I built a conditional political donation system (demo)

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Over the past few years I’ve been building POWERBACK, a web platform that experiments with applying conditional escrow mechanics to campaign finance. Instead of donating directly to a campaign, users commit funds under a publicly defined condition. Funds are intended to be collected up front and released only if the condition is met. (If the condition is not met, funds are not refunded and instead support the platform.) Conditions must be externally verifiable and rule-based. The current v1 focuses on a single condition: whether H.J.Res.54 (a proposed constitutional amendment from Congresswoman Jayapal to overturn Citizens United) receives a floor vote in the House. This demo does not collect funds, create accounts, or process payments. It is an interactive walkthrough of the mechanics and UI flow. The intended full version includes authentication, payment via Stripe, and automated condition resolution. Technical stack: MERN (MongoDB, Express, React, Node), VPS deployment with Nginx reverse proxy, JWT auth in the full build, and background watchers for legislative updates. The hard problems have been structural rather than technical: defining objective conditions, avoiding discretionary fund control, ensuring neutrality, and designing something that does not resemble a quid pro quo. I’m interested in feedback on the escrow model, incentive alignment, legal edge cases, and whether the mechanism is clear. Demo: https://demo.powerback.us Happy to answer questions.

Visit product View on Hacker NewsFeb 26, 2026

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Product HuntOn track for Day 1 leaderboard · Strong signals: model, user, stripe · Missing: mac, agents, macos

86%86% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

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AppSumoMay struggle as an AppSumo deal · Strong signals: platform, users · Missing: plus, intuitive, reviews

46%46% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: nginx, io · Missing: https docs, excited, just released

43%43% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRLess likely to generate early MRR · Strong signals: users · Missing: mobile apps, ios, personal

29%29% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: active · Missing: arr, mrr, revenue

12%12% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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