Store large and cold datasets in Iceberg on S3, query them through Spectrum, and reserve Redshift local tables for workloads that need low latency or high concurrency.
Enterprise AI success depends on scalable architecture, governance automation, AI operations, observability, and developer-first enablement strategies.
Learn how to implement the Planning Pattern with Enterprise Java, Jakarta EE, CDI, and LangChain4j, enabling AI to transform business goals into executable workflows.
REST APIs waste tokens. UMA uses MCP to bridge agents to local Wasm/WASI-NN, slashing costs and latency by replacing raw data with deterministic, executable intent.
Learn how a local LLM agent automates work list generation from reports, enriches tasks from Jira, detects duplicates, and keeps enterprise data secure.
Replacing unreliable “vibe coding” with a rigorous automated evaluation loop using curated datasets, Claude judge agents, and metric tracking for production AI agents.
A silent provider update once invalidated months of LLM scores in a pipeline I owned. Here is what I changed after, and how parenting taught me the same lesson twice.
Sail is an open-source computation framework that serves as a drop-in replacement for Apache Spark (SQL and DataFrame API) in both single-host and distributed settings.
AI integration is more than agents and prompts. Explore seven architectural patterns to choose the right level of autonomy for enterprise applications.
Automate GitHub repo tracking with a local agent using Python, SQLite, and cron. Learn how to build a lightweight monitoring system for open-source projects.
Production AI failures often stem from undocumented behavior. Learn about AIDF, a framework for defining agent decisions, boundaries, and accountability.