AI/ML Resources

Why AI-Generated Code Is Making Regression Testing More Important, Not Less

AI-generated code introduces integration failures that spec-based tests cannot catch. Regression testing grounded in real production behavior is the fix.

July 1, 2026

by Sancharini Panda

· 1,445 Views

AI-Augmented React Development: How I Rebuilt My Workflow Without Losing Control of the Code

AI accelerates React 18 workflows but breaks down in large enterprise codebases. Here’s where it helps, where it fails, and the guardrails your team needs.

July 1, 2026

by Sathwik Nagulapati

· 1,620 Views

If You Can Facilitate a Retrospective, You Can Audit Your AI

Learn how the AI Delegation Audit helps Scrum teams inspect AI workflows, catch automation drift, and keep delegated AI work safe and accountable.

July 1, 2026

by Stefan Wolpers

CORE

· 1,328 Views · 1 Like

Loop Engineering: The Layer After Prompt, Context, and Harness Engineering

This article walks through all four layers side by side, with comparison tables for when to use each one and which agent architecture fits which job.

July 1, 2026

by Vidyasagar (Sarath Chandra) Machupalli FBCS

CORE

· 1,367 Views · 1 Like

The New Senior Developer Job Description: Half Engineer, Half AI Systems Architect

Senior developers now own two roles: traditional engineering plus AI systems architecture. This split reshapes compensation, hiring, and what 'senior' actually means.

June 30, 2026

by Dinesh Elumalai

CORE

· 2,496 Views · 3 Likes

Architecting Trustworthy AI: Engineering Patterns for High-Stakes Environments

This post presents three domain-agnostic engineering patterns for building AI systems that remain safe even when the model is wrong.

June 29, 2026

by Sujay Puvvadi

· 1,208 Views

Building Production-Safe Agentic Remediation With Docker MCP Gateway: Lessons From 43% to 100% Accuracy

We built an AI Docker remediation system on MCP Gateway. First version: 43% correct. After 9 engineering fixes: 100%. Here's what changed.

June 29, 2026

by Mohammad-Ali Arabi

· 1,843 Views

Black Swan Bugs: Paving the Way for New Roles in Software Engineering

By outsourcing more of our thinking to probabilistic systems, we risk weakening the very human habit black swans demand: the habit of making the right questions.

June 29, 2026

by Stelios Manioudakis

CORE

· 3,076 Views · 3 Likes

Why Requirements Are Becoming the Control Layer in AI-Assisted Development

As AI generates more code and tests, requirements become the control layer that keeps delivery consistent, traceable, and aligned with the system context.

June 29, 2026

by Andrei Lavygin

· 1,207 Views · 1 Like

Before the AI Coding Agent Writes Code: Structuring Scattered Requirements With PARA

AI coding agents often fail when the required context is scattered. It is about preparing better context before the agent writes code.

June 29, 2026

by Venkata Naga Satya Sai Vineeth Kondisetty

· 935 Views

The New Insider Threat Isn't Human: Securing AI Agents Before They Secure Themselves

AI agents are becoming powerful insiders. Learn how identity, MCP security, least privilege, and policy enforcement reduce emerging risks.

June 26, 2026

by Igboanugo David Ugochukwu

CORE

· 2,012 Views · 1 Like

Data Pipeline Observability: Why Your AI Model Fails in Production

Your machine learning model had 95% accuracy in testing, but crashes in production. The problem isn't the model, it's your data pipeline.

June 26, 2026

by Abhilash Rao Mesala

· 1,386 Views · 1 Like

Two Clocks Are Running Out at Once, and Almost Nobody Is Watching Both

Quantum computing and AI coding tools are changing security. Learn why crypto-agility and better governance are now critical.

June 26, 2026

by Igboanugo David Ugochukwu

CORE

· 2,104 Views · 2 Likes

What Cloud Engineers Actually Need to Know About AI Infrastructure

AI infrastructure isn’t about GPUs. Most issues come from storage, networking, data pipelines. If GPU utilization is low, check the infrastructure first, not the model.

June 26, 2026

by Naveen Kalapala

· 1,328 Views · 1 Like

Beyond Software Hope: The Engineering Blueprint for AI Execution Truth

This engineering blueprint details how to replace "software hope" with deterministic, hardware-level enforcement via TEEs and the Citadel protocol.

June 25, 2026

by Theo Ezell

· 1,323 Views · 1 Like

Code and Connect: MCP + MuleSoft

Understand MCP, AI agents, and assistants, and learn how Model Context Protocol connects AI applications to tools using MuleSoft.

June 25, 2026

by Ajay Singh

· 1,570 Views

The AI Definition of Done

The AI Definition of Done: human-in-the-loop is not a quality standard; you need a different approach for agent harnesses or operational excellence.

June 25, 2026

by Stefan Wolpers

CORE

· 1,443 Views · 1 Like

AI, OAuth, and Other Platform APIs in the Core

Deeper AI integration in the framework core, modern authentication via OAuth / OIDC and WebAuthn passkeys driven from the system browser, and a few smaller additions.

June 24, 2026

by Shai Almog

CORE

· 1,852 Views · 1 Like

5 Warning Signs Your Data Architecture Needs a Redesign (Before It Falls Apart)

Five signs your data architecture needs redesign. Fix with semantic layer, active metadata, embedded governance, AI security, maintainable design.

June 24, 2026

by Rajanikantarao Vellaturi

· 1,420 Views · 1 Like

AI Broke Your Definition of Done

When a machine writes most of the code, "the code shipped" stops being a finish line. The work that's left is the work your definition of done was already skipping.

June 24, 2026

by Matt Watson

· 1,054 Views · 2 Likes

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