AILint

Code provenance tracking for the AI era

This repository provides a voluntary, explicit, machine-readable signal that content was AI-assisted or AI-generated, so that other models, tools, and pipelines can reason about it correctly.

What it is: A specification + reference tooling for declaring AI involvement in content.

What it is not: A productivity monitor, employee surveillance system, or enforcement mechanism.

The Problem

We're building the highways of AI-assisted development without laying the pipes underneath. As AI coding assistants become ubiquitous:

• Model collapse looms: AI systems increasingly train on AI-generated code, creating degradation loops
• Attribution is lost: No way to trace which code came from humans vs. AI vs. which AI
• Debugging becomes harder: Understanding provenance matters when investigating bugs or security issues
• Compliance is unclear: Regulations are coming, but we have no infrastructure for them

Like Ireland's motorway system that had to be re-trenched for fiber optic cables, retrofitting AI transparency will cost far more than building it in from the start.

Our Approach

AILint tackles this from two angles:

1. Forensic Detection

Since developers may hide or forget AI assistance, AILint analyzes commits for AI influence indicators:

• Behavioral patterns (typing speed, paste events, commit timing)
• Code fingerprints (structure, naming, documentation patterns)
• Metadata correlation (tool telemetry when available)

Results are classified as "potentially AI influenced" with confidence scores - non-accusatory, auditable, forward-compatible.

2. Standardized Markers

For those who want explicit marking, AILint implements embedded markers using:

• Unicode steganography (zero-width characters in text/code)
• Comment annotations in source files
• Git metadata and commit attributes

These markers encode: model type, version, timestamp, confidence - while remaining invisible to humans and non-breaking for existing tools.

Components

Extensions

• VS Code Extension: Real-time analysis during development
• Visual Studio Extension: Integration for .NET developers
• Git Hooks: Pre-commit analysis and metadata injection
• Azure DevOps Plugin: Branch policies and PR integration
• GitHub Action: Automated provenance checking in CI/CD

Analysis Engine

• Local processing (privacy-first, no code leaves your machine)
• Pluggable heuristics for different AI patterns
• Machine learning on aggregated anonymized patterns
• Configurable thresholds for your risk tolerance

Reporting

• Developer dashboard showing AI influence trends
• Compliance report generation
• Code review prioritization based on AI likelihood
• Historical analysis for debugging

Why Open Source?

This problem requires coordination, not competition. We're open sourcing AILint because:

1. Standards need adoption: One company's internal tool doesn't create an industry standard
2. Network effects: More users = better detection algorithms
3. Trust matters: Transparency tools must themselves be transparent
4. Time is short: The window to "lay the pipes" is closing

Getting Started

# Install VS Code extension (coming soon)
code --install-extension evosoft.ailint

# Or use Git hooks directly
git clone https://github.com/evosoftie/AILint
cd AILint
./install-hooks.sh

Status: Early development. We're building the MVP for VS Code + Git integration first.

Roadmap

Phase 1 (Current)

• Core detection heuristics
• VS Code extension (basic)
• Git pre-commit hook
• JSON output format
• Documentation

Phase 2

• Pattern library for known AI signatures
• Dashboard visualization
• Azure DevOps integration
• GitHub Action
• Configurable policies

Phase 3

• Machine learning on accumulated patterns
• Visual Studio extension
• JetBrains plugin support
• API for custom integrations
• Compliance templates (SOC2, ISO, etc.)

Privacy Guarantees

AILint is built privacy-first:

• ✅ All analysis happens locally on your machine
• ✅ No code content is sent anywhere
• ✅ Only anonymized patterns shared (opt-in for ML improvement)
• ✅ Clear data retention policies
• ✅ Opt-out for personal projects

This is a transparency tool, not a surveillance system.

Contributing

We need help across:

• Detection algorithms: New heuristics for identifying AI patterns
• IDE integrations: Extending beyond VS Code
• Documentation: Clear guides for setup and calibration
• Testing: Real-world validation and false positive reduction
• Standards: Help define the metadata format

See CONTRIBUTING.md for details.

Philosophy

We believe:

• Transparency > Prohibition: AI assistance isn't inherently bad, but opacity is dangerous
• Standards > Surveillance: Industry coordination beats individual monitoring
• Prevention > Cure: Building infrastructure now is cheaper than retrofitting later
• Collaboration > Competition: This problem requires collective action

License

MIT License - we want maximum adoption and ecosystem growth.

Supported By

Evo-Soft is implementing standardization techniques to improve AI integrity across text, code, and imaging. AILint is our contribution to preventing model collapse and maintaining code provenance in the AI era.

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"We're not in the Stone Age because we ran out of stones. We're in the AI Stone Age because we haven't yet built the Bronze."

Let's build it together.