Architecture linter built specifically for AI-written code

What to build

A CLI + hosted dashboard that scans AI-assisted codebases (Cursor, Claude, Copilot) for LLM-typical architecture smells: duplicated logic, circular dependencies, inconsistent layering, dead abstractions. Targeted at solo devs and small teams shipping fast with AI-generated code, as a pre-commit/CI check plus a visual dashboard to track architecture drift over time.

AI coding assistants ship fast but introduce a distinct pattern of structural debt; a dedicated linter for that debt is a narrow but real niche, as shown by RepoGuard's Show HN launch.

Demand

Solo developers and small teams using Cursor/Claude/Copilot to ship quickly are accumulating architecture debt faster than they can review it, and existing linters aren't tuned to catch AI-specific smells like duplicated logic or dead abstractions.

• Hacker NewsLaunch post

  Show HN: RepoGuard – Architecture linter for AI-generated code (Cursor, Claude) — 3 points, 0 comments on HN.

• Hacker NewsLaunch post

  Low initial traction (3 points, 0 comments) suggests early-stage, unproven demand rather than a validated rush of interest.

Stack

• Tree-sitter
• dependency-cruiser
• madge
• Node.js CLI (Commander/oclif)
• Supabase (dashboard + auth)
• GitHub Actions API

Solo + AI difficulty

Parsing and dependency-graph analysis across multiple languages is the hard part; a single-language (e.g. TypeScript/JS) CLI with basic circular-dependency and duplication checks is achievable in 1-2 weeks using existing graph/AST libraries. The hosted dashboard and CI integration add another 1-2 weeks.

Entry threshold

Buildable solo in weeks using existing static analysis libraries plus custom rules; low capital needed, main cost is time to tune rules against real AI-generated codebases and avoid false positives.

Window

6-12 months

Where to find first users

• Show HN relaunch with clearer traction proof
• r/ClaudeAI and r/cursor communities
• Cursor/Claude Discord servers
• Indie Hackers and Product Hunt launch

Competitors

• dependency-cruiser
• SonarQube

Counter-signals & risks

• Cursor, Anthropic, and GitHub could build equivalent architecture-smell detection directly into their coding assistants, removing the need for a standalone third-party tool.

• General-purpose static analysis tools (dependency-cruiser, SonarQube, ESLint plugins) can likely be reconfigured to catch the same smells without a new dedicated product.

• The core claim that LLM-generated code has a distinct, consistent 'smell signature' is asserted in marketing but not independently validated with data.

• The Show HN launch itself got minimal traction (3 points, 0 comments), which is weak evidence of real market pull.