sites.northwestern.edu/highd… [Translated from EN to English]
→ View original post on X — @thegautamkamath, 2026-03-31 19:39 UTC
COMPUTING
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Complete AI Stack: Tools and Frameworks for Modern Development
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gemini for research copilot for coding claude for deep searches and agentic code openai for quick searches cerebras for faster ai inferences tavily for search api firecrawl for web scraping langchain for ai app development langsmith for agentic ai observation fastAPI for backend server docker for containerising the app streamlit for frontend interface
→ View original post on X — @avikumart_, 2026-03-31 17:40 UTC
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Agentic Inference: Hybrid GPU-RDU Hardware Strategy for Token Serving
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Agentic inference isn’t a “future trend” anymore — it’s the default. The real question: how do you serve fast, premium tokens without building a multi‑MW Frankenstack? Our take: GPUs for prefill, RDUs for decode. Hybrid > GPU‑only. 🔗 sambanova.ai/blog/agentic-in…
→ View original post on X — @sambanovaai, 2026-03-31 17:30 UTC
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Innventure CEO Claims Stock Undervalued, Accelsius at Center
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A very hot play on AI data centers, Accelsius, continues to be at the heart over the debate about conglomerate Innventure. Innventure CEO says his stock is too cheap; it’s hard to say Bill Haskell, CEO of conglomerate Innventure, which owns a very talked-about maker of technology to cool Nvidia GPUs, Accelsius, says his company’s stock is too cheap relative to the value of Accelsius. Maybe so, although Innventure and Accelsius are still early-stage ventures with a lot to prove. thetechnologyletter.com $INV
→ View original post on X — @tiernanraytech, 2026-03-31 16:08 UTC
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Contact Trust Region: Efficient Robot Manipulation with Linearized Dynamics
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Where can linearized dynamics be trusted w/contact?
— MIT CSAIL (@MIT_CSAIL) 31 mars 2026
MIT's "Contact Trust Region" answers this using manipulation theory, differentiable simulation & control. It enables manipulation w/less compute than RL, powering robots to be more energy-efficient: https://t.co/i0Uza8QveU pic.twitter.com/7kaueisigyWhere can linearized dynamics be trusted w/contact? MIT's "Contact Trust Region" answers this using manipulation theory, differentiable simulation & control. It enables manipulation w/less compute than RL, powering robots to be more energy-efficient: tinyurl.com/mw48m2wf
→ View original post on X — @mit_csail, 2026-03-31 16:00 UTC
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AI System Teaches Humanoid Robots to Play Tennis
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#AI System Teaches Humanoid #Robots to Play Tennis from Human Motion #Data
— Ronald van Loon (@Ronald_vanLoon) 31 mars 2026
by @TheHumanoidHub#Robotics #Engineering #ArtificialIntelligence #Innovation #Technology pic.twitter.com/y6fB6FaAr4#AI System Teaches Humanoid #Robots to Play Tennis from Human Motion #Data
by @TheHumanoidHub #Robotics #Engineering #ArtificialIntelligence #Innovation #Technology -
Windows Developers Face Familiar Tech Initiative Pattern Amid AI Focus
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Windows developers have seen this energy before, UWP, WinUI, Project Reunion, all started with similar momentum before losing executive sponsorship quietly. The difference this time is it aligns with hardware strategy, AI integration and competitive pressure against Apple
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Cerebras threatens NVIDIA’s GPU dominance with single-chip technology
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NVIDIA’s trillion-dollar dominance relies on the complex art of horizontal scaling, but Cerebras poses a dangerous threat by proving that one giant chip can eliminate the communication bottlenecks of massive GPU clusters. If AI workloads shift toward single-system training and… pic.twitter.com/svpfJ2QP2i
— Satya Mallick (@LearnOpenCV) 31 mars 2026NVIDIA’s trillion-dollar dominance relies on the complex art of horizontal scaling, but Cerebras poses a dangerous threat by proving that one giant chip can eliminate the communication bottlenecks of massive GPU clusters. If AI workloads shift toward single-system training and ultra-fast inference, Nvidia's greatest strength—distributed computing—could quickly become an obsolete solution to a solved problem.
→ View original post on X — @learnopencv, 2026-03-31 11:30 UTC
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Unbounded Indexing Challenges in High-Ingestion Systems
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Indexes aren’t the problem. Unbounded indexing in high-ingestion systems is. IoT, observability, AI telemetry, financial feeds all share the same pattern: → continuous ingestion
→ append-only data
→ time-based queries
→ massive retention Eventually the architecture -
AetheroSpace Launches Phobos Satellite for Orbital Intelligence
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Congrats to our friends @AetheroSpace on their (literal) launch! 🚀🛰️ https://t.co/8vyM7te7is
— Gill Verdon (@GillVerd) 31 mars 2026Congrats to our friends @AetheroSpace on their (literal) launch! 🚀🛰️ Edward (@somefoundersalt) Proud to announce that Phobos, the second @AetheroSpace satellite, was successfully launched to orbit earlier this morning We’re partnered with @BoozAllen on this mission to demonstrate capabilities for adaptive event detection using high-fidelity Earth observation data collected on orbit This will enable satellites to achieve faster, smarter, decision-making on orbit, and is a major step towards building the orbital intelligence layer for defense needs! — https://nitter.net/somefoundersalt/status/2038788178002522343#m
→ View original post on X — @bobgourley, 2026-03-31 05:05 UTC