I still don't understand how memes work. The approach itself in this case is trivial, I think it's easy to argue that there are 10s of similar algorithms for using AI coding agents that would result in very similar results. However somehow the weird name + an initial cabal made this into a meme.

But Ralph has problems when you move beyond one-off tasks:

1. Overcooking: The loop runs forever. Leave it too long and the AI starts adding features nobody asked for, refactoring code that was fine, writing documentation for the sake of it.

2. Undercooking: Ctrl+C too early and you're left with half-done features.

3. Fragile state: Ralph uses markdown files (TASKS.md, PLANNING.md) as source of truth. LLMs can corrupt these - add extra formatting, forget sections, change structure.

4. No memory between sessions: Each run starts fresh. The AI can't see what was done yesterday.

5. Vendor lock-in: Ralph was built for Claude Code. Switching to Codex or Gemini means rewriting your workflow.

So I built juno-code to fix these problems.

What juno-code does differently Iteration control: Instead of while :; do, you get -i 5 for exactly 5 iterations, or run_until_completion.sh that stops when kanban tasks are done.

Structured task tracking: Instead of markdown, tasks are stored in NDJSON files via juno-kanban. The format can't be corrupted by LLM formatting errors. You can query tasks programmatically:

./.juno_task/scripts/kanban.sh list --status in_progress Backend agnostic: Switch between Claude, Codex, Gemini, or Cursor with one flag:

juno-code -b shell -s claude -m :opus -i 5 -v juno-code -b shell -s codex -m :codex -i 5 -v juno-code -b shell -s gemini -m :flash -i 5 -v Stuck on a bug? Try a different model's perspective with one word change.

Full traceability: Every completed task links to a git commit. Time travel through development history. The AI can search git history instead of re-reading everything.

Hooks without lock-in: Run tests, linters, or any script at lifecycle points. Works with any backend:

{ "hooks": { "START_ITERATION": { "commands": ["./scripts/lint.sh"] }, "END_ITERATION": { "commands": ["npm test"] } } } Real-time feedback: Send feedback to the running AI without stopping it:

juno-code feedback "found a bug in the auth flow" Quick start npm install -g juno-code

cd your-project juno-code init --task "Migrate from JavaScript to TypeScript" --subagent claude

# Run until kanban tasks are complete ./.juno_task/scripts/run_until_completion.sh -s claude -i 10 -v The key insight Ralph proved that AI works better in loops. juno-code adds the structure that makes loops sustainable:

Controlled cooking time (not infinite) Strict task format (not corruptible markdown) Any AI backend (not vendor locked) Full audit trail (not blended changes) GitHub: https://github.com/askbudi/juno-code npm: https://www.npmjs.com/package/juno-code

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