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Microsoft-owned GitHub says it now has more than 100 million developers using the code-hosting service to contribute to software projects. GitHub's user numbers are up from 73 million in 2021 and 40 million in 2019, which was a year after Microsoft acquired it for $7.5 billion, with 28 million users, and gained cross-platform desktop development framework Electron. If GitHub's 100 million users are all active developers, it perhaps reflects how the nature of software development is changing. Also: Memory safe programming languages are on the rise. Here's how developers should respond SlashData, for example, estimates there are 24 million active developers worldwide.
Computing started in April 1936 with the invention of the first electronic computer by IBM. It gave new hope towards coding languages which was a previously unknown field. What if someone told you, the hunt for codification in languages started way back? Like many ancient Indian forgotten legends. One example is Panini's "Ashtadhyayi".
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Java is also the most widely used programming language by all developers and programmers to develop machine learning solutions and enterprise development. Similar to Python, Java is also a platform-independent language as it can also be easily implemented on various platforms. Further, Java is an object-oriented and scalable programming language. Java allows virtual machine technology that helps to create a single version of the app and provides support to your business. The best thing about Java is once it is written and compiled on one platform, then you do not need to compile it again and again.
Microsoft Azure is the leading SaaS or (Software as a service) platform with various functionalities for developers and creators. It is a popular platform for integrating with other available tools in the market. DevOps is a combination of two words; development & operations, with a tinge of QA (Quality Assurance) thrown into the mix. Together three terminologies make the word DevOps, which is quite the hit in IT & software circles. DevOps offers a quick and'agile' way of developing software, providing a coordinated front for developers and companies to manage their resources seamlessly.
Alekh Verma is the CEO and Founder of eSearch Logix Technologies Pvt. Ltd., a digital positioning and branding firm that provides web development, mobile development and digital marketing services for clients across the globe. Overseeing the company's operations worldwide, he has been responsible for end-to-end management of service and support with a creative and futuristic vision. Building expertise over the years in all aspects of digital product development and marketing channel, he is also fond of contributing articles and information collections regularly to leading publishers online focusing on balancing knowledge, experience and digital industry growth.
This article explains how you can integrate OpenAI GPT-3 with Google Spreadsheets. This allows you to complete spreadsheet tasks with the use of AI. Tip: Make sure to subscribe to above Gist since all future revisions with improvements will be made to this file. Then you can refer to this file later and update your functions. Note: When there are revisions to functions in the Gist file we discussed above, this is the same place you need to update the new revised code as well.
Since the release of the ChatGPT interactive AI assistant it has been surprising to see some of the snide, passive-aggressive reactions from some (not all) members of the software engineering community, in the style of "it's just inference from bad data". Let's get real, folks, it is truly game-changing. Basically, if you need a program element and can describe that need, the assistant will generate it for you. There is no particular restriction on the programming language that you choose, as long as its description and enough examples are available somewhere. The code will be pretty good.
Transfer learning--using a machine learning (ML) model that has been pretrained as a starting point for training on a different, but related task--has proven itself as an effective way to make models converge faster to a better solution with less-labeled data. These benefits have led pretrained models to see a staggering amount of reuse; for example, the pretrained BERT model has been downloaded tens of millions of times. Taking a step back, however, reveals a major issue with the development of pretrained models: They are never updated! Instead, after being released, they are typically used as-is until a better pretrained model comes along. There are many reasons to update a pretrained model--for example, to improve its performance, address problematic behavior and biases, or make it applicable to new problems--but there is currently no effective approach for updating models.