GrindProof: My Architecture Laboratory
Why This Matters to You
When I recommend an architecture pattern or tech stack to clients, they deserve to know it works. GrindProof is where I test those patterns with my own money before putting them in your production.
Key Results
The Truth
This isn't a side hustle distracting me from client work. It's a controlled experiment where I prove architecture decisions work under real load before recommending them.
The Concept: AI Accountability That Actually Works
Content creators struggle with accountability. They set goals ("post 3 times a week") but life happens. Traditional accountability partners are unreliable.
GrindProof's Promise:
- Creators set public commitments
- AI (Claude) checks if they followed through
- If they fail, they pay a penalty (goes to charity or friends)
- Public dashboard shows their streak
Why AI?
- AI doesn't sleep, forget, or have its own problems
- Can verify completion from screenshots, links, or check-ins
- Scales to thousands of users without hiring humans
The Technical Challenge: Trust at Scale
Building an AI accountability system means solving problems most SaaS doesn't face:
The Stakes
- Users are putting real money on the line
- False positives (AI says you failed when you didn't) = lawsuit territory
- False negatives (AI says you passed when you didn't) = platform loses credibility
- Downtime during verification window = user anger
This isn't a todo app where bugs are forgiven. This is "AI holding your money hostage" - it has to be bulletproof.
Architecture Decisions (And Why They Transfer to Client Work)
Decision 1: SvelteKit Over Next.js
Why: Faster server-side rendering, better DX, smaller bundle sizes
Client Benefit
The patterns I build here (form handling, API routes, SSR) transfer directly to client projects. When I say "SvelteKit is faster," I can show the Lighthouse scores from GrindProof.
Decision 2: Supabase for Everything
Why: PostgreSQL + Auth + Realtime + Storage in one package
The Test: Can Supabase handle:
- Concurrent webhook processing (Stripe + AI callbacks)?
- Real-time leaderboard updates?
- Complex row-level security rules?
Result: Yes, but with caveats
- RLS policies get complex fast (documented all edge cases)
- Need connection pooling for serverless (learned the hard way)
- Realtime subscriptions need cleanup or memory leaks (found and fixed)
Client Benefit
When I recommend Supabase, I can say "here's exactly where it will bite you, and here's how to avoid it."
Decision 3: Claude over GPT-4 for Verification
Why: Better at following instructions, less likely to hallucinate
The Test: Can Claude reliably verify accountability from messy user inputs (screenshots, URLs, text)?
// Verification pipeline
async function verifyCommitment(commitment, evidence) {
// 1. Structured prompt (tested 50+ iterations)
const prompt = buildVerificationPrompt(commitment, evidence);
// 2. Call Claude with strict output format
const result = await claude.messages.create({
model: "claude-3-5-sonnet-20241022",
max_tokens: 500,
messages: [{
role: "user",
content: prompt
}]
});
// 3. Parse structured response
const verification = parseVerification(result.content);
// 4. Human review flag for low confidence
if (verification.confidence < 0.85) {
await flagForHumanReview(commitment.id);
}
return verification;
}Learnings
- Claude is more reliable than GPT-4 for binary decisions (did they do it: yes/no)
- Need confidence scores + human review for edge cases
- Structured outputs > parsing free-form text
- Cost per verification: ~$0.02 (cheaper than human reviewers)
Client Benefit
When building AI features, I know exactly how to prompt for reliability, not just "cool demos."
What I'm Testing Right Now
Current Tests:
- Stripe metered billing - Usage-based pricing for AI verifications
- Real-time leaderboards - Supabase Realtime with 1000+ concurrent users
- AI prompt versioning - A/B testing prompts without redeploying
- Progressive enhancement - Works without JS (SvelteKit SSR)
Why This Matters to You:
- Your project won't be the first time I've done metered billing
- I've already debugged the Supabase Realtime scaling issues
- Prompt versioning system is ready to copy-paste into your AI product
The Numbers (Brutal Honesty)
Current Metrics
| Metric | Before | After |
|---|---|---|
| Monthly Active Users | 47 | |
| Revenue | $280 MRR | |
| Infrastructure Cost | $45/month | |
| Gross Margin | 84% | |
| Uptime | 100% (since launch) |
Why I'm Sharing This
Not to brag (47 users isn't impressive). But to show:
- The architecture scales beyond current usage
- Costs are predictable and low
- The system doesn't break under load spikes
When I tell clients "this will cost ~$50/month to run," it's not a guess.
What You Get From This
When you hire me, you're not just getting my consulting time. You're getting:
Battle-tested patterns
Every architecture I recommend has been stress-tested in GrindProof
Honest cost estimates
I know exactly what Supabase/Claude/Stripe costs at scale
Real debugging experience
I've hit and fixed the edge cases before your users do
Code I can show
Want to see how I handle webhook retries? It's in GrindProof
The Lesson
I don't just read Hacker News and recommend trends. I build real products, with real users, and real money on the line.
My Promise
- I won't recommend tech I haven't personally deployed
- I won't guess at costs - I'll show you my bills
- I won't hand-wave scaling - I'll show you load test results
Other consultants talk about best practices. Codefred shows me his production monitoring dashboard.
Want to see it live?
Check out GrindProofWant architecture like this?
Let's talk about your project and how I can apply these battle-tested patterns to your production system.
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