71–80 of 187 posts
At the moment I predominantly work with Python and hence PyCharm as the main IDE. However, I've built this plugin https://plugins.jetbrains.com/plugin/31117-agent-cli to render agentic CLIs as an editor tab in PyCharm and also some notification hooks so I don't have to switch windows and it's easy to jump around the code while the agent is doing its work. Besides that I have a collection of custom skills (plan for JI…
but AI dev workflows get complicated fast
you start with claude code or codex and it's cute, but then you realize - hmm configuration is cheap, the AI can do it!
then you start looking into MCPs and skills, fuck it, oh-my-pi looks awesome!
wait a second? I can just have AI make my own personal AI harness! Next thing you know, you're writing the 5th version of "little-coder" or similar using the Pi library
ahh shit, you just read an article that `tools` are actually crazy important for AIs, using `sed` is dumb when `hashline` + ASTs are way better, lets just start writing our own tools!!
...anyway I just use Zed, simple agent on the left, code on the right
i have some pretty complicated automated workflows that use `linear` + a orchestrator -> implementer -> reviewer -> releaser workflow, but it's less a dev stack and an AI factory
1) Slow code. Let the agent(s) discover and plan, then launch the swarm on the confirmed implementation steps. 2) Use LSP. If nothing works, usually you can connect it via MCP. I think all coding agents support this by now. 3) Add hooks if you want to stop the coding agent from doing something nasty, or hallucinate and give incomplete output. TDD and any verification tool you can think of are your friends. 4) Skills…
Currrent setup:
Zed + Terminal threads (love this!) + Remote machine
Devcontainers + Claude + Pi
[1] Zed https://zed.dev/
[2] Terminal threads https://zed.dev/blog/terminal-threads
[3] Pi https://pi.dev/
As sort of byproduct also replaced Alacritty + Zellij (i just don't have the need to use more, 3 weeks of new setup)
1) Write half pager of markdown by hand - tech, architecture, features
2) Ask 2-3 LLMs from different companies to review for gaps & problems
3) Make LLM turn it into implementation plan with emphasis on modular phases
4) Repeat step 2 but on the implementation plan. Usually the 3rd LLM just goes yeah that looks fine
5) Walk through phases individually, sometimes multiple in one shot depending on vibes. Sprinkle more 2-3 other LLM checks in between again depending on vibes & judged difficulty
There's lots of ways. You have to upskill through the stages IMO. Write code, write w/ agent, write w/ multi agents, write w/orchestrators. My way is to just run a giant AI agent factory engine and make the agents full flow do everything. (plan long term, write prd, task, review). Here's ~4000 commits in last month as an example, i have about ~10k ish including private/work stuff? https://github.com/portpowered/you-a…
For each new feature, I open a worktree, spar with Claude to work up a gherkin spec with @todo on each story. Each agent pushes commits to a WIP PR in GitHub where I review and leave comments or questions. Once the spec is done we mainly interact on the PR. @todo becomes @wip and @done as the agent progresses. I really like gherkin for agentic engineering, it's very clarifying.
I have about 2-4 agents running at a time. Large test suite, linters and formatters enforced on push.
I review agent messages, some specs/plans, and conduct local code reviews with Plannotator [1].
For skills, I have a bunch of custom ones for my own workflow. and for public skills I really only use the interrogate skill from cursor's lauren [2].
Key workflow stuff:
- Almost all work I do gets done in a git worktree.
- ghostty + Mac OS gives me all the organization I need for multi-agenting
- turn off all agent memory, this has only ever caused problems for me.
[2] https://github.com/cursor/plugins/blob/main/pstack/skills/in...