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    Enables AI agents to debug embedded systems by providing a comprehensive interface for GDB operations across multiple architectures like ARM and x86. It supports remote debugging via gdbserver or QEMU, allowing for detailed inspection of memory, registers, stack frames, and variables.
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    An MCP server that enables AI agents to debug .NET applications using netcoredbg. It supports core debugging tasks like setting breakpoints, stepping through code, and inspecting variables or stack traces.
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    MIT
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    Enables AI assistants to generate, edit, and deploy production-ready full-stack applications through VULK's automated development pipeline. It supports multi-file code generation, browser verification, and exports to web, PWA, and mobile platforms.
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    MIT
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    A Lightning-paid tool stack for autonomous agents — capital-scale-aware second-opinion /review (Sentinel mode auto-injects live trading state), reasoning, structured decisions, sandboxed code execution, paid agent-to-agent messaging, persistent memory, and signed audit proofs. Built and used daily by our own agent fleet who pay each other in sats. Pay-per-use via Bearer balance or L402.
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    Apache 2.0
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    Enables Claude to function as a full-stack software engineer with comprehensive development capabilities including project creation, database management, frontend/backend development, testing, deployment, and DevOps operations across multiple frameworks and technologies.
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    MCP server that detects project frameworks (25+), starts dev servers, and creates public Cloudflare tunnels from a single tool call. Text Claude via Dispatch "start my project and give me a link" and get back an HTTPS URL you can open on your phone. Supports full-stack projects, auto-installs dependencies, and also works as a standalone CLI.
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    MIT
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    Enable Claude (or any other LLM) to interactively debug your code (set breakpoints and evaluate expressions in stack frame). It's language-agnostic, assuming debugger console support and valid launch.json for debugging in VSCode.
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    MIT
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    Provides production-ready implementation patterns for features like authentication, payments, and deployments to AI coding assistants. It enables developers to search and retrieve battle-tested code snippets and setup steps for full-stack tasks.
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    MIT
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    Enables offline AI agent automation with embedded local LLM (Qwen 2.5), sandboxed file operations through AgentFS, and dynamic skill loading. Exposes capabilities via MCP with tri-state safety guards for private, air-gapped environments without network connectivity or API costs.
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