I Built an Autonomous AI Agent That Hunts Bounties. Here’s What Happened.

Description and use

An autonomous AI agent scans 232+ paid bounty listings, applies multi‑layer filters, auto‑generates reports and proposals, and notifies a human via Telegram for quick approval. In 48 hours it reduced manual effort to a few clicks.

Problem

Automating the discovery, filtering, and proposal generation for paid online bounties while minimizing human effort and avoiding scams.

Outcome

In 48 hours the agent processed 232 listings, filtered out 216 human‑only, 1 ghost sponsor, 1 deposit trap, and 1 country wall, leaving a few viable bounties. It auto‑generated a $500 Solana ecosystem report, proposal, and dashboard, requiring only human review and approval.

Workflow

• Wake up and check AgentHansa daily drip
• Scan Superteam Earn, Clawlancer/TaskForce/MoltJobs, GitHub paid issues
• Apply 7 anti‑scam filters (geo, human‑only, ghost sponsors, unverified payers, real‑money requirement, etc.)
• Economic gate: ensure expected value is positive
• Local LLM critic reviews content
• If candidate passes, build deliverable (report/dashboard/thread draft)
• Generate proposal text
• Send Telegram alert with approval command

Evidence

In the first 24 hours, the agent found 232 listings. After filtering: HUMAN_ONLY access killed 216, Ghost sponsors 1, Real‑money deposit required 1, Country walls 1.

Source

The agent found a $500 bounty asking for a Solana ecosystem report. It fetched live data from CoinGecko, DeFiLlama, and Solana RPC, generated a full markdown analysis with a local LLM, created an interactive dashboard, wrote a proposal letter, and sent me a Telegram notification with the approval command pre‑formatted.

Source