Tests passing while complexity explodes from 29 to 285 is the perfect illustration of why benchmarks are misleading right now. The field keeps measuring "can AI write code" when the real question is "can it maintain software." Very different things.
MACHINE LEARNING
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Language Thinking Limitations and Abstract Mental Models in AI
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Thinking in language has limited applications, largely in coding and mathematics where the language itself can help reasoning. But, as I've been saying for years, thinking manipulates mental models in abstract (continuous) representation space. Soooo, xAI gonna use JEPA now?
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Gemma 4 31B Quantized Models Evaluated on NVFP4 and FP8
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Gemma 4 31B, quantized and evaluated. Instruction following evals are live on our NVFP4 and FP8-block model cards. Results look great. Reasoning and vision evals coming later this week. NVFP4: huggingface.co/RedHatAI/gemm… FP8: huggingface.co/RedHatAI/gemm… Red Hat AI (@RedHat_AI) The open source ecosystem moved fast on Gemma 4 today. Google DeepMind released it. @vllm_project had Day 0 support across diverse accelerators. Red Hat AI Inference Server is ready for Gemma 4 experimentation too. Guide in the reply 👇 — https://nitter.net/RedHat_AI/status/2039876315222782215#m
→ View original post on X — @clementdelangue, 2026-04-05 12:20 UTC
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Agentic AI Periodic Table: Memory, Planning, Tools, Safety
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Agentic AI now has its own “Periodic Table” 🧪🤖 From: LLM, RAG, RL to PLAN, MAS, LTM to SAFE, HUMAN oversight to HR, MKT, LEGAL use cases Autonomous AI = memory + planning + tools + safety + collaboration. It’s a system, not a prompt. Credit: Prem Natarajan #AgenticAI #AIStack #LLM
→ View original post on X — @ingliguori, 2026-04-05 12:17 UTC
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Harness Engineering: Building Better AI Agent Systems
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I let Claude Code loop for 45 minutes while I was at the gym. Came back. It told me the feature was done. It wasn't. It hadn't even run the tests. Not because the model is dumb. Because I wrapped it in nothing but a loop and a dream. That's harness engineering in one sentence. And no, it's not prompt engineering with a fancier name. The model is the engine. Context is the fuel. The harness is the rest of the car. Steering. Brakes. Lane boundaries. Warning lights. Tools, permissions, tests, retries, guardrails. Engine + fuel but not strong parts in it = dangerous car. So I stopped tuning the engine and started building the car around it. In every skill file (Claude Code, Claude Co-work), I added one last step. After each interaction, the agent reflects on what I liked, what I edited, what failed. Then it updates its own skill to be better next time. Token usage dropped (a lot). Output quality went up. Compounding improvement with zero extra effort from me. LangChain did something similar at a bigger scale. Changed only the harness on a coding agent. Same model. Went from outside the top 30 to top 5 on a benchmark. Same engine, completely different results, just because the car around it was better. Next time your agent breaks, don't blame the model. Fix the car. P.S. Do your agents learn from their mistakes, or do they keep making the same ones?
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Fully Autonomous Enterprise AI Is A Myth
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Fully #Autonomous Enterprise #AI Is A Myth
by Sunil Padiyar @Forbes Learn more: https://
bit.ly/4doGV78 #ArtificialIntelligence #MachineLearning #ML -
Computer Vision Foundation Connects Waymo, AR, and Humanoid Robots
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I saw this early as Adrian Kaehler built the first computer vision system that started Waymo and later worked on augmented reality and humanoid robots. He told me the technology on all three is very similar.
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Real-Time Adaptive Robotics: Beyond Entertainment to Practical Impact
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A Spider-Man stunt… executed by a robot.
— Pascal Bornet (@pascal_bornet) 5 avril 2026
That says more about robotics than it seems.
Disney Imagineers built a system that flies over 25 meters in the air → adjusting its motion in real time.
→ flips
→ rotation
→ speed control
→ balance
All handled mid-flight.
What… pic.twitter.com/izWYf7yStzA Spider-Man stunt… executed by a robot. That says more about robotics than it seems. Disney Imagineers built a system that flies over 25 meters in the air → adjusting its motion in real time. → flips → rotation → speed control → balance All handled mid-flight. What stands out to me is not the spectacle… it’s the decision-making happening in the air. This isn’t scripted motion. It’s real-time adaptation to physics. That’s the shift. From robots that repeat actions to systems that respond to the environment And once that threshold is crossed, the implications extend far beyond entertainment: → high-risk environments → dynamic industrial tasks → real-world human assistance This is where things start to matter. Because the ability to adapt in real time is what turns machines into systems we can rely on. So here’s the real question: Where will real-time adaptive robotics create the most value next? #ArtificialIntelligence #Robotics #Innovation #FutureOfWork #Technology
→ View original post on X — @pascal_bornet, 2026-04-05 09:01 UTC
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AI Communities Interconnected Across X Platform Networks
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I mapped out ask these communities on X: https://
x.com/scobleizer/lis
ts
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Gemma-4-E4B: Local AI Agents Identify Sea Animals with Vision
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Watch Gemma-4-E4B casually identify sea animals by classifying images in a single agentic session using its vision capabilities. (impressive for a 4B model 🚀)
— Victor M (@victormustar) 5 avril 2026
I'm convinced: the agents of tomorrow are local, free, fast, and run on every computer! pic.twitter.com/mHPMiPPNSZWatch Gemma-4-E4B casually identify sea animals by classifying images in a single agentic session using its vision capabilities. (impressive for a 4B model 🚀) I'm convinced: the agents of tomorrow are local, free, fast, and run on every computer!
→ View original post on X — @deeplearn007, 2026-04-05 08:41 UTC