It’s all a lot to take in, but you can tackle FHE development just like an elephant sandwich: one bite at a time. Here is my checklist for how to approach FHE application development. By following this guide, you can take your first steps toward never exposing your sensitive data in the cloud again.

For Python developers unfamiliar with industrial protocols, OPC UA PubSub (IEC 62541-14) is a standard that decouples data producers from consumers by allowing devices to broadcast telemetry via stateless middleware like UDP Multicast. For industrial engineers new to Python concurrency, asyncio is a standard library that uses an event loop to handle thousands of simultaneous network operations concurrently without the heavy overhead of traditional threading.

Each article in this series addresses a single topic by providing insights into what the topic is, why we are interested in exploring that topic, where to get started with the topic, and how to get hands-on with learning about the topic as it relates to the Fluent Bit project.

Each article in this series addresses a single topic by providing insights into what the topic is, why we are interested in exploring that topic, where to get started with the topic, and how to get hands-on with learning about the topic as it relates to the Fluent Bit project.

I was browsing Apache Airflow's open issues one evening — something I do when I want to understand the parts of the codebase I don't use every day. Issue #59935 caught my attention immediately.

The report was simple: pool names in Airflow can contain any characters — spaces, emojis, anything. But the metrics reporting system requires stats names to contain only ASCII letters, numbers, underscores, dots, and dashes. When you created a pool with a name like 'pool name with whitespace and emoji,' Airflow would happily accept it — and then crash silently when it tried to report metrics for that pool.

Today, engineers are expected to do more than build systems — they are expected to influence decisions, communicate across teams, and demonstrate impact beyond their immediate environment. Yet most career advice still focuses solely on improving technical skills.

The answer depends on what the solution is for. For creative endeavours such as ideation or writing fiction, non-deterministic responses can be a strength. But I'm sure we can agree that software that relies on precise results, such as those used in finance or scientific research, cannot accept non-determinism.

Genkit Java is an open-source framework that makes building AI-powered applications in Java as straightforward as defining a function. Pair it with Google’s Gemini models and Google Cloud Run, and you can go from zero to a production-deployed generative AI service in minutes, not days.

Introduction

Google NotebookLM lets you upload your documents and get insights about the documents using Google's Gemini models. It is a very powerful and convenient tool. 

Manual red-teaming and hand-crafted evaluation suites have been the standard approach, but they can be very hard to scale. They're expensive, time-consuming, and worst of all, they become obsolete the moment they're published, since models can be trained on them.

In theory, this leads to efficiency; in practice, if you operate within highly regulated sectors — Finance, Utilities, Defense, Healthcare, etc. — the journey often slows down due to significant networking and compliance requirements.

Diskless Kafka refers to a Kafka architecture in which brokers use no local disk storage. Instead, all event data is stored directly in cloud object storage such as Amazon S3, Google Cloud Storage, or Azure Blob Storage.

Cloud Copilots vs. Local Copilots

Cloud-based copilots have become the default entry point for many developers, especially in workplace settings, offering seamless integration with cloud-hosted repositories and services. However, there are trade-offs — namely recurring subscription costs and potential exposure of sensitive code or data.

This is especially true in finance and enterprise environments where analysts rely on Excel for modeling and reporting.

Vision-language-action (VLA) models represent a critical breakthrough in this evolution by combining visual perception, language understanding, action generation, and the potential for generalization. VLA models are poised to redefine what machines can do in the physical world. We will go over different VLA models in the industry today that you can leverage in your work.

What Are Vision-Language-Action (VLA) Models

Vision-language-action (VLA) models combine visual perception and natural language understanding to generate contextually appropriate actions. Traditional computer vision models are designed to recognize objects, whereas VLA models interpret scenes, reason about them, and guide physical actions in real-world environments.

Common approaches include traditional control panels, pre-configured virtual machine (VM) images, and container-based setups. Each offers a different path to the same goal: a functional, secure, and maintainable service. This article compares these methods based on practical experience, focusing on their strengths, limitations, and suitability for different use cases. The goal is not to advocate for any single solution, but to help developers make informed decisions based on their technical needs and operational constraints.

To unpack what’s new and what’s next, I spoke with Anil Inamdar, Global Head of Data Services at NetApp Instaclustr. Anil has decades of experience helping enterprises adopt and operate open source data technologies at scale. In this conversation, he explains why 3.0 matters not just for developers already on OpenSearch, but for any engineering team rethinking how they search, monitor, and analyze data in a distributed world.

To avoid legal threats, many researchers opt to avoid commercial software, products, and applications and instead turn their attention to open source. Open-source teams welcome contributions to improve security, offer transparency through pull requests, and are used throughout the industry. Where closed-source software may respond with a legal threat, open source responds with an enthusiastic thank-you, allowing security researchers to make an impact and talk about their work.

Every Monday at 8 AM, our team's Teams chat explodes. "Hey, the dashboard is down." "Perf team can't access their pods." "Build agents crashed overnight."

When it comes to distributed databases, there is no one-size-fits-all solution. Database-as-a-Service (DBaaS) options may not always meet customers' data sovereignty or portability needs. On the other hand, managing database clusters manually is complex and resource-intensive. What’s needed is a balanced approach that automates routine tasks like updates and backups, while simplifying operations such as scaling, failover, and recovery. This is precisely where Kubernetes excels — bridging automation with operational simplicity.