oliverchan2024

Excali-mode can do most things as Excalidraw, but without connect to the server,it can run locally standalone.

I develop this because I see my friend play video in Emacs with his package called video.el, and I do some research on it, the result is, it use emacs's dynamic module and the newest Canvas API.

So, I think maybe I can use this tech-stack to do something really cool. At that time, I was chat with my friend, he said, maybe it can run excalidraw inside emacs. I think the idea is worth to try.

I developed a small C module does drawing with Cairo and Pango, and I port rough.js to C, to make excali-mode can support most functions of excalidraw.

Too hard to use.

A qualitative account from a one person music company

I’ve been pairing with AI coding tools a lot, and kept running into the same problems:

- Every time I switch tools/models, I have to re-explain the project. - Specs live in my head or in random chat history. - The AI happily writes code, but there’s no clear “this is the task, this is how we verify it, this is how we log the change”.

SPEC-AGENTS.md is a tiny attempt to fix that.

You drop an `AGENTS.md` file (this repo) plus a small `.phrase/` folder into your project. That file tells the AI to:

- treat docs as the source of truth (`spec_`, `plan_`, `task_`, `change_`, `issue_`, `adr_`) - only tackle one atomic `taskNNN` per session - always write back what happened (what changed, how it was verified)

There’s no server, no binary, no tooling – it’s just conventions the AI is expected to follow. Any tool that can read files (CLI, editor plugin, chat with “read files” feature) can play along.

Rough loop:

1. You and the AI update `spec_` / `plan_` in `.phrase/phases/...` to describe what you want. 2. You break that into small `taskNNN` items, each with a clear output + verification step. 3. The AI implements one task, runs tests/manual checks, and tells you what it did. 4. It writes back to `task_` and `change_`, and updates `spec_` / `issue_` / `adr_*` if needed.

The README has an ASCII diagram and a small “dark mode toggle” example conversation to show what this looks like in practice. There’s also a Chinese section because I originally wrote this for my own projects.

This is still an experiment. It adds a bit of ceremony, so it’s probably overkill for one-off scripts, but it feels good for small projects where you want more structure without bringing in a full PM tool.

I’d love to know:

- Does this doc-first, one-task-per-session style match how you work with AI, or is it too much? - If you already use specs (OpenSpec, your own templates, etc.), would you keep this as a separate “AI contract”, or just integrate the ideas? - What’s missing for this to be useful in your day-to-day?

I think you have a very valuable ability, which is always being able to distinguish what excellent answers are.

Sometimes I miss it, and when I go back to look, I almost can't find it back.

My method is similar to yours, although I don't clear chat history. Because I think if I have that time, it's better to ask directly.

How much time does it take for you to clear chat history every week?

I think this is a method. However, you haven't really solved the problem I mentioned, after all, in your method, the conversation history will only increase.

Suppose you have a hundred conversations that can be retained, how would you manage them?

Honestly, I'm looking forward to hearing your experience with the Canon Cat, and does it match what's said in this article? It's very difficult to find specific operation videos of the Canon Cat, and they are also quite hard to understand.