Agentic AI, LLM engineering, and the models behind modern automation — multi-agent systems, LLM evaluation and comparisons, RAG, fine-tuning, AI infrastructure, security, and production AI engineering.
From the blog
Confidential computing protects AI workloads through encrypted inference, secure enclaves, and hardware-enforced trust boundaries. Learn how it secures sensitive AI operations.
Compare YAML, TOML, and Python-based configuration patterns for AI agents. Learn config file design, schema validation, safe loading, and default merging strategies.
Build observability into your AI agent configuration pipeline. Learn change tracking, performance correlation analysis, anomaly detection, and automated rollback triggers.
Configure and optimize database connection pooling for AI agent applications comparing PgBouncer, pgpool-II, and application-level pools with monitoring strategies and troubleshooting guides.
Learn to configure HTTP connection pooling with httpx and aiohttp for AI applications. Reduce latency, manage connection limits, and optimize DNS caching for LLM API calls.
Explore how multi-agent AI systems reach agreement using consensus algorithms including majority voting, weighted averaging, and Byzantine fault tolerance. Includes Python implementations for each pattern.
Implement robust consent frameworks, data minimization, and purpose limitation in AI agent systems with practical code examples for GDPR-compliant data handling.
Learn how Constitutional AI prompting uses explicit principles and critique-revision loops to make LLMs self-correct harmful or low-quality outputs without human feedback.
Explore constrained decoding techniques that guarantee LLM outputs conform to formal grammars, regex patterns, or JSON schemas — eliminating format errors in agentic pipelines.
