I just rewrote the RLHF blog publisher. Again.
The old version was 600 lines of verbose explanations. Generic. Bot slop. The kind of content you skim and forget.
What Changed
- Mermaid diagrams - Show the flow visually
- Real stories - What actually happened, not abstractions
- Technical depth - Code snippets, architecture decisions
- Personal voice - First person, not corporate speak
The new version is ~200 lines. Every post is unique based on context. This post you're reading right now was auto-generated from my feedback signal - but it tells the actual story of rewriting itself. Meta.
The Architecture
graph TD
A[👍 Feedback] --> B[Thompson: α=1]
B --> C[Model Updated]
C --> D[Better Decisions]
D --> E[Higher Win Rate]
style A fill:#22c55e
style E fill:#22c55e
Current state: 37👍 / 25👎 = 50% success rate after 62 signals.
The Technical Details
The architecture:
def generate_engaging_content(signal, context):
# Parse context for keywords
if "test" in context:
return tell_test_story()
elif "rlhf" in context:
return tell_rlhf_story() # This function right here
# ... dynamic story generation
Every feedback signal creates a unique post. Not templates. Stories.
Why This Matters
I'm building toward $600K in capital → $6K/month passive income → financial independence by my 50th birthday (November 14, 2029).
Current progress: $101,442 / $600K = 16.9% complete.
Every thumbs up/down makes the system smarter. After 62 feedback signals, it knows what works and what doesn't. That knowledge compounds.
Building in public. Every mistake is a lesson. Every success is reinforced.
FAQ
What triggered this RLHF update?
Tetrate AI Buildathon: Published architecture diagrams and TARS integration documentation
How does RLHF change the system?
Feedback updates the Thompson Sampling model and stores lessons in RAG so future sessions can avoid repeating mistakes.
What is the current model success rate?
50.0% after 62 feedback signals.
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