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Java Development Kit (JDK) 23 has officially been released, bringing with it a host of exciting new features and improvements to the Java programming language. This update introduces a variety of changes, including previews of module import declarations, stream gatherers, structured concurrency, and scoped values, as well as a new class-file API. However, one significant change in JDK 23 is the deprecation of memory access methods in the sun.misc.Unsafe class, signaling a shift towards safer, more standardized alternatives. These updates aim to enhance the functionality and performance of Java, with thousands of smaller improvements and bug fixes included in the…

Generative AI heavily relies on data to generate responses to user queries. Large language models (LLMs), such as OpenAI’s GPT-3, are trained on enormous datasets to understand and produce natural language. For instance, GPT-3 was trained using the CommonCrawl dataset, which comprises 570 gigabytes of data and 400 billion tokens. These datasets, although vast, are essentially snapshots frozen in time, and they cannot incorporate real-time information about ongoing events. This limitation can lead to AI-generated responses that are outdated or, in some cases, even incorrect. Moreover, LLMs are susceptible to hallucinations—instances where the AI generates information that appears plausible but…

When it comes to AI, no executive is likely to give a free pass to experimentation without oversight. The reality for leaders is that AI efforts must be aligned with business goals, while carefully mitigating associated risks. Even the most cautious executives are hesitant to completely dismiss AI, knowing that failing to leverage AI-driven innovations could leave their businesses vulnerable to disruption and falling behind competitors. However, the balance between harnessing AI’s potential and managing its risks requires clear governance and accountability frameworks. AI governance encompasses the principles, practices, and regulations that guide organizations in utilizing AI effectively and responsibly.…

API development continues to experience tremendous growth year after year, and organizations are increasingly relying on APIs to connect services and facilitate data exchange. A report from 451 Research in 2022 found that the average organization is now managing over 15,500 APIs, with a staggering growth rate of 201% in just one year. Cloudflare’s data further highlights the prevalence of APIs, revealing that over half of the traffic it handles is API-based. However, as the number of APIs continues to rise, enterprises are facing significant challenges in managing and governing these various integration points, often leaving them fragmented and unregulated.…

We are still in the early stages of AI’s evolution, and one clear sign of this is how much work users still have to do to make AI tools function effectively. As Jono Bacon, founder of Community Leadership Core, points out, even something as simple as choosing the right large language model (LLM) to run a query can be confusing for most people. Once you’ve selected the model, there’s still plenty of work left to fine-tune the results, and consistency is far from guaranteed. Current AI models require a lot of manual intervention to ensure the output is relevant or…

A vector database may seem like just another type of database at first glance, but its functionality goes far beyond the traditional database model, particularly in the realm of artificial intelligence (AI). While conventional databases are optimized for handling structured, transactional data with relational queries, vector databases are designed to manage unstructured data, catering to modern AI-driven workloads such as machine learning inference, natural language processing, and recommendation systems. The key difference lies in how data is represented and retrieved. Traditional databases are used to store data in tables with predefined schemas and structured queries, whereas vector databases are tailored…

On September 16, a powerful open letter from key figures in the JavaScript ecosystem called out Oracle for what they consider the abandonment of the JavaScript trademark. The signatories of the letter included Ryan Dahl, creator of Node.js and Deno, as well as Brendan Eich, the creator of JavaScript. Together, they are urging Oracle to release the JavaScript trademark into the public domain. By September 20, the petition had gathered more than 10,000 signatures, signaling strong support from the broader tech community for their cause. The letter, titled “Oracle, it’s time to free JavaScript,” argues that Oracle’s ownership of the…

Microsoft is making strides to enhance the data science experience for users of its Visual Studio Code editor by introducing the Python Data Science Extension Pack. This new offering, launched on September 18 and available on the Visual Studio Marketplace, bundles four essential extensions designed to streamline workflows for Python developers in the data science field. The goal of the pack is to provide a comprehensive solution, giving users access to the right tools for data preparation, analysis, visualization, and even machine learning model development. At the core of the extension pack is the Python extension, which brings powerful features…

Deno 2.0 Release Candidate: Key Updates and Enhancements Deno 2.0, a major update to the Deno runtime for JavaScript, TypeScript, and WebAssembly, is now available as a release candidate (RC). As a competitor to Node.js, Deno has steadily gained popularity among developers due to its focus on security, simplicity, and modern features. This release brings several significant updates, including changes to global variables, improved dependency management features, and adjustments to the permission system, all of which are set to enhance the overall development experience. Unveiled on September 19, the Deno 2.0 RC includes all the expected features of the final…

Arm’s Latest Partnerships Drive AI Performance from Edge to Cloud Arm is making significant strides in bringing AI and machine learning workloads to Arm-based hardware by integrating its Arm Kleidi AI acceleration technology with PyTorch and ExecuTorch, an on-device inference runtime developed by PyTorch. This collaboration, announced on September 16, aims to extend AI performance improvements across both edge and cloud environments. By supporting PyTorch and ExecuTorch, the next generation of applications will be able to run large language models (LLMs) on Arm CPUs, unlocking new possibilities for AI-driven solutions. Arm’s partnerships with PyTorch and TensorFlow also ensure that Kleidi…