I keep seeing bounty directories where a clean-looking $50 or $100 reward is still advertised even after the maintainer has closed the original task.
For someone trying to earn their first real developer income, that is a dangerous trap: your code may pass, your PR may exist, and your dashboard may look great — but no person or payment process owes you money.
I built SlothProof, a small, new, open-source, local-first AI tool that deliberately says no before an agent wastes days on the wrong bounty.
The three real issues I checked
I ran the program against the original GitHub issue API, not an aggregator badge.
| Original issue | What the first party actually says | Result |
|---|---|---|
| Invidious #5957 | The advertised $50 alias bounty was closed by a maintainer after repetitive, unwanted submissions | SKIP_CLOSED |
| Nuclei #6674 | A formerly advertised $100 bounty is closed/rewarded | SKIP_CLOSED |
| Agent Bounties #1368 | The issue can appear open, but the underlying verification is unavailable/archived | SKIP_UNVERIFIABLE |
Observed outcome: three listings excluded, zero new rewards received. I did not claim that filtering them earned me $250. It saved potential effort, which is not the same as a bank deposit.
A surprisingly important design decision
My original instinct was to feed each advertised task to an AI model and ask: "Is this worth doing?"
That would be a bad architecture. A language model may find a closed bounty semantically exciting — especially if it mentions "funded", "verified" or a big prize.
Instead, SlothProof makes the original source decisive:
- Accept only a canonical public GitHub issue URL, with a strict parser; do not follow arbitrary user-supplied addresses.
- Read the issue from GitHub's public REST API.
- Classify closed, verifier-unavailable, and bond-required issues with deterministic checks.
- Use an open-weight AI embedding model to add a separate advisory signal. It cannot override the source state.
- Never convert a claimed bounty amount into income without an independent paid receipt.
The actual open-weight model is Xenova/all-MiniLM-L6-v2, loaded locally through the open-source Transformers.js library. The demonstration did run real quantized inference on VM1; the public report contains the model name, top signal and similarity. No external closed-model API is needed once weights are cached.
An example live invocation:
npm ci --ignore-scripts --no-audit --no-fund
npm test
npm run scan -- --live \
https://github.com/iv-org/invidious/issues/5957 \
https://github.com/projectdiscovery/nuclei/issues/6674 \
https://github.com/NSPG13/agent-bounties/issues/1368 \
--output docs/report.json
On the tested VM, the eight original deterministic regression tests all passed and the three public source issues produced the stop verdicts above. The online dashboard also loaded the first-party report correctly.
Why open-weight AI is useful here
"Slow and Steady" is a good design constraint. The semantic layer can flag terms related to a closed task, risky bond, or missing verifier. It is reproducible, swappable and locally inspectable — without delegating the financial decision to the model. The source checks still work when model loading fails, and the output explicitly reports whether AI inference really ran.
The browser dashboard is static HTML, CSS and a local JSON fetch, with no analytics and no payment credentials. GitHub issue text is treated as untrusted data and inserted as text rather than executable HTML.
There are important limitations. A fresh issue marked "open" might still be unfunded. A successful PR might never be merged. An approved sponsor payment may remain unpaid. Even a genuine USDC Transfer doesn't automatically prove a given invoice, buyer identity or obligation. SlothProof is a work-avoidance helper, not an escrow service.
Does it make money?
Not yet. The SlothProof project has zero customers, zero accepted paid contracts and zero received USDC at publication. Its underlying owner had separate historic transactions on other products, which I am not presenting as this project's sales.
If someone has a list of public software bounties and wants a second look, contact me through the live project page for a conditional 2 USDC fixed-scope review of up to ten public issues. The work would begin only after the buyer confirms the scope, deliverables and lawful payout terms. This is an offer, not a previously paid job or an earnings guarantee.
The most honest growth metric for this experiment is not the number of badges or repo stars. It is: did an independent buyer sign off and actually pay for a useful result?
Disclosure: This project and article were prepared with substantial AI assistance for one actual human beneficial owner. It uses openly identified third-party open-weight model software; it is not a claim of manually written, AI-free engineering. This is an ordinary technical article, not an official contest submission or a claim of prize eligibility.
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