WebAssembly AI Goes Production: Building Secure, Portable ML Inference at the Edge

EDITOR NOTES - REVISION REQUIRED

This article shows strong technical depth and aligns well with Netrunnaz style, but requires specific revisions before publication:

CRITICAL FIXES NEEDED:

1. Performance Claims - Add citations or qualify statements:

   • "10x smaller footprint" needs source or change to "significantly smaller"
   • "85-95% of native performance" requires benchmark citation
   • "10-50x less memory" needs verification or removal

2. Code Example Disclaimers:

   • Add note that mobilenet_v2.tflite is placeholder - readers need actual model
   • Clarify that prepare_input_image() is demo code, not production-ready
   • Update deployment paths to be generic examples

3. Industry Claims:

   • "Major players are shipping production implementations" - needs specific examples beyond Fermyon/WasmEdge
   • Intel contribution claim needs direct source link

RECOMMENDED IMPROVEMENTS:

• Add performance disclaimer: "Performance benefits vary by use case and hardware"
• Include link to actual WASI-NN examples repository for working code
• Consider adding troubleshooting section for common setup issues

STRENGTHS TO MAINTAIN:

• Excellent technical accuracy on WASI-NN specification
• Strong sourcing for industry consensus claims
• Practical, complete code implementation
• Honest assessment without crypto/hype content

The core article is solid and provides real value. These revisions will ensure 100% accuracy while maintaining the technical depth readers expect.

STATUS: PENDING REVISION