Weekdone, weekly status reporting for teams

Hacker News

Hey HN friends, This week we launched in public Weekdone (http://weekdone.com/), a really easy and simple to use weekly team status reporting tool. We already have a lot of startups using us, even in small teams of 4-5 people. Many use us in addition and in parallel to Basecamp, Asana, Jira, Trello, Pivotal etc. While there is a lot of data in those systems, nobody has time to read what others are up to. So each team member posting their key plans, progress and problems weekly is something everyone has time to read. We'll add import of headlines from the systems above in the future as well. Any feedback? Would you use this at your startup?

Visit product View on Hacker NewsJan 22, 2013

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Product HuntOn track for Day 1 leaderboard · Strong signals: using · Missing: mac, agents, macos

79%79% 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.

Hacker NewsStrong engagement from HN community · Strong signals: io · 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.

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

51%51% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

49%49% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

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

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

18%18% 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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