AI

An old habit of mine: bookmarking as learning. I scroll through community posts, group chats, and GitHub trending every day, and every new term looks familiar — until I close the laptop and find I can hardly string a single full sentence together. A few days ago I came across a Rust blogger explaining the Socratic method, using it to guide his kid’s studying. Today I ran into a new term, the Jev model, with absolutely no concept of what it was — so I figured I’d try this learning method on it and see if anything sticks. ...

My last post covered day one of wiring up a multi-agent coding workflow with GitHub Projects and Orca — that day it merely ran. This is the five-days-later sequel: it has grown into a complete system. Numbers first: 122 commits, 111 merged PRs, 127 issues filed. The six work packages of V0.1 MVP for my side project (panta-log, a local-first voice journaling tool) are all shipped. No chronological diary here — this post breaks down the system’s full feature set, layer by layer. Every layer was forced into existence by a real, stepped-in pit, and the pits are included. ...

Notes on wiring GitHub Projects and Orca into a working AI coding pipeline, pitfalls included. macOS as the example. Background: I have a side project (panta-log, a local-first voice review tool) whose issue/PR conventions had been sitting in another repo, unused. This time the goal was concrete: a kanban board for tasks, Orca running the agents, and the whole chain of “pick a task → develop → test → merge → status transitions itself” working end to end within one day. ...