Open Source Dashboard Reveals Actual Claude Code Compute Costs
What This Tool Does
A developer built an open source dashboard that calculates your actual compute costs when using Claude Code. This addresses a visibility gap: Anthropic's /limits command only shows a percentage without context, and there's no built-in way to see real compute costs.
How It Works
The developer reverse-engineered the rate limit formula from API response headers. Claude Code uses a weighted token cost model where input, output, cache creation, and cache read are all priced differently. Once you know the weights, you can calculate exact dollar burn per session.
Dashboard Features
- Real-time usage percentage (matches Anthropic's internal number exactly)
- Actual dollar cost
- Burn rate
- Peak hours tracking
- Shows which skills/hooks are firing
Key Finding
The developer discovered their $100/month maximum plan burned $13,286 in equivalent API compute in one month. This reveals the current level of subsidization, suggesting many users will face cost shocks when subsidies end.
Compatibility and Setup
The dashboard works for TUI, VS Code, and T3 Code users. It's particularly useful for IDE users who currently have zero visibility into their limits. The tool runs locally and takes approximately 60 seconds to set up.
📖 Read the full source: r/ClaudeAI
👀 See Also
Warp Terminal Goes Open Source with Agentic Dev Environment
Warp is now open-source, rebranding as an agentic development environment with a built-in coding agent and support for bringing your own CLI agents like Claude Code, Codex, and Gemini CLI.
Comparing Multi-Agent AI Systems: Anthropic's Harness vs Agyn's Engineering Org Model
Anthropic published a harness design for long-running application development, while Agyn's multi-agent system for team-based autonomous software engineering was open-sourced last month. Both systems reject monolithic agents in favor of role separation, structured handoffs, and review loops.
AVP Protocol Enables LLM Agents to Share KV-Cache Instead of Text for Token Efficiency
AVP (Agent Vector Protocol) allows LLM agents to pass KV-cache directly between them instead of text, reducing token processing by 73-78% and achieving 2-4x speedups across Qwen, Llama, and DeepSeek models. The protocol works with HuggingFace and vLLM connectors and is available as a Python package.
altRAG: Replace Vector DB RAG with 2KB Pointer Files for AI Coding Agents
altRAG is a Python tool that replaces vector database RAG with lightweight pointer files. It scans Markdown/YAML skill files to create a 2KB skeleton file mapping sections to exact line numbers and byte offsets, allowing AI agents to read only needed sections instead of entire files.