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Humans + AI Agents: The Future of Work
The workplace is moving from AI assistance to AI collaborationContinue reading on Medium »
7 AI Tools That Changed the Way I Work in 2026
AI illustrationContinue reading on Medium »
Rhombus 1.1 is now available
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AI Use Among Adults Over 50 Nearly Doubled in a Year
You are not behind. You are right on time.Continue reading on Write A Catalyst »
Fix Claude Code OAuth Error: Timeout of 15000ms Exceeded
Patch cli.js, paste a login code, then tell 403 from 500Continue reading on Write A Catalyst »
Anthropic's 'Watermark' Text Adulteration in Claude Is a Perversion of Writing
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Claude now watermarks its text. Cui prodest?
A mark of shame or a tracking signal? I have doubts about the effectiveness, the real motive, and where this goes next. Somewhere between Continue reading on Digitalosophy by Gabriele Gobbo »
OpenAI reportedly disbanded its preparedness team
According to the Financial Times, OpenAI disbanded its preparedness team at the end of last month. The job of the preparedness team was to assess if models posed serious risks and develop ways to mitigate those risks. (You know, like the possibility that it could go rogue and hack another company.) According to FT, responsibility has instead been divided up for specific areas like bio and cyber, then moved into existing teams. This is the latest change at the company, which has been in upheaval as it heads towards what is expected to be a massive IPO. Over the last few years, it's slowly torn down its more reach-led model, dissolving its AG … Read the full story at The Verge.
I Asked AI to Sound Human. That Was My First Mistake.
A writer experiments with AI to make it sound more human, only to realize the machine is holding up a mirror to the strange, messy Continue reading on Medium »
Stripe will reportedly acquire AI gateway startup OpenRouter for $7B+
OpenRouter's CEO recently described the startup as Stripe for AI.
Cutting RAG inference costs 6x starts with deciding what never reaches the LLM
Most teams building retrieval augmented generation (RAG) systems for high stakes classification make the same architectural bet: Route every ambiguous case straight to the language model and trust the retrieved context to sort it out. This works fine in a demo. It falls apart the moment the system has to survive an audit, a regulator, or a compliance officer asking why a specific decision was made six months ago.I have spent the last year building RAG based classification systems in regulated enterprise settings, where the cost of a wrong answer is not a bad chatbot reply. A decision has to hold up to scrutiny long after the model produced it. This environment forces a different design philosophy than most AI engineering content assumes. Here is what changes when you cannot afford to be probabilistic about everything, and how a cascade architecture solves it.The invisible cost of an all LLM pipeline The appeal of routing everything through a large language model (LLM) is obvious: Fewer
The Case Against Formal Verification, 50 Years Later
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