Taking Down a $10K Bounty with Nothing but AI

The Idea

I had a simple question: could I use AI to take down a serious open-source bounty? Not a $50 CSS fix — a real one. Something that would require research, strategy, architecture, and execution. I wanted to see how far a single person could get by treating AI as a true collaborator rather than just an autocomplete engine.

Gumroad — the creator economy platform founded by Sahil Lavingia — open-sourced their entire codebase and started putting bounties on GitHub issues. Some small, some big. I didn't know which ones existed or what they involved. So I started with a completely open-ended prompt:

I gave it no direction on which bounty, no preference on size or complexity. I wanted to see what it would find and how it would reason about the options.

The Research

But I wasn't convinced yet. Maybe there was something easier with less competition. So I pushed back:

Going Big

That was enough for me. If we were going to do this, we were going all the way. I didn't want to submit another thin API wrapper — I wanted something that would make Sahil sit up in his chair.

This was where it got interesting. I asked the AI to research Sahil specifically — his values, his writing, what he responds to — and factor that into the design. Not just "build a CLI" but "build a CLI that this specific person will be excited about."

The Move Nobody Else Made

The plan was already strong, but then I had an idea. The bounty was for an "AI-native" CLI. Everyone else was interpreting that as "a CLI that AI can shell out to." But what if we went further?

This was the moment the submission went from "competitive" to "different category." We weren't just wrapping an API — we were setting a new standard for what "AI-native developer tool" means.

The Build

With the plan locked in, I gave the go-ahead:

And then it just... built the whole thing. 40 files of clean TypeScript in a single session. The AI asked a few clarifying questions (npm package name, my GitHub username, whether to use Anthropic or OpenAI for the natural language mode) and then started cranking.

The full feature set:

• Complete API coverage — Products, sales, licenses, subscribers, payouts, offers, webhooks, variants, custom fields. Every command supports --json mode and automatic pagination.
• OAuth browser login — gumroad auth login opens a browser, user clicks Authorize, done. No copying tokens around. (Getting this to work required some debugging of Gumroad's OAuth scope configuration — the joys of working with a real API.)
• MCP server — gumroad mcp starts a Model Context Protocol server that any compatible AI agent can connect to directly.
• One-command AI setup — gumroad setup cursor, gumroad setup claude-code, gumroad setup all.
• Live sales watcher — gumroad watch for a real-time sales feed in your terminal.
• Status dashboard — gumroad status for an at-a-glance revenue view.
• Natural language mode — gumroad ai "how much did I make this week?"
• Easter eggs — gumroad ping with animated webhook checking, ASCII art logo.
• Shell completions — bash, zsh, fish.

Testing It Live

I created a Gumroad API application, got a token, and we tested every command against the real API. Everything worked — product listing, user profiles, status dashboard, the OAuth flow, the setup commands, completions, all of it. We iterated on a few things (fixing display name handling, making the auth process exit cleanly after success), but the core build was solid on the first pass.

The Submission

The Gumroad repo restricts pull requests to collaborators, so the process is to comment on the issue with your implementation. I recorded a Loom walkthrough demonstrating every feature and posted it alongside the GitHub repo link.

Looking at the competition:

What I Learned

A few takeaways from treating AI as a full collaborator on a real project with real stakes:

Prompting is strategy, not instruction. The most valuable prompts weren't "write this function" — they were "go research the competitive landscape" and "understand what this specific person values." Treating the AI like a strategist rather than a typist produced dramatically better results.

Pushing back matters. When the AI recommended the $10K bounty immediately, I said "find me easier ones." It came back with evidence that the easy ones were actually harder (swarmed, bad ROI, deployment barriers). That back-and-forth led to a more informed decision than either of us would have made alone.

The best ideas come from building on AI suggestions. The AI proposed the MCP server. I proposed the multi-tool integration skills on top of that. Neither of us would have gotten there alone. The compounding effect of iterating on each other's ideas is where the real leverage is.

AI can do the 0-to-1, but you need to direct the 1-to-100. Left to its own devices, the AI would have built a competent CLI. It was the human direction — "go way above and beyond," "understand Sahil," "build skills for every AI tool" — that turned it into something that stands out.

Current Status

The submission is live on the GitHub issue. As of this writing, Sahil hasn't responded yet. The bounty is still open. If you're reading this, Sahil — check your notifications.

Regardless of the outcome, this was a proof of concept. One person, one AI, one session, going after a $10,000 bounty against professional developers — and producing something that meaningfully outscoped the competition. The tools are here. The question is just how ambitious you're willing to be with them.