AI Coder

AI Coding Agent

The human role shifts from "coding" to environment design, specification writing, and feedback loop construction.

Project Folder (Repository) = System of Record: All knowledge must exist explicitly within the repo.

AGENTS.md should be kept short, with actual knowledge distributed in docs/ structure.
Documents are also mechanically verified with CI and lint.

Agent Legibility First: Code style prioritizes structures that agents can easily understand over human preferences.

Architecture layer structure: Types → Config → Repo → Service → Runtime → UI
Dependency direction enforced by lint. Rules are enforced by code, not documentation.

Agents directly handle UI, logs, and metrics

Chrome DevTools Protocol integration.
LogQL / PromQL access available, feedback loop must be automated

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.

https://aavetis.github.io/ai-pr-watcher/

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