A tool for conducting a virtual card sorting

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TL;DR: I ended up yak shaving a tool for conducting a card sorting online, and wanted to share it because I couldn't find one myself. Also I don't have to maintain or host it so it should stay up for a long time, hopefully. https://github.com/indigane/cardsort The long version of why: I wanted to conduct a quick, one-off card sorting for a side project, but after spending hours looking at the available commercial and open source solutions, I couldn't find one that worked for me. The commercial ones either had too restrictive free tier with only 20 cards or so, or they lacked basic features such as open sorting vs. closed sorting, or they would have cost me upwards of $100/month. The open source ones would have required me to self-host them, which I wasn't too excited about, and after trying docker-compose up on one of them and hitting errors after errors, I was even less excited so I gave up on that. I also looked for demos of open source projects that could have worked for the one-off use I needed, but no luck there. I wanted to avoid spending time developing my own solution, as I already had the side project I was supposed to be working on. I also especially wanted to avoid spending time on maintaining and hosting. If I could get away with a static site, no database, thus no need for me to host it, and get it done without spending too much time on it, it might be worth it, and maybe someone else could benefit from it too. And the result was this. The lack of a backend has some obvious trade-offs, but the benefit is that it is fire and forget, and I can hopefully come back years later and use it again for some other one-off thing.

Visit product View on Hacker NewsMay 17, 2022

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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.

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

51%51% 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 · Strong signals: host · Missing: plus, platform, intuitive

47%47% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Strong signals: dock, open · Missing: mac, agents, macos

39%39% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

31%31% 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

11%11% 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.

Incorrect prediction on native model

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