Trash Data Collection App for Cleanup Volunteer Groups

Hacker News

An open-source tool for collecting and sharing trash collection data, built using RedwoodJS. Context Around the world, grassroots volunteer groups organize cleanup efforts of all kinds – inland, coastal, urban, underwater, mangroves, etc. Some collect data about what they collect and some don't, and there are many, many tools available. Collecting good quality, consistent, and consolidated data about trash collection can help to: 1) influence public behavior 2) coordinate future volunteer and paid cleanup efforts What Problem This Solves Most of the tools available are suited for specific kinds of cleanups and data recording practices. This is intended to offer different and easier ways for any cleanup group to record and normalize whatever data they have.

Visit product View on Hacker NewsJul 17, 2024

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Product HuntOn track for Day 1 leaderboard · Strong signals: context, using, open · Missing: mac, agents, macos

77%77% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

67%67% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive

52%52% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

44%44% 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: way · Missing: mobile apps, ios, personal

40%40% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · 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 · Strong signals: paid · Missing: web3, chat, crypto

2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

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