FishDex An offline fishing log built to show my students

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I’m a teacher, not a professional developer. I’ve been trying to track my fishing life-list in Excel for the last year, but it was clunky and I eventually gave up. I wanted to show my students that the "technical wall" shouldn't stop them from building tools they actually need. I "vibe coded" this entire app in about a day using AI to handle the syntax while I focused on the logic and the user experience. The Goal: To prove that with modern tools, you can move from "spreadsheet fatigue" to a functional, custom application without a CS degree. The App: 100% Local: No sign-ins, no databases, no tracking. It uses IndexedDB to keep data on the device. Offline First: It’s a PWA, so it works on the water where there’s no signal. Open Source: I’ve put it under an MIT license so my students (and anyone else) can fork it and break it to see how it works. I’d love feedback—not just on the code (which I know is AI-assisted), but on the utility. Is this a viable way to teach "Creation over Consumption" to the next generation?

Visit product View on Hacker NewsFeb 27, 2026

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

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

best fitHighest predicted score across all platforms for this description.

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

76%76% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

49%49% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

42%42% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

33%33% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

15%15% 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.

Correct prediction on native model

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