AI/ML Resources

How Does an LLM Request and Response Cycle Work? A Full Walkthrough

Learn how an LLM request and response cycle works, from the second you hit send to the moment the last word lands on your screen.

July 28, 2026

by NaveenKumar Namachivayam

CORE

· 2,819 Views · 2 Likes

From DevOps to AIOps: How Agentic AI Tamed Our Multi-Substrate Chaos

Legacy VMware on-prem, reactive AWS, and a ticket queue that never emptied — we deployed agentic AI across both substrates and changed how the team operates entirely.

July 27, 2026

by Mayank Jain

· 1,870 Views · 1 Like

Stop Fine-Tuning Everything: A Decision Framework for Model Adaptation

When to prompt, when to fine-tune, when to pre-train — a decision framework to get the most out of your data and the most bang for your buck.

July 27, 2026

by Supriya Vijay

· 2,729 Views

Securing Model Context Protocol Servers: 4 Gates From Code to Production

Secure MCP servers against prompt injection, data leaks, and denial-of-wallet with four practical, OWASP-aligned gates from code to production. Runnable code.

July 24, 2026

by Viquar Khan

CORE

· 4,106 Views · 2 Likes

Building a Runtime Control Plane for Agentic AI: Lessons From Shipping Real Agents in Production

Practical patterns, code examples, and hard-earned lessons from implementing enforceable guardrails around autonomous agents in real enterprise environments.

July 24, 2026

by Geetha Sreenivasa Rao Repalle

· 3,709 Views · 2 Likes

Building Production-Grade Semantic Search With GPT-5 and Microsoft Foundry, From Scratch

Learn key concepts like embeddings, vector search, chunking, hybrid search, semantic ranking, and how these pieces fit into a scalable, enterprise-grade AI application.

July 24, 2026

by Jubin Soni, FBCS

CORE

· 2,460 Views

Engineering Production Agentic Systems: An Introduction

Explore a three-part field manual on building reliable production AI agents through context engineering, guardrails, and human-in-the-loop architecture.

July 24, 2026

by Ram Ravishankar

· 3,130 Views · 1 Like

How to Build Living AI Coding Assistants With Quarkus Agent MCP

Supercharge local development with the standalone Quarkus Agent MCP server, allowing AI assistants to run, monitor, and debug Java applications.

July 23, 2026

by Daniel Oh

CORE

· 4,719 Views · 3 Likes

Why AI-Generated Code Fails Security Reviews 45% of the Time

AI coding tools produce security flaws in 45% of outputs, yet developers trust the code more despite no security gains in two years.

July 23, 2026

by sunil paidi

· 3,126 Views

AI and Agentic: Promise, Peril, and Predictability

As the use of AI and agentic AI grows, organizations need to implement strong governance and guardrails to protect their users and themselves.

July 23, 2026

by Bikram Sinha

· 3,856 Views

Treat Your AI's Output Like User Input

AI's output is untrusted input. Validate it, fence it in, and never hand it raw to anything that can do damage — the same reflex you already have for form data.

July 23, 2026

by Naveen Birru

· 2,196 Views

How to Build a Solid Test Pipeline in the Era of Agentic AI Development

Learn to build a strong testing pipeline to ensure code quality, as in enterprises today most code is generated by AI agents at a huge volume and scale.

July 22, 2026

by Sai Rakshit Yerram

· 5,526 Views · 2 Likes

How Agentic AI Is Turning Traditional Automation Into a Tool Layer?

Agentic AI doesn’t replace traditional automation. It uses existing workflows, APIs, and systems as tools while handling decisions, exceptions, and complex tasks.

July 22, 2026

by Sagar Sahay Bhatnagar

· 3,587 Views

Tracing the Agentic Loop: Monitoring Multi-Round-Trip MCP Calls With OpenTelemetry

The latest MCP spec mandates W3C tracing. Use Quarkus and OpenTelemetry to easily visualize disjointed, multi-round-trip AI agent workflows in production.

July 22, 2026

by Daniel Oh

CORE

· 3,446 Views · 1 Like

Building Production-Ready AI Vector Search in Databricks: Chunking, Embeddings, ML Pipelines, and RAG

Build production-ready Databricks vector search with chunking, embeddings, Delta sync, index freshness, version control, and governance.

July 22, 2026

by Seshendranath Balla Venkata

· 2,366 Views · 1 Like

The Production-Readiness Gap in AI-Generated Full-Stack Apps

AI-generated apps can look complete quickly, but production readiness still depends on security, backend design, data quality, and observability

July 22, 2026

by Satyam Nikhra

· 3,507 Views

Beyond the Model: Building Real-World Machine Learning

Learn how MSOE bridges the gap between machine learning theory and production systems through data pipelines, deployment, monitoring, and automation.

July 21, 2026

by Craig Hardy

· 2,924 Views

Will AI Keep Us Stuck in 2020 Architectures?

AI coding assistants are deeply fluent in current Spring conventions, which raises a real question: does that lock architecture into 2020-era patterns? It turns out, no.

July 21, 2026

by Daniel Sagenschneider

· 3,341 Views · 1 Like

One Click From Requirements to Production: The Promise and the Reality

AI deployment demos always end before the hard part begins. Writing code is 20% of delivery. This article breaks down the hidden 80% that demos never show you.

July 21, 2026

by Sanketh Kumar Divveda

· 3,033 Views · 2 Likes

Search Is Becoming the Control Plane for AI Agents

The future of AI agents is searchable tool discovery, not hardcoded APIs. MCP and semantic search are turning tools into capabilities agents can find and use at runtime.

July 21, 2026

by sunil paidi

· 2,493 Views

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