This is such an inspiring video. It also makes me feel proud that we are building AI tools to empower these amazing scientists to expand our knowledge. Modern physics is forcing us to rethink existence | Michelle Thaller: Fu… piped.video/LcC5ilQKQGc?si=Gvdn… via @YouTube
@nandodf
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Microsoft’s MAI-Transcribe-1: The Underrated AI Infrastructure Layer
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The most underrated infrastructure play in AI just landed — and almost nobody is talking about it. @Microsoft quietly released three new MAI models this week, and one of them changes the game for voice-first products. MAI-Transcribe-1 delivers state-of-the-art speech-to-text across 25 languages at 2.5x the speed of any existing Azure offering. Why does this matter more than another chatbot upgrade? Because the next billion AI users will not type. They will speak. In Lagos, in Jakarta, in Sao Paulo — the default interface is voice. And whoever owns the transcription layer owns the entry point to every downstream agent, workflow, and decision. This is the same pattern we saw with cloud storage a decade ago. Nobody built a business on S3. But almost every important business ran on top of it. Transcription is becoming the S3 of agentic AI — invisible, essential, and a winner-take-most market. @SatyaNadella is not competing with @OpenAI on vibes. He is competing on plumbing. And plumbing tends to win. If you are building anything voice-adjacent — customer support, health intake, field operations, education — this week's release should be on your radar. Daily rec: @benedictevans on infrastructure economics in AI. Always worth reading twice.
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The Well: Open-Source Library of Physics Simulations for AI
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Imagine trying to teach someone how to swim just by letting them read books about water. That is how we have been training AI on physics, using text descriptions. To really learn, you need to get in the water. "The Well" is that water. Polymathic AI has released a massive 15TB open-source library of physics simulations. It allows AI models to experience physical phenomena directly. Instead of reading about a supernova, the model processes the actual data of the explosion. Instead of reading about aerodynamics, it analyzes the fluid flow. This moves us from [Generative AI] (making things up) to [Scientific AI] (discovering truth). A huge step forward for open science. GitHub Repo: github.com/PolymathicAI/the_well/ [Translated from EN to English]
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Complete AI Learning Roadmap: Videos, Repos, Books, Papers, Courses
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Stop wasting hours trying to learn AI. 📘📚 I have already done it for you. With one list. Zero confusion. And no fluff 📹 Videos: 1. LLM Introduction: lnkd.in/dMqbaZdK 2. LLMs from Scratch: lnkd.in/dYYwEhYy 3. Agentic AI Overview (Stanford): lnkd.in/dArmMt2i 4. Building and Evaluating Agents: lnkd.in/dBWd2W8u 5. Building Effective Agents: lnkd.in/dHfdebqw 6. Building Agents with MCP: lnkd.in/dXuNHrRJ 7. Building an Agent from Scratch: lnkd.in/da3ANw3w 8. Philo Agents: lnkd.in/dq-BfZE5 🗂️ Repos 1. GenAI Agents: lnkd.in/d3UDtwwv 2. Microsoft's AI Agents for Beginners: lnkd.in/dHvTmJnv 3. Prompt Engineering Guide: lnkd.in/gJjGbxQr 4. Hands-On Large Language Models: lnkd.in/dxaVF86w 5. AI Agents for Beginners: lnkd.in/dHvTmJnv 6. GenAI Agentshttps://lnkd.in/dEt72MEy 7. Made with ML: lnkd.in/d2dMACMj 8. Hands-On AI Engineering:lnkd.in/dgQtRyk7 9. Awesome Generative AI Guide: lnkd.in/dJ8gxp3a 10. Designing Machine Learning Systems: lnkd.in/dEx8sQJK 11. Machine Learning for Beginners from Microsoft: lnkd.in/dBj3BAEY 12. LLM Course: lnkd.in/diZgGACG 🗺️ Guides 1. Google's Agent Whitepaper: lnkd.in/gFvCfbSN 2. Google's Agent Companion: lnkd.in/gfmCrgAH 3. Building Effective Agents by Anthropic: lnkd.in/gRWKANS4. 4. Claude Code Best Agentic Coding practices: lnkd.in/gs99zyCf 5. OpenAI's Practical Guide to Building Agents: lnkd.in/guRfXsFK 📚Books: 1. Understanding Deep Learning: lnkd.in/dgcB68Qt 2. Building an LLM from Scratch: lnkd.in/g2YGbnWS 3. The LLM Engineering Handbook: lnkd.in/gWUT2EXe 4. AI Agents: The Definitive Guide – Nicole Koenigstein: lnkd.in/dJ9wFNMD 5. Building Applications with AI Agents – Michael Albada: lnkd.in/dSs8srk5 6. AI Agents with MCP – Kyle Stratis: lnkd.in/dR22bEiZ 7. AI Engineering: lnkd.in/gi-mQcXa 📜 Papers 1. ReAct: lnkd.in/gRBH3ZRq 2. Generative Agents: lnkd.in/gsDCUsWm. 3. Toolformer: lnkd.in/gyzrege6 4. Chain-of-Thought Prompting: lnkd.in/gaK5CXzD. 🧑🏫 Courses: 1. HuggingFace's Agent Course: lnkd.in/gmTftTXV 2. MCP with Anthropic: lnkd.in/geffcwdq 3. Building Vector Databases with Pinecone: lnkd.in/gCS4sd7Y 4. Vector Databases from Embeddings to Apps: lnkd.in/gm9HR6_2 5. Agent Memory: lnkd.in/gNFpC542 Repost for your network ♻️
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Gemma 4 31B Dominates Open Source Leaderboard with Superior Performance
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Gemma 4 31B shifts the Pareto frontier, scoring +30 Arena points above similarly priced models like DeepSeek 3.2. Its position on the Pareto frontier is based on early pricing indicators from third parties. Arena.ai (@arena) Gemma 4 by @GoogleDeepMind debuts at 3rd and 6th on the open source leaderboard, making it the #1 ranked US open source model. By total parameter count, Gemma 4 31B is 24× smaller than GLM-5 and 34× smaller than Kimi-K2.5-Thinking, delivering comparable performance at a fraction of the footprint. — https://nitter.net/arena/status/2039782449648214247#m
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Ilya Sutskever Recognized for Deep Learning Revolution Contributions
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Ilya Sutskever (@ssi) helped launch the deep learning revolution. From AlexNet to GPT, his work has pushed the boundaries of AI, powering advances from language models to image generation and beyond. Read more: ow.ly/AAIe50YCSXF #NASaward #ArtificialIntelligence
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MultiGen: Level Design for Editable Multiplayer Worlds in Diffusion Engines
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MultiGen
— AK (@_akhaliq) 3 avril 2026
Level-Design for Editable Multiplayer Worlds in Diffusion Game Engines
paper: https://t.co/UT01uUYBxj pic.twitter.com/AMY0bDusAjMultiGen Level-Design for Editable Multiplayer Worlds in Diffusion Game Engines paper: huggingface.co/papers/2603.0…
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τ³-bench: Interactive Agent Evaluations for Knowledge and Voice
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Really excited for the release of 𝜏³-bench, which brings interactive agent evals ever closer to real-world use cases across two dimensions: 1. 𝜏-knowledge evaluates agents that need to operate over noisy knowledge bases to figure out the correct policies/tools to use while serving a user 2. 𝜏-voice tests voice agents in interactive customer service style settings. If you are developing embedding or voice models for AI agents, 𝜏³ is a great testbed for you to see how your models would perform in a realistic downstream use case. Blog: sierra.ai/blog/bench-advanci… Tweets from @BenShi34 and @keshav_57: nitter.net/benshi34/status/203436… nitter.net/keshav_57/status/20346…
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Microsoft’s MAI-Image-2 AI Model Generates Creative Images
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Here are few image create using Microsoft's new ai image model – MAI-Image-2 with prompts.
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Claude Code Skill Converts ArXiv Papers to Working Code
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I made a Claude Code skill that turns any arxiv paper into working code.
— pdawg (@prathamgrv) 3 avril 2026
Every line traces back to the paper section it came from & any implementation detail the paper skips will be flagged, and not assumed.
open sourcing it –https://t.co/sSio4JfpIo pic.twitter.com/5XqlGgQsqCI made a Claude Code skill that turns any arxiv paper into working code. Every line traces back to the paper section it came from & any implementation detail the paper skips will be flagged, and not assumed. open sourcing it – github.com/PrathamLearnsToCo…