Designing scalable lease coordination in CockroachDB, focusing on key distribution, concurrency, and reducing transaction conflicts in multi-region systems.
Microservices assume predictable callers. AI agents break this with non-deterministic calls, fan-out, and retries. Here are 5 core assumption breaks and fixes.
AI is transforming multi-cloud integration with real-time, decentralized, secure systems — improving compliance, APIs, and scalability across industries.
Building effective AI models for real-world applications requires clear problem definition, quality data, the right algorithms, continuous testing, and optimization.
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.
AI-generated code broke three of the five classical non-functional quality pillars — readability, maintainability, and security — while creating two new dimensions
AppSec focuses only on code, leaving AI supply chains exposed. Effective security embeds AI checks into workflows, scanning PRs and AI components continuously.
DuckDB is an embeddable analytical database that runs inside your Python process with zero setup. It can query CSV files, Parquet, and pandas DataFrames.
AI doesn’t replace engineering discipline; it amplifies it. Used carefully, AI speeds up good design and clean code; used carelessly, it accelerates technical debt.
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