I Built a Task Board for Makers

Hacker News

As crazy as it sounds, I really created my own task board because no one worked for me. There is just a huge gap between Jira (which is a mess) and something like Todoist (which is too simple). So I build 7todos for myself. A task board to fill the gap. And over time a lot of people have joined and love it as well. One of my favorite feedback "In my opinion, it does that better than all the other more comprehensive tools like DoneDone, Easynote, or Anywhere. Other tools may have more features or focus on teamwork, but when I work with them, I consistently feel like they slow me down. Not so with 7todos." The kind of feedback you love to hear, especially as an indie maker 7todos aims to help you to get an overview of all workspaces and all your tasks across projects, and maybe it can also help you :)

Visit product View on Hacker NewsMay 12, 2022

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

78%78% 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: tasks · Missing: mac, agents, macos

77%77% 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, intuitive

47%47% 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 released

36%36% 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, personal

32%32% 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

14%14% 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.

Correct prediction on native model

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