Tag: ai

Saturday morning, before exercise, I asked Claude one stray question. By afternoon there was a new repo, a pensive, four issues, and a working tool I'm using to write this post.

The raw chat transcript that produced pair v1 — from 'can you inject text into a TUI?' to a working nvim-driven launcher with multi-session picker, image paste, and a pile of zellij gotchas. Companion to "A Better Claude Input Interface".

What is an AI Native Company?

Two weeks of building the substrate underneath an AI-native personal workflow: a base agentic development environment, a personal data access layer, a credential gateway, and a private canvas where ideas form, collide, and grow. Not ready for release yet — here's a sneak peek.

Adding tool calls to Parley on a flight, watching Claude make a mess, then drawing the architecture boundary for Claude and watching it execute flawlessly within it. The eureka: Claude Code is the execution harness, Parley is the interface for human to inspect and influence — both running on the same shared filesystem state.

Reflecting on a year of tinkering and adapting to the ever changing world of AI. After a year and 400+ commits on Parley.nvim, I'm convinced the future is humans setting up the environment, with full automation of the coding inside it. Here's my journey so far.

A follow-up parley: log.md is the least controllable file in an agentic issue structure because it depends on the model voluntarily writing during execution. Drop it. Let git history and plan.md diffs serve as the log instead.

A parley conversation on why agentic coding state — issues, plans, logs — wants to live inside the repo, beside the code. Single-threaded topological execution as the sane default; minimal files per issue beat reinventing Jira on disk.

When agentic coding lets a vim user write a custom mouse-aware floating picker because Telescope couldn't do that. The implication for SaaS: programmability stops being a nice-to-have, it becomes the moat.

An iterative spec flow built on a small Makefile and a handful of markdown files — issue, todo, lessons, specs — cuts through the opaque magic of workflow tools. The trick is treating specs as sketches that bound the agent's search space, not exact behavior to confine it. Let AI bringing in world's knowledge!