AI Coding Agent
Problem for agent is Language Model Context Size
The advent of large language models could potentially reduce software development costs to nothing, sparking a rapid and diverse growth in software akin to the content boom or Cambrian explosion.

LLMs should be used in conjunction with other tools to prevent the human review process from becoming a bottleneck.
One approach to reinforcement learning involves generative and discriminative models, such as GAN. Typical high-level AI development follows this approach and requires automation. While images can be compared visually, it's much harder to evaluate text, code, and audio. Therefore, a good AI coding assistant should not just provide results, but should help by breaking tasks down into smaller, easily verifiable steps. In other words, the importance of verifiability aligns with Verifiable Reward, suggesting that larger units like code blocks or video clips should be gradually incorporated.
AI Coder Services
Codex
Opensource
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Claude Code
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Jules
Opensource
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Github Copilot
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Devin
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Gemini CLI
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Gemini Code Assist
Opensource
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Phind
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LLM LS
Opensource
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Tabby ML
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Continue Dev
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Tabnine
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Cody
Opensource
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CodeWhisperer
Opensource
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Codeium
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Codey
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Q Developer
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Aider-chat
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Meticulous
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OpenCode
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TurinTech
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AI Coding Agents
AI Coder Models
AI Wireframe Tools
Design Arena
Current limitations
- Stop Digging; Know Your Limits
- Mise en Place
- Scientific Debugging
- The tail wagging the dog
- Consistent formatting
- Read the Docs
- Use Static Types
Leaderboard
PR workflow integration Git Flow
Designing tools for developers means designing for LLMs too
Most large language models (LLMs) aren't great at using less popular frameworks.