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I built a weekly sprint planner for individuals

Hacker News

I built a weekly sprint planner for individuals

Hello all I know there are plenty of productivity apps. Still, I found myself using many different apps for my productivity needs: Todoist for tasks, calendar for time-blocking, a physical planner for weekly/quarterly/yearly planning, Day One for journaling, and Roam for notes. QTR is my attempt to blend these together. It's like Trello mishmashed with a calendar. You start with tasks for the year, and then drag them to different quarters, then zoom into a quarter to then drag tasks to weeks, and then zoom into a week to then drag tasks to days, and then zoom into a day to then drag tasks to time slots. This way you start with the big picture and then narrow down your focus step by step. The cool thing is these contexts (year, quarter, week, day, time) are all connected, so if you add a task directly to a day, it automatically also adds it to the week, quarter, and year. And each context comes with its own journal and notes, which means you can maintain separate journals and notes for daily, weekly, quarterly, and yearly (something you can't do in Day One). The other cool thing is when a task comes to mind, you don't have to assign a day to it. You can assign a week, month, quarter, or year. And you get a reminder when you get closer to the timeframe. Anyhow, I need a lot of feedback, and I'm hoping you all can help.

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Product HuntOn track for Day 1 leaderboard · Strong signals: apps, context, physical · Missing: mac, agents, macos
75%75% 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.
TrustMRRFits verified-revenue profile · Strong signals: apps, month, way · Missing: mobile apps, ios, personal
72%72% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Indie HackersFits the IH revenue-focused audience · Strong signals: para · Missing: supports, reddit linkedin, podcasting
69%69% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Missing: https docs, excited, just released
55%55% 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
40%40% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: arr · Missing: mrr, revenue, profit
21%21% 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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