DEV Community: satya-anguluri

I built an AI engineering course where every lesson is a runnable Spring Boot project (Ollama, no API key)

satya-anguluri — Thu, 27 Aug 2026 19:19:03 +0000

Hi everyone — I’m a Java/Spring Boot engineer with 17+ years of experience, and I’ve spent the past several months building EngineerPrep.

I kept running into the same problem while learning AI engineering: many courses either stay at the conceptual level or demonstrate everything inside notebooks. They explain what tokens, embeddings, RAG and agents are, but not how these pieces behave inside a production application.

So I built the kind of learning path I wanted:

73 focused AI engineering lessons

Short chapters instead of long video lectures

Visual walkthroughs of what happens inside the system

Production incidents and failure scenarios

Hands-on implementation labs

Runnable Maven projects with local Ollama support

Optional OpenAI and Amazon Bedrock configurations

An AI mentor that answers within the context of the current lesson

The curriculum progresses through:

LLM Foundations

Prompt Engineering

Structured Output and Validation

RAG and Embeddings

AI Memory

Agents and Tool Calling

Model Evaluation

AI Security

Production AI Systems

The complete LLM Foundations module is free: 15 lessons, hands-on labs and a starter project that runs locally with Ollama. No paid AI API or credit card is required.

EngineerPrep is intentionally focused: one structured path for working software engineers who want to understand how AI systems are designed, implemented and debugged in production—especially with Java and Spring Boot.

You can try it here:

https://engineerprep.io

I would genuinely appreciate feedback on three things:

Does the first lesson make the value clear quickly?

Is the lesson → incident → project structure useful?

What would prevent you from completing the free module?

I built this independently, so direct criticism is welcome. It will help me decide what to improve next.

Hi everyone. This is Satya , java developer from the past 18 years. Interested very much in AI stuff.

satya-anguluri — Tue, 14 Jul 2026 21:27:39 +0000

Capstead

satya-anguluri — Mon, 13 Jul 2026 16:36:02 +0000

I built Capstead: A Governance & Observability Layer for AI Capabilities in Spring Boot

As more teams integrate AI into their Spring Boot applications, I kept running into the same questions:

Which AI capabilities does our application expose?
Who owns each capability?
How much does each capability cost?
Which capabilities are failing or exceeding latency budgets?

Frameworks like Spring AI provide excellent model integrations and metrics, but I wanted visibility at the business capability level rather than just individual model calls.

That's why I built Capstead, an open-source governance and observability control plane for AI capabilities in Spring Boot.

With a simple @Capability annotation (or a bodyless @CapabilityClient interface), Capstead provides:

📋 Live capability catalog
💰 Per-capability cost attribution, token usage, latency, and success rate
🎯 Daily budget enforcement
🌳 Durable execution history with parent/child execution trees
🤖 MCP export so capabilities can be exposed as agent tools
🔌 Provider-neutral support (Spring AI, LangChain4j, custom SDKs, or any HTTP client)

The goal is to provide a governance layer on top of existing AI integrations without changing how developers build AI applications.

I'd love feedback from engineers building AI-enabled systems:

Is this a problem you've encountered?
What governance or observability features are you missing today?
What integrations would you like to see next?

GitHub: https://github.com/satya-anguluri/capstead

I'd really appreciate your thoughts and suggestions!