dc
dcmntr.com
Hacker Newsdcmntr.com
Hey guys, I am recreating dcmntr.com from what it used to be (a social bug tracking/discussion/documentation tool) to a private bug tracking/task tracking application. I know there are a lot of bug tracking applications out there, but I'm trying to make it super easy to use and not overwhelming for people that want to do basic bug tracking or aren't very computer/technologically saavy. The project is still in heavy development, but if any of you would be brave enough to test it and rip it to pieces, that would be awesome. The website is http://dcmntr.com (short for documenter) Thanks!
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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting68%68% 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: computer · Missing: mac, agents, macos64%64% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive48%48% 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 released43%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 · Missing: mobile apps, ios, personal37%37% 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, revenue12%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, crypto0%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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