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I tried to reverse engineer Claude Code usage limits

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I tried to reverse engineer Claude Code usage limits

I hit my Claude Code usage limit yesterday in the middle of regular work. Not doing anything unusual - just a couple of coding sessions. It felt like it came out of nowhere. Then today Anthropic announced they're tightening 5-hour limits during peak hours. It feels like a rug pull is coming and we're all just supposed to accept "you've used too much" with no explanation of what "too much" means. Usage limits have always been opaque. A vague percentage bar. No dollar figure. No formula. No way to plan your work around it. So I built a proxy that captures the rate-limit headers Anthropic sends back on every response — headers that exist but Claude Code never shows you. Here's what my data looks like for the last 2 days: My 5h budget is worth ~$78–$282 Estimated 5h Budget (11 sessions observed) Range: $34 – $399 Median: $164 p25-p75: $78 – $282 Estimated 7d Budget Estimate: ~$1,949 I'm paying $200/month for Max 20x. My estimated weekly budget is ~$1,949 in API-equivalent usage. The quota is price-weighted, not token-based 95.1% of my input tokens were cache reads (427M tokens). They barely moved the quota. The 5.5M fresh input and 1.8M output are what actually burned through the budget (as expected). If you're on Pro or a different Max tier, your data would help a lot. It’s open source so more people can run it and we can figure out the real numbers together. https://github.com/abhishekray07/claude-meter

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Product HuntOn track for Day 1 leaderboard · Strong signals: claude, coding, code · Missing: mac, agents, macos
78%78% 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 · Missing: supports, reddit linkedin, podcasting
68%68% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: exist, open source, io · Missing: https docs, excited, just released
53%53% 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: month, way · Missing: mobile apps, ios, personal
50%50% 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.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
21%21% predicted probability of success on AppSumo, 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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