Building a Slack bot with traditional APIs led to 400 lines of code. Using MCP and AWS Bedrock reduced complexity, enabling scalable, tool-driven automation.
Integrate AI into Java apps with Jakarta EE, CDI, MicroProfile Config, and LangChain4j. Build AI services from simple prompts to type-safe domain-driven interactions.
MuleSoft MCP and A2A shipped in 2025. Zero practitioner guides exist beyond basic setup. 17 recipes reveal the implementation ladder teams are missing.
Multi-scale feature learning helps CNNs and U-Net models combine global context with fine details, improving accuracy in tasks like image segmentation.
Every major software wave added new business capabilities. AI’s real impact will come when it powers adaptive, intelligent business systems — not just faster development.
Static analysis for LLM agents that flags prompt-injection risks—like confused deputy flows and dynamic prompts—before runtime, improving security and auditability.
RAG answers can stay stable while evidence shifts. Learn why evidence stability matters for reproducibility, auditability, and debugging — and how to check it.
Moving a hardcoded LangGraph React agent into LaunchDarkly AI Configs so prompts, models, tools, tracking, and rollout testing can be changed without redeploying.
My goal here is to experiment with an alternative approach leveraging Java's tried-and-tested, robust functionalities that have been available since JDK 1.5.
Many MVPs get too big because teams treat several user-facing systems and vendor-dependent workflows as one app instead of planning one complete path first.