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Aamu.app – all-in-one productivity tool

Hacker News

Aamu.app – all-in-one productivity tool

Hi there HN. Aamu.app is an app that I've made and I hope to get some feedback. It aims to have all the most important productivity tools (for companies) in one package. It will hopefully get more of those features in time, but currently there are already quite a few – tasks, docs, video meetings and so on. Why did I do it? Because I thought the current ones were not that good, and I'm talking about things like Microsoft's offering now. The different parts of their software were clumsy and they were not integrated very well into the whole – i.e. different parts were not designed in collaboration in mind. I realize it's quite a big app for one person to do, but let's just say that I felt I needed to do and finish it. Couldn't stop after I had started it. --- Here's an url you can try it without registering into it, and the usernames/passwords: https://piper.aamu.app/ username: richard ... password: richard1 username: gilfoyle ... password: gilfoyle username: bighead ... password: nbighead username: dinesh ... password: xxdinesh --- The main url of the site: https://aamu.app/ Hope to get some feedback, thanks!

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4points
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Did not reach leaderboard

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Indie HackersFits the IH revenue-focused audience · Strong signals: started · Missing: supports, reddit linkedin, podcasting
75%75% 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: user, tasks · Missing: mac, agents, macos
59%59% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, pipe, io · Missing: https docs, excited, just released
59%59% 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 · Strong signals: video · Missing: mobile apps, ios, personal
44%44% 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
32%32% 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
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.

Incorrect prediction on native model

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