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Your AI Agent Needs a Menu, Not a Mystery

Every AI agent in 2026 ships with the same onboarding: a blank text box. No indication of what it can do. No signal when it learns something new. Users type “hi,” get a generic response, and never come back. We solved this for BrightHire’s Slack Hiring Agent with a capabilities registry — a single module that tells the user what the agent can do, tells the LLM what it can do, and forces the developer to describe every new feature. One source of truth, three audiences, zero drift.

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I A/B Test My Prompts Like a Scientist

Most teams evolve prompts by feel. Change something, eyeball the output, ship it if nobody screams. This is alchemy, not engineering. When your system is non-deterministic, a single successful run proves nothing. I built an eval harness that runs prompt versions head-to-head — 10 runs each, scored on behavior, measured on cost and latency. No vibes. No guessing. Just data that tells you exactly what improved, what regressed, and what it costs.

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AI Vision Is a Game Mechanic Now

There’s a category of game that couldn’t have shipped two years ago. Not because the hardware didn’t exist, or the game design theory wasn’t there, or the players weren’t ready. The core mechanic was impossible. It required a machine that could look at a picture, understand a natural language question about it, and answer accurately — in real time, at scale. That machine now exists. And it unlocks game designs that no one has explored yet.

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Build Exactly What You Want

For decades, your computer experience was dictated by what someone else decided to build. You bought the operating system. You paid for the productivity suite. You subscribed to the project management tool that did 60% of what you needed and learned to live with the other 40% — the missing features, the clunky workflows, the integrations that never worked quite right. That era is over. Agentic coding means you describe what you want in natural language, and a frontier AI agent builds it. Not eventually. Now.

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