According to GitHub's recently published Octoverse 2024 report, Python has emerged as the most popular programming language on the platform, surpassing JavaScript. Additionally, the use of Jupyter Notebooks has seen a remarkable increase. This trend highlights the growing emphasis on data science, artificial intelligence, and machine learning within the GitHub community.
Python’s versatility is evident in its widespread application across various domains, including machine learning, data analysis, scientific research, web development, and even home automation. The report indicates that the surge in Python’s adoption is part of a broader expansion of open-source activities that extend beyond traditional software development practices. GitHub has also observed a heightened interest in AI agents and smaller models that demand less computational power.
Released on October 29, 2024, the Octoverse report analyzes GitHub’s activity from October 1, 2023, to September 30, 2024. It challenges the perception that AI could potentially replace developers. Instead, the report reveals that as AI technology evolves, developers are increasingly integrating AI models into their applications and participating in AI-related projects on GitHub. This year has also witnessed an unprecedented influx of developers from around the world joining the platform.
The report outlines the leading developer communities on GitHub from 2019 to 2024, which include the United States, India, China, Brazil, the United Kingdom, Russia, Germany, Indonesia, Japan, and Canada. Furthermore, GitHub highlights that Africa is cultivating a burgeoning community of developers. In 2024, nearly 1 billion contributions to open-source and public repositories were made by developers globally.
In terms of security, GitHub reported that developers utilized CodeQL secret scanning tools in 2024, successfully detecting over 39 million instances of secret leaks. The most prevalent security vulnerabilities identified were related to injection flaws, broken access controls, insecure designs, and cryptographic failures.
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Meta AI on WhatsApp is set for a major upgrade, transforming it into a personalized assistant tailored to user preferences. This exciting new update is likely to be powered by a Chat Memory feature, which is currently under development. This feature aims to deliver more relevant and customized responses based on user interactions with Meta AI.
Also Read | Meta Layoffs: Jobs Cut Across WhatsApp, Instagram, and Reality Labs
According to a report from WABetaInfo, the Chat Memory feature will enable Meta AI to retain certain details from conversations, such as personal interests, dietary preferences, birthdays, allergies, and even your preferred conversation style. With this stored information, Meta AI can offer personalized responses that align with the user’s lifestyle and preferences.
For example, if you ask Meta AI for food suggestions, it could use your saved information to automatically filter out dishes you dislike or are allergic to.
Also Read | WhatsApp Introduces New Low Light Mode for Video Calls: Here's How to Enable It
A screenshot shared by WABetaInfo suggests that Meta AI will come with a disclaimer stating that it “automatically remembers certain parts of your chat to provide more relevant responses." Users will also have the option to instruct Meta AI to remember specific details using the "remember this" command.
For those concerned about privacy, WhatsApp will give users full control over what information Meta AI retains. You will have the option to update or delete this information at any time, ensuring your data remains in your control.
WABetaInfo discovered the Chat Memory feature in the WhatsApp beta for Android version 2.24.22.9. Although there has been no official announcement from Meta, WhatsApp is expected to roll out the update to a broader audience soon. However, this feature is still not available in our independent testing of the WhatsApp beta version.
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BMC Software announced a new generative AI assistant at its Connect 2024, said to make it easier to add AI capabilities inside mainframe environments.
The AI assistant is part of the BMC Automated Mainframe Intelligence (AMI) framework, used in automating mundane tasks. BMC AMI Assistant helps take advantage of tools such as BMC AMI DevX Code Insights, that explains how a piece of mainframe code is structured and works.
In addition to this, BMC AMI Assistant will be integrated with BMC AMI Ops Insights. Here, the beta tool utilizes LLMs to enhance and optimize mainframe applications.
These systems become more accessible for the new generation of IT specialists as the number of AI tools grows," Priya Doty, vice president of Industry Solutions Marketing at BMC, added, saying the assistant will preserve crucial knowledge about mainframe systems, especially as professionals retire.
According to Chief Technology Advisor for The Futurum Group Steven Dickens, many mainframe applications are those versions built on old, poorly documented code. Updating and modernizing such essential systems becomes much easier with the introduction of AI.
One of the AI-powered tools BMC AMI DevX Code Insights is part of many unified consoles that BMC aims to provide. In fact, these AI models can either be developed in-house by BMC or on top of third-party platforms; additionally, they may include the organization's own LLMs if needed.
Earlier this year, BMC invited organizations to join its Design Program, with early access to new generative AI features as they're rolled out. Ultimately, managing one's mainframe will be done much like any other distributed computing platform, eliminating much of the need for specialty skills.
This shift in AI usage will therefore change the IT team structures and roles to allow the creation and deployment of applications across mainframes and other platforms, as cross-functional teams of developers will be able to build and deploy applications.
Meanwhile, mainframe usage is up-and-coming as more Java and Python applications are landing on these platforms. Python, in particular, will likely serve as the catalyst for new workloads of high-performance real-time AI analysis of transactions that their mainframes deal with to preserve their unparalleled performance at scale. Increasingly diverse workloads' management is challenging going forward, even decades after being hailed as also-ran relics destined for obscurity since enterprises remain critically involved with the mainframes.
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