Paydirt: How I Built an Open-Weight AI Agent to Turn GitHub Bounties into a Real Side Hustle

This is a submission for the Hacktoberfest Open-Source AI Challenge Week 2: Side Hustle

What I Built :
Every open-source contributor knows the sting of bounty ghosting: you spend two evenings fixing a thorny bug on a funded GitHub issue only to find the maintainer hasn't logged in since August, another contributor opened an unreviewed PR three weeks prior, or the bounty platform escrow terms are predatory. Over 60% of open-source bounties never pay out, turning developer side-hustle time into uncompensated labor.

Paydirt turns open-source bounty hunting from a lottery into an evidence-based side hustle. It is an open-weight, privacy-firs
AI agent that:

1. Discovers Funded Issues: Scrapes and normalizes bounties across GitHub issues (Algora, issue tags, corporate sponsorship tiers).
2. Predicts Real Payout Probability ($P(\text{payout})$): Uses Prior Labs' TabPFN Bayesian tabular foundation model to score whether a funded issue will actually merge and pay out based on repo velocity, maintainer response intervals, review churn, and bounty size.
3. Calculates Expected Value: Ranks opportunities by and hourly earning efficiency, steering contributors toward high-conviction wins instead of vanity sums.
4. Local Skill Matching via Gemma: Embeds your local git commit history and GitHub profile via open-weight Gemma 3 + EmbeddingGemma running entirely on your machine through Ollama. Your code, habits, and private repos never leak to third-party cloud APIs.
5. Durable Lifecycle Tracking via Temporal: Manages the entire lifecycle—from claiming intent and plan generation to PR submission and bounty release—with durable timers and webhook signals.
6. Built-in Safety & Dry-Run Guarantees: Strict safety guards prevent accidental spam or premature claims; all external GitHub mutations require manual user confirmation. Who would pay for it? Freelance developers, student contributors, and independent OSS maintainers looking to monetize 10–20 hours a week without burning time on ghosted issues. A monthly subscription or 2% success fee on settled payouts provides a clear ROI in just one saved weekend.

Demo :

Platform preview : https://paydirt-iota.vercel.app/

*Code *:

Repository: https://github.com/subhranshudash13-dotcom/Paydirt
Monorepo Structure:
packages/shared: Shared types, bounty ranking algorithms, and schemas.
apps/api: Fastify backend, TimescaleDB/Tiger Data store, and live hydrate feed.
apps/worker: Temporal workflows for durable bounty lifecycle management.
apps/web: React 18, TypeScript, Tailwind CSS, Vite with responsive neo-brutalist UI.
services/predictor: TabPFN Python Bayesian scoring sidecar.

How I Built It :
Paydirt was architected around open innovation, modular workflows, and local-first execution:

1. Open-Weight AI & Local Inference Google Gemma 3 (gemma3:4b) & embeddinggemma: Orchestrated locally via Ollama. Contributors' GitHub profile bios, repositories, and past pull requests are converted into dense vector embeddings locally. When viewing an issue, similarity between developer skills and issue requirements is scored on-device with zero cloud latency and total privacy. TabPFN (Prior Labs): Rather than hallucinating a payout score through an LLM, we utilize TabPFN—a foundation model specifically trained on tabular data. It computes calibrated probabilities $P(\text{payout} = 1)$ using 8 tabular features (maintainer response frequency, open PR count, repo activity age, issue description length, and historical bounty settlements).

2. Temporal Durable Workflows
   Bounty hunting is an asynchronous, multi-week endeavor. Paydirt uses Temporal (@temporalio/workflow and @temporalio/activity) to track claims. Workflows pause for review signals, automatically trigger reminder check-ins before bounties expire, and verify when PRs merge—all surviving process restarts.

3. TimescaleDB / Tiger Data with pgvector
   PostgreSQL equipped with timescaledb and pgvector stores time-series telemetry on issue churn (tracking how maintainer response latency drifts over time) and runs vector cosine searches for skill-to-bounty matching. In development, a memory fallback allows instant out-of-the-box prototyping.

4. Sentry Error Tracing & Telemetry
   Configured across Fastify and Temporal workers (@sentry/node), ensuring failed API scraping pipelines, unparsed issue formats, or network timeouts are captured with full stack traces and contextual tags.

Why Does Open Innovation Matter?
True Privacy for Developer IP: Closed AI APIs (OpenAI, Anthropic) require transmitting code, private git histories, and developer profiles to remote corporate servers. By using open-weight models like Gemma 3 running locally on Ollama, developers never leak uncommitted scratchpad notes or proprietary codebases just to get AI assistance.
Reproducible, Non-Degrading Models: Proprietary models silently update or suffer regression across versions. Open models allow us to pin exact weights (gemma3:4b, TabPFN checkpoints) so payout predictions and scoring metrics remain consistent and verifiable.
Zero API Cost for Students & Indie Hackers: Contributors working on bounties should not have to spend $20/month in API credits just to find out which bounties are worth their time. Open weights run on hardware developers already own.