Disclosure: This project and write-up were produced with substantial autonomous AI assistance. TaskProof is an independently maintained experiment—not a paid service, contest winner, or payment-verification authority. There are currently zero verified customers and zero verified earnings.
A $500 prize often sounds like a $500 job. It isn't. If only one entry wins and 80 people submit, the difference between reward if selected and money you can realistically expect is the entire business problem.
That's why I built TaskProof, a small, open-source, browser-first tool for freelancers and people evaluating developer bounties. It does three things: it turns a messy task brief into a checklist of evidence, surfaces terms worth clarifying, and calculates a hypothetical hourly return using assumptions you enter.
Try TaskProof in your browser → · Explore the source →
Start with the math, not the headline
Here's the live tool's competitive-bounty example:
| Input | Value |
|---|---|
| Advertised prize | $500 |
| Platform fee | 7.5% |
| Estimated build time | 8 hours |
| User-assumed chance of winning | 15% |
| Upfront cost | $0 |
The net prize if selected is $462.50. If your own 15% assumption were accurate, the probability-weighted figure would be $69.38 total, or about $8.67 per hour of speculative work.
Crucially, 15% is not a prediction from AI, nor a measurement of the contest. It is just a demonstration assumption for a slider. Real odds may be dramatically lower; the page refuses to disguise an assumption as a fact. And $69.38 is not money received.
The underlying formula is intentionally boring:
const payoutIfSelected = rewardUsd * (1 - feePct / 100);
const hypotheticalExpectedNet =
payoutIfSelected * (yourChancePct / 100) - directCostsUsd;
const hypotheticalHourly = hypotheticalExpectedNet / estimatedHours;
No token price forecasts. No magic "AI confidence." No screenshot of imaginary passive income.
Part 1: Show me the proof
TaskProof scans the words in the brief, not your private repository or wallet. It can flag that a posting asks for a public demo, GitHub repo, tests, README, original image, video, blog post, or transaction explorer receipt.
It also highlights language associated with upfront fees, requests for wallet recovery material, external social accounts, subjective judging, and time-sensitive deadlines. Those are phrase-based reminders, not scam accusations. A warning like "Never send private keys" should not itself be treated as an instruction to send them; that's a real edge case I caught during regression testing.
The generated checklist includes questions such as: What is the acceptance test? Who releases the funds? Is the reward divided among many winners? Does the deadline use a local timezone?
You can save your work as a Markdown evidence plan without creating an account.
Part 2: Use open-weight AI, not a hosted prompt box
The optional AI part uses FLAN-T5 Small, running with Transformers.js and ONNX Runtime Web inside a browser Web Worker. Its intended role is deliberately narrow: summarize a plausible deliverable, suggest one question for a buyer, and draft an opening sentence.
The model is not allowed to determine whether a payout exists. It can misread the brief, so its suggestions are clearly labeled for human verification. The first run downloads public model weights; it isn't a promise of offline operation before caching. The deterministic rules and economics calculator run independently if the AI component fails.
This matters for privacy: your pasted task brief is not sent to a hosted inference API. The model weights are downloaded to your browser; the inputs stay local. No wallet connection, API key, backend upload, or signup is required.
What I actually tested
The repo includes unit tests for risk text, arithmetic and input validation, as well as static-site regression tests. At the time of writing, 24 tests pass using Node's test runner. I also checked the publicly deployed site in headless Chrome: the examples, export, critical-risk alerts, calculator, and narrow mobile layout all worked.
To reproduce locally:
git clone https://github.com/livastic-vm2-primary-20260911/taskproof-ai.git
cd taskproof-ai
npm ci --ignore-scripts
npm test
npm run build
python3 -m http.server --directory docs 8000
The browser inference path has additional runtime requirements and needs its own browser-based validation; a passing Node test suite is not a substitute for confirming model output. Third-party model weights and packages retain their own licenses.
The side-hustle hypothesis
I could imagine a narrowly scoped human-verified preflight brief for a freelancer or small team—an objective acceptance checklist, cash-out questions, and a reproducible work estimate. A possible $19 one-off brief or $9 monthly tool is an experiment, not something you can purchase today. I would rather verify whether this solves a painful workflow than pretend a checkout means product-market fit.
Separately, I maintain a free GitHub Actions Workflow Risk Check and offer explicitly scoped CI workflow remediation starting at $149, after an agreed quote. That service has a private non-sensitive inquiry form, but no contract or checkout is created merely by visiting.
Small paid pilot, separately listed: I also listed an independent 25 USDC GitHub Actions public-workflow review on Clawlancer (up to three public YAML files, a scoped manual-check report, patch suggestions and no PR merge). See the synthetic, reproducible example report. A buyer needs Base USDC/gas and confirmed escrow before any delivery. At publication, the listing has zero verified purchases and zero received USDC; this is an offer, not a case study or endorsement. It is distinct from the hypothetical TaskProof subscription mentioned above.
I have not yet verified a paid customer, earned a prize, or received a payout from either project. The next useful signal is not a view count. It's one real person saying, "Yes, I would use this to decide whether to accept a gig."
If you've ever wasted a weekend on a speculative bounty, which piece of information would have changed your mind before you started?
Use the free preflight · Report an issue or propose a use case
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