H100 GPUs are best for flexibility, fast iteration, and custom CUDA work. TPU v5p wins on GCP for large-scale LLM training with better cost efficiency and scaling.
This article demonstrates a "Threat-Model-First" workflow where we use AI not just to write code, but to aggressively attack our logic before we implement it.
This article combines practical mentoring experience with an analysis of how AI alters the junior career path, focusing on what teams should realistically expect today.
High-concurrency systems — especially retail, travel, ticketing, or any “hot product” scenarios — often face cache stampedes (aka "thundering herd", "dogpiling"). Here's a practical pattern for managing concurrent data requests.
In this article, I want to take a closer look at the pitfalls of popular SaaS scaling strategies, drawing on my own experience, and share the lessons learned.
A real Gemini Pro session shows how AI can look “done” while skipping uploaded data and ignoring STOP commands — creating silent failure risk without verification.
Cloud systems drift when exceptions accumulate, and decisions lose connection to original objectives. Clear requirements and early security design prevent sprawl.
AI-driven development is outpacing security teams. This piece examines where AI-powered security actually help, where they fail, and how teams can use them responsibly.
Agent identity and its audit history will enforce zero-trust access for agents based on both identity and past behavior. This makes agent access more secure and reliable.
Retrieval-Augmented Generation (RAG) is transforming enterprise AI by bridging the gap between general-purpose language models and organization-specific knowledge.
Keep GenAI cheap and fast: cache aggressively, route models by confidence, cap tokens and tools, compress context, and monitor cost per successful outcome.
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