Technology

Apache Spark is a data processing framework that can quickly perform processing tasks on very large data sets, and can also distribute data processing tasks across multiple computers, either on its own or in tandem with other distributed computing tools. These two qualities are key to the worlds of big data and machine learning, which require the marshalling of massive computing power to crunch through large data stores. Spark also takes some of the programming burdens of these tasks off the shoulders of developers with an easy-to-use API that abstracts away much of the grunt work of distributed computing and big data processing.

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• A near-Universal (Local) Linux Elevation of Privilege vulnerability
• TechCrunch informed AT&T of a 5 year old data breach
• Signal to get very useful cloud backups
• Telegram to allow restricted incoming
• HP exits Russia ahead of schedule
• Advertisers are heavier users of Ad Blockers than average Americans!
• The Google Incognito Mode Lawsuit
• Canonical fights malicious Ubuntu store apps
• Spinrite update
• A Cautionary Tale

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Hosts: Steve Gibson and Leo Laporte

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Bun 1.1, the latest version of the Bun toolkit and drop-in Node.js replacement for building, testing, and running JavaScript and TypeScript, now supports Windows 10. The latest version is also more compatible with Node.js.

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2023 has been a breakout year for developers and generative AI. GitHub Copilot graduated from its technical preview stage in June 2022, and OpenAI released ChatGPT in November 2022. Just 18 months later, according to a survey by Sourcegraph, 95% of developers report they use generative AI to assist them in writing code. Generative AI can help developers write more code in a shorter space of time, but we need to consider how much of a good thing that may be.

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Talk to anybody about generative AI in the cloud, and the conversation goes quickly to GPUs (graphics processing units). But that could be a false objective. GPUs do not matter as much as people think they do, and in a few years, the conversation will likely shift to what is much more critical to the development and deployment of generative AI systems in the cloud.

The current assumption is that GPUs are indispensable for facilitating the complex computations required by generative AI models. While GPUs have been pivotal in advancing AI, overemphasizing them might detract from exploring and leveraging equally effective and potentially more sustainable alternatives. Indeed, GPUs could quickly become commodities like other resources that AI systems need, such as storage and processing space. The focus should be on designing and deploying these systems, not just the hardware they run on. Call me crazy.

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It’s hard to believe that the agile software development methodology officially turned 20 years old last year. What once was an outlying practice for startups collaborating in colocated spaces with stickies and whiteboards is now a sophisticated, scalable, and widely used set of agile software development processes and tools.

There’s a rich history behind agile software development and why organizations use agile methods such as scrum and kanban to modernize applications, improve customer experience, and implement digital transformations. There’s also a tremendous body of knowledge around these methodologies and their intersections with design thinking, product management, and devops. Fewer people today ask, “What is agile?” More are seeking guidance for how to align their teams on agile best practices.

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