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.
*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.
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?
1 comment
[ 3.4 ms ] story [ 15.2 ms ] threadI'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?
*Demo:* https://asciinema.org/a/wpZmOSOgFXp8HRzTltncgN7e3