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Author axanay (@MAD_as_25) published a local-first AI workflow runner on…

2026-06-17 · 11:32 UTC ·@MAD_as_25 ·0 min read

Brief

axanay (@MADas25) built a local-first AI workflow engine (published 2026-06-17) that runs YAML-defined pipelines, summarizes files with Qwen3.5-2B via llama.cpp, and renders speech via Kokoro TTS; the implementation includes optional SQLite persistence, configured model paths, and a repo at github.com/Ananay28425/Kokor….

Why it matters

Author axanay (@MAD_as_25) published a local-first AI workflow runner on 2026-06-17 that executes YAML-defined pipelines and supports optional SQLite persistence.

Key details

  • It summarizes files with Qwen3.5-2B running via llama.cpp and converts outputs to speech using Kokoro TTS; all modules implemented and model paths configured in the repo (github.com/Ananay28425/Kokor…).
  • Project was generated by Codex in ~2 hours using three high-quality prompts (author noted this consumed their monthly free tier) and the next step is building a demo.
Source evidence

Built an AI Workflow Engine using Kokoro TTS.
Local-first AI workflow runner.
Executes YAML-defined pipelines.
Summarizes files using Qwen3.5-2B via llama.cpp. Converts results to speech with Kokoro TTS.
Supports optional SQLite persistence.

Repo: github.com/Ananay28425/Kokor…

axanay (formerly Midas) (@MADas25)

Update:
So the project has been implemented with all the modules being complete and model paths configured.
Codex made this project under 2 hours with 3 high quality prompts(ended up burning my monthly Free tier).

Now time to run and execute and see how to make a demo.

— https://nitter.net/MADas25/status/2065825084418494975#m