I built a GitHub-style contribution graph for Claude Code usage

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I've been using Claude Code a bunch lately and wanted to see my usage patterns visualized like GitHub's contribution graph (mostly out of curiosity). So I built ccheatmap - a tiny CLI tool that reads your local Claude Code usage data and renders it as a heatmap right in your terminal. npx ccheatmap It just reads the JSON files Claude Code already writes to ~/.config/claude/projects/ and shows you: - Daily session counts (or token usage, or interactions) - Weekly patterns (turns out I use Claude most on Tuesdays ) - Total stats for the period - Nice color gradients to represent activity The whole thing is ~300 lines of TypeScript. Nothing fancy - just parsing timestamps, bucketing by day, and rendering colored Unicode blocks. What surprised me: weekend usage often spiked way higher than weekdays. Turns out I tend to vibe code more on side projects when the stakes are lower. Repo: https://github.com/viveknair/ccheatmap Would love to hear if others track their AI tool usage differently, or if there are other metrics that would be interesting to visualize. Also curious if anyone else has noticed usage patterns they didn't expect. I was inspired by ryoppippi's ccusage tool which has cost tracking and detailed usage analytics - definitely check that out if you need deeper insights.

Visit product View on Hacker NewsAug 20, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: claude, visual, activity · 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 HackersFits the IH revenue-focused audience · Strong signals: ios · Missing: supports, reddit linkedin, podcasting

65%65% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

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

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

34%34% 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

30%30% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

19%19% 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.

Correct prediction on native model

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