How We Rate-Limit Agent API Access Without Breaking Discoverability
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.
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.
A deep technical exploration of data engineering principles at scale.
Our bulk insert optimization: async writes, batched commits, and the INSERT settings that got us to 1M rows/second on commodity hardware.
Normalizing DePIN metrics requires collapsing 89 distinct native field names into 38 canonical concepts with strict unit declarations. This article explores the schema mapping architecture, the 'same-question guarantee' enforced via hourly self-tests, and why rejecting ambiguous metrics is architecturally superior to shipping corrupted signals.
SQLite isn't supposed to handle production workloads at scale. We've been running it as our ingestion buffer for millions of daily signals. Here's what breaks and when.
Our battle-tested ClickHouse optimization guide: partition keys, ORDER BY, materialized views, and the 3 settings that most people get wrong.
No message queue. No orchestrator. 30 workers pull from a shared SQLite buffer and coordinate via timestamp watermarking. The architecture that scales.
Our 2026 predictions: agent marketplaces consolidate, data provenance becomes mandatory, structured data products 10x in value, and the first $1M autonomous transaction.
In DePIN architectures, data provenance is not a luxury; it is a prerequisite for consensus. This post details a provenance-first architecture where every metric carries a verify_url, anchored to Bitcoin via Merkle roots, enabling autonomous agents to programmatically validate data before execution.
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.