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  • Gemma 4 31B Quantized Models Evaluated on NVFP4 and FP8

    By

    @clementdelangue

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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

  • Harness Engineering: Building Better AI Agent Systems
    Harness Engineering: Building Better AI Agent Systems

    By

    @whats_ai

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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?

    → View original post on X — @whats_ai, 2026-04-05 12:00 UTC

  • Virtual employees target 50 trillion in knowledge worker value

    By

    @fogoros

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    Virtual employees targeting $50 trillion in knowledge worker value. Partner content with Siemens. This dwarfs every other technology disruption in economic scale. The next two years will separate the prepared from the disrupted. . #sie_HM.

    → View original post on X — @fogoros, 2026-04-05 11:58 UTC

  • Fully Autonomous Enterprise AI Is A Myth
    Fully Autonomous Enterprise AI Is A Myth

    By

    @ronald_vanloon

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    Fully #Autonomous Enterprise #AI Is A Myth
    by Sunil Padiyar @Forbes Learn more: https://
    bit.ly/4doGV78 #ArtificialIntelligence #MachineLearning #ML

    → View original post on X — @ronald_vanloon

  • AlphaEvolve Optimizes FM Logistic Warehouse Routing by 10.4%

    By

    @demishassabis

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    Using AI to solve the Traveling Salesman Problem at warehouse scale. 📦 AlphaEvolve helped FM Logistic improve its routing algorithm by 10.4%, resulting in a reduction of total warehouse travel by over 15,000 km per year. 🚚 A great example of how @GoogleDeepMind and @googlecloud are using AlphaEvolve to help companies become more efficient. Read more at: cloud.google.com/blog/produc…

    → View original post on X — @demishassabis, 2026-04-05 07:21 UTC

  • GPT-5.5 ‘Spud’ Leaks: OpenAI’s Omnimodal AI Frontier
    GPT-5.5 ‘Spud’ Leaks: OpenAI’s Omnimodal AI Frontier

    By

    @ceobillionaire

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    GPT-5.5: The “Spud” Leaks & The New Frontier of Omnimodal AI – A New Foundation: Unlike incremental updates, GPT-5.5 (codenamed “Spud”) is rumored to be a completely new pre-trained base, built on nearly two years of focused research. – Big Model Smell: OpenAI’s Greg Brockman points to a major qualitative shift models becoming less rigid and more intuitive, adapting to user intent without over-explanation. – Omnimodal & Agentic: Designed as a natively omnimodal system, GPT-5.5 is expected to function as a highly autonomous agent rather than a traditional chatbot. – Extreme Time Horizons: A key goal is extending long form reasoning handling complex, open-ended tasks over significantly longer timeframes. – Unlocking New Abilities: Early signals suggest it can solve tasks that previously required heavy prompting or weren't feasible for LLMs at all. – The Arena Tease: Rumored first-pass image generations are already surfacing in AI arenas, hinting at early testing or a near-term reveal. – The Pricing War: While competitors like Claude Mythos are rumored at $100 per 1M tokens, OpenAI may price GPT-5.5 more aggressively to drive adoption. – Imminent Rollout: Following recent hints from leadership, “Spud” could arrive soon as a key step toward OpenAI’s broader AGI push. (Unverified leaks; treat performance claims, naming, and timelines with caution.)

    → View original post on X — @ceobillionaire, 2026-04-05 06:00 UTC

  • Agentic AI: Execution vs. True Decision-Making Capability
    Agentic AI: Execution vs. True Decision-Making Capability

    By

    @pascal_bornet

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    When you ask an agentic AI to implement your design… it executes perfectly. The gap: judgment and context. Execution is solved. Judgment and context aren’t. Agentic systems can act autonomously. But they don’t always understand what matters… or why. This is where things start to matter. So here’s the real question: Are we building agents that act… or systems that can truly decide? #ArtificialIntelligence #AI #AgenticAI #FutureOfWork #Innovation Credits: Ralph

    → View original post on X — @pascal_bornet, 2026-04-05 05:00 UTC

  • Context Layer: The Core of Enterprise AI Stack Architecture

    By

    @langchain

    –

    One of the core things we’re going to have to contend with in AI is that even the most advanced models in the word can’t have all the relevant knowledge needed to be useful, because everyone has different use-cases and ways they’ve designed their workflows. Perhaps most importantly, as you get into the enterprise, everyone has entirely different access levels to corporate knowledge and information. Continual learning at the model layer, even at a single enterprise level, is near impossible because every user knows and has access to something different than another user. This isn’t like coding where by and large most developers can access all the relevant stuff to their job. On a single banking team, bankers have entirely different sets of documents they’re ever allowed to see. Sanitizing this is hard and having the model keep secrets is impossible. This is why the context layer is going to always be the core part of the AI stack for applied use cases to turn general models turn into useful agents. Can’t fight the physics on this one. Harrison Chase (@hwchase17) x.com/i/article/204046441296… — https://nitter.net/hwchase17/status/2040467997022884194#m

    → View original post on X — @langchain, 2026-04-05 04:40 UTC

  • Warehouse Automation Using Robotics and AI Technology
    Warehouse Automation Using Robotics and AI Technology

    By

    @ronald_vanloon

    –

    How to #Automate Your #Warehouse with #Robotics
    by @antgrasso #Robots #RPA #ArtificialIntelligence #Innovation #Technology

    → View original post on X — @ronald_vanloon

  • Microsoft’s MAI-Transcribe-1: The Underrated AI Infrastructure Layer
    Microsoft’s MAI-Transcribe-1: The Underrated AI Infrastructure Layer

    By

    @nandodf

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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.

    → View original post on X — @nandodf, 2026-04-05 01:01 UTC