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The context that actually matters isn't in your database. It's in the tools your users live in every day. Multi-stage agents stall the moment they hit a step they can't see. And every missing integration is a different OAuth flow, a different token lifecycle, weeks of plumbing before the agent reads a single record.
Founders need to lead equally with the product and company philosophy. They need to build a public track record of specific, falsifiable claims about their domain and make their teams visible. A lot of value is migrating now as things commoditize. Companies are now competing on the quality of their predictions about their own roadmaps.
A skill is a markdown file Claude reads when a task calls for it. Anyone in a company can write one in an afternoon. Inside companies, people are writing them faster than they can think of new ones, complaining "I could build ten times more of these, I just can't think where to start." A public marketplace doesn't exist yet but it's coming, with security review and governance built in. Twelve months from now, "we have a skill for that" will be a buying question the way "we integrate with X" was a decade ago.
This developer turned a two-hour-long task for their accounting team into a five-minute-long task for an AI agent. This dramatically impacted how their team operated as the same flow was used across many similar processes. This article presents their six-step process for building an accounting AI agent. By iterating and improving the agent, the team eventually developed some major time savings.
Your VPs are debating whether to add multi-region support or go deeper in one industry. Forty minutes later they've quoted Andy Grove, mentioned Stripe twice, and shipped nothing. The conversation that would actually matter is which specific feature would have closed the customer who ghosted you Friday. If your team can't name that feature in one sentence, no framework or whiteboard session will conjure it.
Search is now a lot different from just two years ago. Two new terms have shown up to describe the work of being visible inside those answers: Answer Engine Optimization (AEO), the work of being the source an answer engine uses, and Generative Engine Optimization (GEO), the work of appearing inside answers a model produces. Each pulls from a different web index, but most of these indexes are the result of the same crawl, rendering, and quality work. This post walks through how to optimize for this new type of search. It provides sources for every recommendation.
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Fin Operator is an AI-powered system designed for teams to configure, monitor, and improve Fin, a customer-facing AI agent. Fin now resolves more than two million customer issues each week across its 8,000 global customers. The operational complexity behind its systems has exploded, leaving support operations teams overwhelmed. Operator collapses the entire operational loop into a conversational interface.
Experimenting is becoming a standard operating layer across Google Ads campaign management. Advertisers can now run and compare experiments across AI Max campaigns, Video campaigns, Demand Gen campaigns, and Performance Max campaigns. The platform's recent update changes how developers should structure decision-making. This article explains what the extended experiment support means, what changed in the API, how the changes fit into Google Ads' current experiment architecture, and more.
Every AI lab is currently running at a loss. There is a large gulf between what companies pay for AI subscriptions and what it actually costs to serve these services. Every organization that has built workflows, products, or entire business units on top of these subsidized prices will find itself one day looking at a bill it did not budget for. The subsidy creates dependency, and that dependency will trap companies when the inevitable price increase occurs.
Dario buried "work and meaning" at the very back of Machines of Loving Grace and admitted the section was short because he didn't have answers. Demis says we'll need new philosophers. Every AI roadmap right now describes capex and scaling laws in tight numbers and the human side in fuzzy gestures about meaning and adaptation. If you can't describe what one specific person does on a normal Tuesday in the world you're building, you don't have a future, you have an aesthetic.
The VP of Sales is an easy scapegoat, but project velocity is the new growth lever.
AI will enable professionals to do more, but these professionals need to be supported by a pit crew that helps them handle the underlying details of using the technology.
Nobody has a project on their to-do list called "buy a CRM."
Stop reviewing the code and start reviewing the spec before the agent writes a single line.