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In this article, you will learn practical prompt-engineering patterns that make large language models useful and reliable for time series analysis and forecasting. Topics we will cover include: How to frame temporal context and extract useful signals How to combine LLM reasoning with classical statistical models How to structure data and prompts for forecasting, anomalies, and domain constraints Without further delay, let’s begin. Prompt Engineering for Time Series AnalysisImage by Editor Introduction Strange as it may sound, large language models (LLMs) can be leveraged for data analysis tasks, including specific scenarios such as time series analysis. The key is to…

We are extending Azure public regions with options that adapt to our customers’ evolving business requirements without forcing trade-offs. Organizations running mission‑critical workloads operate under stricter standards because system failures can often affect people and business operations at scale. They must ensure control, resilience, and operational autonomy such that innovation does not compromise governance. They need agility that also maintains continuity and preserves standards compliance, so they can get the most out of AI, scalable compute, and advanced analytics on their terms. For example, manufacturing plants need assembly lines to continue to operate during network outages, and healthcare providers need…

The Amazon EMR runtime for Apache Spark is a performance-optimized runtime for Apache Spark that is 100% API compatible with open source Apache Spark. With Amazon EMR release 7.9.0, the EMR runtime for Apache Spark introduces significant performance improvements for encrypted workloads, supporting Spark version 3.5.5. For compliance and security requirements, many customers need to enable Apache Spark’s local storage encryption (spark.io.encryption.enabled = true) in addition to Amazon Simple Storage Service (Amazon S3) encryption (such as server-side encryption (SSE) or AWS Key Management Service (AWS KMS)). This feature encrypts shuffle files, cached data, and other intermediate data written to local…

We have covered packer-as-a-service offerings from the computer underworld in the past, previously dissecting impersonation campaigns and the rise of HeartCrypt, both popular among ransomware groups. However, it is a fast-changing landscape, and now we are watching a new incarnation of the same type of service: the Shanya crypter — already favored by ransomware groups and taking over (to some degree) the role that HeartCrypt has played in the ransomware toolkit. We’ll look at its apparent origins, unpack the code, and examine a targeted infection leveraging this tool. Sophos protections against this specific packer are covered at the end of…

Seven Regional Clean Energy Companies Graduate from Cleantech San Diego Startup Accelerator Shannon Bresnahan   |   Press Releases SAN DIEGO, CA, March 11, 2025 – Cleantech San Diego today announced the graduation of seven local clean energy companies – ChargeNet Stations, Helicoid, Paired Power, Redoxblox, Sonocharge, Uprise Energy, and Xtelligent – from its Southern California Energy Innovation Network (SCEIN).   SCEIN is an accelerator program for clean energy startups based in San Diego, Riverside, San Bernardino, and Imperial counties that are developing technologies to help California meet its clean energy goals. The program offers free business services through a consortium of regional…

The ternary operator is one of those things that will exist in virtually any modern programming language. When writing code, a common goal is to make sure that your code is succinct and no more verbose than it needs to be. A ternary expression is a useful tool to achieve this.What is a ternary?Ternaries are essentially a quick way to write an if statement on a single line. For example, if you want to tint a SwiftUI button based on a specific condition, your code might look a bit as follows:struct SampleView: View { @State var username = “” var…

Artificial Intelligence (AI) at the edge is popular among smart video devices. For example, Smart Home cameras and video doorbells revolutionized home monitoring. What began as simple recording and remote viewing tools has evolved into intelligent observers. With AI infusion, today’s cameras can actively analyze scenes, alert users to motion events, recognize familiar faces, spot package deliveries, and dynamically adjust their recording behavior. Enterprise surveillance cameras are another example. These cameras have superior resolution, enhanced computing power, and can drive more sophisticated AI models. These enhanced capabilities result in sharper detection at greater distances. As illustrated, customers demand intelligent monitoring…

While the training of large language models provides deep knowledge that is excellent for common tasks—like creating Compose UIs—training concludes on a fixed date, resulting in gaps for new libraries and updated best practices. They are also less effective with niche APIs because the necessary training data is scarce. To fix this, Android Studio’s Agent Mode is now equipped with the Android Knowledge Base, a new feature designed to significantly improve accuracy and reduce hallucinations by grounding responses with authoritative documentation. This means that instead of just relying on its training data, the agent can actively consult fresh documentation from…

Two-dimensional (2D) materials have garnered notable research interest due to their extraordinary properties. Assembling two or more 2D materials into heterostructures introduces properties that are not present in any individual components, leading to a spectrum of nanodevices and applications. The lifetime and performance of nanodevices can be largely dictated by the working temperatures, and the heat dissipation in 2D materials and heterostructures is vital to the reliability and functionality of devices. However, mechanical effects encountered can potentially impact thermal transport. A comprehensive understanding of the interplay between mechanical loadings and thermal transport in 2D materials and their heterostructures is fundamental…

The discourse about to what level AI-generated code should be reviewed often feels very binary. Is vibe coding (i.e. letting AI generate code without looking at the code) good or bad? The answer is of course neither, because “it depends”. So what does it depend on? When I’m using AI for coding, I find myself constantly making little risk assessments about whether to trust the AI, how much to trust it, and how much work I need to put into the verification of the results. And the more experience I get with using AI, the more honed and intuitive these…