Year in Code – Wrapped for Claude Code Users

Hacker News

Hey HN! I built Year in Code to turn your Claude Code usage into a beautiful Wrapped-style report (like Spotify Wrapped). If you use Claude Code with ccusage, it's super quick to try: Setup (2 minutes): 1. Run: `npx -y ccusage@latest daily --since 20250101 --until 20251231 --json > wrapped.json` 2. Upload at yearincode.xyz/2025 3. Get your report! *What you'll see:* - Total tokens - Activity streaks & patterns - Top models used - Shareable report Key features: - 100% secure: 100% browser processing, no server uploads - 2-minute setup: One npx command, upload JSON, done - Free & open source Built with Next.js. Source on GitHub: https://github.com/isaadgulzar/year-in-code Would love feedback from the HN community!

Visit product View on Hacker NewsDec 27, 2025

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Did not reach leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: claude, model, user · Missing: mac, agents, macos

95%95% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

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

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

37%37% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: open source · Missing: https docs, excited, just released

36%36% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

28%28% 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

26%26% 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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