AI coding didn’t just help me build MacMater faster. It helped me notice the small frictions people live with every day. On macOS, there still isn’t a simple native way to right-click in Finder and instantly open a file or folder with the app you actually want. There also isn’t a clean built-in way to right-click and create a new file from your own templates. So people install one app for Finder tweaks. Another for clipboard history. Another for mouse gestures. Another for input switching. But ordinary people shouldn’t need a folder full of tiny utilities just to make their computer feel right. That became the idea behind MacMater: an all-in-one native Mac utility that brings these daily improvements together. Open with your favorite apps. Create new files from templates. Switch input methods automatically. Make your mouse feel better. Bring back anything you copied. AI wasn’t a magic button. It was more like a patient teammate. It helped me unfold ideas, question tradeoffs, rewrite messy thoughts, and keep asking: “Is this actually useful to a real person?” The biggest lesson: AI coding does not remove human judgment. It demands more of it. You still have to know what matters. You still have to say no. You still have to choose simplicity over cleverness. In the end, the best technology disappears. What remains is a small moment: someone opens their Mac, does their work, and feels like the machine finally understands them a little better.

AI reliability can't come from "self-reflection" alone. Welcome to the era where a separate agent audits the answer before you get it 🔬 Title: Apodex-1.0: A Verification-Centric Agent Team for Discoverative Intelligence URL: 🔬 Overview A system that shifts from a single-agent reasoning loop to a verification-centric distributed agent team. In heavy-duty mode it becomes an asynchronous team that specializes, cross-checks, and audits its own evidence before answering. ❓ Challenges Solved Reliability on hard, open-ended problems can't come from a model's parametric memory alone. The premise: the hardest research problems are bounded not by model capacity but by what the model is allowed to interact with. 💡 Methodology & Proposed Approach ・A main agent asynchronously spawns specialized sub-agents with independent contexts and tools ・A shared report pool aggregates parallel findings without blocking on slower tasks ・A verification agent team handles conflict resolution, fact-checking, and draft review ・The core idea is verification as external audit: the reasoning agent and auditing agent are separated, and the verifier is free to disagree ・It coordinates up to 150 sub-agents over 15,000+ steps in a single task 📊 Experimental Results ・BrowseComp 90.3 / DeepSearchQA 94.4 / BrowseComp-ZH 84.1 ・FrontierScience-Research 46.7 (+8 vs competitors) / SuperChem 74.2 (+12 over next-best) ・Heavy-duty mode lifts the base by +14.8 on BrowseComp and +18.4 on FrontierScience-Research ・The open-source 4B-SFT beats every 30B-class open-source model on BrowseComp #AIAgents# #DeepResearch#

AI is pushing data centers to their limits, and cooling is becoming one of the biggest bottlenecks. @Ferveret is taking a nuclear reactor-inspired approach to fix it. Their system outperforms state-of-the-art liquid cooling by 15% and delivers 35% more tokens from the same power— with zero water consumption.

AI transformation doesn’t start with experiments. It starts with leadership. At #DellTechWorld#, @MichaelDell shared a clear message: ➡️ Don’t start small. Go after the biggest processes in your business. The companies moving fastest into AI aren’t just adopting new tools—they’re rethinking how work gets done, end to end. And the fastest path forward? Find your super users. Empower them. Scale what works. 🎥 Watch the full conversation with @siliconangle & @theCUBE: