34 articles in this category
A developer's guide to querying DePIN supply telemetry, on-chain revenue, and network metadata across 296 decentralized infrastructure networks — with verify-yourself provenance on every value.
A deep technical exploration of data engineering principles at scale.
The 10 best bugs of 2025: the off-by-one that cost 800 records, the timezone bug that shifted all timestamps by 7 hours, and the ClickHouse query that ran for 4 days.
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.
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.
A provenance-first snapshot of four decentralized compute networks — Akash, io.net, Aethir and Nosana — with the metric depth we track on each and live supply, market and revenue values, every figure carrying a source and a verify_url.
Every DePIN value we serve carries the source it was read from and a link you can open right now to check it. Every daily batch is Merkle-rooted and timestamped to Bitcoin. Here is exactly how it works, and what it does and does not prove.