I finally understand the difference between LLMs, RAG, and AI Agents. After two years of building production AI systems, I realized most people are treating them like competing tools when they’re actually three layers of the same intelligence stack. 1. The LLM is the brain.

1/16 You've seen it in movies: a lone genius AI solves everything in seconds. But in reality, even the smartest person (or AI) hits a wall. A new paper from Microsoft Research suggests the next leap in AI isn't about being a lone genius. It's about learning to be a world-class

I just fell down a rabbit hole reading a new AI paper and I think my brain is broken. An AI system called Kosmos just did what collaborators estimate is 6 months of PhD-level scientific research. It did it in 12 hours. 1/12 I know, I know. We hear "AI scientist" and think

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🚨 Google just dropped an ace 64-page guide on building AI Agents From ADK to AgentOps, Vertex AI Agent Engine to Agentspace, this guide is the clearest path yet from experimentation to scalable production 🔥 Download link (free!) in 🧵 ↓

To celebrate five years of #AlphaFold, we’re making The Thinking Game available on YouTube. 🧬 Get a candid look at the triumphs, the challenges and the pivotal moments that led to a breakthrough on a 50-year-old grand challenge in biology. Stream for free on YouTube →

This is insane 🤯 A new system called Paper2Video can read a scientific paper and automatically create a full presentation video slides, narration, subtitles, even a talking head of the author. It’s called PaperTalker, and it beat human-made videos in comprehension tests.

🚨 This project just made most AI agents look outdated. It’s called Better Agents and it supercharges your coding assistant (Kilocode, Claude Code, Cursor, etc), making it an expert in any agent framework you choose (Agno, Mastra, etc) and all their best practices. This is the

Turn any GitHub repo into a visual, interactive diagram in one click 🤯 Just replace `github` with `gitdiagram` in the URL… or go to: → gitdiagram.com - It maps your entire repo - Shows all files - Reveals every dependency 100% open-source and free. Repo in 🧵↓

"Data Structures & Algorithms using Python" This book covers all types of data structures from Arrays to Graphs. Simple to complex algorithms. Available FREE. Comment if you need it.

One of the most detailed visual explainer for self-attention in transformers:

The AI prompt library your competitors don't want you to find → Biggest collection of text & image prompts → Unlimited custom prompts → Lifetime access & updates Grab it before it's gone 👇 godofprompt.ai/pricing

New MIT paper asks what really caused big AI efficiency gains and finds most of them come from scaling, not tiny tricks. They challenge an earlier claim of about 22000x algorithm gains over a decade and argue the true figure is far smaller. In small experiments they toggle

This Stanford University paper just broke my brain. They just built an AI agent framework that evolves from zero data no human labels, no curated tasks, no demonstrations and it somehow gets better than every existing self-play method. It’s called Agent0: Unleashing

"Agentic Design Patterns: A Hands-On Guide to Building Intelligent Systems" Read the Online eBook from Google at docs.google.com/document/d/1rs… Buy hardcopy version at amzn.to/3IyOrPx

When it comes to AI & ML, These 10 channels will teach you more than any degrees. 1. Andrej Karpathy youtube.com/andrejkarpathy 2. sentdex youtube.com/@sentdex 3. Sebastian Raschka youtube.com/@SebastianRasc… 4. Jeremy Howard youtube.com/@howardjeremyp 5. MIT OpenCourseWare

Banger paper from Stanford University on latent collaboration just dropped and it changes how we think about multi-agent intelligence forever. "Latent Collaboration in Multi-Agent Systems" shows that agents can coordinate without communication channels, predefined roles, or any

A solid 65-page long paper from Stanford, Princeton, Harvard, University of Washington, and many other top univ. Says that almost all advanced AI agent systems can be understood as using just 4 basic ways to adapt, either by updating the agent itself or by updating its tools.

🔥 I can’t believe this exists… someone finally wrote the secret playbook every AI agent startup has been faking. A research team just dropped “A Practical Guide for Designing, Developing, and Deploying Production-Grade Agentic AI Workflows” and it’s basically the internal

Check out their blogs if you are into AI/ML. 1) Andrej Karpathy Neural networks & LLMs explained from first principles by one of the OGs of modern AI. - karpathy.ai/?utm_source=li… 2) Sebastian Raschka, PhD Deep dives into LLM training and fine-tuning with super clear code