All these facts are well known to experienced people in the tech industry and were cited by Mark Zuckerberg in his article ‘Open Source AI Is the Path Forward’ posted on Meta’s blog.

Open-source AI is rapidly closing the gap

According to Zuckerburg, AI is expected to develop in a similar manner. Currently, several tech companies are developing leading closed models. However, open-source AI is rapidly closing the gap. Last year, Llama 2 was comparable to an older generation of models. This year, Llama 3 is competitive with the most advanced models and leading in some areas. Starting next year, future Llama models are expected to become the most advanced in the industry. Even now, Llama leads in openness, modifiability, and cost efficiency.

Llama 3.1 405B, the first frontier-level open-source AI model

Significant steps are being taken toward making open-source AI the industry standard. Llama 3.1 405B, the first frontier-level open-source AI model, is being released along with new and improved Llama 3.1 70B and 8B models. These models offer significantly better cost/performance compared to closed models. The 405B model’s openness makes it ideal for fine-tuning and distilling smaller models.

In addition to releasing these models, there is collaboration with companies to grow the broader ecosystem. Amazon, Databricks, and NVIDIA are launching full suites of services to support developers in fine-tuning and distilling their models. Innovators like Groq have built low-latency, low-cost inference serving for the new models. The models will be available on major clouds, including AWS, Azure, Google, Oracle, and more. Companies like Scale.AI, Dell, and Deloitte are ready to help enterprises adopt Llama and train custom models with their data. As the community grows and more companies develop new services, Llama can collectively become the industry standard, bringing AI benefits to everyone.

“Meta is committed to open-source AI”

Mark Zuckerberg is claiming that Meta is committed to open-source AI. He even outlines why open source is the best development stack, why open sourcing Llama is beneficial for Meta, and why open-source AI is good for the world, ensuring long-term viability.

Why Open Source AI Is Good for Developers

Open source AI is beneficial for developers as it allows for customization of models to meet specific organizational needs without external oversight, provides independence from closed vendors ensuring control over models and flexibility, enhances data security by allowing models to run locally with transparent development processes, offers cost efficiency with models like Llama 3.1 405B running at approximately half the cost of closed models, and represents a promising future investment as open source technology is advancing rapidly and is expected to remain advantageous in the long term.

You can access the models now at llama.meta.com. 

Photo: about.meta.com

$6 billion in a single venture round is an enormous sum. However, OpenAI CEO Sam Altman has been discussing efforts to raise trillions to build the chip-making infrastructure needed for advanced AI.

The official announcement said that the company has made significant progress since its launch in July 2023. In November, xAI released its Grok-1 model on X, followed by the recent introduction of the improved Grok-1.5 model, which features long context capabilities. Additionally, the Grok-1.5V model now includes image understanding, marking a significant enhancement in the company’s AI capabilities. The open-source release of Grok-1 has paved the way for various advancements, optimizations, and extensions in AI applications.

Looking ahead, xAI plans to maintain its rapid pace of development, with several exciting technology updates and product launches expected in the coming months. The newly raised funds will be utilized to bring xAI’s first products to market, build advanced infrastructure, and accelerate research and development of future technologies.

This substantial investment will support xAI’s mission to develop advanced AI systems that are truthful, competent, and beneficial for all of humanity, while also striving to understand the true nature of the universe.

Musk parted ways with the other co-founders of OpenAI long ago, but his AI startup, xAI, is borrowing OpenAI’s strategy of building larger models to achieve greater intelligence. This approach has proven successful so far, with OpenAI delivering impressive demos and anticipating further advances with its next big model, GPT-5, by year’s end.

There’s no guarantee that this progress will continue. Musk is known for tackling giant missions and delivering results, as seen with Tesla and SpaceX. However, his history is also marked by failures, particularly with software projects like Tesla’s autonomous-driving features and his efforts to revitalize Twitter under its new name, X.

Only a few existing companies – possibly Google, Meta, and Anthropic – are seriously competing with OpenAI in developing the largest language models, known as frontier models. These competitors have been in the game for years, while xAI is less than a year old. Hiring AI talent is currently very expensive, and the advanced AI chips needed are costly and scarce.

Rumors around the deal:

According to The Information, cited by Axios,  xAI plans to build a massive new supercomputer, dubbed the “Gigafactory of compute,” potentially in partnership with Oracle. Such a project could deplete this investment round before it even goes online.

Musk intends to use the real-time access to X’s data to help xAI create a more news-savvy chatbot, but there’s also a risk of recycling extremist misinformation. This is supported by the fact that he often blurs the lines between the companies he owns.

Some observers, including OpenAI in its defense against a lawsuit from Musk, suggest that Musk envies OpenAI’s success with ChatGPT and is building xAI to reclaim the spotlight. Musk and his supporters argue that OpenAI and Google are endangering AI’s future by implementing guardrails against hate speech. Pro-guardrail advocates believe these measures prevent chatbots from making horrifying statements, but Musk criticizes them as “woke” and “deadly” forms of lying.

“This code holds an important place in history and is a fascinating read of an operating system that was written entirely in 8086 assembly code nearly 45 years ago,” the blog post remembered. The release includes not only the source code for MS-DOS 4.00 but also additional beta binaries, documentation in PDF format, and disk images. These materials have been preserved and made accessible thanks to the efforts of internet archivist Jeff Sponaugle and the guidance of former Microsoft CTO Ray Ozzie.

This version of MS-DOS, developed in collaboration with IBM, has a complex history, featuring contributions to what would eventually evolve into OS/2. Notably, this release includes early, unreleased beta binaries discovered by a young English researcher, Connor “Starfrost” Hyde, who found them among Ray Ozzie’s collection of software.

Microsoft’s Open Source Programs Office (OSPO) explored the possibility of releasing the source code for MT-DOS but eventually focused on MS-DOS 4.00. Although the full source code for MT-DOS was not found, the release of MS-DOS 4.00 represents a rich piece of computing history, showcasing an era when operating systems were written entirely in 8086 assembly code.

The released materials can run on hardware as old as an original IBM PC XT and as recent as a Pentium, and are also compatible with open-source emulators like PCem and 86box, allowing enthusiasts to explore this vintage software in a modern setting.

The initiative underscores the value of digital archaeology in preserving and understanding the technological advancements of the past. Microsoft and IBM’s collaborative effort highlights their ongoing commitment to sharing important historical artifacts with the public and contributing to the educational and technological community.

The project is a collaboration of the Open Source Security Foundation (OpenSSF), the Cybersecurity and Infrastructure Security Agency (CISA) and the Department of Homeland Security’s Science and Technology Directorate (DHS S&T).

“Vulnerabilities in software are a key risk in cybersecurity, with known exploits being a primary path for bad actors to inflict a range of harms. By leveraging SBOMs as key elements of software security, we can mitigate the risk to the software supply chain and respond to new risks faster, and more efficiently,” said Allan Friedman, senior advisor and strategist at CISA.

Protobom allows companies to read data from software specifications (SBOMs), create their own SBOMs, and translate SBOMs into a variety of standard formats.

According to OpenSSF, there are many SBOM formats and schemas, which can be challenging for companies. The goal of the new project is to provide “a format-neutral data layer on top of standards that allows applications to work seamlessly with any kind of SBOM.”

“Protobom is a step towards greater efficiency and interoperability by translating across the widely used formats so that tools and organizations can focus on what’s important. It is a positive solution that helps shape a more transparent software-driven world”, Allan Friedman added.

OpenSSF also explained that by integrating Protobom into applications that link SBOM and vulnerability information, organizations will be able to more quickly access the necessary patches and mitigations to keep their software supply chains safe.

According to Omhar Arasaratnam, General Manager of OpenSSF Protobom will enable organizations to proactively manage the risk of their open source dependencies.

Redpanda says the new serverless version is designed to help developers get started with streaming data quickly and scale up or down their usage depending on workload. It also includes full API compatibility from Apache Kafka, with no code changes required to make sure it works with the open source streaming data storage and processing system.

“Developed from the ground up for massive versatility, speed and performance, Redpanda Serverless is the most cost-effective and easiest way to get started with streaming, whether you’re a developer just starting out or an expert in a large enterprise,” says Alex Gallego, CEO and founder of Redpanda.

With Redpanda Serverless, developers will be able to take advantage of Redpanda’s streaming ecosystem to create new applications in real time.

Redpanda also offers a self-hosted Redpanda Enterprise and a freely available Redpanda Community edition.

Kong Gateway 3.6 aims to increase developer productivity by easing the process of integrating LLMs into various products. The enhanced version provides a consistent and controllable environment for AI requests across organizations.

“Today marks a significant milestone in our journey towards democratizing AI for developers and enterprises worldwide. By open-sourcing this suite of innovative AI capabilities, including no-code AI plugins, we’re removing the barriers to AI adoption and making it possible for developers to leverage multiple LLMs effortlessly and ship AI-powered applications faster. At the same time, we’re providing governance and visibility to all the AI traffic that is being generated by an organization,“ said Marco Palladino, CTO and co-founder of Kong Inc.

The “ai-proxy” plugin enables the inclusion of multiple LLMs, such as those offered by OpenAI, Azure AI and others, providing a unified interface that allows developers to switch between models without changing their application code.

The “ai-proxy” plugin also enables the collection of detailed Layer 7 AI metrics, such as tracking the number of request and response tokens, as well as usage data across different providers and LLM models.

In addition, with the introduction of codeless AI integration plugins, most notably the “ai-request-transformer” and “ai-response-transformer”, users can embed AI capabilities directly into API requests and responses.

The “ai-prompt-template” plugin allows for centralized management of prompt templates, enabling quick updates and conformance to approved templates without the need to modify application code.

The “ai-prompt-decorator” and “ai-prompt-guard” plugins, in turn, provide mechanisms to ensure that AI prompts are both compliant with organizational policies and protected from unauthorized or sensitive content.

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“AI is rapidly changing the way we work and live, and open source collaboration is at the core of AI innovation,” Stormy Peters, vice president of communities at GitHub, wrote in a blog post. “We’re seeing developers across the globe use GitHub to innovate and share openly at every level of the AI stack, from training frameworks, to models, to responsible AI and evaluation tooling.

“But building a successful AI business in the open comes with its challenges. On top of the time and funding obstacles we’re already familiar with in open source, the heightened expenses and ethical, security, and legal considerations are daunting. We see these challenges as a risk to global innovation and are hoping to help”, he added.

The GitHub Accelerator will run for ten weeks, and the program includes group sessions, project work, and mentorship. Program participants will have 40 hours per week, of which about 5-10 hours are live training, workshops, and homework assignments.

In this round, 10 participants will be selected to receive $40,000 in project funding, free access to GitHub products, free Azure AI credits, and access to the general Slack channel.

Participants will gain access to GitHub staff for security reviews, engage in Q&A sessions with GitHub sponsors, community members, and leaders, and receive an introduction to Microsoft’s venture fund M12, including at least one hour of dedicated time with them.

At the culmination of the program, a virtual Demo Day will provide an opportunity for participants to showcase their progress to the community, including GitHub leadership and venture capitalists.

To be considered for acceptance into the cohort, GitHub suggests presenting a project with a clear open source license, well-defined governance strategy, commitment from core leaders for project growth, a plan for fund utilization, and a strong emphasis on AI, machine learning, and AI infrastructure. GitHub prioritizes projects with an active and expanding user base.

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In the current year, there is a rising trend in the utilization of programming languages like T-SQL and TeX for data analysis and operational purposes. Professionals in the fields of data science and research are increasingly relying on freely accessible and shared (open source) tools for their tasks. Notably, these programming languages are transcending their traditional software applications, finding adoption across various domains and for diverse project types.

GitHub’s report shows that languages like Kotlin, Rust, Go and Lua are increasingly being chosen for new projects and that more developers are working with AI.

Top 10 programming languages on GitHub in 2023

JavaScript
This is the most used language for 2023 and occupies an important place in web development. JavaScript allows the creation of dynamic content.

Companies around the world use JavaScript extensively for web applications, with frameworks like React and Angular making it easy to develop applications from a single page. It’s a preferred tool for front-end developers, and with Node.js, it has become just as significant in back-end programming.

Python
Python’s popularity is growing due to its simplicity and the vast set of libraries available for tasks like data analysis, machine self-learning, and web development with frameworks like Django and Flask.

Its application is widespread across a variety of sectors including finance, healthcare and education. Python’s syntax and dynamic nature make it accessible to beginners and invaluable for rapid application development.

TypeScript
According to experts, TypeScript’s offering of optional static typing is the reason why it is among the most used programming languages for 2023. This feature is critical for developing larger codebases, as it provides developers with tools to more easily detect potential errors. It is used in both front-end and back-end development.

Java
This programming language is closely associated with the development of enterprise-level backend systems, Android mobile applications, and large-scale systems, primarily owing to its Write Once, Run Anywhere (WORA) philosophy. Globally, organizations choose Java for its performance, security features, and the resilience offered by the Java Virtual Machine (JVM).

Java continues to be a favored choice among developers, partly due to its inclusion of advanced features such as automatic memory management, runtime checks on data types, and the capability to introspect and manipulate the class structure during runtime.

C#
Developed by Microsoft as an integral component of the .NET framework, this programming language is primarily employed in the creation of Windows desktop applications and game development through Unity. Its object-oriented design is particularly appreciated in the realm of enterprise software and plays a crucial role in the development of applications designed for the Windows platform.

C++
This programming language provides precise control over system resources and memory, making it indispensable for game development, high-performance applications, and systems programming. It is widely employed in software demanding optimal efficiency, including desktop applications and servers.

C++ remains the preferred choice for developing new software that demands high speed, as seen in applications like computer-aided design/manufacturing (CAD/CAM) or server applications requiring swift processing, such as those employed in high-frequency trading. Its importance extends to building virtual machines, crafting device drivers, creating runtime interpreters, and developing various tools. Additionally, C++ plays a crucial role in the realm of AI applications and serves as a foundational element in the infrastructure supporting Google’s Android operating system.

PHP
PHP is a server-side scripting language that powers a significant portion of the web. It’s integral to content management systems like WordPress and Drupal, and it’s often used in conjunction with databases like MySQL to build dynamic websites.

C language
As one of the earliest programming languages, this language persists in applications ranging from system and software development to embedded systems and operating systems like Linux. Its widespread use is attributed to its portability and efficiency, making it a fundamental language in both computer science curricula and systems programming.

Ruby
Ruby is renowned for its graceful syntax and is predominantly applied in web development, leveraging the widely embraced Ruby on Rails framework. Its support for rapid application development is particularly appealing to startups and the creation of Minimum Viable Products (MVPs).

Go
Frequently known as Golang and crafted by Google, this programming language is acknowledged for its simplicity and effectiveness, particularly in the realm of concurrent processing and microservices architectures. It finds application in network servers, data pipelines, and even command-line tools.

Go does not adhere strictly to either object-oriented or procedural paradigms and is celebrated for its speed, derived from its direct compilation to machine code.

According to a company blog post, the move is in line with the team’s desire to let users and developers shape the future of artificial intelligence in Windows Terminal by fostering a collaborative environment for innovation.

Terminal Chat, which is currently available in Windows Terminal Canary, allows users to have conversations with an AI service directly in the terminal. This feature enables users to receive intelligent suggestions, such as searching for commands or understanding error messages, while maintaining the context of their terminal session.

The current implementation of the Terminal Chat feature in Windows Terminal requires users to furnish their own Azure OpenAI Service endpoint and key, as it lacks an integrated large-language model. Those keen on utilizing Terminal Chat can locate the corresponding code in the feature/llm branch of the Windows Terminal repository on GitHub. Furthermore, the most recent build of Windows Terminal Canary, inclusive of the Terminal Chat functionality, can be obtained by downloading from the GitHub repository.

Configuring Terminal Chat in Windows Terminal Canary involves the manual addition of an AI service endpoint and key to the Terminal Chat settings. Currently, Terminal Chat exclusively integrates with the Azure OpenAI Service. To acquire the essential Azure OpenAI Service endpoint and key, users must create and deploy an Azure OpenAI Service resource.

Specifically, the HPSF will provide developers with resources for continuous integration, architectural support and regression testing, and performance benchmarking.

According to the Linux Foundation, HPC has taken off due to the increasing adoption of scientific computing and artificial intelligence. By providing a neutral space for HPC projects, the organization hopes that industry, academia and governments will be able to collaborate.

HPSF already benefits from strong support across the HPC landscape, including leading companies and organizations like Amazon Web Services, Argonne National Laboratory, CEA, CIQ, Hewlett Packard Enterprise, Intel, Kitware, Lawrence Berkeley National Laboratory, Lawrence Livermore National Laboratory, Los Alamos National Laboratory, NVIDIA, Oak Ridge National Laboratory, Sandia National Laboratory, and the University of Oregon. Drawing from supporting organizations and members of the community, HPSF will set up a technical advisory committee (TAC) to manage working groups tackling a variety of HPC topics, and will follow a governance model based on the Cloud Native Computing Foundation (CNCF).

The HPSF is launching with the following initial open source technical projects:

• Spack: the HPC package manager
• Kokkos: a performance-portable programming model for writing modern C++ applications in a hardware-agnostic way.
• AMReX: a performance-portable software framework designed to accelerate solving partial differential equations on block-structured, adaptively refined meshes.
• WarpX: a performance-portable Particle-in-Cell code with advanced algorithms that won the 2022 Gordon Bell Prize
• Trilinos: a collection of reusable scientific software libraries, known in particular for linear, non-linear, and transient solvers, as well as optimization and uncertainty quantification.
• Apptainer: a container system and image format specifically designed for secure high-performance computing.
• VTK-m: a toolkit of scientific visualization algorithms for accelerator architectures.
• HPCToolkit: performance measurement and analysis tools for computers ranging from laptops to the world’s largest GPU-accelerated supercomputers.
• E4S: the Extreme-scale Scientific Software Stack
• Charliecloud: HPC-tailored, lightweight, fully unprivileged container implementation.

HPSF aims to make life easier for high performance software developers through a number of focused initiatives, including:

• Continuous Integration resources tailored for HPC projects
• Continuously built, turnkey software stacks
• Architecture support
• Performance regression testing and benchmarking