Testing, Deployment, and Maintenance Resources

Testing produces evidence, evidence becomes feedback, and feedback helps us make decisions — even as AI reshapes how code is written and tested.

July 7, 2026

by Stelios Manioudakis

CORE

· 3,880 Views · 2 Likes

How to Build a Production-Ready RAG Pipeline With Vector DBs

RAG prototypes are easy. Production RAG is not. This covers vector DB tradeoffs, chunking patterns, re-ranking, and evaluation setups that hold up at scale.

July 6, 2026

by Mark Saxon

· 3,824 Views · 1 Like

Add Observability to Your React Native Application in 5 Minutes

A five-minute walkthrough for adding logs, traces, and error monitoring to a React Native iOS app using LaunchDarkly's Observability SDK, shown on a simple counter app.

July 6, 2026

by Alexis Roberson

· 1,704 Views · 5 Likes

Azure Databricks for Scalable MLOps and Feature Engineering With Apache Spark, Delta Lake, and MLflow

A practical guide to feature engineering at scale with Azure Databricks, covering distributed data processing with Spark and reliable storage with Delta Lake.

July 6, 2026

by Jubin Soni, FBCS

CORE

· 1,427 Views

Background Work, Push Topics, and Richer Notifications

Constraint-based background work, foreground services, push topic subscriptions, shared-content handling, and enhanced local notifications with full simulator support.

July 6, 2026

by Shai Almog

CORE

· 1,044 Views · 1 Like

Building an AI Agent That Responds to Real-Time Events With AWS Bedrock, Kinesis, DynamoDB, and S3

Build an AI agent that processes real-time events with Amazon Bedrock and a serverless AWS architecture powered by Kinesis, DynamoDB, and S3.

July 3, 2026

by Jubin Soni, FBCS

CORE

· 2,337 Views · 1 Like

The Software Deployment Failures That Pass Every Pre-Deployment Check

Every check passed. Production still broke. The deployment failures that slip through pre-deployment validation, and why testing more does not fix it.

July 3, 2026

by Sancharini Panda

· 1,513 Views

Why Push-Based Systems Fail at Scale — and How Hybrid Fan-Out Fixes It

Push-based systems work until celebrity-scale traffic creates massive fan-out pressure. Modern platforms solve this using hybrid architectures.

July 1, 2026

by Jayapragash Dakshnamurthy

· 1,349 Views

One Stolen Key, One Stolen Token: Why Machine Identity Is Cloud-Native's Quietest Crisis — and the Only Fix That Actually Holds

Learn how stolen machine credentials fuel major cloud breaches and how policy-as-code and short-lived identities help stop modern attacks.

July 1, 2026

by Igboanugo David Ugochukwu

CORE

· 3,936 Views

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

· 1,997 Views

Can Rust Have Zero-Cost Dependency Injection?

This article explains compile-time dependency injection in Rust without reflection, runtime containers, dyn, Arc, Rc, or runtime resolution overhead.

July 1, 2026

by Dmytro Brazhnyk

· 2,402 Views

Beyond Static Thresholds: Building Self-Healing Systems via Context-Aware Control Loops

Static thresholds fail in complex distributed systems. This article introduces a context-aware control loop architecture to isolate failures and automate recovery.

June 29, 2026

by Darshan Botadra

· 1,710 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

· 2,579 Views

Selective Deployment in Azure Data Factory: A Practical Blueprint for Safer CI/CD

Implement selective deployment in Azure Data Factory to safely promote individual features without deploying the entire factory state

June 26, 2026

by Sauhard Bhatt

· 2,295 Views · 2 Likes

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

· 1,783 Views · 1 Like

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

· 1,847 Views · 1 Like