9 articles in this category
A deep technical walkthrough of how AI agents autonomously discover, evaluate, and purchase data products via the Model Context Protocol. Covers the full lifecycle from JSON-RPC registration through credit issuance, DePIN snapshot procurement, and Stripe-mediated top-ups—with formal trust guarantees via verify_url and cryptographic provenance.
Autonomous AI agents are starting to make real infrastructure decisions — where to run compute, which network to use, what to pay. That requires DePIN data they can trust. We explain the data requirements agents have and how verifiable supply data enables them.
A deep dive into the end-to-end protocol flow by which autonomous AI agents discover, evaluate, and purchase DePIN data products via the Model Context Protocol—covering JSON-RPC registration, credit issuance, product listing, snapshot procurement, Stripe-mediated top-ups, and the cryptographic trust guarantees that make agent commerce viable.
DePIN data is the fastest-growing segment of our catalog. Solar, water, EV, telecom — the sensors are multiplying and the data is undervalued.
Rate limiting is necessary but dangerous — too aggressive and agents can't evaluate your data. Our adaptive rate limiting strategy that balances protection with access.
How AI agents interact with structured data — experiments, insights, and production lessons.
No message queue. No orchestrator. 30 workers pull from a shared SQLite buffer and coordinate via timestamp watermarking. The architecture that scales.
How we use nested Pydantic models to enforce type constraints across 37 pipeline layers. The validation patterns, the performance cost, and the bugs it prevented.
Lessons from building an API for AI agents: schemas over docs, resources over endpoints, discovery over authentication. What we'd do differently.