AI Coding
Analysis | Diagrams | Innovators
What is optimum architecture for AI Coding? What is the optimum architecture for developing AI Agents?
Code is only as good as it remains, understandable, maintainable and free of technical debt while helping people to produce valuable outcomes with minimal effort and prior experience.
Build software to expand potential to deepen insights
Subject Expertise
- Best Practice Principles
- Replicate SaaS Apps and Integrate
IDE Toolkit
What are the best tools and practices for evolving solutions with a clean architecture?
| IDE | Notes |
|---|---|
| VS Code | Legacy |
| Aider | Most innovative? Python |
| Bolt New | Web Green Fields |
| Cline | VS Plugin |
| Copilot | Outpaced |
| Cursor | All Purpose |
| Devin | Enterprise |
| Replit | Web Green Fields |
| v0 | UI/UX Design |
| qodo | Enterprise |
| coderabbit.ai | Dev Ops |
Performance Benchmarks
SWE Benchmark is a tool for evaluating the performance of AI coding agents.
See best practices for using coding agents
Use Cases
Use JTBD Analysis to build software to expand potential to deepen insights to build closer connections.
Software is a Commodity, Culture is your only Competitive Advantage
- Save money on software with features you don't use or need
- Maximize Advantage of Trade Secrets and IP
- Take control of your data footprint
- Optimize integration and data flows
- Control your own destiny
Prototyping Ideas
- Quickly building proof-of-concept applications
- Iterating on designs through conversation with AI
Automating Tasks
- Writing exploratory code to try out structures
- Simplifying and trimming down large codebases
- Automating monotonous tasks and one-off scripts
- Converting programs to more efficient languages for performance improvements
- Building entire web applications with unfamiliar technologies
Code Optimization and Refactoring
- Identifying opportunities to improve code efficiency
- Suggesting refactoring to simplify complex codebases
Documentation and Logic Explanations
- Generating code documentation
- Explaining complex code or algorithms
Context
- AI Prompts: Build a library of prompts tied to context for using them
- Model Providers: Base layer to innovate upon
- AI Agent Frameworks: Build agents with deep domain knowledge