I've been working on Octomind, an AI development assistant that addresses two problems I kept running into:
1. *Cost blindness* - Most AI tools don't show you what you're spending until the bill arrives 2. *Model inefficiency* - Using GPT-4 for everything when simpler tasks could use cheaper models
*What makes it different:*
*Real-time cost display:* Every interaction shows exactly what you're spending: ``` [~$0.05] > "Explain this algorithm" [~$0.12] > "Refactor for better performance" [~$0.18] > "Generate comprehensive tests" ```
*Intelligent model routing:* Configure different models for different task types. Simple queries go to cheap models (Claude Haiku, GPT-3.5), complex reasoning goes to premium models (Claude 3.5 Sonnet, GPT-4).
*MCP server integration:* This is the part I'm most excited about. You can add specialized AI agents through configuration alone:
```toml [mcp.servers.code_reviewer] model = "openrouter:anthropic/claude-3-haiku" ```
Now `agent_code_reviewer(task="review this function")` is available in your session. No custom code needed.
*Multimodal CLI:* ``` > /image screenshot.png > "Debug this UI layout issue" ```
*Technical details:* - Written in Rust for performance - Supports 6+ AI providers (OpenRouter, OpenAI, Anthropic, Google, Amazon, Cloudflare) - Session-based architecture with full conversation history - Built-in development tools via MCP (file operations, shell commands, etc.)
*Real usage example:* Last week I used it to refactor a complex authentication system. Total cost: $0.23 for what would have been 3+ hours of manual work. The cost visibility helped me optimize my prompts and model selection.
*Installation:* ```bash curl -fsSL https://raw.githubusercontent.com/muvon/octomind/main/instal... | bash ```
The project is open source (Apache 2.0): https://github.com/muvon/octomind
I'd love feedback from the HN community. What features would make AI development tools more useful for your workflows? Are you tracking AI costs in your projects?