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  • Gamma 31b Unexpectedly Outperforms Qwen 3.5 397B Model
    Gamma 31b Unexpectedly Outperforms Qwen 3.5 397B Model

    By

    @kimmonismus

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    Gamma 31b model outperforming Qwen 3.5 397B is nuts to me.

    → View original post on X — @kimmonismus

  • Waymo Autonomous Vehicles Transform Customer Driving Preferences

    By

    @scobleizer

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    Totally. Been interviewing Waymo customers and they love them. Getting three rides in one changes them deeply. Many say that before they got that experience they thought they "loved to drive" or "could never trust a computer to drive." After? They say: 1. I'm never going

    → View original post on X — @scobleizer

  • Google Releases Gemma 4 Open-Source Models with Impressive Performance

    By

    @kimmonismus

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    Here we go: Gamma 4 released: ""Outperforms models 20x its size" Google dropped Gemma 4 under Apache 2.0, full open-source, big licensing shift. Built on Gemini 3 tech, four sizes: E2B, E4B, 26B MoE, 31B Dense. Price-performance: 31B is #3 open model on Arena AI, 26B MoE is #6

    → View original post on X — @kimmonismus

  • Gemma4 vindicated: dense models triumph over mixture of experts
    Gemma4 vindicated: dense models triumph over mixture of experts

    By

    @jeremyphoward

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    Gemma4 is amazing. You'll read that everywhere. Let's focus on what is HUGE here: the revenge of dense models…. Throw away your b200, not needed anymore, throw away the millions of lines of code we had to write to make MOEs faster, training stable etc… throw away your router-aware kernel, your EP DEEP GEMM, throw away the auxiliary loss function. Welcome to simplicity, dense is the new king. FINALLY hating MoEs is back to being chad. For those who know me: I was always a moe doomer

    → View original post on X — @jeremyphoward, 2026-04-02 16:23 UTC

  • Microsoft Redefines Superintelligence From Godlike Intelligence to Product Value
    Microsoft Redefines Superintelligence From Godlike Intelligence to Product Value

    By

    @garymarcus

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    Talk about moving goalposts. This one from MSFT’s Suleyman may well take the cake. “Superintelligence” just went from intelligence beyond all humans to merely “delivering product value”.

    → View original post on X — @garymarcus

  • MIT Measures AI Capabilities in Economic Tasks
    MIT Measures AI Capabilities in Economic Tasks

    By

    @mit_csail

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    A first-of-its-kind study from MIT measures how successful AI is in completing thousands of tasks done by workers in the US economy. Across these real-world tasks, they found that AI capabilities are improving quickly, but performance is rising smoothly: bit.ly/3Q1JQZD [Translated from EN to English]

    → View original post on X — @mit_csail, 2026-04-02 16:01 UTC

  • Waymo Autonomous Cars Becoming Normal in Daily Life

    By

    @scobleizer

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    Totally. And my autonomous car is feeling normal. I noticed that by interviewing Waymo customers. They see it as normal life now. One guy has taken one to work every day for two years.

    → View original post on X — @scobleizer

  • Enterprise AI Playbook: Key Lessons from 51 Successful Deployments
    Enterprise AI Playbook: Key Lessons from 51 Successful Deployments

    By

    @erikbryn

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    What do successful deployments of AI have in common? It was awesome working with Elisa Pereira and @AGraylin on this research. We studied 51 companies and summarized the results. Alvin has a nice summary below. Check out digitaleconomy.stanford.edu/… for the full report. Alvin Wang Graylin (@AGraylin) New Research💡: “The #Enterprise #AI Playbook — Lessons from 51 Successful Deployments” Excited to share new research from Stanford @DigEconLab, I co-authored with @erikbryn and Elisa Pereira . We spent 5 months interviewing executives across 41 organizations, 9 industries, and 7 countries — focusing exclusively on AI deployments that actually delivered measurable value. Not hype. Not predictions. What’s working right now, and why. A few findings that challenged even our assumptions: The hard part isn’t the AI. 77% of the toughest challenges were invisible costs — change management, data quality, process redesign. Technology was consistently described as the easiest part. Same use case, wildly different timelines. One company deployed AI customer support in weeks. Another took years. Same models. The difference was always the #organization — its #leadership, processes, and willingness to fail. #Agentic AI works — but most firms haven’t tried it yet. Only 20% of our cases were agentic, but they delivered 71% median gains vs. 40% for high-automation. This gap will widen fast. The model is increasingly a #commodity. For 42% of implementations, model choice was fully interchangeable. The durable advantage is in orchestration, data, and process — not the foundation model. With productivity increase, headcount #reduction is common (45%), but not the majority outcome. Redeployment, hiring avoidance, and acceleration strategies accounted for 55% of cases.🚨 The window for experimentation is closing. This is no longer a question of whether AI delivers value. It’s whether organizations can evolve fast enough to capture it — and whether leaders will take responsibility for smoothing the transition for workers and communities along the way. Full report (free): digitaleconomy.stanford.edu/… @StanfordHAI — https://nitter.net/AGraylin/status/2039729157676921185#m

    → View original post on X — @erikbryn, 2026-04-02 15:48 UTC

  • Salesforce Agentforce Transforms Government Service Delivery with AI

    By

    @yuhelenyu

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    How is @Salesforce’s Agentforce impacting government? AI in the public sector has moved from hesitation to execution. Agents are now live and reducing costs, improving service delivery, and handling millions of citizen interactions. #sponsored The bigger shift is from reactive systems to proactive, personalized government. This is a redefinition of how government serves citizens: piped.video/EKH6cmeJC3E #AI #GovAI #Agentforce #Missionforce #SalesforcePartner

    → View original post on X — @yuhelenyu, 2026-04-02 15:45 UTC

  • AI Agents Managing Startups: YC-Bench Tests Profitability and Survival
    AI Agents Managing Startups: YC-Bench Tests Profitability and Survival

    By

    @dair_ai

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    Can an AI agent run a startup for a year without going bankrupt? Turns out most can't. New benchmark from Collinear AI puts 12 models to the test. YC-Bench tasks agents with running a simulated startup over hundreds of turns: hiring employees, selecting contracts, and maintaining profitability in a partially observable environment with adversarial clients and compounding consequences. Only three models consistently surpass the $200K starting capital. Claude Opus 4.6 leads at $1.27M average final funds, followed by GLM-5 at $1.21M with 11x lower inference cost. Scratchpad usage, the sole mechanism for persisting information across context truncation, is the strongest predictor of success. Adversarial client detection accounts for 47% of bankruptcies. Long-horizon coherence, not raw intelligence, separates the winners from the bankrupt. Paper: arxiv.org/abs/2604.01212 Learn to build effective AI agents in our academy: academy.dair.ai/

    → View original post on X — @dair_ai, 2026-04-02 15:37 UTC