My
My Day – daily planner to get things done
Hacker NewsMy Day – daily planner to get things done
Has a Kanban view and a timeline view, which are also mixed together in a TODO list with a smart sorting. Prioritized tasks are interleaved with scheduled ones. The philosophy being explored is that there is a big overlap between calendar items and todo items, and calendar items never happen exactly as scheduled. Scheduled items gets converted to "Doing" state automatically at the start time. Try it here: https://joexo.codeberg.page/myday/
Share cardActual performance
2points
Did not reach leaderboard
Launch Intel predictions
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Product HuntUnlikely to reach the leaderboard · Strong signals: tasks, code · Missing: mac, agents, macos47%47% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal46%46% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released38%38% 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, intuitive37%37% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting34%34% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue19%19% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: smart · Missing: web3, chat, crypto6%6% predicted probability of success on BetaList, based on ML models trained on real launch data.
Correct prediction on native model
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