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  • Generative AI: 90% Accuracy, 10% Unacceptable Errors
    Generative AI: 90% Accuracy, 10% Unacceptable Errors

    Imagine if your car randomly went out of control 10% of the time. That's commercial-grade generative AI web search. rat king 🐀 (@MikeIsaac) glass half full: 90 percent accuracy is an impressive accuracy rate glass half empty: 10 percent error rate for a company that does more than 5 Trillion search queries per year is still a gigantic number nytimes.com/2026/04/07/techn… — https://nitter.net/MikeIsaac/status/2041535422623609174#m [Translated from EN to English]

    → View original post on X — @garymarcus, 2026-04-07 16:16 UTC

  • DeepSeek Version 4 Launch: New Expert Model Update
    DeepSeek Version 4 Launch: New Expert Model Update

    Looks like a new Deepseek is launched. Verison 4 incoming? Teortaxes▶️ (DeepSeek 推特🐋铁粉 2023 – ∞) (@teortaxesTex) DeepSeek update is here. Let's see what "Expert" brings. — https://nitter.net/teortaxesTex/status/2041545727034224781#m

    → View original post on X — @kimmonismus, 2026-04-07 16:05 UTC

  • Google’s 10% Error Rate: Shift from Pre-ChatGPT Standards
    Google’s 10% Error Rate: Shift from Pre-ChatGPT Standards

    10% error rate at any scale would never have been tolerated by Google pre-ChatGPT. The company has fundamentally changed rat king 🐀 (@MikeIsaac) glass half full: 90 percent accuracy is an impressive accuracy rate glass half empty: 10 percent error rate for a company that does more than 5 Trillion search queries per year is still a gigantic number nytimes.com/2026/04/07/techn… — https://nitter.net/MikeIsaac/status/2041535422623609174#m

    → View original post on X — @garymarcus, 2026-04-07 15:51 UTC

  • AI Agents Limited by Access, Not Intelligence: Browser as Solution

    There’s a growing narrative that AI agents are being held back by model limitations. From what I’ve observed, that’s only part of the story. The bigger constraint is access. Most of the internet was designed for humans navigating interfaces, clicking buttons, filling forms, and handling imperfect flows. APIs expose a clean layer, but they represent only a fraction of where real work actually happens. The moment an agent moves beyond a controlled demo and tries to operate in the wild, things start breaking. Pages render unpredictably. Authentication becomes messy. Workflows lack standardization. Assumptions about APIs fall apart. So the problem shifts. It becomes less about how well the agent can reason and more about whether it can operate. That’s why @browserbase caught my attention. Treating the browser as the primary interface for agents feels like a fundamental shift. Instead of forcing the world into APIs, it allows agents to interact with the web as it already exists. Once agents can reliably log in, navigate, and execute tasks across real systems, the outcome evolves from assistance to execution. The next phase of AI will be defined by reliable execution in real environments. Kudos to @pk_iv and the @Browserbase team for pushing this forward 👏♥️ Paul Klein IV (@pk_iv) Your agents suck when using the web because 85% of it doesn't have an API. Browserbase gives them everything they need to do work online. Leading AI companies like Ramp, Lovable, and Clay trust us to power agents that do real work on behalf of real people. With a single API key, your agent gets everything it needs to navigate the wild web: browsers, search, fetch, identity, a sandbox runtime, and model gateway. Stop waiting on integrations, build agents that can browse and interact with the web just like humans. — https://nitter.net/pk_iv/status/2041518621290266632#m

    → View original post on X — @scobleizer, 2026-04-07 15:27 UTC

  • Business Value of Agentic AI: Three Key Pillars for Scaling
    Business Value of Agentic AI: Three Key Pillars for Scaling

    What's The #Business Value Of #AgenticAI? Three Key Pillars For Scaling #AIAgents by Preetpal Singh @Forbes Learn more: buff.ly/d3CwrxT #LLM #GenerativeAI #ArtificialIntelligence #MachineLearning

    → View original post on X — @ronald_vanloon, 2026-04-07 15:23 UTC

  • AI’s Hybrid Strategy: Sharing Influence and Competitive Control
    AI’s Hybrid Strategy: Sharing Influence and Competitive Control

    A new divide appears. For our free newsletter this week, we discuss how the AI industry is moving toward a new divide, open sourcing enough to spread influence while reserving their strongest models to preserve their strategic edge. @IrenaCronin and I write this newsletter every week. The AI industry is moving toward a hybrid strategy in which companies share enough of their models and tools to build adoption, developer loyalty, and ecosystem influence, while keeping their most advanced systems closed to protect competitive advantage, control risk, and capture more value. Instead of a simple open versus closed divide, AI is increasingly becoming a spectrum shaped by business strategy, safety concerns, and market competition. Read for free at unaligned.io and please subscribe! [Translated from EN to English]

    → View original post on X — @scobleizer, 2026-04-07 15:20 UTC

  • AI-RAN: Artificial Intelligence Built Into Mobile Networks
    AI-RAN: Artificial Intelligence Built Into Mobile Networks

    What is AI-RAN? It’s when AI is built directly into the mobile network—not just running in the cloud. That means faster decisions, lower latency, and smarter systems. I explain it simply here: linkedin.com/pulse/how-netwo… #MWC26 @SoftBank @SoftBank_RandD @ericsson @techiemats

    → View original post on X — @haroldsinnott, 2026-04-07 15:11 UTC

  • Automated competitor analysis: From manual stalking to couch relaxation.

    I used to spend Sunday nights manually stalking competitor metrics. This week I asked PokeeClaw to handle it. Competitor analysis → visual report → emailed to me → Slack notification to my team. All while I was on the couch doing nothing.

    → View original post on X — @aibreakfast

  • Enterprise AI Adoption: Fragmentation Problem Beyond Implementation

    Every enterprise is “doing AI”… But almost none are actually operating with it. The real problem isn’t adoption. It’s fragmentation. AI is everywhere, yet decisions are still slow. Thanks to @Adapt for partnering with me on this, because this is exactly the problem they’re solving. Here’s what most leaders are missing… #AdaptPartner

    → View original post on X — @ronald_vanloon, 2026-04-07 15:00 UTC

  • Frontier AI Models: From Expensive Today to Affordable Tomorrow

    I am experiencing this now and it’s definitely the future Marc Andreessen 🇺🇸 (@pmarca) Magical OpenClaw experiences that use frontier models cost $300-1,000/day today, heading to $10,000/day and more. The future shape of the entire technology industry will be how to drive that to $20/month. — https://nitter.net/pmarca/status/2041397922940801170#m

    → View original post on X — @clementdelangue, 2026-04-07 14:50 UTC