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Codesprint – A LeetCode Typing Trainer

Hacker News

Codesprint – A LeetCode Typing Trainer

Hi HN! I built CodeSprint because I realized getting good at typing code requires practicing syntax fluency, not just logic. I found myself screwing up on mock interviews not because I didn't know the algorithm, but because I fumbled the syntax. I needed a way to drill "Depth First Search in Python" or "Ring Buffer in C++" until my fingers actually knew the shape of the code. The Engineering: Renderer: Uses a heavily customized Monaco Editor instance. I'm using deltaDecorations to paint diffs directly onto the editor model without breaking syntax highlighting, and getScrolledVisiblePosition for a custom, low-latency caret. Data Pipeline: I didn't want to hardcode snippets. I wrote a custom Bun script (scripts/sync-leetcode.ts) that reverse-engineers the LeetCode GraphQL API to pull real problem snippets, sanitizes them, and normalizes indentation. Latency: To handle high WPM without UI lag, the typing engine isolates keystroke processing from the main React render cycle where possible. Repo: https://github.com/cwklurks/codesprint Live Demo: https://codesprints.vercel.app/ I'm a 15-year-old student from Vancouver. Would love feedback on the typing engine feel vs. native VS Code!

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Actual performance

3points
1comments
Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
78%78% 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: model, new, using · Missing: mac, agents, macos
69%69% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: pipe, io · Missing: https docs, excited, just released
55%55% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
39%39% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal
37%37% 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
17%17% 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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