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Kaado.io – Unifying Personal Productivity

Hacker News

Kaado.io – Unifying Personal Productivity

Hi HN, a while ago I discovered a problem with problem with personal productivity tools: Every time you find a tool that works well in one regard (say, habit tracking), you neglect your previously working systems (say, your to do list system). So I set about to build a system which unifies all the different aspects of personal productivity. Everything is represented in cards (like flash cards) which are defined in Markdown. The following types of cards exist: * Learning : Classic front/back prompts to memorize things. Think "What is the capital of Argentina?" on the front and "Buenos Aires" on the back. Works with a spaced repetition algorithm so time between prompts is optimized. * Habits : Recurring actions you want to do - you can set the frequency individually. For example "Stand up and walk around your room" every two hours. * Checks : Recurring prompts, similar to habits, but always questions referring to the past. For example a daily "Did you drink enough today?" prompt * To do : One time actions that are deleted upon completion. * Reading List : Used for books, articles, talks etc. There are always a limited number of reading list items active, which will prompt you to read a page a day until you are done with the given book. * Other : Everything else - interesting websites, quotes, art or even memes that you will occasionally see. The idea is that there is a single queue for all types of cards, only ever showing you one item that's due next - whether habit, learning prompt or a random note. This prevents choice fatigue, the dread of lengthy to-do lists and the boredom of endless dry learning prompts. I've been using the app successfully for myself for quite some time now but would love external feedback. You can check it out at https://kaado.io. It's currently completely free (and in fairly early stage development). Simple email signup is needed but you won't get any unprompted mails. Apart from that I would love your takes and ideas regarding personal productivity and what works for you.

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
90%90% 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: email, single, using · Missing: mac, agents, macos
72%72% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: exist, ide, io · Missing: https docs, excited, just released
67%67% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoStrong fit for a featured deal · Strong signals: occasional · Missing: plus, platform, intuitive
55%55% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: personal, way · Missing: mobile apps, ios, entrepreneurs
48%48% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: recurring, active · Missing: arr, mrr, revenue
16%16% 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.

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