Automate Power Platform deployments by resolving connection references and activating cloud flows after import, so every environment is ready when the pipeline finishes.
LLMs can quickly generate web application code, but AI-written code may contain security vulnerabilities. This article reviews testing methods for LLM systems.
A walk-through of the new JDWP-based on-device debugging pipeline for ParparVM iOS apps and Android apps, with a step-by-step IntelliJ tutorial for each.
Finding bugs is what testing produces; understanding quality is why it exists. QA's future belongs to those who understand products, customers, and risks, not just bugs.
Learn how Conversational Risk Accumulation (CRA) helps detect session-level risks in long AI chats using telemetry, drift tracking, and soft guardrails.
The article focuses on moving away from traditional, "imperative" ETL processes to a modern, "declarative" approach using the Databricks Lakeflow platform.
Learn how a local LLM agent automates work list generation from reports, enriches tasks from Jira, detects duplicates, and keeps enterprise data secure.
Automate GitHub repo tracking with a local agent using Python, SQLite, and cron. Learn how to build a lightweight monitoring system for open-source projects.
Production AI agents can trigger cascading failures when observability tracks what broke, but not whether the system can safely absorb remediation actions.