Most teams don’t need vector databases. PostgreSQL + pgvector handles the majority of AI workloads with less complexity, lower cost, and comparable performance.
Multi-cloud sounds strategic, but usually happens by accident. Networking, IAM, and observability all break at boundaries. Only attempt it if you have no choice.
Three protocols are shaping how AI agents interact with tools, other agents, and users. Here's what each one does, how they fit together, and when to reach for which.
Designing scalable lease coordination in CockroachDB, focusing on key distribution, concurrency, and reducing transaction conflicts in multi-region systems.
AI is transforming multi-cloud integration with real-time, decentralized, secure systems — improving compliance, APIs, and scalability across industries.
We analyzed 1,000 data pipeline incidents across 500+ environments and found that code-related failures still account for ~10% of all data quality issues.
DuckDB is an embeddable analytical database that runs inside your Python process with zero setup. It can query CSV files, Parquet, and pandas DataFrames.
RAG failures stem from retrieval, not models. Replace one-size-fits-all vector search with a decision framework, hybrid flow, and guardrails for reliable systems.
Retries can silently DDoS your wallet — amplifying failures into massive costs. Without limits, jitter, and circuit breakers, “resilience” becomes self-inflicted damage.
PostgreSQL CDC often fails after WAL reading: snapshot handoff gaps, unsafe checkpoints, bad ordering, and retry logic can silently corrupt replicated data.