Author: Arie

In the era of generative AI and large language models (LLMs), the quality and accessibility of data have become the primary differentiators for enterprise success. However, many organizations remain trapped in the architectural paradigms of the past — centralized data lakes and warehouses that create massive bottlenecks, high latency, and “data swamps.” Enter the Data

We are living in an era where software systems are growing in size with each passing day and often face a constant tension between the scale, performance, and security, where each of them is essential and non-negotiable. Security tools must process large volumes of data in real time (network logs, user activity, login attempts, password

The architecture diagram shows clean rectangles. “Stateless API tier,” someone wrote in Lucidchart, then drew an arrow to a managed database. The presentation went well. Everyone nodded. Six months later, after the third incident where a rolling deployment dropped active uploads and the on-call engineer spent two hours discovering that session affinity was secretly enabled

Edge computing has become a practical way to reduce latency and enable real-time decision-making. Running AI models on edge devices can lead to significant performance gains, especially in manufacturing, health care, transportation and infrastructure. However, distributing data across a network of thousands of devices introduces unique security concerns compared to traditional IT environments. For organizations

September 2024. A fintech company in Austin — I can’t name them, NDA — invited me to review their AI agent deployment. They’d built an expense processing system that was supposed to handle receipt scanning, categorization, approvals. Worked great in testing. Three months into production, it was generating fake restaurants. Their accountant found it during

Building generative AI applications in Java used to be a complex, boilerplate-heavy endeavor. You’d wrestle with raw HTTP clients, hand-craft JSON payloads, parse streaming responses, manage API keys, and stitch together observability, all before writing a single line of actual AI logic. Those days are over. Genkit Java is an open-source framework that makes building

As discussed in the MultiCloudJ introduction, it is fairly common to use more than one cloud provider in enterprises. This can happen for many reasons, like mergers, choosing the best services from different clouds, or moving gradually from one cloud to another. Because of this, you may have compute running on Google Cloud while your data or

Imagine deploying a Node.js/TypeScript backend for user authentication that works flawlessly in development, only to watch users get mysteriously logged out or unable to log in shortly after launching to production. Everything ran fine on your local machine, but in the live environment, users start losing their sessions en masse. Requests to protected endpoints begin

The numbers tell a troubling story. Forty-five percent of cyberattacks in 2024 exploited weaknesses in CI/CD pipelines, according to industry tracking data. Not application code. Not user credentials. The build and deployment infrastructure itself. This represents a fundamental shift in how attackers think. Why spend weeks crafting an exploit for production systems when you can

It’s very easy to talk about secure GenAI. But did you ever think about whether your agents are running only the prompts, tool schemas, router rules, and semantic models you intended — especially after many weeks of rapid iteration? It is very hard to prove this. Most teams freeze application code and container images, but