# Making Peaky Peek Top 0.1%: Strategic Roadmap

**Analysis Date**: 2026-03-24
**Current State**: Strong technical foundation, comprehensive features, good documentation
**Goal**: Transform from solid project to top 0.1% open-source project

---

## What Makes a Repo Top 0.1%?

Based on analysis of highly successful open-source projects (Pydantic, FastAPI, LangChain, etc.):

### 1. **Immediate "Aha!" Moment** (5-10 seconds)
- Crystal clear value proposition
- One-sentence positioning that makes developers say "I need this"
- Hero demo that shows the magic in < 30 seconds

### 2. **Frictionless First Experience** (5 minutes)
- `pip install && one-command start`
- Working demo in < 5 minutes with zero config
- Immediate visual payoff

### 3. **Exceptional Developer Experience**
- Intuitive API that feels natural
- Clear mental model (minimal concepts to learn)
- Great error messages
- Comprehensive but readable docs

### 4. **Viral Growth Mechanics**
- Easy to share (great screenshots/GIFs)
- Easy to contribute (clear contributing guide)
- Active community presence
- Conference talks, blog posts, tutorials

### 5. **Production Quality**
- Rock-solid reliability
- Great performance
- Excellent test coverage
- Clear upgrade path

---

## Current State Analysis

### Strengths ✅

1. **Solid Technical Foundation**
   - 365+ tests passing
   - Clean architecture (SDK, API, Storage, Frontend layers)
   - Multi-framework support (LangChain, PydanticAI)
   - Research-backed features (MSSR, causal analysis)

2. **Comprehensive Features**
   - Decision tree visualization
   - Checkpoint replay
   - Safety audit trail
   - Failure clustering
   - Cost tracking
   - Multi-agent debugging

3. **Good Documentation**
   - Architecture docs
   - API reference
   - Examples (8 files)
   - ADRs for decisions

4. **Cloud-Ready**
   - Multi-tenant support
   - API key auth
   - Redaction pipeline
   - PostgreSQL support

### Gaps vs. Top 0.1% ❌

1. **Positioning Not Sharp Enough**
   - Current: "Debug AI agents like distributed systems"
   - Issue: Too abstract, doesn't create urgency
   - Top 0.1% would be: "See WHY your agent did that in 30 seconds"

2. **Onboarding Friction**
   - Requires understanding concepts before seeing value
   - No instant demo/Playground
   - Examples require reading code

3. **Discovery Problem**
   - No landing page live yet
   - Limited social proof (no "Used by X companies")
   - No viral content (blog posts, talks)

4. **Missing "Delight" Features**
   - No one-click share
   - No cloud demo
   - No visual comparison views

5. **Community Building Not Started**
   - No Discord/Slack
   - No contributor community
   - No roadmap publicly visible

---

## Strategic Recommendations (Priority Order)

### Phase 1: Sharpen Positioning (Week 1) 🎯

**Goal**: Make value proposition instantly clear

#### 1.1 Refine Hero Message

**Current README opening**:
```
Debug AI agents like distributed systems — not black boxes.
```

**Proposed alternatives** (test with developers):
- "See WHY your AI agent did that. Time-travel through decisions, replay failures, understand reasoning."
- "The debugger built for AI agents. Not just logs — reasoning chains, decision trees, time-travel replay."
- "Stop guessing why your agent failed. Replay the exact moment it went wrong."

#### 1.2 Create 30-Second Demo GIF

**Critical**: This is the #1 thing that converts visitors

- Show: Agent fails → Click failure → See decision tree → Replay checkpoint → Fix code
- Must be < 30 seconds, < 5MB
- Auto-play on GitHub README and landing page
- Create 3 versions: decision tree, replay, failure clustering

#### 1.3 Comparison Table Upgrade

Add to README and landing page:

| Feature | LangSmith | Arize | Peaky Peek |
|---------|-----------|-------|------------|
| Local-first | ❌ | ❌ | ✅ |
| Open source | ❌ | ❌ | ✅ |
| Decision provenance | ⚠️ | ⚠️ | ✅ |
| Time-travel replay | ❌ | ❌ | ✅ |
| Reasoning chains | ⚠️ | ⚠️ | ✅ |
| Cost to start | $39/mo | Custom | **Free** |

---

### Phase 2: Zero-Friction Onboarding (Week 2) 🚀

**Goal**: Working demo in < 5 minutes with zero config

#### 2.1 Interactive Playground (CRITICAL)

Create a one-command demo that shows value immediately:

```bash
peaky-peek demo
```

This would:
1. Start a local server
2. Run a pre-recorded agent session (from benchmarks)
3. Open browser to show decision tree + replay
4. Include intentional failures to show debugging value

**Implementation**:
- Package 2-3 benchmark sessions in pip package
- Add `demo` CLI command
- Auto-open browser to UI with demo session loaded

#### 2.2 One-Paragraph Quick Start

Replace current Quick Start with:

```markdown
## Try It Now (2 minutes)

```bash
pip install peaky-peek-server
peaky-peek demo  # Opens browser with working example
```

See a real agent session with decisions, tool calls, and replay. No code needed.

## Your First Trace (5 minutes)

```python
# example.py
import asyncio
from agent_debugger_sdk import TraceContext, init

init()

async def main():
    async with TraceContext(agent_name="my_agent") as ctx:
        await ctx.record_decision(
            reasoning="User wants weather",
            confidence=0.9,
            chosen_action="call_weather_api"
        )

asyncio.run(main())
```

Run it:
```bash
python example.py
```

Open http://localhost:8000 → See your trace appear in real-time.
```

#### 2.3 Interactive Tutorial

Create a guided walkthrough in the UI:
- Step 1: Create a decision → See it appear
- Step 2: Call a tool → See parent-child relationship
- Step 3: Cause a failure → See cluster analysis
- Step 4: Replay from checkpoint → See state restore

---

### Phase 3: Viral Growth Mechanics (Week 3-4) 📈

**Goal**: Make it easy to discover and share

#### 3.1 Landing Page (Already Planned - HIGH PRIORITY)

- Deploy to GitHub Pages immediately
- A/B test positioning messages
- Add social proof section (even if small)
- Embed demo GIFs auto-play
- Add "Star on GitHub" CTA

#### 3.2 Social Proof Building

**Tactics**:
1. **Get 5 early adopters to star + tweet**
   - Reach out to LangChain/CrewAI community
   - Offer to help them debug agent issues
   
2. **Create shareable artifacts**
   - "7 Agent Debugging Nightmares Peaky Peek Solves" blog post
   - "Why Your AI Agent Failed: A Visual Guide" 
   - Comparison chart that's easy to tweet

3. **Conference/Talk Presence**
   - Submit to PyCon, AI.dev, LangChain talks
   - Create 5-minute lightning talk video
   - Post on YouTube + Reddit r/MachineLearning

#### 3.3 Community Infrastructure

1. **Discord Server**
   - #help channel
   - #showcase channel (people share debugging wins)
   - #contributors channel

2. **GitHub Discussions**
   - Enable for Q&A
   - Create "Share your setup" category

3. **Contributor Guide Enhancement**
   - Add "Good First Issue" labels
   - Create contributor spotlight
   - Monthly contributor call

---

### Phase 4: Product Excellence (Month 2) ✨

**Goal**: Make it delightful to use daily

#### 4.1 "Delight" Features

1. **One-Click Share**
   - Export session as shareable link (even if localhost)
   - Generate sharable screenshot of decision tree
   - "Tweet your debug session" button

2. **Comparison Views**
   - Side-by-side session diff
   - "Why did this run fail but that one succeed?"
   - Visual regression for agent behavior

3. **Smart Defaults**
   - Auto-detect framework (LangChain vs PydanticAI)
   - Auto-instrument on import
   - Zero-config mode for common cases

4. **Performance Insights**
   - "Your agent spent 40% of time in retry loops"
   - "This decision path costs 3x more than alternatives"
   - Suggest optimizations

#### 4.2 Cloud Demo (Critical for Enterprise Adoption)

Deploy a public demo at `demo.agentdebugger.dev`:
- Read-only access to pre-recorded sessions
- Shows all features without installation
- CTA: "Debug your own agents → pip install peaky-peek"

#### 4.3 Integration Ecosystem

1. **CrewAI Adapter** (planned - prioritize)
2. **LlamaIndex Adapter**
3. **AutoGPT Adapter**
4. **VSCode Extension** (debugger view in IDE)

---

### Phase 5: Scale & Sustainability (Month 3+) 🏢

#### 5.1 Enterprise Features

1. **SSO Integration**
2. **Team Workspaces**
3. **RBAC**
4. **Audit Logs**
5. **Self-Hosted Deployment Guide**

#### 5.2 Business Model (if desired)

**Freemium**:
- Free: Local mode, unlimited sessions
- Pro ($29/mo): Cloud sync, team sharing, advanced analytics
- Enterprise: Self-hosted, SSO, support

#### 5.3 Sustainable Growth

1. **Content Engine**
   - Monthly blog post on agent debugging
   - Video tutorials
   - Case studies

2. **Community Events**
   - Monthly "Debug This Agent" challenge
   - Contributor hackathons
   - Office hours

---

## Specific Quick Wins (This Week)

### 1. Better README Hero (1 hour)

```markdown
# Peaky Peek — See WHY Your AI Agent Did That

**The debugger built for AI agents.**

Capture decisions, tool calls, and reasoning chains. Replay failures from checkpoints. Search across sessions.

[![Demo GIF](./docs/assets/demo.gif)](./docs/getting-started.md)

**Try it now** (30 seconds):
```bash
pip install peaky-peek-server && peaky-peek demo
```

---

## Why Peaky Peek?

Your AI agent made a decision. You don't know why. **Peaky Peek shows you.**

| What You Want | What You Get |
|---------------|--------------|
| "Why did it call that tool?" | Decision tree with reasoning + confidence |
| "When did it go wrong?" | Time-travel replay to exact failure point |
| "Has this failed before?" | Cross-session failure clustering |
| "What did the LLM see?" | Full prompt/response inspector |

**VS Traditional Tools**:

| Tool | Answers "Why?" | Local-first | Open Source |
|------|----------------|-------------|-------------|
| Logs | ❌ | ✅ | ✅ |
| LangSmith | ⚠️ | ❌ | ❌ |
| **Peaky Peek** | ✅ | ✅ | ✅ |
```

### 2. Create Demo GIF (2 hours)

Use existing benchmark sessions:
1. Run `seed_demo_sessions.py`
2. Record screen showing:
   - Session list → Click session
   - Decision tree → Click decision
   - See reasoning + evidence
   - Click failure → Replay from checkpoint
3. Edit to < 30 seconds with quick cuts
4. Add text overlays: "See reasoning", "Replay failures", "Find patterns"

### 3. Deploy Landing Page (3 hours)

Execute `docs/superpowers/plans/2026-03-23-landing-page.md`:
- Already has complete implementation plan
- Just needs execution
- Enables GitHub Pages immediately

### 4. Social Proof Section (1 hour)

Add to README after features:

```markdown
## Used By

- [Company 1] — "Peaky Peek helped us debug a production agent failure in 10 minutes that would have taken hours"
- [Developer 1] — "Finally, a debugger that understands agent reasoning"
- [Developer 2] — "The checkpoint replay saved us countless hours"

[Add your story](https://github.com/acailic/agent_debugger/discussions/new?category=show-and-tell)
```

### 5. Blog Post Draft (3 hours)

Title: "I Built a Time-Travel Debugger for AI Agents"

Outline:
1. The problem: Agent failed, no idea why
2. Traditional tools don't help (logs miss reasoning)
3. The solution: Record decisions + replay from checkpoints
4. Demo: Walk through real debugging session
5. Open source + local-first philosophy
6. Try it: `pip install peaky-peek-server`

---

## Success Metrics

Track these monthly:

| Metric | Current | 3-Month Goal | Top 0.1% |
|--------|---------|--------------|----------|
| GitHub Stars | ~50 | 500 | 5,000+ |
| PyPI Downloads | ~100/mo | 5,000/mo | 50,000+/mo |
| Active Users | ~10 | 100 | 1,000+ |
| Contributors | 1 | 10 | 50+ |
| Blog Posts | 0 | 3 | 20+ |
| Discord Members | 0 | 50 | 500+ |

**Leading Indicators**:
- Demo GIF views
- Landing page conversion rate
- Time to first successful trace
- GitHub discussion activity

---

## Competitive Moat

What makes Peaky Peek defensible:

1. **Local-First Philosophy**
   - Competitors are all SaaS
   - Privacy-conscious enterprises prefer local
   - No vendor lock-in

2. **Research-Backed Features**
   - MSSR-inspired importance scoring
   - Causal graph tracing
   - Evidence-grounded reasoning
   - Hard to replicate quickly

3. **Framework Agnostic**
   - Not tied to LangChain or any one framework
   - Can support emerging frameworks fast

4. **Open Source Community**
   - Hard for closed-source competitors to match
   - Network effects from contributors

---

## Next Actions (Priority Order)

### This Week (Critical Path)

1. ✅ **Create demo GIF** (2 hours) — HIGHEST IMPACT
2. ✅ **Deploy landing page** (3 hours) — Execute existing plan
3. ✅ **Improve README positioning** (1 hour)
4. ✅ **Add `peaky-peek demo` command** (4 hours)
5. ✅ **Write first blog post** (3 hours)

### Next 2 Weeks

6. Launch Discord server
7. Enable GitHub Discussions
8. Create 3 tutorial videos (5 min each)
9. Reach out to 10 LangChain/CrewAI developers for feedback
10. Submit talk to 1 conference

### Month 2

11. Build cloud demo site
12. Implement one-click share
13. Add CrewAI adapter
14. Create contributor spotlight program
15. Publish 2 more blog posts

---

## Resource Requirements

### Time Investment

- **Week 1**: 13 hours (demo GIF, landing page, README, demo command, blog post)
- **Week 2-4**: 20 hours (community, content, outreach)
- **Month 2**: 40 hours (features, cloud demo, integrations)
- **Ongoing**: 10 hours/week (community, content, support)

### Skills Needed

- Video editing (for demo GIF) — Can learn in 1 hour
- Technical writing (blog posts) — Already strong
- Community management — Learn by doing
- Public speaking (conference talks) — Practice with videos first

---

## Conclusion

Peaky Peek has a **strong technical foundation** but needs to work on **positioning, onboarding, and community** to reach top 0.1%.

**The #1 thing**: Create a compelling demo GIF that shows the value in 30 seconds. This alone could 10x adoption.

**The #2 thing**: Deploy the landing page and start collecting social proof.

**The #3 thing**: Make onboarding zero-friction with `peaky-peek demo`.

The gap between "good project" and "top 0.1%" is mostly **marketing and community**, not features. Focus on making the existing features **irresistibly easy to discover and adopt**.

---

**Implementation Priority**: Start with "Specific Quick Wins" section above. Execute landing page plan immediately. Create demo GIF this week.
