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AI

Edge AI is a Game-Changer for Embedded Devices

AI at the edge is built for embedded systems. And no need for tons of compute power— most of the heavy lifting happens during training so the models run efficiently on minimal hardware. With microcontrollers like STM32N6 optimizing for AI workloads, the potential is growing fast. Is AI at the edge part of your embedded strategy this year?

The Future of SEO: Predictions and Preparations

Search Engine Optimization better known as SEO, has been part of digital marketing right from the year 2000. With growth in technology and alterations in the manner that users interact on the online platform, SEO changes as well. It will particularly apply to businesses and marketers who want to improve on their positions as far as the internet is concerned. An SEO agency possesses industry knowledge to assist in updating strategies to match current knowledge and implementations of an algorithm.

Using a transformer-based text embeddings model to reduce Sentry alerts by 40% and cut through noise

Sentry uses Issue Grouping to aggregate identical errors and prevent duplicate issues from being created, and duplicate alerts being sent. One of the chief complaints we’ve heard from our users is that in some cases the existing algorithm did not sufficiently group similar errors together, and Sentry would create separate issues and alerts, causing unnecessary disruption–or at least annoyance–to developers.

How AI-powered anomaly detection is transforming APM for SREs

Site reliability engineers (SREs) often face challenges in keeping an organization’s sites running smoothly as the complexity of distributed systems steadily increases. With the rise of microservices, cloud-native architectures, and massive data volumes, manual monitoring and troubleshooting are no longer sustainable. SREs must navigate hurdles like alert fatigue, incident response delays, and the constant pressure to maintain system reliability.

How AI is Transforming the Way We Analyze Data

In 1956, when IBM's engineers unveiled the first hard disk drive, it stored only five megabytes-an amount dwarfed today by a single high-quality photo on your smartphone. But that wasn't the fascinating part; it was the vision. They anticipated a future where data would not only be stored but also analyzed on an unprecedented scale. Fast forward to the 21st century, and data is growing exponentially. Every second, trillions of bytes are created, tracked, and stored across the globe. But storing it isn't the challenge anymore; making sense of it is.

How AI Can Misinterpret Data and Lead to Errors

While AI systems can analyze vast amounts of data quickly, they may also misinterpret that data and lead to significant errors. Understanding how AI misjudgments occur will improve algorithms and ensure they provide accurate results. From biases in data to linguistic ambiguities, various factors can contribute to an AI's misinterpretation of information. Look closely at how these systems work and reveal why you should address these issues right below.