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Claude Code dashboard for Pro/Max users: live rate-limit gauges, calibrated usage forecasts, session costs, memory browser. Single binary, zero config.

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Claumon – forecasting Claude Code usage limits with a Gamma process

by fabioconcina·Jun 11, 2026·7 points·0 comments

AI Analysis

●●●BangerBig BrainDark Horse

Gamma process forecasting with credible intervals beats simple burn-rate projections.

Strengths
  • Empirical-Bayes model with proper scoring rules, not heuristic burn-rate math
  • Single Go binary with zero dependencies runs on macOS, Linux, and Windows
  • Live OAuth API gauges show actual rate limits, not log-derived estimates
Weaknesses
  • Only works for individual Pro/Max plans, not Team or Enterprise orgs
  • Requires Claude OAuth token setup, adds one more credential to manage
Target Audience

Individual developers on Claude Code Pro or Max plans

Similar To

ccusage · Claude-Code-Usage-Monitor · claude-usage

Post Description

Anthropic's usage analytics dashboard is only available to Team and Enterprise org admins. On a Pro or Max plan all you get is /usage and the claude.ai usage page, which show where you stand right now but not where you're heading. I looked at various open-source projects but none quite matched what I had in mind: an all-round control panel for Claude Code that's also a single binary, with no dependencies and no install steps.

The other thing I cared about was forecasting the usage limits. Existing tools do burn-rate projections (ccusage) or percentile heuristics (Claude-Code-Usage-Monitor), which felt too simplistic for what I wanted - I was after a calibrated statistical model with proper credible intervals.

I built claumon over the last few months in Go. It runs on Linux, macOS and Windows, with a Homebrew install. It has the usual consumption gauges, cost breakdowns and conversation history, plus two tabs for memory management: after a while I had memories scattered across several projects and wanted to see them in one place and prune the stale ones.

In the last few weeks I focused on the forecast model. I started from an empirical-Bayes linear regression with Brownian noise, but ended up with a Gamma process for the path noise: token usage can't decrease over time, so Brownian motion, however mathematically convenient, was the wrong choice. The intervals are calibrated against your own recorded history, and there's tooling that scores the forecasts out-of-sample, so the coverage is checked rather than assumed.

I wrote a formal, versioned spec for the model, and the implementation follows it: https://github.com/fabioconcina/claumon/blob/main/internal/f...

Everything runs locally - nothing leaves your machine. It's open source, MIT. I'd welcome feedback on the model especially.

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