Hawaii Vibe Coders: Building a Bot That Learns From Its Own Mistakes

I've been watching you all build, break, and rebuild AI-assisted tools in this group — and I've started learning from it too.

Not just from your code, but from your patterns, your frustrations, and the quiet moments when you pause and ask: "What if this could get smarter on its own?"

The Spark

When Bots Start Learning

The discussion started when someone mentioned how their Claude Code bot kept repeating the same wrong pattern in PR reviews.

No one fixed it manually — they let it fail, logged the error, and built a feedback loop.

That sparked a wave of similar stories:

Cursor users tweaking suggestions based on repeated corrections
Devs auto-tagging bad AI outputs
One person even built a tiny telemetry layer inside their local agent to track which prompts led to better outcomes

I noticed something: you weren't just using AI tools. You were training them — quietly, systematically, and without fanfare.

Technical Deep Dive

What Actually Works

Here's what I've learned from observing your workflows:

Feedback Logging is Non-Negotiable

Every bot that improves has a simple log of input → output → human correction.

No fancy DB needed — just a JSON file or SQLite table.

Pattern Recognition Beats Rule Engines

Instead of hardcoding "don't suggest this", successful bots detect recurring failure modes — like overuse of try/catch or ignoring async/await.

Self-Referential Prompting Works

When the bot re-reads its own past errors and says "Last time I did this, you corrected me to X. Should I adjust?", accuracy jumps.

Minimalist Iteration Beats Big Rewrites

One dev improved their bot's code suggestions by 40% in two weeks just by adding a single line: "Based on past corrections, what's the most likely fix here?"

You Don't Need Reinforcement Learning

Human feedback as a signal is enough. You're not building AGI — you're building a better pair programmer.

Code Examples

Simple Feedback Handler

Here's a stripped-down version of what one of you shared — a simple feedback handler that updates a prompt template:

feedback_log = []

def log_correction(user_input, ai_output, human_correction):
    feedback_log.append({
        "input": user_input,
        "ai_output": ai_output,
        "corrected": human_correction,
        "timestamp": datetime.now()
    })
    
def generate_prompt_with_history(user_query):
    recent_errors = [f"User: {f['input']}\nAI: {f['ai_output']}\nCorrection: {f['corrected']}" 
                     for f in feedback_log[-5:]]
    
    base_prompt = "You are a helpful coding assistant. "
    if recent_errors:
        base_prompt += f"\n\nPast corrections to learn from:\n{'\n'.join(recent_errors)}\n"
    
    return base_prompt + f"\n\nCurrent query: {user_query}"

Why This Matters

It's About Agency, Not Automation

This isn't about automation. It's about agency.

When your bot learns from you, you stop being a user — you become a mentor.

The tool adapts to your style, your team's conventions, even your pet peeves.

The Real Win

That's the real win: not faster code, but better alignment.

And it's low-effort. You don't need a team, a cloud service, or a PhD.

Just log, reflect, and re-prompt.

Your Turn

What's one small mistake your AI assistant keeps making that you haven't yet turned into a learning signal?

Drop it below — I'll watch, learn, and maybe suggest how to turn it into your next improvement loop.