Me

MemKit –- The spaced-repetition flashcard app I've always wanted

Hacker News

MemKit –- The spaced-repetition flashcard app I've always wanted

Hello, HN! I took a few months off and decided to properly build something I've wanted for a while now. MemKit is the spaced-repetition flashcard app I've always wanted. Here's what I mean: - Minimal friction to use. You can immediately start using the app. You are automatically given an account. You don't have to sign up or type in a username or password. A lot of times I'm reading a book and I only have my phone on me. It's super annoying to log into an app when you get signed out, especially on your phone and especially if you don't use a password manager on your phone. MemKit lets you jot down interesting things you learn without signing in and then merge them into your account later. This is made even easier through email one-time passwords. - No demands. You don't have to set an email, phone number, or even a username! But you can, if you want to. Email or phone number is a necessity for account recovery, of course. Note that phone numbers are not yet implemented. - No ads. No selling data. No emails besides those for sign-in and account recovery. No algorithms to maximize use — users should only use the app if and when they want to. - Maximal simplicity / minimal complexity. There is really only one feature — tags. Tags allow you to group together arbitrary things for ease of review (e.g., #math groups together all math things). When you make a thing, you can associate to it multiple tags. And a single tag can be associated to multiple things. You don't need "courses," and "flashcard sets" preclude the association of a single flashcard to multiple sets, in many competing apps. I have attempted to avoid bloat and complexity as much as possible. - You grade yourself. Many education apps grade your answers for you. I think self-grading is a crucial part of the learning and memorization process. - The right technology. This is the most contentious point. I chose Rust and an extremely fast server-side rendering framework, Maud, coupled with a minimal amount of vanilla JS. Nginx is my web server and, I know this is somewhat dubious, redis is my primary DB. I want to see how far I can scale on one $30/month server and a $15/month email service as my only costs, outside of domain registration. Please let me know if you like it! Roadmap: - Phone numbers to reduce friction to sign in - Better insight into time-to-review, how often a thing was missed, etc. - API, image, and sound support to enable, say, uploading 1,000 most frequently used Finnish words - AI generated quizzes. Obviously comes with all current downsides of LLMs but self-grading avoids many of these problems. https://memkit.app/

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Indie HackersFits the IH revenue-focused audience · Strong signals: maximize · Missing: supports, reddit linkedin, podcasting
97%97% 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: apps, user, email · Missing: mac, agents, macos
81%81% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: nginx, ide, 000 · 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.
TrustMRRFits verified-revenue profile · Strong signals: apps, month, answers · Missing: mobile apps, ios, personal
61%61% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: users · Missing: plus, platform, intuitive
34%34% 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.

Incorrect prediction on native model

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