High performance computing (HPC) software ecosystems are inherently heterogeneous, comprising scientific applications that depend on hundreds of external packages, each with distinct build systems, options, and dependency constraints. Tools such as Spack automate dependency resolution and environment management, but their effectiveness relies on manually written build recipes. As these ecosystems grow, maintaining existing specifications and creating new ones becomes increasingly labor-intensive. While large language models (LLMs) have shown promise in code generation, automatically producing correct and maintainable Spack recipes remains a significant challenge. We present a systematic analysis of how LLMs and context-augmentation methods can assist in the generation of Spack recipes. To this end, we introduce SpackIt, an end-to-end framework that combines repository analysis, retrieval of relevant examples, and iterative refinement through diagnostic feedback. We apply SpackIt to a representative subset of 308 open-source HPC packages to assess its effectiveness and limitations. Our results show that SpackIt increases installation success from 20% in a zero-shot setting to over 80% in its best configuration, demonstrating the value of retrieval and structured feedback for reliable package synthesis.

Hardware acceleration can revolutionize robotics, enabling new applications by speeding up robot response times while remaining power-efficient. However, the diversity of acceleration options makes it difficult for roboticists to easily deploy accelerated systems without expertise in each specific hardware platform. In this work, we address this challenge with RobotCore, an architecture to integrate hardware acceleration in the widely-used ROS 2 robotics software framework. This architecture is target-agnostic (supports edge, workstation, data center, or cloud targets) and accelerator-agnostic (supports both FPGAs and GPUs). It builds on top of the common ROS 2 build system and tools and is easily portable across different research and commercial solutions through a new firmware layer. We also leverage the Linux Tracing Toolkit next generation (LTTng) for low-overhead real-time tracing and benchmarking. To demonstrate the acceleration enabled by this architecture, we use it to deploy a ROS 2 perception computational graph on a CPU and FPGA. We employ our integrated tracing and benchmarking to analyze bottlenecks, uncovering insights that guide us to improve FPGA communication efficiency. In particular, we design an intra-FPGA ROS 2 node communication queue to enable faster data flows, and use it in conjunction with FPGA-accelerated nodes to achieve a 24.42% speedup over a CPU.

Consumers often don't understand AI's power and impact. "AI is just everywhere in our lives today and the average consumer has no clue how it works or what undermines the technology," Roetzer told me. We need help understanding what responsible AI looks like. Tech companies don't have all the answers. The burden of building and using AI responsibly falls on technology companies, which don't always have incentives to build systems that prioritize people over profit.

Welcome to Signals & Threads, in-depth conversations about every layer of the tech stack, from Jane Street. Today, I'm going to have a conversation with Andrey Mokhov about build systems. Build systems are an important but I think poorly understood and often unloved part of programming. Developers often end up with only a hazy understanding of what's going on with their build system learning just enough to figure out what arcane invocation they need to get the damn thing working and then stop thinking about it at that point, and that's a shame because build systems matter a lot to our experience as developers. A lot of what underlies a good developer experience really comes out of the build system that you use and also there's a lot of beautiful ideas and structure inside of build systems. Sadly, a lot of that beauty is obscured by a complex thicket of messy systems of different kinds and a complicated ecosystem of different build systems for different purposes, and I'm hoping that ...

The decision-making process must be made explicit and transparent. Voice assistants usually have female voices, reflecting the fact most real-world assistants are women. This is inherently wrong because it embeds societal assumptions about women in these systems, reproducing so-called "women's work" and reinforcing stereotypes. When systems are designed in this way, it looks bad for the company because it reflects its values. Alexa now reprimands users for being rude to her, which shows that Amazon takes abuse against women seriously.

One of the most important innovations in the present era for the development of highly-advanced AI systems has been the introduction of Reinforcement Learning (RL). It has the potential to solve complex decision-making problems. It generally follows a "trial and error" method to learn optimal policies of a given problem. It has been used to achieve superhuman performance in competitive strategy games, including Go, Starcraft, Dota, among others. Despite the promise shown by reinforcement algorithms in many decision-making problems, there are few glitches and challenges, which still need to be addressed.

Our lives are already enhanced by AI – or at least an AI in its infancy – with technologies using algorithms that help them to learn from our behaviour. As AI grows up and starts to think, not just to learn, we ask how human-like do we want their intelligence to be and what impact will machines have on our jobs? We are well on the way to a world in which many aspects of our daily lives will depend on AI systems. Within a decade, machines might diagnose patients with the learned expertise of not just one doctor but thousands. They might make judiciary recommendations based on vast datasets of legal decisions and complex regulations.

Today, nearly 40 percent of students at U.S. colleges are age 25 or older. They often work at least part time to afford tuition and living costs, and many are juggling school and family responsibilities like caring for children. Time is a precious resource for them. These "nontraditional" students require flexibility so that they can accommodate all their responsibilities while pursuing their higher education. As the demand for more flexibility grows, so does the demand for online learning.

Just two years ago, AI software programs were writing full movie screenplays, predicting the Kentucky Derby and beating video game world champions - and the technological breakthroughs did not stop there. Since then businesses, including many Fortune 500 companies, have started to grapple with what AI can do for their business and operations. As they experiment, a clear pattern is emerging: anything which is a repeatable process can and will be taken over by AI and machine learning (ML). If you decide to extrapolate these technologies, then all normal processes will be automated, and the job of humans will be relegated to handling the complex exceptions. For example, a recent Harvard Business Review article highlighted how Stitch Fix, an online clothing subscription service, uses a machine learning engine to help make personalized recommendations for customers.

Scientists are using artificial intelligence to build systems that can analyze the quality of classroom instruction and student engagement. School leaders and education researchers often rely on test scores to judge how well students are learning. But that ignores many important aspects of learning, such as the liveliness of classroom discussion or how engaged and motivated the students are. Expert observers in a classroom can immediately pick up on these unquantifiable moments of great teaching. But human observations are time-consuming and expensive.