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
Implement soft deletes, data retention policies, GDPR-compliant deletion, and conversation archival for AI agent systems with PostgreSQL patterns, automated cleanup, and audit trails.
How to transition from building agents alone to leading a team of AI engineers, covering team structure, project planning, code review practices, and knowledge sharing for agentic AI projects.
Compare specialized and general-purpose AI agent architectures. Learn when to deploy narrow experts versus flexible generalists, with decision frameworks and trade-off analysis.
Learn how speculative decoding uses lightweight draft models to generate candidate tokens that a large target model verifies in parallel, achieving 2-3x inference speedups without quality loss.
Explore speculative execution techniques for AI agents including prediction models, cache warming, speculative tool calls, and rollback strategies that reduce perceived latency by pre-computing likely outcomes.
A practical comparison of the three leading STT engines for voice AI agents — OpenAI Whisper, Deepgram, and AssemblyAI — covering accuracy, latency, streaming capabilities, language support, and pricing.
Build production-grade AI agent database layers with SQLAlchemy 2.0 including async sessions, relationship loading strategies, model definition patterns, and Alembic migration workflows.
Learn how to apply Site Reliability Engineering principles to AI agent systems, including defining SLIs and SLOs, managing error budgets, reducing operational toil, and running effective incident management for autonomous agent workloads.
Compare and implement state management patterns for AI agent interfaces using React Context for simple state, Zustand for client state, and TanStack Query for server state.
