Hands-on guides to LLMs, RAG, agents, and MCP, built with Java and Spring Boot, with lessons from real projects.
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Reposition your Java skills for the AI era, learn how LLMs work, then ship RAG and agents.
From LLM basics to retrieval patterns, memory, and a working Spring AI RAG app.
End-to-end builds: MCP agents, Ollama, document workflows, and LLM vs type-safe trade-offs.
Get real value from Copilot and coding agents on large Java codebases without losing the fundamentals.
A practical comparison of type-safe Java design and LLM-assisted code generation in Spring Boot, with guidance on where each approach adds the most value.
A practical blueprint for building AI agents that stay useful, observable, and safe when real users push them beyond the happy path.
A practical map of how large language models work: tokens, transformers, training, sampling, and the patterns that keep answers grounded.
Understand the internal mechanics, memory footprints, and CPU cache costs of Collections, Generics, Lambdas, and Streams to write high-performance Java code that outpaces AI assistants.
A practical walkthrough of PaperStock, a hybrid paper-trading platform that combines deterministic strategy rules with explainable AI risk gating.
Moving from a senior software engineering or architecture track to a Forward Deployed AI Engineer (FDAIE) role is a highly strategic transition. Companies like OpenAI, Anthropic, Scale AI, and Palantir are hiring aggressively for this position.
Imagine meeting a brilliant coworker who can instantly draft a marketing strategy, debug your code, or translate ancient Greek
the architectural guidelines for developing, securing, and deploying AI/ML applications within the enterprise
Think of RAG like an open-book exam. Instead of guessing from memory, the AI looks at a library of books (your data) to find the right facts before it answers you.
A step-by-step guide to installing Visual Studio Code on Windows, macOS, and Linux, including interface overview and plugin setup.
A Retrieval-Augmented Generation (RAG) system built with Spring AI, designed to chat with local documents.
Learn AI Core Concept: Intelligence is just 'sensing, thinking, and acting.'
Think of Anthropic (the company that makes Claude) as a car manufacturer. They make different models for different needs
Learn how AI Copilot Optimizer cuts token usage and latency for large Java codebases through sanitization, cache reuse, and similarity search.
A modern Java curriculum for the AI era teaches developers to design contracts, review generated code critically, and verify correctness instead of only writing syntax.
A deep technical comparison of Java and Python architecture, memory model, performance, and migration patterns.
The AI Corporate Travel Reimbursement System is a full-stack Spring Boot application for managing employee travel reimbursements.
A practical guide for traditional software developers to transition into AI engineering by layering AI capabilities onto existing skills and systems.
A complete guide on building and configuring a Spring Boot AI application that acts as an MCP Server for Splunk log analysis.
A detailed tutorial explaining how the Astro-based Aigrama blog works, including content flow, layouts, deployment, analytics, comments, and domain integrations.
A production-ready training guide for Java, Spring Boot, Spring Batch, Servlets, JSP, MS-SQL, React, and Storybook workflows.
A comprehensive guide to Java Generics — covering bounded wildcards, PECS principle, type erasure gotchas, and practical patterns for writing reusable, type-safe APIs.
Learn how to write high-performance async Python code using asyncio — covering coroutines, task groups, async context managers, and real-world HTTP client patterns.
Learn how to use Mermaid.js diagrams to document distributed system architectures — including sequence diagrams, flowcharts, and ER diagrams — embedded directly in Markdown.
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