If you’re a backend engineer working with AWS and curious about how we went from autocomplete-style AI to agentic, this one breaks down the architecture shifts.
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.
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.
This comprehensive technical guide breaks down the essential architectural, storage, and integration patterns required to scale enterprise big data platforms.
When optimizing Spring Boot integration tests, developers often focus on obvious metrics, but they do not always explain why an integration test suite is slow.
Learn about how middleware in AI agent frameworks enables request rewriting, tool filtering, and context control — capabilities callbacks alone can’t support.
Most agent frameworks observe model calls and allow rewriting them only after they reach the model, making an understanding of callbacks and middleware essential.
Part 3 of a step-by-step tutorial that decorates the implementation with Spring AI advisors to demonstrate how certain production concerns may be addressed.