Trane-rustlings, learn Rust with Trane and rustligns

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Last week, I shared my project Trane ( https://github.com/trane-project/trane ), an automated system for learning new skills. There are some courses at https://github.com/trane-project/trane-music , but I figured it'd be nice to have a self-contained course to showcase Trane and something related to programming since Trane is in an early stage, and it's not likely non-technical users will try it. Given that Trane is my first Rust project, I figured it'd be nice to augment rustlings ( https://github.com/rust-lang/rustlings/ ) with Trane. The result is trane-rustlings ( https://github.com/trane-project/trane-rustlings ). This course is an example of how existing educational materials can be easily augmented with Trane. The exercises in this course just reference the exercises in rustlings by name. By solving the rustlings exercises in the order Trane presents them, you progressively gain mastery of all of them, while reinforcing them as you go along. If you find you are being shown the same exercises too many times (specially at the beginning) then you can either finish your study session and continue later to have time to absorb the material or add the exercise to the blacklist, so it's not shown ever again (Trane will act as if that exercise has been fully mastered). I also added a documentation site (still under development): https://trane-project.github.io/ Instructions on how to use the command-line interface are there. If you end up trying it, let me know what you think.

Visit product View on Hacker NewsJul 11, 2022

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Hacker NewsStrong engagement from HN community · Strong signals: exist, existing, io · Missing: https docs, excited, just released

69%69% predicted probability of success on Hacker News, based on ML models trained on real launch data.

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

55%55% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Strong signals: user, new · Missing: mac, agents, macos

50%50% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: interface, users · Missing: plus, platform, intuitive

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

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

38%38% predicted probability of success on TrustMRR, 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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