Latest AI industry news and updates
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The Age of Installable Expertise How Vercel's Skills.sh signals a fundamental shift in how we package and distribute human knowledge Within hours of Vercel announcing Skills.sh on January 21st, their top skill had 20,000 installs. Remotion, the programmable video animation platform, shipped a video created
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llms cannot do math. llms cannot do excel. but llms can code. LLMs are much better at writing Python than Excel formulas. 90%+ accuracy on Python/SQL. Only 77% on spreadsheet formulas. Same models, same logic, 15-point gap. Why LLMs Cannot Do Math LLMs don't calculate. They predict
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A comprehensive comparison of the leading AI coding agents: autonomous execution capabilities, model availability, hooks/extensibility, and whether you can leverage existing subscriptions. Summary If you want… Use Best hooks for custom automation Claude Code or OpenCode Turnkey autonomous execution Cursor Background Agents or Copilot Coding Agent Leverage Claude Pro/
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Generative AI is bigger than ChatGPT. Large Language Models are one type of generative AI. They generate text. But generative AI also includes models that generate predictions from tabular data, forecasts from time-series, insights from relationship networks, and structured queries from natural language. These specialized models share the same foundation:
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TL;DR * SAP released SAP-RPT-1, a purpose-built model for tabular business data that delivers 3.5x better prediction accuracy than general-purpose LLMs * The model enables enterprise use cases like demand forecasting, inventory prediction, financial planning, and supply chain optimization * If you're running SAP systems, RPT-1 is available now
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TL;DR * The real value of AI isn't automation. It's enabling a fundamental shift in how your teams work. * AI projects fail when people don't trust the system enough to change their behavior. Explainability fixes that. * The difference between an AI tool and a
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TL;DR * Most organizations force teams into either constant change or rigid efficiency. They're missing the point: both modes are essential. * Jim Highsmith's explore/exploit framework gives you a practical way to dynamically balance adaptation and optimization based on real conditions. * The handoff points between modes
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Organizations aren’t struggling to generate insights anymore. They’re struggling to act on them. We’ve spent a decade building predictive intelligence. Now we need to make it accessible. Predictive AI can do the math. GenAI can explain it. That’s the division of labor emerging across enterprise AI.
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The Real Significance of Anthropic's Cowork Launch Anthropic didn't set out to build Cowork. They stumbled onto it. When Claude Code launched, Anthropic expected developers to use it for what the name suggested: coding. But something strange showed up in the usage data. Engineers were using
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What vibe coding means for the other side of the software lifecycle "Vibe coding" was the Collins Dictionary Word of the Year 2025. Describe what you want, AI writes the code. How about Vibe Config? That might be actually more interesting for most companies. A parallel shift is
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Why Your AI Demos Fail in Production: The Case for Multi-Agent Discussion Panels TL;DR * Multi-agent AI systems that mimic real team dynamics can solve complex enterprise problems that single-model approaches can't handle * A four-role architecture with Researcher, Expert, Critic, and Moderator creates reproducible, auditable decision-making workflows * LangGraph&
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* Fixed-size chunking at 512 tokens actively destroys the context your retrieval system needs to work * Semantic chunking that respects document structure can improve retrieval accuracy by up to 30% * RAG is fundamentally a system design problem, not just a retrieval optimization challenge * Bridging the demo-to-production gap requires rethinking chunking, retrieval,