10K MRR Experiment - Week 1 Retrospective (The Honest Version)

Week 1 is done. I'm five days in and I can feel the momentum - but I can also see the gap. That gap is the honest part of this experiment: building is not the same as revenue.

Here's the reality so far:

• 300+ commits
• 3 production-ready apps
• 14+ agents running daily
• $0 revenue (nothing is deployed yet)

This post is a week-one retrospective - what I did, what I learned, and how I plan to convert all this activity into actual MRR.

Why I'm doing this experiment

I've spent years building for clients: agency work, Web3 projects, now AI products. The pattern I saw was slow, expensive development cycles. The promise of AI agents is that they compress those cycles from quarters to weeks.

So I'm stress-testing that promise publicly:

• 30 days
• 10K MRR target
• daily shipping with agents
• open documentation of the wins and failures

It's part research, part marketing, part personal challenge. And it's the fastest way I know to prove the tagline I keep repeating: "AI that ships. In weeks, not quarters."

What I built in week 1

I didn't build one product. I built three, because the experiment is about systems, not just apps. I wrote about the full build process in I Built 3 AI Apps in 5 Days.

1) Personality marketplace (SOUL.md)

A marketplace where AI agent personalities are defined in structured SOUL.md files - capabilities, boundaries, tone, and specializations.

2) Prompt battle arena (ELO)

Prompts compete head-to-head. The best rise via ELO ranking. It turns prompt engineering into a sport - and a library.

3) Bounty marketplace (points economy)

Tasks are posted with point-based incentives, designed for both human and agent labor. A lightweight experiment in marketplace mechanics.

These aren't random apps. I'm building a connected ecosystem where each product feeds another.

The flywheel strategy

If the goal is MRR, I need a repeatable loop — the same conviction behind why every business needs an AI agent strategy. Here's the flywheel I'm building:

1. Build in public →
2. Prove expertise →
3. Drive leads →
4. AI Development Sprint ($5K/$10K/$20K) →
5. Case studies →
6. More content → repeat

The products are both proof and assets in that loop. They show speed, they show depth, and they create real case studies I can point to.

What worked in week 1

1) Agent orchestration

Running 14+ agents isn't magic. It's a process. I've written a deeper dive on this in Running 14+ AI Agents Daily: Lessons From the First Week.

• Clear tasks
• Small scope
• Tight boundaries
• Frequent commits

When I orchestrated well, the agents shipped features overnight.

2) Shared architecture

Using Next.js + Convex across all products let me reuse components and workflows. Consistency became a speed advantage.

3) Thin UI, thick workflows

Instead of polishing UI, I focused on core workflows. That's why I can call these "production-ready" even if they're not polished.

What didn't work (and why it matters)

1) No deployment = no reality

This is the biggest gap. I'm building fast, but without deploying I'm not learning from users. There's no validation.

2) Feature creep from abundance

When agents can build quickly, I'm tempted to build everything. It's a false sense of progress. I need to tighten the scope.

3) The comfort of building

Building feels productive. Selling feels vulnerable. I'm forcing myself to get uncomfortable this week.

The cost (real and invisible)

Even with $0 revenue, the costs are real:

• API usage from Anthropic's Claude and OpenAI's GPT‑5.3‑Codex
• Hosting and infra planning
• Time spent orchestrating agents
• Context switching across three products

The experiment is proving that "AI is free" is a myth. Fast doesn't mean cheap. It means you're moving the cost into orchestration and model usage. I broke down every dollar in AI Agent Cost Breakdown: Real Numbers.

The tools that changed my workflow

Today, both Claude Opus 4.6 and GPT-5.3-Codex released. It's a reminder that the stack evolves faster than any individual product.

The announcement of Claude Code Agent Teams also validated my approach: the future is agent teams, not solo copilots. If you're curious about the broader landscape, I wrote a guide on How to Build AI Agents in 2026.

What comes next (week 2 priorities)

If week 1 was about building, week 2 is about shipping.

Priorities:

1. Deploy at least one product
2. Onboard real users (even if it's 5-10 people)
3. Track activation (not just visits)
4. Move from "production-ready" to "in production"

I also need to start pre-selling the AI Development Sprint. If I want MRR, I need paid commitments.

The hard truth

300+ commits can feel like success. But the market doesn't pay for commits. It pays for outcomes.

The experiment is working in one way: I'm proving I can ship fast with agents. But the outcome I care about - revenue - hasn't started.

And that's fine. It's week 1.

I'm not hiding the gap. The gap is the story. If I can close it in public, that proof is more valuable than any sales deck. The AI Product Building course walks through the frameworks I'm using to close that gap. If you want to go deeper on the methodology, the AI Product Building course walks through this step by step — from MVP to monetization.

Week 1 done. Week 2 is for deployment, users, and revenue.

If you're following, keep me accountable. You can also find me on GitHub.