Agentic AI is turning QA from scripted execution into autonomous, risk-driven orchestration. Faster releases, smarter testing, but still guided by humans.
This article walks you through why Git repos grow, how to find and remove hidden large files, and how to optimize pack files to get your repo back into shape.
As AI agents accelerate software delivery, teams need automated trust controls, signed provenance, and runtime enforcement to keep releases fast and verifiable.
In this article, learn about Qwen Code, a terminal-based AI coding assistant optimized for Qwen3-Coder. Learn setup, commands, testing, and workflow tips.
Vibe coding speeds prototyping, but SDLC gains need guardrails, tests, specs, repo context, and secure workflows-optimizing feedback and quality, not code generation.
Intent-based chaos engineering tests AI systems with calculated stress, using topology, sensitivity, and SLA insights to ensure predictable resilience.
Learn all about scalable, cloud-native architectures with microservices and serverless technologies, boosting agility, performance, and cost-efficiency.
RAG alone doesn’t stop hallucinations. I use five guardrails: relevance scoring, forced citations, NLI checks, staleness detection, and confidence scoring.
A secure MCP server can still break production. Twenty heuristic rules score readiness by catching missing timeouts, unsafe retries, and absent error schemas.
At GTC 2026, Jensen Huang, Aravind Srinivas, Harrison Chase, Mira Murati, and Michael Truell made a compelling case that the future of AI belongs to open agent systems, not just open models.
Migrating from DLT to Lakeflow is mostly an API refactor, swapping DLT for pipelines, separating streaming and materialized tables, and updating CDC logic.
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