AI/ML Resources

How AI Is Actually Changing SRE Tools, Part 2: ITOps, Chaos Engineering, and the Rest of the Job

Across every category, AI is good at surfacing options and drafts; the SRE still owns the judgment call with real consequences.

August 20, 2026

by Vidyasagar (Sarath Chandra) Machupalli FBCS

CORE

· 1,277 Views · 3 Likes

Securing AI Retrieval Pipelines and Adding Identity-Aware Access Controls to RAG Systems

A step-by-step tutorial for adding per-chunk permission enforcement, prompt injection defenses, and audit logging to RAG pipelines — without rebuilding your data.

August 19, 2026

by Shekar Munirathnam

· 1,462 Views · 2 Likes

Deploying an Enterprise LLM Chatbot on Databricks With RAG, MLflow, Vector Search, and Model Serving

Build and ship a production chatbot: author the RAG chain, log it with MLflow, register it to Unity Catalog, trace it, then deploy to Model Serving.

August 19, 2026

by Seshendranath Balla Venkata

· 1,226 Views · 1 Like

How Docker Is Becoming an AI Development Platform

Local AI dev chaos fixed by moving LLM, vector DB, and app into one Compose file, reproducible, but it's not a Kubernetes replacement.

August 19, 2026

by Pruthvi Raj Seknametla

· 18,036 Views · 3 Likes

Why Is the Agent Card Important?

Build AI agents with A2A and Agent Cards to enable seamless agent discovery, communication, and task collaboration across specialized agents.

August 19, 2026

by Ajay Singh

· 1,223 Views · 1 Like

Containerizing LLMs: Best Practices for Docker-Based AI Workloads

Bloated LLM Docker images and silent OOM kills taught me: separate weights from images, use runtime, not devel bases, and budget GPU/host memory separately.

August 19, 2026

by Pruthvi Raj Seknametla

· 16,281 Views · 1 Like

Multi-Agent Software Engineering: Can AI Teams Build Production Systems?

Multi-agent AI reaches production readiness through orchestration, durable workflows, observability, and resilience rather than simply adding more models.

August 19, 2026

by Uthej Mopathi

· 1,331 Views · 2 Likes

Java Enterprise Is Already Ready for the AI Era

Java Enterprise is ready for AI today. Jakarta EE integrates with AI providers and frameworks, while Jakarta Agentic AI and Jakarta EE 12 strengthen it.

August 18, 2026

by Otavio Santana

CORE

· 1,870 Views · 5 Likes

MCP vs A2A vs ACP: How AI Agents Talk to Each Other

Compare MCP, A2A, and ACP to understand how AI agents access tools, delegate work, manage tasks, and communicate securely in production systems.

August 18, 2026

by Yakaiah Bommishetti

· 1,581 Views · 2 Likes

Member Spotlight: Pavan Belagatti

In the first article of this series, we caught up with Pavan to talk about his tech journey, what keeps him learning, and his passion for agentic AI and AI/ML.

August 18, 2026

by Dominique Roller

· 863 Views · 1 Like

The Embedding Model You Choose Matters More Than Your LLM

Everyone debates GPT-4 vs Claude vs Gemini. Meanwhile, the biggest RAG performance bottleneck is your embedding model. Here's the data, and the fix.

August 17, 2026

by David Taiwo Balogun

· 1,393 Views · 3 Likes

Engineering as a Service Is What Happens When You Let Vibe Coding Win

Vibe coding makes non-engineers capable of prototyping software. What survives is production intuition: knowing how systems fail in ways that were never in the spec.

August 17, 2026

by Dinesh Elumalai

CORE

· 960 Views · 2 Likes

Your AI Agent Is a Distributed System, Not a Chatbot

Most AI agent failures are execution failures, not reasoning failures. Durable orchestration helps enterprise agents survive retries, outages, and long-running workflows.

August 17, 2026

by Anuj Kapoor

· 1,143 Views · 3 Likes

Using AIDLC to Build Documents (Not Just Code)

I turned a pile of design docs into a ready-to-deliver KT presentation using AIDLC, with every decision gated, traceable, and logged.

August 17, 2026

by Shaleen Mathur

· 788 Views

Six Patterns for Building Production-Grade AI Quality Systems

AI quality engineering platform should be built on 6 patterns. It is a unique PTAO cognitive loop (Perceive → Think → Act → Observe) that quality-gates.

August 14, 2026

by samarpana rani Nagaiah

· 1,801 Views · 2 Likes

5 Infrastructure Controls for Securing AI Agents

Prompt-based guardrails fail under adversarial pressure. Here are the five controls that helps to validate along with the configuration to implement them.

August 14, 2026

by Shekar Munirathnam

· 1,626 Views · 1 Like

Code Generation Is Solved; Trust Is the Bottleneck

Polygraph is an open-source Claude Code plugin that finds bugs in stateful code (reducers, workflows, checkout flows, session managers) in any language.

August 14, 2026

by Jean-Jacques Dubray

· 1,650 Views · 1 Like

AI-Powered API Development With Spring AI

Learn how to build intelligent, production-ready REST APIs using Spring AI, enabling your Spring Boot applications to integrate LLMs.

August 14, 2026

by Muhammed Harris Kodavath

· 1,311 Views · 2 Likes

Thoughts on Developing With A(ccelerated) I(nference)

Our collaboration with AI is an ongoing experience. Let's explore it, use it as a tool, and let it help us focus on the important parts of problem-solving.

August 14, 2026

by Horatiu Dan

CORE

· 1,489 Views · 3 Likes

Graph Engineering: The Layer After Loop Engineering

A single agent is just the smallest possible graph: one node with an edge back to itself. Most tasks should stay that simple.

August 14, 2026

by Vidyasagar (Sarath Chandra) Machupalli FBCS

CORE

· 1,695 Views · 3 Likes

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