Ra-AID SUPER AGENTIC Coder - This 3-STEP Opensource Agentic CODER Beats CLINE & CURSOR?

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AI Summary

Summary of Video Transcript

• Introduction to a new open-source AI coder named Raid
  • Built on Lang graph’s agent-based task execution framework
  • Optionally integrates with Ader
  • Aims to develop software autonomously
  • Features a three-stage architecture:
    • Research: Analyzes codebases and gathers context
    • Planning: Breaks down tasks into actionable steps
    • Implementation: Executes planned steps sequentially
  • Concerns about token consumption for the workflow
• Mention of Dart AI
  • AI-powered project management tool
  • Features include generating project plans, detecting duplicate tasks, creating subtasks, and task assignment to AI
  • Free for teams up to four people, with paid plans available
  • Integrations with GitHub, Slack, Discord, and an API for custom workflows
• Installation and Setup of Raid
  • Install using pip
  • Set up API keys for the desired provider (Anthropic Cloud models by default)
  • Export API keys as environment variables
    • For Anthropic: export ANTHROPIC_API_KEY='your_key_here'
    • For OpenAI: export OPENAI_API_KEY='your_key_here'
  • Other provider options include Open Router, Deep Seek, and Gemini
• Usage Patterns of Raid
  • Basic usage with the -m operator for task execution
  • Research-only mode with --research-only flag
  • Human in the loop mode with --hil flag for interactive coding
  • Chat mode with --chat flag for natural conversation interface
  • Web research capability using Tav API
    • Set Tavali API key with export TAV_API_KEY='your_key_here'
  • Configure expert tool with --expert-provider flag for complex logic and debugging
  • Configure different models for different workflow stages
  • Integrate with Ader using --use-ader flag
  • Configure Ader’s behavior with AER_FLAGS environment variable
• Testing Raid with an Image Cropper Tool
  • Task: Add an option to overlay an image on another image
  • Raid consumed about 600k tokens for the task
  • Implementation was not fully satisfactory
  • Potential use with free Gemini API for less capable coding tasks
  • Not recommended for use with already capable tools like CLA or Ader
• Conclusion
  • Raid holds potential but could be improved for simplicity and ease of use
  • Invites viewers to share thoughts and subscribe to the channel

Detailed Instructions and URLs

• No specific CLI commands or URLs were provided in the transcript for the installation and setup of Raid or Dart AI.