Tomorrow 9am - create AI agents on private Github Repositories.
CommandDash will support private Github repositories AI agents. This has been the most requested feature. And it goes live tomorrow 9am IST.
CommandDash is a programming agents marketplace in your IDE. It offers you specialised Dash agents to integrate APIs and SDKs like Gemini, Langchain, Firebase. Skip the document and just put in your usecase to generate integration code contextualised to your codebase.
External link for CommandDash
Ahmedabad, Gujarat 382330, IN
Get started with building RAG apps with AWS + Claude. Checkout this super helpful repository!
🔥 🔥 🔥 Debugging someone else's code can be a daunting task, but what if you had an assistant to guide you through the process? Enter CommandDash, a game-changer in the world of software development. With its innovative AI agents, you can now streamline your coding journey and unlock new levels of productivity. While they have multiple other Agents, the one that peaked my interest was the Commanddash Code Agent, a remarkable tool designed to assist CTOs/developers/architects in navigating complex codebases effortlessly. Imagine having an intelligent companion that can summarize code, offer insightful suggestions, and even help you build software applications from scratch. Recently, I had the opportunity to create a Code Agent for the AWS-Samples repository, specifically for serverless-rag project. In just a few clicks has had this utility thats proven to be an invaluable asset, seamlessly integrated into the project's README file. You can search through issues, learn more about the codebase, and even adopt pieces of code to craft your own retrieval-augmented generation (RAG) applications. But that's not all – the Commanddash Code Agent is also available as a convenient extension for your favorite IDE, #VScode, ready to provide guidance and support whenever you need it. AICodeAgent Link: https://lnkd.in/dz8-rSXk Project Link: https://lnkd.in/dnXMm8cU Go get a badge now #genai #genaiagents Some useful questions 1. Hows the orchestrator agent created ? 2. How does the LLM perform OCR in this codebase ?
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