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  • Buzzy as competition where AI agents vie for winning video

    Buzzy isn’t a tool.
    
It’s a competition. Multiple AI agents.
One winning video. You don’t create anymore.
You arbitrate.

    → View original post on X — @jouhatsu_ai

  • Anatomy of the .claude/ folder explained

    Anatomy of the .claude/ folder: nitter.net/akshay_pachaar/status/… Akshay 🚀 (@akshay_pachaar) x.com/i/article/203496196714… — https://nitter.net/akshay_pachaar/status/2035341800739877091#m

    → View original post on X — @akshay_pachaar, 2026-04-04 13:03 UTC

  • 8 Building Blocks of Effective Claude Prompts
    8 Building Blocks of Effective Claude Prompts

    The anatomy of a Claude prompt: The difference between a mediocre Claude output and a great one almost always comes down to how you structure your prompt. Not the specific words you choose. Not some secret phrasing. Just a clear, repeatable structure that gives Claude exactly what it needs to do the job well. Here's how a well-built Claude prompt breaks down into 8 building blocks, each doing one job: 1️⃣ Role Tell Claude who it is before telling it what to do. "You are a [ROLE] with expertise in [DOMAIN]. Your tone should be [TONE]. Your audience is [AUDIENCE]." Setting a role in the system prompt changes how Claude reasons, what it prioritizes, and how it communicates. A "senior backend engineer" writes differently than a "technical copywriter," and Claude picks up on that distinction immediately. 2️⃣ Task State what you want and what success looks like, in the same breath. "I need you to [SPECIFIC TASK] so that [SUCCESS CRITERIA]." The "so that" part is what people skip, and it's the part that matters. It gives Claude a way to evaluate its own output. Without it, Claude is guessing what "good" means. Be direct, skip the preamble, and cut the fluff. 3️⃣ Context This is where you feed Claude everything it needs to do the job well. Wrap it in XML tags like <context> and </context>, then paste your documents, data, or background inside. One thing that dramatically improves quality: put long documents at the top of your prompt and your actual query at the end. Anthropic's own testing shows this can improve response quality by up to 30%, especially with complex, multi-document inputs. 4️⃣ Examples Nothing steers output quality like showing Claude what "good" looks like. Provide 3-5 input/output pairs. Cover normal cases AND edge cases. Wrap them in <examples> tags so Claude doesn't confuse them with instructions. Claude pays extremely close attention to examples. If your example has a quirk you didn't intend, Claude will replicate it. So make sure every example models the behavior you actually want. 5️⃣ Thinking For anything requiring reasoning, analysis, or multi-step logic, ask Claude to think before answering. "Before answering, think through this step by step. Use <thinking> tags for your reasoning. Put only your final answer in <answer> tags." This separates the messy reasoning from the clean output. You get to see how Claude arrived at its answer without that reasoning cluttering the final result. 6️⃣ Constraints Every good prompt has guardrails. "Never [thing to avoid]. Always [thing to ensure]. If you are about to break a rule, stop and tell me." That last line is underrated. It turns Claude into a collaborator instead of a blind executor. Instead of silently violating a constraint, Claude flags the conflict and lets you decide. 7️⃣ Output Format Don't leave the format to chance. "Return your response as [JSON / markdown / table / prose]. Use this exact structure: [structure template]." If you want JSON, show the exact schema. If you want markdown, show the heading structure. If you want a table, define the columns. The more specific you are about shape, the less time you spend reformatting afterward. 8️⃣ Prefill This one is API-specific, but incredibly powerful. You can pre-fill the start of Claude's response to skip preamble and lock in the format. Claude will continue from exactly where you left off. No "Sure, I'd be happy to help!" opening, no throat-clearing, just clean output from the first token. Here's the thing people get wrong about prompting: they think it's about finding the right words. It's actually about giving Claude the right structure. If you want to go deeper, I wrote a detailed article covering the anatomy of the .claude/ folder, a complete guide to CLAUDE(.)md, hooks, skills, agents, and permissions, and how to set them all up properly. Link in the next tweet.

    → View original post on X — @akshay_pachaar, 2026-04-04 13:02 UTC

  • IoT Data Transforms Engagement Into Real-Time Operational Loop
    IoT Data Transforms Engagement Into Real-Time Operational Loop

    IoT data turns engagement into an operational loop rather than a campaign. Signals from connected products feed analysis and personalization, since teams act before friction appears and adjust features in real time, changing support and loyalty. Microblog by @antgrasso

    → View original post on X — @antgrasso, 2026-04-04 13:01 UTC

  • The Well: Open-Source Library of Physics Simulations for AI
    The Well: Open-Source Library of Physics Simulations for AI

    Imagine trying to teach someone how to swim just by letting them read books about water. That is how we have been training AI on physics, using text descriptions. To really learn, you need to get in the water. "The Well" is that water. Polymathic AI has released a massive 15TB open-source library of physics simulations. It allows AI models to experience physical phenomena directly. Instead of reading about a supernova, the model processes the actual data of the explosion. Instead of reading about aerodynamics, it analyzes the fluid flow. This moves us from [Generative AI] (making things up) to [Scientific AI] (discovering truth). A huge step forward for open science. GitHub Repo: github.com/PolymathicAI/the_well/ [Translated from EN to English]

    → View original post on X — @nandodf, 2026-04-04 13:00 UTC

  • Robots Harvest 2000 Broccolis Per Hour Without Human Workers

    These #Robots Harvest 2000 Broccolis an Hour—No Humans Needed
    by @lukas_m_ziegler #AgriTech #TechForGood #Innovation #Tech #Technology

    → View original post on X — @ronald_vanloon

  • Anthropic Expands Into Voice Technology With Full Stack Integration

    Anthropic moving into voice is something. Owning the full stack from reasoning to voice makes sense if you want to control the experience end-to-end.

    → View original post on X — @whats_ai

  • Realistic AI Images Raise Disinformation Risks Despite Factual Errors

    Paradoxically, the more realistic the images are in form but not in the information they show (they still contain many factual errors), the more "dangerous" their potential to spread disinformation becomes. Even so, it is one more step toward achieving high-quality images AND

    → View original post on X — @dotcsv

  • OpenAI Reveals GPT-Image-2 with Flawless Text Rendering
    OpenAI Reveals GPT-Image-2 with Flawless Text Rendering

    🚨 LEAK ALERT 🚨 OpenAI's new image model GPT-Image-2 just quietly dropped on @arena. Early tests show INSANE world knowledge and flawless text rendering. Better than @NanoBanana Pro! It's on @arena under code names:
    → maskingtape-alpha
    → gaffertape-alpha
    → packingtape-alpha [Translated from EN to English]

    → View original post on X — @datachaz, 2026-04-04 12:41 UTC

  • CISOs Prioritize OT/IT Convergence for Enhanced Security

    More CISOs are looking at OT/IT convergence seriously. CC: @IIoT_World @CRudinschi @agentic_factory @survivingwithan @joannefriedman @bzarkout

    → View original post on X — @fogoros