Shared shopping list with reoccurring items

Hacker News

Create a list, add items to it, share it with friends or family. Check an item, decide if you want it to be automatically relisted in N days or at X date (or never). That's it: no AI, no notifications, no nothing. Backstory: As a product manager, I always wanted to build things but never had time to learn how to code them. Six months ago, I asked people around if I should invest 20-something weeks into learning React or, in that timespan, LLMs would be capable of doing programming for me. The consensus was that I should learn to code. I disagreed with it. Two weeks ago, I paid for GitHub Copilot and, with o1-preview's help, devised the plan and developed the actual code of my PWA. It works as a charm, so I, my family, and friends use it every day, precisely as planned. Now, I can comfortably say that I still don't know how to code, but that won't stop me and shouldn't stop any other non-coder from developing something they want anymore.

Visit product View on Hacker NewsDec 16, 2024

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Actual performance

2points

Did not reach leaderboard

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

88%88% 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: code · Missing: mac, agents, macos

84%84% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released

72%72% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRFits verified-revenue profile · Strong signals: month, way · Missing: mobile apps, ios, personal

50%50% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

29%29% 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

18%18% 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

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

Incorrect prediction on native model

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