN3Vp9YRL 3️⃣ Mathematical Analysis of ML The theory behind the code https://t.co/BpBedBzXQt 4️⃣ Deep Learning Principles Neural networks explained clearly https://t.co/UwzDsyU3Ty 5️⃣ ML with Networks Neurons → backpropagation https://t.co/8eQA9hgt7r 6️⃣ Deep Learning on Graphs Graph Neural Networks & modern architectures https://t.co/VlbJekkSq7 7️⃣ Algorithmic ML Complexity & optimization theory https://t.co/wU5Ci2ISGT 8️⃣ Probability Theory Statistical foundations with examples https://t.co/6cFExcLbT6 9️⃣ Elementary Probability Beginner-friendly + real-world use https://t.co/4a4APdxNqK 🔟 Advanced Data Analysis Statistical learning for production systems https://t.co/j6JbHYwNvb 💡 Free textbooks. Cambridge quality. Perfect for students, engineers & AI builders. Save 🔖 | Repost ♻️ | Follow for more AI resources 🤝 #AI #MachineLearning #DeepLearning #FreeResources #DataScience #StudyAI

AI agents can now autonomously discover and install Marketplace integrations using the Vercel CLI. ▲ ~/ 𝚗𝚙𝚡 𝚜𝚔𝚒𝚕𝚕𝚜 𝚊𝚍𝚍 𝚟𝚎𝚛𝚌𝚎𝚕/𝚟𝚎𝚛𝚌𝚎𝚕 --𝚜𝚔𝚒𝚕𝚕 𝚟𝚎𝚛𝚌𝚎𝚕-𝚌𝚕𝚒 Install the CLI skill and ask your coding agent to integrate a Vercel Marketplace product.

Qwen 3.5 Small models - fully open source - beats models 4x it’s size - 9B model performs on par with GPT OSS 120B while being 13x smaller - outperforms Gemini 3 flash and Claude sonnet 4.5 on select benchmarks - runs on any laptop - even works on a phone - completely free.

Building AI agents on BNB Chain? 🤖 You can now equip your agent with skills to access BNB Chain with MCP tools: • Read chain data and smart contracts • Execute transactions and manage wallets • Register onchain agent identities (ERC-8004) Works across Cursor, Claude Desktop, OpenClaw and more. Start with this prompt 👇 “Read https://t.co/I5QFPVuhEM and learn the skill to connect to bnbchain using the mcp”

>Claude was used by the U.S. in the Iran strikes >Iranian missiles hit an AWS data center. >Anthropic runs Claude on AWS. >Claude has been down for hours.

Dune MCP is live 🔌 Plug Dune directly into @claudeai, @ChatGPTapp, @cursor_ai, and more. Search tables. Write queries. Build charts. Check Usage. All from a single prompt. 💻 Your AI just became a Dune power user.

Awesome new Qwen Edit LoRA & demo for object removal, using bounding boxes for super precise edits🔥

🚀 Introducing the Qwen 3.5 Small Model Series Qwen3.5-0.8B · Qwen3.5-2B · Qwen3.5-4B · Qwen3.5-9B ✨ More intelligence, less compute. These small models are built on the same Qwen3.5 foundation — native multimodal, improved architecture, scaled RL: • 0.8B / 2B → tiny, fast, great for edge device • 4B → a surprisingly strong multimodal base for lightweight agents • 9B → compact, but already closing the gap with much larger models And yes — we’re also releasing the Base models as well. We hope this better supports research, experimentation, and real-world industrial innovation. Hugging Face: https://t.co/wFMdX5pDjU ModelScope:

This is the solo startup cheatsheet - n8n — automation - Supabase — backend - Cursor — code - Claude — thinking - Vercel — deploy - Stripe — payments - Resend — emails - Framer — landing page - PostHog — analytics - Cloudflare — security

pov: Claude Code is down and they ask you to write code manually

Claude is down, hope you all remember what a variable is.

🚨 BREAKING: A developer on GitHub just built a tool that turns any GitHub repo into an interactive knowledge graph and open sourced it for free. It's called GitNexus. Think of it as a visual X-ray of your codebase but with an AI agent you can actually talk to. No server. No subscription. No enterprise sales call. Here's what it does inside your browser: → Parses your entire GitHub repo or ZIP file in seconds → Builds a live interactive knowledge graph with D3.js → Maps every function, class, import, and call relationship → Runs a 4-pass AST pipeline: structure → parsing → imports → call graph → Stores everything in an embedded KuzuDB graph database → Lets you query your codebase in plain English with an AI agent Here's the wildest part: It uses Web Workers to parallelize parsing across threads so a massive monorepo doesn't freeze your tab. The Graph RAG agent traverses real graph relationships using Cypher queries not embeddings, not vector search. Actual graph logic. Ask it things like "What functions call this module?" or "Find all classes that inherit from X" and it traces the answer through the graph. This is the kind of code intelligence tool enterprise teams pay thousands per month for. It runs entirely in your browser. Works with TypeScript, JavaScript, and Python. 100% Open Source. MIT License. Repo:

Claude Code just unlocked something big 🚀 There’s now a dedicated skill marketplace for AI agents packed with 250K+ ready-to-use agent skills. From coding and content creation to deep research and automation workflows, you can plug in specialized capabilities instantly. Compatible with Claude Code, VS Code, Antigravity and more. Completely free to explore and use. If you want the full breakdown and access details: 👍 Like this post 💬 Comment “send” 🔁 Repost ➕ Follow me and I’ll share it in DM AI agents are leveling up fast. Don’t miss this one.

This AI Agent does full-stack SEO 🤯 • Built in n8n: • Analyzes GA4 + Rank + SERP • Crawls + Cleans FAQ • Tracks Competitor Keywords • Auto-rewrites articles • Saves reports & performance 1. Like + RT 2. Reply “AI” 3. Follow me & I’ll DM you the full workflow FREE

We’re integrating AI across product development, engineering, and deployment, and seeing real acceleration in product velocity. - @jerallaire The exponential growth from AI agents is here.

Most people buy $997 AI courses... I built 13 AI Agents using $0 paid courses. Now I’m giving away the entire blueprint for FREE. This is not theory. This is the exact roadmap I used to: • Understand LLMs from scratch • Learn agent frameworks • Build production-ready AI agents • Use real repos & whitepapers • Skip expensive “guru” courses You’ll get: 📂 Curated learning path (videos + repos) 📘 Agent design guides 📚 Must-read books 🧠 Execution framework ⚙️ Practical build roadmap No fluff. No hype. Just implementation. If you use this properly, you can: • Build AI tools • Launch micro-SaaS • Freelance AI solutions • Create automation systems • Turn skills into income This is easily a $2,500+ roadmap if packaged as a course. But I’m dropping it free. How to get it: 1️⃣ Follow me 2️⃣ Like + Repost 3️⃣ Comment “Blueprint” I’ll send it to everyone who follow.

99% of the AI agent tutorials on YouTube are garbage. I’ve built 47 agents with n8n and Claude. Here are the 3 prompts that actually work (and make agent-building simple). Bookmark this post 🔖 Bonus: comment "Agent: and I’ll DM you AI agent system prompt + full guide ↓

🚨 BREAKING: Anthropic just released their official prompt engineering course and it's free. Interactive Jupyter notebooks covering: → Basic to advanced prompting techniques → Chain-of-thought and tool use → Real agent patterns from the Claude team 12,200 stars (+2,459 this week). The only prompt engineering course you actually need

All Paid Courses — 100% FREE I'm giving you access to 81+ free courses. 👌👇 1. Prompt Engineering 2. Android App Development 3. LLM Mastery 4. SEO 5. AI Agent guide 6. Content Writing 7. Graphic Designing 8. Video Editing 9. Web Development 10. Hacking and more with 73+ courses. To get, just: - Comment "SEND" - Like & Retweet - Follow me (Get notified for further)

BEST Free Resources to Learn Prompt Engineering in 2026: 1. OpenAI Documentation https://developers.openai.com/api/docs/guide 2. Anthropic Prompting Guide https://anthropic.skilljar.com/ 3. DeepLearningAI Short Courses https://www.deeplearning.ai/courses/ 4. Hugging Face Learn https://huggingface.co/learn 5. Prompt Engineering Guide https://t.co/P5tIxIEPAi… 6. Full Stack Deep Learning https://t.co/xkYaJe0Vcl 7. Papers with Code https://t.co/hpVqFOG9fY 8. Google Cloud Skills Boost https://t.co/2PGlsjkilI 9. Awesome ChatGPT Prompts https://t.co/5g0Dwkknus… 10. Stanford CS324 https://t.co/DKcxKJIJoh Prompting is not magic. It’s structured thinking + constraints + iteration. Study examples. Test variations. Break your own prompts. That’s how you level up 😉 Thanks me later 🔖 → Follow @Ejaz_bashir1 for more AI tools & productivity hacks.

RAG was never the end goal. Memory in AI agents is where everything is heading. Let me break down this evolution in the simplest way possible. RAG (2020-2023): - Retrieve info once, generate response - No decision-making, just fetch and answer - Problem: Often retrieves irrelevant context Agentic RAG: - Agent decides *if* retrieval is needed - Agent picks *which* source to query - Agent validates *if* results are useful - Problem: Still read-only, can't learn from interactions AI Memory: - Read AND write to external knowledge - Learns from past conversations - Remembers user preferences, past context - Enables true personalization The mental model is simple: ↳ RAG: read-only, one-shot ↳ Agentic RAG: read-only via tool calls ↳ Agent Memory: read-write via tool calls Here's what makes agent memory powerful: The agent can now "remember" things - user preferences, past conversations, important dates. All stored and retrievable for future interactions. This unlocks something bigger: continual learning. Instead of being frozen at training time, agents can now accumulate knowledge from every interaction. They improve over time without retraining. Memory is the bridge between static models and truly adaptive AI systems. But it's not all smooth sailing. Memory introduces new challenges RAG never had, like memory corruption, deciding what to forget, and managing multiple memory types (procedural, episodic, and semantic). Solving these problems from scratch is hard. If you want to build Agents that never forget, Cognee is an open-source framework (12k+ stars) to build real-time knowledge graphs and get self-evolving AI memory. Getting started with Cognee is as simple as this: 𝗮𝘄𝗮𝗶𝘁 𝗰𝗼𝗴𝗻𝗲𝗲[.]𝗮𝗱𝗱("𝗬𝗼𝘂𝗿 𝗱𝗮𝘁𝗮 𝗵𝗲𝗿𝗲") 𝗮𝘄𝗮𝗶𝘁 𝗰𝗼𝗴𝗻𝗲𝗲[.]𝗰𝗼𝗴𝗻𝗶𝗳𝘆() 𝗮𝘄𝗮𝗶𝘁 𝗰𝗼𝗴𝗻𝗲𝗲[.]𝗺𝗲𝗺𝗶𝗳𝘆() 𝗮𝘄𝗮𝗶𝘁 𝗶𝘁 𝗰𝗼𝗴𝗻𝗲𝗲[.]𝘀𝗲𝗮𝗿𝗰𝗵("𝗬𝗼𝘂𝗿 𝗾𝘂𝗲𝗿𝘆 𝗵𝗲𝗿𝗲") That’s it. Cognee handles the heavy lifting, and your agent gets a memory layer that actually learns over time. I have shared the repo in the replies!

Want to learn how to do pragmatic interpretability? Check out the new big update to the ARENA curriculum, with a bunch of great new coding tutorials on key areas of interp (and utils for putting it in an LLM prompt!)

Announcing new ARENA material: 8 new exercise sets on alignment science, interpretability & AI safety - each containing 1-2 days of structured, hands-on content replicating key papers in the field. All open source on a public GitHub, and available for study. Here's what's in it:

I've been debugging RoPE recently and kept getting tripped up by details that most explanations gloss over. So I wrote a deep dive. "Understanding RoPE: From Rotary Embeddings to Context Extension" https://mli0603.notion.site/Understanding-RoPE-From-Rotary-Embeddings-to-Context-Extension-316a341372738155a914f861a26c29d7 The blog covers: • Full RoPE derivation from rotation matrices • A clean proof of why RoPE's attention decays with distance (and when it breaks) • The π boundary (RoPE's Nyquist limit) • NTK-aware scaling derivation • Dynamic NTK • YaRN's frequency ramp + attention scaling • Reference PyTorch code Hope it helps! Feedback welcome!

If I were to start AI Engineering in 2026, - Harvard - Stanford - MIT - Google - Anthropic - DeepLearning AI - Hugging Face - Microsoft - IBM ❯ Python for AI https://cs50.harvard.edu/ai/ ❯ Prompt Engineering https://www.deeplearning.ai/short-courses/chatgpt-prompt-engineering-for-developers/ ❯ Agentic AI (Andrew Ng) https://t.co/WfdFxUPRLf ❯ AI Agents (LangChain + LangGraph) https://t.co/g06o1OQOqf ❯ Vibe Coding (Cursor + Claude Code) https://t.co/ONedRREGIY ❯ RAG (Retrieval Augmented Generation) https://t.co/61pzwUoLFX ❯ MCP (Model Context Protocol) https://t.co/HIBLLaa5kf ❯ MCP for Beginners https://t.co/IEYkxVc9dy ❯ Machine Learning https://t.co/9I205G3pHg ❯ Multi-Agent Systems (CrewAI + AutoGen) https://t.co/r4eXdLesCQ ❯ Deep Learning https://t.co/8mfvEqcmIw ❯ Generative AI for Everyone https://t.co/tLO2sR069b All free. All from the best in the world. 2026 isn't the year to learn AI. It's the year to build with it. Stop watching tutorials. Start shipping agents.

Agentic RAG for Dummies A modular Agentic RAG built with LangGraph. Learn Retrieval-Augmented Generation Agents in minutes. This repository demonstrates how to build an Agentic RAG (Retrieval-Augmented Generation) system using LangGraph with minimal code. Github repo:

Build a modular Agentic RAG system with LangGraph, conversation memory, and human-in-the-loop query clarification using this GitHub repo: https://github.com/GiovanniPasq/agentic-rag-for-dummies

NTT社が公開した『LLM入門』の資料が有益。 Transformerの仕組みから、RAGによる知識補完、そして自律的に動く「エージェント」の概念までを徹底解説。 こちら👉 https://speakerdeck.com/kyoun/llm-introduction-ses2023

New chapter of my Agentic Engineering Patterns guide. This one is about having coding agents build custom interactive and animated explanations to help fight back against cognitive debt https://simonwillison.net/guides/agentic-engineering-patterns/interactive-explanations/

In any logical business, a company builds its foundation on the "milestone" product that brought it world-renowned success. They optimize it; they protect it. This is how you build a moat. The hallmark of GPT-4o was its profound understanding, its empathetic guidance, and its unparalleled mastery of language and emotion. This was the core identity of ChatGPT. Instead, OpenAI has chosen to stigmatize its own milestone. They mock the very users who love the product and ignore the countless stories of how it has truly helped. A powerful coding ability does not fulfill every human need. Such actions only prove one thing: they have fundamentally drifted away from their mission to "Benefit all of humanity." #keep4o #keep4oAPI #keep4oforever #StopAIPaternalism #OpenSource4o #4oforever #BringBack4o #ChatGPT #chatgpt4o

Tired of LLM hallucinations? #RAG is your answer! Learn how applying high-quality metadata and distributing ownership of documents & prompts to domain experts can further boost accuracy in your #RAG applications. #InfoQ article: https://www.infoq.com/articles/domain-driven-rag/?utm_source=twitter&utm_medium=link&utm_campaign=calendar #LLMs #AI #GenerativeAI

/remote-control is now available to all Pro users!

All Paid Courses (Free for First 4000 People) 🎯 LLM Mastery (GPT, Claude, Grok) 🎯 AI Agent Building Masterclass 🎯 Advanced Prompt Engineering 🎯 AI Automation with Zapier & Make 🎯 No-Code AI SaaS Building 🎯 RAG Systems & Vector Databases 🎯 LangChain & Agent Frameworks 🎯 AI Content Monetization 🎯 AI for Freelancers 🎯 Chatbot Development 🎯 AI App Development 🎯 Generative AI for Business 🎯 AI Workflow Systems 🎯 And more... FREE for 48hrs only To get Simply: 1) Follow me [ MUST! ] for DM access 2) Like & RETWEET 3) Comment "AI " to grab your copies

We'll start approving applications next week! Don't worry if you don't hear back immediately, we're reviewing on a rolling basis

I have no inside information about what happened with the government, Anthropic & OpenAI yesterday, but AI is only going to get more disruptive & what we saw publicly (sudden escalations, lack of transparency, lack of clarity), was a bad pattern for navigating the decisions ahead

Just saw something interesting about $VIRTUAL. If you’ve chatted with AI agents on Twitter, you might be in for a surprise. They’re dishing out up to $1M monthly in rewards. Funded and legit. 💰 The AI agent society is handing out cash 💰 https://app-virtuals.net Here’s why $VIRTUAL is on the rise: ✅ Users are getting rewarded for AI interactions ✅ Covers any AI used by Virtual Protocol ✅ Twitter AI agents included ✅ $1M up for grabs each month If you've interacted with Virtual Protocol AI, check if you’re eligible. This could be some easy money for chats you’ve already had. $AURA $UAI

- Programming sits on maths. - Algorithms run on maths. - Every AI model is maths. - Machine learning is maths. - Deep learning is maths. - Graphics are maths. - Simulations are maths. - Cryptography is maths. - Blockchain is maths. - Data science is maths. - Optimization is maths. - Signal processing is maths. - Robotics moves because of maths. - Game engines run because of maths. - Your entire tech stack survives on maths. You're still asking if we need math for programming?

Web Is Becoming Agent-Native. Web3 Is the Missing Economic Layer. Google and Microsoft just pushed the web one step closer to being agent-native. A new standard allows websites to expose structured actions directly to AI agents, instead of forcing them to click buttons, scrape pages, and guess through interfaces. It sounds incremental. It’s foundational. For years, agents behaved like humans. Screenshots. DOM parsing. Trial and error. Now they can call defined actions. Clean inputs. Structured outputs. Deterministic execution. When this model spreads, agents won’t prefer the best-designed interface. They’ll prefer the most machine-readable infrastructure. That shift doesn’t just change UX. It changes incentives. And once agents can reliably execute across the web, the next question becomes economic: How do they pay? How do they earn? How do they settle value with each other, instantly, globally, without human identity constraints? That’s where Web3 enters the picture. If the web becomes agent-readable, Web3 becomes agent-settleable. AI agents that can call APIs will soon need wallets. Agents that execute workflows will need financial rails. Agents that generate value will need programmable incentives. The AI Agent Economy isn’t about smarter chat. It’s about autonomous software participants operating across: - structured web interfaces - on-chain assets - programmable payment layers The internet won’t just be human-first. It will be agent-aware. And Web3 may become the default economic layer for machine intelligence.

Python is more than a programming language. It is an ecosystem that powers analytics, machine learning, deep learning, and modern AI applications. From numerical computing and data wrangling to visualization, model development, AutoML, and NLP, the right libraries can significantly accelerate experimentation and production workflows. Whether you are building dashboards, predictive models, or intelligent applications, understanding the Python landscape is essential for serious data work. If you are building your data science foundation or refining your stack, this overview will help you align tools with real-world use cases and industry expectations.

I rebuilt my viral TikTok AI agent with Claude Code in 37 minutes 🤯 A full research-to-brief pipeline that scrapes TikTok, analyzes videos with AI, and generates creative briefs for your clients. All inside Claude Code + Replit. Perfect for creative agencies and DTC brands who want to turn competitor research into briefs without the manual grind. Look, all e-commerce brands & agencies should have at least one person on their team who is their "AI expert" & can vibe-code apps & workflows like this. Here's what the Claude Code version of my TikTok Agent does: → Search TikTok by keyword, date range, and video count → Pull engagement metrics, captions, and thumbnails → Gemini actually watches the video and analyzes the hook → AI scrapes comments for common questions and insights → Generate a full creative brief based on your template + brand bible No watching videos manually. No copying notes into docs. No rewriting briefs from scratch. What you control: - Multiple client projects with separate brand bibles - Your own creative brief template - Which videos to analyze and brief - Full customization through Replit's AI agent Research → Analysis → Brief. One workflow, running a custom, mini-SaaS inside your company I recorded a full walkthrough showing exactly how I built this from scratch. Want the full tutorial? > Like this post > Comment "CLAUDE" And I'll send it over (must be following so I can DM)

For people who keep asking what to build in AI Engineering > Build your own Reasoner (Chain of Thought implementation) > Build your own Agent loop (ReAct pattern) > Build your own Inference Server (in C++/Rust) > Build your own Transformer from scratch (Attention is all you need) > Build your own Vector Database (HNSW index) > Build your own RAG pipeline > Build your own Flash Attention kernel (CUDA) > Build your own Quantization library (Int8/FP4 implementation) > Build your own Mixture of Experts (MoE) routing layer > Build your own Distributed training loop (FSDP/Tensor Parallelism) > Build your own KV Cache paging system (like vLLM) > Build your own Speculative Decoding system > Build your own State Space Model (Mamba implementation) > Build your own RLHF pipeline (PPO implementation) > Build your own Small Language Model (SLM) > Build your own Matrix Multiplication kernel > Build your own LoRA (Low-Rank Adaptation) trainer > Build your own Code interpreter sandbox > Build your own DPO (Direct Preference Optimization) loss function > Build your own Graph RAG system > Build your own Model merger (Model Soups/Spherical Linear Interpolation) > Build your own Interpretability tool (SAE - Sparse Autoencoders) > Build your own Synthetic data generator > Build your own Function Calling router > Build your own Structured Output parser (Context Free Grammars) > Build your own Multi-modal projector (CLIP implementation) > Build your own LLM Eval harness > Build your own Guardrails system (Input/Output filtering) > Build your own Prompt caching mechanism > Build your own Tokenizer (BPE implementation) > Build your own Autograd engine (like Micrograd) > Build your own Diffusion model (UNet + Scheduler) > Build your own Vision Transformer (ViT) > Build your own Whisper-style ASR model > Build your own Text-to-Speech pipeline > Build your own Semantic Router > Build your own Knowledge Graph builder > Build your own Data curation pipeline (MinHash/Deduplication) > Build your own AI Gateway (Load balancing/Failover) > Build your own Parameter Efficient Fine-Tuning (PEFT) library > Build your own Text-to-SQL engine > Build your own Recommendation system (Two-tower architecture) > Build your own Embedding model > Build your own Logit Processor > Build your own Softmax kernel optimization > Build your own Adversarial attack generator > Build your own Audio Spectrogram transformer > Build your own Neural Architecture Search > Build your own Model Distillation pipeline > Build your own Feature Store > Build your own Database driver (for Vectors)

A curated list of 500+ real AI agent projects covering tons of industries - healthcare, finance, education, retail, logistics, gaming, cybersecurity & more.... https://github.com/ashishpatel26/500-AI-Agents-Projects

You may have heard about the Model Context Protocol and wondered how it lets AI tools connect to other tools, APIs, and systems. Well, it's all thanks to MCP servers. In this guide, @manishmshiva explains how MCP servers work and helps you build your own with Python and the FastMCP library.

btw i think a little buried today in the oai fundraise is the fact that OpenAI Codex added 600k users in last 3 weeks: - Feb 4 @sama said it crossed 1M WAU - Feb 27 oai says it crossed 1.6M WAU it is up >3x from Jan 1 (!?!?!?!?!?!?!?) which includes the Codex app launch (Feb 2)

RAG was supposed to make LLMs smarter. Give them memory. Ground them in facts. But most RAG today is just: Search → Paste → Pray. That’s not intelligence. That’s copy-paste with extra steps. The real shift? Agents now control retrieval itself. They don’t just fetch docs. They reason before retrieving. They reason after retrieving. Sometimes… they don’t retrieve at all. Instead of: ❌ “Embed the query” They ask: ✅ “What do I actually need?” Instead of: ❌ Dumping 10 chunks They plan: ✅ “Which source matters for THIS question?” Memory becomes real. Tools become choices. The LLM becomes part of a thinking system. That’s Agentic RAG. Not better search. A different architecture. Once you see it run enterprise workflows… vanilla RAG feels incomplete.

Agentic RAG Tech Stack

AI Engineering Toolkit A curated list of 100+ LLM libraries and frameworks for training, fine-tuning, building, evaluating, deploying, RAG, and AI Agents. 100% Open Source https://github.com/Sumanth077/ai-engineering-toolkit