Banger tweet, i think about it often because i feel it becoming a bottleneck. what's in my brain is "private" information for the LLM – it doesn't know what I know or don't know and I don't know what I don't know… The mind meld is only medium effective.
INNOVATION
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Robot intelligent nettoie et ramasse les objets avec un bras
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This Smart Cleaning #Robot Picks Up Clutter and Scrubs Floors with a #Robotic Arm
— Ronald van Loon (@Ronald_vanLoon) 31 mars 2026
by @XRoboHub#EmergingTech #Technology #Innovation pic.twitter.com/kcCM6YUminThis Smart Cleaning #Robot Picks Up Clutter and Scrubs Floors with a #Robotic Arm
by @XRoboHub #EmergingTech #Technology #Innovation -
Claude’s Secret Pomodoro Mastery Mode Revealed
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BREAKING: Claude has a secret mode called "Francesco Cirillo's Pomodoro Mastery Coach." It goes way beyond setting a 25-minute timer. It assesses where you're stuck, builds a full daily execution plan in Pomodoro units, teaches you to track and crush every interruption, and
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Structured AI reasoning over raw scale
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The takeaway for builders:
The future of reliable AI isn’t just bigger parameters. It’s giving models structured, verifiable environments to reason in. Typed control flow > open-ended code generation. Paper: http://
arxiv.org/abs/2603.20105
Code: http://
github.com/lambda-calculu
s-LLM/lambda-RLM
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Optimize AI inference with smaller models
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You’re probably overpaying for AI inference. Researchers just proved a small model with the right scaffolding crushes a model 50x its size on long-context tasks. Read this thread and you’ll know how to get better results with fewer resources:
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LLMs struggle with long inputs
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The problem: LLMs choke on long inputs. The usual fix? Bigger context windows. More parameters. More RAM. But there’s a deeper issue. When you let a model write its own recursive code to manage memory, you get infinite loops, broken outputs, and unpredictable costs. Brute
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T-Mobile and NVIDIA Explore Edge AI for Reduced Latency Performance
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From #GTC26: AI performance depends heavily on architecture. @TMobileBusiness and NVIDIA are exploring how edge compute can reduce latency by processing data closer to where it’s created. ⚡ fierce-network.com/broadband… T-Mobile for Business Partner #EdgeAI
→ View original post on X — @haroldsinnott, 2026-03-31 00:42 UTC
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AI Systems Still Struggling With Basic Visual Tasks
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amazing. i was giving examples like this for DALL-E three years ago. systems are still struggling with some basics.
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SquareMind Raises $18M for AI Skin Cancer Detection Robotic Platform
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This goal hasn’t changed https://
x.com/theahmadosman/
status/1978790002835267707?s=46
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