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"AI doesn't take your job. AI makes you the CEO." Balaji Srinivasan joins a16z’s Erik Torenberg for a conversation on the future of the AI economy, decentralization, and how work changes in an AI-native world, including: - How distillation and open source could decentralize AI power - Why AI lowers the cost of creation but raises the cost of verification - The shift from global internet to “trusted tribes” and private AI - Why humans are the sensor and AI is the actuator 00:00 Intro 02:06 Why you want AI inside the trusted tribe, not outside it 05:35 The Problem with AI slop 09:25 Where AI works 17:08 "AI can't read your mind, but it can read your body." 30:10 "AI doesn't take your job. AI makes you the CEO." 46:01 The SaaSpocalypse: Real or overblown? 49:19 What happens if AI companies get bigger than governments? @balajis @eriktorenberg

Gemma 4 + OpenClaw + Ollama + Discord — Full Local AI Setup for Free 🔥 Google just dropped Gemma 4 and we wired it directly into Discord 🔹 Gemma 4 31B pulled via Ollama — completely local 🔹 Fresh OpenClaw install from scratch 🔹 Full Discord bot setup — Developer Portal, intents, OAuth2, permissions 🔹 OpenClaw + Discord pairing walkthrough 🔹 Chat with Gemma 4 directly from your Discord server 🔹 DuckDuckGo web search enabled — no API key needed Watch the full setup below 👇

Today we're releasing Gemma 4, our new family of open foundation models, built on the same research and technology as our Gemini 3 series. These models set a new standard for open intelligence, offering SOTA reasoning capabilities from edge-scale (2B and 4B w/ vision/audio) up to a 26B parameter MoE model and a 31B dense model. By releasing Gemma 4 under the Apache 2.0 license, we hope to enable more innovation across the research and developer communities. Our earlier Gemma 3 models were downloaded 400M times and over 100,000 variants of those models have been published, so we're excited to see what the community will do with the even better Gemma 4 models! Learn more at blog.google/innovation-and… and goo.gle/gemma-4-apache… Great work by everyone involved! #Gemma4 #AI #OpenSource #ML

Mistral just open-sourced a 4B TTS model that clones any voice from 3 seconds of audio. - 68.4% win rate over ElevenLabs Flash v2.5 - 9 language support w/benchmarks - Sub-second latency, 32 concurrent streams on a single H200 - Strong expressivity, emotion + naturalness

Spotted the Aranda Lee Kuan Yew orchid in full bloom at the VIP Orchid Garden in SG Botanic Gardens ytdy. Mr Lee died eleven years ago today. The world has changed, but the unity, resourcefulness, and resolve of our forefathers remains our strength. – LHL go.gov.sg/j1ap3l

The link between material and moral flourishing is real timharford.com/2026/03/the-li…

Congratulations to Charles Bennett and Gilles Brassard for winning this year's @theofficialacm Turing Award! 🎉 They were recognized for their work on quantum information science & quantum cryptography. Google is proud to support the award to recognize groundbreaking CS work.

We've raised $6.5M to kill vector databases. Every system today retrieves context the same way: vector search that stores everything as flat embeddings and returns whatever "feels" closest. Similar, sure. Relevant? Almost never. Embeddings can’t tell a Q3 renewal clause from a Q1 termination notice if the language is close enough. A friend of mine asked his AI about a contract last week, and it returned a detailed, perfectly crafted answer pulled from a completely different client’s file. Once you’re dealing with 10M+ documents, these mix-ups happen all the time. VectorDB accuracy goes to shit. We built @hydra_db for exactly this. HydraDB builds an ontology-first context graph over your data, maps relationships between entities, understands the 'why' behind documents, and tracks how information evolves over time. So when you ask about 'Apple,' it knows you mean the company you're serving as a customer. Not the fruit. Even when a vector DB's similarity score says 0.94. More below ⬇️

karpathy just broke the internet with something called auto research it’s basically an ai research agent that runs experiments for you 24/7 you give it a goal like “make this model better” “find a higher converting landing page” “lower customer acquisition cost” then it runs a loop: 1) plan an experiment 2) edit the code or config 3) run a short test on a gpu 4) read the metrics 5) keep the winner 6) try again over and over while you sleep by the morning you wake up to the best version actual tested improvements think of it like a robot research intern that runs hundreds of experiments and only keeps the winners this is link to his repo github.com/karpathy/autor… for your to mess around with it in the latest episode of @startupideaspod i break down: • what auto research actually is • how it works step by step • 10 business ideas you can build with it • how to install it and start using it this one is saucy because tools like this change how startups get built watch

MIT offers 12 Books on AI & ML (FREE TO DOWNLOAD): 1. Foundations of Machine Learning cs.nyu.edu/~mohri/mlbook/ 2. Understanding Deep Learning udlbook.github.io/udlbook/ 3. Algorithms for ML algorithmsbook.com 4. Reinforcement Learning andrew.cmu.edu/course/10-703/... 5. Introduction to Machine Learning Systems mlsysbook.ai/book/assets/do… 6. Deep Learning deeplearningbook.org 7. Distributional Reinforcement Learning direct.mit.edu/books/oa-monog… 8. Multi Agent Reinforcement Learning marl-book.com 9. Agents in the Long Game of AI direct.mit.edu/books/oa-monog… 10. Fairness and Machine Learning fairmlbook.org 11. Probabilistic Machine Learning ❯ Part 1 : probml.github.io/pml-book/book1… ❯ Part 2 : probml.github.io/pml-book/book2…

study calculus because calculus isn’t just doing math; it’s training your mind to see change everywhere why it’s worth it:  • rate of change → understand how things move, grow, or decay in real time  • accumulation → see how tiny changes add up to big effects  • models → describe physics, nature, economics, any system in motion  • intuition → turn abstract symbols into tools for prediction and control  • problem solving → break complex systems into patterns you can actually work with this is why studying calculus isn’t “cramming formulas” it’s learning to think in motion.

We are hiring engineers and scientists! Join one of our teams: - Deception - stress-test and train against lie detectors - Integrity - red-team frontier models and write evals for new threats - Foundations - develop infrastructure for scalable ML workflows NEW 3-6mo fellowship👇

📣 Deal of the Day 📣 Mar 11 Save 45% TODAY ONLY! Fabulous Adventures in Data Structures and Algorithms & selected titles: hubs.la/Q046lRJb0 Author Eric Lippert introduces fabulous solutions using uncommon algorithms and data structures. #algorithms #datastructures #stochasticprogramming #probabilisticprogramming This unique book introduces a collection of amazing algorithms that have the potential to change the way you program. You’ll upend the way you think about lists, learn the algorithms behind powerful developer tools, and rethink how to handle stochastic quantities in modern programming languages.

Another brief book review👇 🌟 Building LLMs for Production 🌟 TLDR: A hands-on technical book with code-heavy examples using LangChain, RAG and data pipelines. 👍 What’s good about the book: → Hands-on, with lots of code samples and explanations. → Focuses on building a real apps → Good for readers who like learning by reading code. 👎 What can be better: → Text-heavy and would benefit from more diagrams and colour. → A lot of the code is already showing its age, it is langchain, llamaindex, AutoGPT → Given how fast this space moves, some parts are already be outdated. 👉 Overall: Good hands-on book for readers who like learning through code, but it would benefit from a refresh and stronger visual presentation.

I accidentally discovered how to compress a semester of learning into 48 hours. A grad student at MIT showed me his NotebookLM setup. I thought he was just organized. Then I watched him pass a qualifying exam on a subject he'd never studied before. Here's exactly what he did: First: he didn't upload a textbook. He uploaded 6 textbooks, 15 research papers, and every lecture transcript he could find on the subject. Then he asked NotebookLM one question: "What are the 5 core mental models that every expert in this field shares?" Not "summarize this." Not "explain this topic." Mental models. The stuff that takes professors years to develop. But the next part is what broke my brain. He followed up with: "Now show me the 3 places where experts in this field fundamentally disagree, and what each side's strongest argument is." In 20 minutes he had a map of the entire intellectual landscape of the field: the debates, the consensus, the open questions. Most students spend a full semester just figuring out what those debates even are. Then he did something I've never seen before. He asked: "Generate 10 questions that would expose whether someone deeply understands this subject versus someone who just memorized facts." He spent the next 6 hours answering those questions using the source material. Every wrong answer triggered a follow-up: "Explain why this is wrong and what I'm missing." By hour 48, he could hold a conversation with his thesis advisor without getting destroyed. The tool didn't change. The questions did. Most people treat NotebookLM like a fancy highlighter. These students are using it like a private tutor who has read everything ever written on the subject. The difference between a semester and 48 hours isn't the amount of content. It's knowing which questions to ask.

This guy shares Claude's most effective prompt structure.

7 Proven Methods To Learn Faster:

Best GitHub Repos to Build Real AI Skills in 2026: 1. OpenAI Cookbook github.com/openai/openai-… 2. OpenAI Agents SDK github.com/openai/openai-… 3. OpenAI Evals github.com/openai/evals 4. PydanticAI github.com/pydantic/pydan… 5. Hugging Face Agents Course github.com/huggingface/ag… 6. AI for Beginners github.com/microsoft/AI-F… 7. Hugging Face 101 Course github.com/huggingface/10… 8. Hugging Face Smol Course github.com/huggingface/sm… 9. AI Engineer Handbook github.com/DataExpert-io/… 10. AI Engineering Field Guide github.com/alexeygrigorev… Follow @DipanshuKu55175 Bookmark this.

"Everything You Wanted To Know About Mathematics" PDF at: math.cmu.edu/~jmackey/151_1…