TAKT
Stop babysitting AI coding agents — reviews can't be skipped
What it does
Open-source CLI that turns AI coding agents (Claude Code, Codex, Cursor & more) into repeatable YAML workflows: plan → implement → review → fix loops with per-step roles, isolated worktrees, and traceable reports. Reviews can't be silently skipped.
Stop babysitting AI coding agents. TAKT orchestrates multiple AI agents through YAML-defined workflows with structured review loops, managed prompts, and guardrails.
Define planning, implementation, review, and fix loops in YAML. Let TAKT coordinate your AI agents with structured workflows. Refine the instruction in a conversation, save it as a task, then run takt run . TAKT coordinates planning, implementation, and parallel review in an isolated worktree. A task moves through a repeatable workflow: describe, queue, run, and review. Animated preview, 35 seconds, no audio. Watch the full tutorial Continue with the complete hands-on walkthrough on YouTube. AI coding agents are powerful, but they don't automatically create a stable development process. Describe your task to the AI assistant. TAKT helps you refine requirements and queue it. Execute tasks in…from nrslib.github.io
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I trained a 125M-parameter transformer to autocomplete piano performances in real time (~108 notes/sec on an iPhone 15). The idea is basically GitHub Copilot or Tabnine, except instead of prompting it with code, you prompt it by playing a few notes on a MIDI piano. The model then continues what you played, entirely on-device. The app is free if anyone wants to try it. Happy to answer questions about the model, training, Core ML, or the many things that didn't work.
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I trained a 125M-parameter transformer to autocomplete piano performances in real time (~108 notes/sec on an iPhone 15). The idea is basically GitHub Copilot or Tabnine, except instead of prompting it with code, you prompt it by playing a few notes on a MIDI piano. The model then continues what you played, entirely on-device. The app is free if anyone wants to try it. Happy to answer questions about the model, training, Core ML, or the many things that didn't work.
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