AI/ML Resources

AI-Era Cybersecurity, Part 1: Mapping Glasswing, Daybreak, Lightwell and the Tools Racing to Find and Fix Bugs

AI is now used on both sides of the vulnerability fight: it finds bugs faster for defenders and shortens the time attackers need to weaponize them.

October 14, 2026

by Vidyasagar (Sarath Chandra) Machupalli FBCS

CORE

· 1,414 Views · 2 Likes

The LLM Evaluation Score Improved. Production Quality Didn't.

Higher LLM evaluation scores can hide production regressions when benchmarks, judges, datasets, and metrics miss real-world system behavior.

October 13, 2026

by Uthej Mopathi

CORE

· 1,405 Views · 1 Like

Who Should Grade Your AI? Rules vs LLM Judges vs Humans

Learn when to use rule-based graders, LLM-as-a-judge, or human review to build more accurate, scalable, and reliable AI evaluation systems.

October 13, 2026

by Yakaiah Bommishetti

· 1,196 Views · 1 Like

Building a Scope-Constrained AI Agent for Automated CI Failure Repair

Build a scope-constrained AI agent that diagnoses CI failures, limits code changes, generates minimal patches, and validates repairs through targeted tests and CI gates.

October 12, 2026

by Uthej Mopathi

CORE

· 1,392 Views · 1 Like

The Jobs Matrix for AI: Four Boxes Instead of 40 Tools

Learn how a 2×2 AI matrix helps Agile teams classify AI use cases as chatbots or agents based on authority, scope, ownership, and review needs.

October 12, 2026

by Stefan Wolpers

CORE

· 1,464 Views

Columnar Formats for AI Telemetry: Parquet vs Vortex With DuckDB

This article compares Parquet and Vortex with DuckDB for AI inference telemetry, showing how file format and row ordering affect query speed and storage.

October 12, 2026

by Bhala Ranganathan

CORE

· 1,978 Views

Running TypeSafe AI's Jev Inside Neo4j for Confidence-Aware Sentiment Decisions

Call TypeSafe AI's Jev from inside a Java UDF, returning a typed sentiment decision with explicit uncertainty information directly from Cypher.

October 12, 2026

by Akmal Chaudhri

CORE

· 1,804 Views

The Code Generation Bottleneck Is Gone. The Maintenance Bottleneck Isn't.

AI removes code-generation bottlenecks, but maintainability, ownership, review, and governance become software engineering’s new constraints.

October 12, 2026

by Uthej Mopathi

CORE

· 1,315 Views · 3 Likes

Production RAG and LLM Systems: A Simple Q&A Guide

Building a small RAG demo is easy. Making it work at production scale — millions of documents, real users, tight latency — is a different job entirely.

October 12, 2026

by Ram Ghadiyaram

CORE

· 1,156 Views

Why Standardized Connectivity Is Only Half the Enterprise AI Equation

Model Context Protocol can eliminate substantial integration friction, but production AI agents still need governance, tool discipline, and orchestration.

October 9, 2026

by Grant Spradlin

· 1,344 Views · 1 Like

Does a Bigger Context Window Mean Better Memory: Understanding Context Windows, Model Memory, and RAG

Context is temporary, model memory is learned, and RAG retrieves external knowledge. A larger context window means more input, not perfect recall or permanent memory.

October 9, 2026

by Yakaiah Bommishetti

· 1,762 Views · 2 Likes

Member Spotlight: Raghava Dittakavi

DZone Core Member Raghava Dittakavi shares why great engineers question assumptions, understand the impact behind every change, and optimize AI to accelerate their work.

October 9, 2026

by Dominique Pugh

· 1,425 Views · 3 Likes

Threat Modeling Context-Aware AI: A STRIDE Walkthrough

How to secure enterprise AI pipelines: a hands-on walkthrough of six threat categories, one reference architecture, and a prioritized list of fixes.

October 9, 2026

by Sandeep Gadde

· 1,519 Views · 4 Likes

Why Your GPU Fleet Is Both Full and Idle: Building a Capacity Orchestration Layer

Managing GPU capacity manually produces fleets that are simultaneously fully allocated and poorly utilized. A capacity orchestration layer can fix the problem.

October 9, 2026

by Ankit Sinha

· 1,544 Views · 1 Like

Production-Grade LLM Regression Testing: Contracts, Evals, and Quality Gates

AI regression testing combines deterministic contracts, behavioral evaluations, production failures, and baseline comparisons to prevent silent LLM regressions.

October 9, 2026

by Uthej Mopathi

CORE

· 1,284 Views · 1 Like

AI Governance Belongs in Your Pipeline, Not in Spreadsheets

Shift AI governance from spreadsheets to code by embedding automated Risk Appetite Statements into CI/CD pipelines as Architectural Fitness Functions.

October 8, 2026

by vikram isanaka

· 1,292 Views · 1 Like

Supercharging AI Agents with Azure Context: A Hands-On Guide to Azure MCP

Here’s a step-by-step guide for cloud engineers on how to bridge local AI assistants with live Azure infrastructure using the Model Context Protocol.

October 8, 2026

by Ammar Ekbote

· 1,552 Views · 1 Like

Putting Guardrails Around What Your AI Agent Is Allowed to Touch

Governance that stops at the model is theater. Put a guardrail gate at the runtime boundary: bind identity, scope retrieval, gate actions, log everything.

October 8, 2026

by Avinash Maddineni

· 1,850 Views · 1 Like

Engineering AI Accountability: Control the Execution Path, Not the Model

True AI accountability requires governing runtime execution paths, explicit authority boundaries, and structured audit evidence, not just model weights.

October 8, 2026

by Maira Ata

· 1,059 Views · 1 Like

Remember Me, Safely: Durable and Governed Memory for Enterprise AI Agents

Agent memory is durable state, not a model feature: isolate threads by principal, checkpoint at boundaries, redact before writes, and resume idempotently.

October 8, 2026

by Harish Gaggar

· 2,228 Views · 1 Like

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