Ponytail made my AI agent terser. Neckbeard makes it prove its work. Why I ditched the viral coding skill for one designed to catch mistakes, verify at the real boundary, and say what it couldn’t check. The architecture, authority gates, and evaluation suite inside.
A practical method for earning AI autonomy by finding workflow bottlenecks, strengthening adjacent capabilities, and expanding only with evidence.
Postmortems matter when their lessons survive the meeting and constrain the next release, without turning blameless learning into automated blame.
Andrej Karpathy predicted a small AI model that would trade encyclopedic knowledge for raw reasoning. Just over a year later, it is a product category, and it changes everything about how we should think about AI infrastructure.
Human organizations have a maximum perceivable rate of change. Above that threshold, they don’t accelerate their response. They slow down. They treat a category shift as a tool upgrade. The shift from deterministic to probabilistic computing has exceeded the threshold, and the three gaps we see everywhere are not failures of leadership. They’re symptoms of an organizational immune system working exactly as designed for a world that no longer exists.
Shannon Ryan’s talk at AgileRTP on why AI adoption fails when organizations fixate on tools instead of the human systems that use them.
As AI coding agents accelerate implementation, the bottleneck shifts upstream to spec creation. A practical walkthrough of how recorded stakeholder meetings, AI agent skills, and open-source profiles can break through the spec ceiling.
Taking on the genuinely hard problems: speaker diarization, meeting auto-detection, and what this fork actually proves about open-core and vibe coding.
Actually forking the MIT codebase: rebranding across 69 files, stripping 928 lines of telemetry, and having an AI coding agent implement a locked feature in a single pass.
A speculative roadmap for forking an open-core AI meeting assistant, with a worked example of how to use AI coding tools to implement it.
We treat workplace accommodations as individual exceptions you have to request, document, and fight for. A growing body of evidence says that model is backward.
Most companies are playing a game they don’t understand the rules to. They’re standing in a room full of competitors, all of them staring at the same two doors. One door says OpenAI. The other says Anthropic. They believe the strategic decision is which door to walk through. They believe this so deeply that they’re paying enormous premiums for inference that’s past the point of diminishing returns, and getting nothing their competitors aren’t also getting.
Anthropic released an open-source Claude Code skill that takes a founder from idea to a live managed agent in minutes. Hermes Agent already has most of the same primitives. Here’s the mapping, the gaps, and the blueprint.