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Self-Tuning Loop
Agents & Architecture Complete 3 posts

Self-Tuning Loop

Recovering learning signal between AI drafts and final outputs — a $0 self-tuning system from problem statement to reference implementation.

About this series

Humans take an AI draft and reshape it in their own voice before publishing. The "draft → final" delta in between usually evaporates — Self-Tuning Loop is the $0 system that recovers that learning signal.

For solo builders, writers, and researchers regularly working from AI drafts; PMs at companies that adopted AI but never closed the learning cycle; engineers looking for a pragmatic implementation of self-evolving agents.

Read in order: Wasted Signal (problem) → System Anatomy (architecture) → Build Your Own (reference implementation). Map each diagnosis to a stage of your own workflow and the recoverable signal becomes visible.

3 episodes

  1. 01
    Anchor post

    The Wasted Learning Signal — The Gap Between AI Drafts and What You Actually Publish

    Introducing Self-Tuning Loop: capture implicit feedback from human edit diffs, analyze patterns periodically, and auto-evolve prompt guidelines. Includes academic gap analysis (DSPy, TextGrad, POHF).

  2. 02

    Cron + Telegram + Claude: Anatomy of a Self-Improving System at $0

    The actual production system implementing Self-Tuning Loop from Part 1. Data collection (35 sources), AI curation, Telegram input pipeline, weekly auto-review, and Syncthing-based zero-deploy prompt evolution.

  3. 03

    Build Your Own Self-Tuning Loop — Reference Implementation Guide

    Self-Tuning Loop 4 steps (Generate → Capture → Analyze → Evolve) extracted as a universal module. Supabase DDL, diff capture utilities, analysis/evolution prompts, email/blog examples, GitHub reference implementation.