zuzuleinen

zuzuleinen / algotutor

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AI-powered algorithmic training system using Claude Code

19
2
100% credibility
Found Apr 19, 2026 at 19 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Go
AI Summary

AlgoTutor is a flashcard reviewer tailored for algorithm concepts and problems, using spaced repetition to schedule reviews for optimal learning retention.

How It Works

1
📚 Discover AlgoTutor

You hear about a handy tool that helps practice algorithm problems through smart flashcards that space out reviews just right for better memory.

2
✏️ Build your flashcard deck

You create simple notes with questions, answers, and topics for the algorithms you want to learn.

3
🚀 Start a review session

You open the app, and it welcomes you with a snapshot of today's due cards and how they're grouped by concept.

4
❓ Tackle a card

A question pops up about an algorithm – you think it through and type your own solution.

5
🔍 Reveal the answer

You peek at the correct explanation to see how you did.

6
⭐ Rate your memory

You pick a button for how easy it was to recall (again, hard, good, or easy), and it smartly plans when to show it next.

7
📊 Wrap up the session

After finishing all due cards, you get a colorful summary of your progress and ratings.

🧠 Boost your skills

Your brain gets trained efficiently, with cards coming back only when needed to lock in algorithm knowledge for good.

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AI-Generated Review

What is algotutor?

Algotutor is a terminal-based flashcard reviewer for algorithmic training, pulling due cards from a JSON store to drill problems via spaced repetition. You get quick sessions showing problem fronts, type your solution, reveal the back with source links, then rate difficulty (1-4) to schedule future reviews—powered by FSRS for optimal timing. Built in Go with a sleek Bubble Tea TUI, it tracks stats by concept like sorting or graphs, perfect for AI-powered algorithmic training without browser bloat.

Why is it gaining traction?

It stands out with gorgeous TUI screens: welcome dashboard of due counts per concept, inline code rendering, progress bars, and session summaries—no setup beyond `make review`. FSRS handles smart scheduling better than basic Anki imports, and JSON persistence means instant portability across machines. Devs dig the zero-config flow for daily algo reps, unlike clunky web apps.

Who should use this?

LeetCode grinders prepping interviews, CS students tackling DP or trees, or backend devs refreshing graph algos between projects. Ideal if you live in the terminal and want offline, distraction-free drills tied to real problems.

Verdict

At 19 stars and 1.0% credibility, it's raw—sparse docs (binary README) and no tests signal early days, so expect tweaks for production use. Worth a spin for Go fans needing a lightweight SRS for algotutor academy-style practice; fork and contribute if it clicks.

(178 words)

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