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trane-leetcode, practice Leetcode using spaced repetition

Hacker News

trane-leetcode, practice Leetcode using spaced repetition

Hi, HN I showed my project Trane ( https://github.com/trane-project/trane ) a few weeks ago. I have kept working on it, but I would like more feedback from users, which is difficult at the moment because there's only a command-line interface at the moment and not a lot of material for it yet. So I figured I would create some more technical courses to hopefully get more technical users to try it out. I just created a set of courses to practice Leetcode exercises, using the set from neetcode.io. The result is trane-leetcode ( https://github.com/trane-project/trane-leetcode ). Unlike an exercise asking you to perform a piece of music or to solve a chess puzzle, the problems in these courses are more open-ended. What solving the problem means is up to you. You could force yourself to code the solution from scratch every time you are shown a problem, but this could get old soon. You could also code it the first time(s), and just draft a solution in pseudocode afterwords. Or you could simply review the solution and assign yourself a score based on how much of it you remembered. Let me know if you tried it and have any feedback on either the courses or specially on Trane itself. Other courses: - trane-music: https://github.com/trane-project/trane-music - trane-ruslings: https://github.com/trane-project/trane-rustlings

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Indie HackersFits the IH revenue-focused audience · Strong signals: created · Missing: supports, reddit linkedin, podcasting
70%70% 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.
Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released
61%61% 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: interface, soon, users · Missing: plus, platform, intuitive
56%56% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: users · Missing: mobile apps, ios, personal
39%39% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Strong signals: user, using, code · Missing: mac, agents, macos
39%39% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
11%11% 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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