If you are responsible for integrating zkML into a real product, I recommend starting with these three "practical" aspects for stress testing.

A. DeFi risk control: A model evaluates some of the characteristics of lending/credit decisions, and DeepProve then outputs a verifiable score. The frontend only receives the score and proof, while the backend retains the complete input and model version. The advantage is explainability and auditability, but the disadvantage is cost and latency—depending on whether you're using it for access control or credit limit increases.

B. Education/Exams: The “problem-solving chain” generated by the model must be verifiable to prevent subsequent editing; the proof must be tied to timestamps/invigilation evidence to prevent cheating.

C. Content Confirmation: Write "model version, prompt word hash, and important intermediate layers" into the certificate. When the work is put on the shelf, it will be attached with a verifiable label "from a certain model/a certain prompt", and copyright disputes can be traced.

To achieve these three goals, don't just focus on the white paper. Develop metrics directly: single proof generation time, gas/fee range, retry rate for failed transactions, and P95 end-to-end latency. Also, track the difference in business conversion compared to not using proofs. Only when the benefits of more stable risk control, fewer disputes, and clearer copyright laws offset the costs of proofs can you truly connect zkML to business.

Don't forget the realities of the network: Lagrange's Prover Network has already delivered a complete proof of GPT-2 reasoning and is being run by over 85 operators on EigenLayer. This allows you to outsource computing power and stability to the network, rather than relying on your own data center. Roadmaps and engineering updates indicate support for LLaMA, Gemma, Claude, and Gemini as the next steps, providing a time window and planning space for product launch.

Interaction: In which scenario would you prefer to try "proven AI" first? A. Risk control scoring, B. Exams/certifications, C. Content rights verification. In the comments section, write A/B/C and the metric you care about most.

@Lagrange Official #lagrange $LA