AI workflows fundamentally depend on real-time data movement: ingesting training data streams, feeding live data to models for inference and distributing predictions back to applications. But strip ...

All technological breakthroughs come with some side effects. Electric power generation brings pollution, vehicles cause ...

CPU architectures in a single system, whether that is a single system-on-chip (SoC) or a larger electronics platform ...

A Threat That Continues to Accelerate Ransomware attacks increased by 11% globally in 2024, reaching 5,414 incidents, with ...

Coding with large language models (LLMs) holds huge promise, but it also exposes some long-standing flaws in software: code ...

This creates what you might call the AI workflow paradox: the faster we can generate code, the more critical it becomes to ...

With software engineers adopting AI faster than other industries, some workers say they've experienced reduced everyday ...

Silence is often associated with monastic vows. In the monastery, with its strict schedule and constrained setting, time and ...

Implementing agentic software engineering requires more than connecting an AI model to your repositories—it starts by ...

In today’s rapidly evolving tech landscape, centralized architectural decision-making can become a bottleneck to delivery performance and innovation. Through stories from our own journey, we’ll share ...

Abstract: Architectural knowledge (AK) of existing systems is essential for software engineers to make design decisions. Recently, Large Language Models (LLMs) trained on large-scale datasets, ...