Span Code Detector
ExternalSpan AI Code Detector is an advanced ML-powered tool (span-detect-1) trained on millions of AI and human code samples to identify AI-generated code across languages like Python, JavaScript, TypeScript, Java, Ruby, Go, and Kotlin. It provides engineering leaders with tool-agnostic, integration-free analysis of semantic code chunks, delivering ~95% accuracy and metrics like AI Code Ratio and defect rates via the Span platform. Ideal for teams measuring AI's real impact on code quality, velocity, and ROI without relying on self-reports.
Description
Span AI Code Detector is an advanced ML-powered tool (span-detect-1) trained on millions of AI and human code samples to identify AI-generated code across languages like Python, JavaScript, TypeScript, Java, Ruby, Go, and Kotlin. It provides engineering leaders with tool-agnostic, integration-free analysis of semantic code chunks, delivering ~95% accuracy and metrics like AI Code Ratio and defect rates via the Span platform. Ideal for teams measuring AI's real impact on code quality, velocity, and ROI without relying on self-reports.
Key capabilities
- Detects AI-generated code via ML classifier (span-detect-1) trained on millions of samples
- Supports Python, JavaScript, TypeScript, Java, Ruby, C++, Go, Kotlin
- Analyzes semantic code chunks for classification: AI, human, or abstain
- Works with any AI coding tool, no integrations required
Core use cases
- 1.Measuring aggregate AI code ratio in repositories
- 2.Tracking AI impact on production velocity, quality, and defects
- 3.Providing visibility into team AI tool adoption and proficiency
Is Span Code Detector Right for You?
Best for
- Engineering leaders tracking team/repo-level AI adoption and impact
- Organizations integrating with Span for developer intelligence metrics
Not ideal for
- Individuals or academics needing line-level detection
- High-stakes uses like individual punishment (95% accuracy too low)
- Users of currently unsupported languages
Standout features
- Browser-based paste-and-check analysis
- High accuracy (~95%) with ~5-10% abstain rate on boilerplate
- Span platform integration for dashboards (AI Code Ratio, defect rates)
- Chunk-level semantic analysis outperforming peers
Reviews
Based on 0 reviews across 0 platforms
User Feedback Highlights
Most Praised
- Outperforms other detectors in independent evaluations
- Quick, impressive results praised by users on Hacker News
- Enables credible, shipped-code-based AI metrics
- Helps leaders optimize AI usage for better ROI
Common Complaints
- Limited initial language support, expanding soon
- Chunk-level only, sacrifices line-level granularity
- Vulnerable to adversarial edits, messy code, or comments
- False positives on old human code or AI docs; false negatives possible