Can we make AI reasoning much faster and more efficient without losing its smarts? Researchers from the University of Maryland, Washington University in St. Louis, and UNC Chapel Hill introduce Parallel-Probe. This innovative, training-free controller uses "2D probing" to
AI
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LLMs struggle to generate useful APL code effectively
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I think there's a fair reaction. OTOH, when using LLMs with APL, which is an extremely efficient and well-designed language, AI is hasn't been able to create any useful code at all for me so far. So their conclusions may be correct anyway…
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Cursor Launches Early Alpha of New Glass Interface
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We're also sharing an early alpha of our new interface. https://
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Resource allocation strategies for technology projects
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The new “how many resources should we put on this project”
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Micron’s Incredible Q2 Results Could Push Stock to $700
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Micron could go as high as $700 as the numbers are just incredible Micron Technology turned in an astounding second-quarter report, with gross profit of seventy-five percent, and a twenty-billion-dollar-per-quarter DRAM business that is tripling. Price targets are as high as $700. And yet, people are selling on the news. The chief risk to Micron is over-supply, and Micron itself doesn’t know how much supply would be too much. Probably, over-supply risk is remote at the moment, even if it’s not zero. thetechnologyletter.com $MU $NVDA
→ View original post on X — @tiernanraytech, 2026-03-19 19:48 UTC
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Neural nets are overkill, especially for inference
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Neural nets are an overkill. Especially for inference.
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Perplexity Computer Integrates Health Apps and Wearable Devices
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excited to try this out !! https://t.co/jjY8HYU1OH
— Lewis Hamilton (@LewisHamilton) 19 mars 2026excited to try this out !! Perplexity (@perplexity_ai) Perplexity Computer now connects to your health apps, wearable devices, lab results, and medical records. Build personalized tools and applications with your health data, or track everything in your health dashboard. — https://nitter.net/perplexity_ai/status/2034668608375382346#m
→ View original post on X — @aravsrinivas, 2026-03-19 19:38 UTC
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Reasoning versus Pattern Matching: Causal and Correlative Models
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To make it very short: reasoning generates causal models of the data, pattern matching uses associative/correlative models of the data.
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Model Limitations: Why AGI Needs True Metalearning Capabilities
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The fact that you need to provide a specialized harness clearly shows the model *does not* encode the kind of metalearning knowledge and problem-solving strategies that humans use. Humans solve novel problems without being told how to proceed step by step. AGI would *not* need a
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Image Generation Prompt Optimization: Disabling Chaos and Using HD
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great test prompt! lots of tiny hands and feet and ice cream. we found turning off chaos/exp and going –hd helps but you still may need to try a few times to get every detail