Introduction

> The landscape of AI is evolving rapidly, and with it comes a deluge of complexity. "Physics isn't going back to where we came from; it's only gonna get weirder from here on out," and that’s why the goal of what we’re building is to create a universal platform that absorbs the chaos and allows developers to write code that remains relevant regardless of the hardware shifts.

> Mojo represents a pivotal advance with its blend of Python's usability and the performance of C/C++, showcasing how we can achieve “over 30,000x speed up over Python.” It’s exhilarating to contribute to a framework that empowers innovation without the constant need to adapt to new technologies.

Mojo programming language

> The vision for Mojo is rooted in addressing the evolving landscape of AI, ensuring that we can effectively leverage cutting-edge hardware while making machine learning accessible to a broader audience. “We need to be able to take machine learning... and make it way more accessible, way more usable, way more understandable.”

> I believe that innovation can and should be fun, which is why Mojo allows for emojis as file extensions. “Emojis are colorful. They're visual. They're beautiful, right?” It’s about reflecting the vibrant nature of programming in the modern era, blending functionality with creativity.

> While Mojo is designed primarily with AI at its core, it also aims to serve as a fully general programming language. “When you do that... you realize that the solution to these problems isn't actually an AI-specific solution.” The goal is to optimize through simplification and provide a seamless experience across various computing needs.

Code indentation

> "Python has millions of programmers and owns machine learning," and that's why building Mojo as a superset of Python feels right. It's about enhancing Python, not replacing it; it's the perfect foundation to address Python's performance gaps while maintaining its beauty and elegance, especially through its indentation style.

> The challenge of merging Python's dynamic metaprogramming capabilities with performance efficiency for computationally intensive tasks is "super hard," but it's about creating a unified experience where "programming at runtime and at compile time" feels seamless. This is crucial for making Mojo not just another language, but an accessible tool for all programmers.

The power of autotuning

> The concept of auto-tuning in Mojo has me genuinely excited; it's about letting the compiler handle the complexities of hardware for you, so you can focus on writing algorithms without needing a degree in hardware engineering. "Most programmers actually don't wanna know this stuff," and with auto-tuning, you can simply specify your algorithm and let the system find the most efficient execution parameters.

> The transformative speed of Mojo compared to traditional Python is remarkable. "We can provide a 35,000x speedup over Python," primarily because Mojo operates as a compiled language, removing the overhead of interpretation and enabling direct exploitation of modern architectures. This shift not only improves performance but opens the door for better product experiences and environmental efficiency.

> Ultimately, it's all about designing a system that balances abstraction and performance. By removing the tangled complexity that comes with legacy languages, we can empower developers to build scalable solutions without getting bogged down by the minutiae of hardware and software interaction. "It’s very, very powerful weapons against slowness," and I love that Mojo can elevate the work of so many machine learning practitioners.

Typed programming languages

> Mojo is designed to combine the flexibilities of Python with the performance benefits of static typing. By allowing for both dynamic and static types, developers can choose how they want to optimize their code. You can start with a dynamic, Python-like feel, and progressively introduce static types as needed, which leads to improved performance and better code completion since the compiler can make optimizations based on type information.

> One of the standout aspects of Mojo is its approach to types and structural elements. Unlike Python's somewhat fragmented implementation of types, Mojo makes types a core part of the language. When you specify a type in Mojo, it’s strictly enforced by the compiler. This not only helps in catching errors at compile time but also makes Mojo suitable for high-performance, low-level programming. Additionally, Mojo's combination of classes and structs allows for a blend of dynamic and static functionalities, enhancing its versatility.

> Releasing Mojo early, despite it being in a 0.1 state, was a conscious decision made to harness community feedback and build the language transparently. This open development philosophy, though challenging, ensures that Mojo evolves in a way that meets the needs of its diverse user base, from hardware engineers to high-level machine learning practitioners. Engaging with the community helps in squashing bugs quickly, introducing features methodically, and ultimately aims to bridge gaps between different programming communities.

Immutability

> The concept of immutability and value semantics is crucial as it transforms how we think about data management in programming. By treating collections as proper values, "if I pass you an array, it’s your array," meaning modifications don't affect the original, it fundamentally reduces bugs and confusion, making code much safer and more predictable.

> The design principle of lazily copying data when it’s modified is a game changer. Instead of creating copies upfront—resulting in unnecessary performance hits—what Mojo does is "gives you copies only when you need them," striking a balance between efficiency and safety, which is incredibly powerful in practical applications.

> There’s a significant evolution in the computing landscape as we shift from single-core to multi-core architectures. This transition, fueled by the need to optimize performance using specialized hardware, makes understanding systems programming and concurrent programming critical. The rise of tensors and how they enable arithmetic abstractions helps us seamlessly tap into this parallelism, driving forward innovations in AI and beyond.

Distributed deployment

> “The industry has been struggling with the deployment of machine learning models, often leading to inefficiencies where it can take weeks or months to get a model into production." This disconnect between research and deployment teams results in long back-and-forth waits, ultimately hampering progress.

> “Both TensorFlow and PyTorch have their strengths and weaknesses, but neither was designed for the world of large language models (LLMs).” The complexity of deploying modern ML models requires a new approach that combines usability for researchers with effective deployment strategies.

> “Complexity is the bitter enemy that Modular is fighting against." The multitude of hardware, evolving model architectures, and the myriad of serving requirements create a tangled web that inhibits innovation, making it difficult for developers to create scalable solutions.

> “What Modular is doing is not just about building another programming language; it’s about creating a comprehensive stack that allows developers to express their ideas freely and effectively.” The goal is to democratically enable innovation across various hardware platforms without getting bogged down by intricate details that don't add value.

> “With innovations like kernel fusion, we can keep computation in the accelerator to avoid costly trips to memory,” thereby optimizing performance. The importance of having a system that adapts to rapid changes in hardware and models is paramount to successfully iterating in this fast-paced environment of machine learning.

Mojo vs CPython

> Mojo is designed as a superset of Python, allowing for seamless integration and execution of Python code. "Without changing any code, [...] you can get 12x speed ups," which is a testament to its potential, and the goal is to make it fully compatible, enabling arbitrary Python code to run efficiently.

> The integration with CPython is not just a bridge; it symbolizes a careful balance between maintaining compatibility and enhancing performance. By leveraging this integration, "we built an integration layer" that allows existing Python packages to run as they are, while paving the way for future optimization.

> The journey of building Mojo is about executing on a vision that respects the complexity of Python, avoiding the mistakes of the past, and ensuring that "we don’t want to repeat the Python 2 to Python 3 transition." This commitment is both a technical and philosophical challenge, aiming to enrich the ecosystem without sacrificing the tools that developers rely on.

Guido van Rossum

> First, collaborating and getting feedback from Guido was crucial; his concern about community fragmentation resonates deeply with me. I'm determined to ensure that Mojo can coexist with Python, allowing us to "borrow lessons" from the transition between Swift and Objective-C. My vision is to see Mojo evolve as a "member of the Python family" that enhances rather than disrupts the existing ecosystem.

> Second, the aim is to facilitate smooth adoption of Mojo within large codebases by emphasizing incremental integration. This means that developers can gradually transition using Mojo for new features without having to rewrite everything at once. By creating a Python interface for Mojo types, we can bridge the gap between communities, allowing both Pythonistas and Mojicians to collaborate and thrive together.

Mojo vs PyTorch vs TensorFlow

> One key insight is that the Modular engine is not meant to replace tools like PyTorch and TensorFlow, but rather to enhance them by providing better performance and tools, without requiring users to rewrite all their existing code. It aims to make the world of AI better by lifting existing frameworks and improving the overall experience.

> Another important point is that for processes where performance is crucial, like training large language models, transitioning to writing code in Mojo can significantly boost efficiency compared to Python. By offering a unifying solution that eliminates the need for hand-writing low-level code like CUDA kernels, Mojo can accelerate progress in developing advanced machine learning models and streamline the development cycle.

Swift programming language

> "Leaving Apple was a bold move motivated by my desire to dive deeply into AI and comprehend its technology and applications. It led me to Google during a time when TPUs were really taking off, ultimately giving rise to projects like Swift for TensorFlow, which, although it didn’t pan out as hoped, contributed significantly to the field."

> "One major lesson was that, while Swift is a fast and efficient language, 'Swift is not Python', and that made adoption challenging. I learned that meeting the world where it is, rather than trying to shift paradigms, is critical for driving new ideas forward."

Julia programming language

> One of the unexpected outcomes when we launched Mojo was the strong reaction from the Julia community, emphasizing that while Julia is a fantastic system, it's fundamentally not Python. Our goal with Mojo is to build on Python's widespread use and beloved syntax, taking what's great about it and enhancing it further, which addresses Python programmers specifically without requiring them to relearn syntax.

> Python's immense popularity comes from its extensive package ecosystem, its low startup time which makes it feel like scripting, and its seamless integration in interactive environments like notebooks. It's like the "glue of computing" because of its simple object representation and dynamic metaprogramming capabilities, making Python both incredibly powerful for technical tasks and supported by a strong, growing community, especially in machine learning.

Switching programming languages

> One key insight shared is the importance of solving real problems to drive tech adoption. The focus is on making the switch to a new technology valuable and low-cost for users. The goal is for Mojo to address pain points and offer a smoother transition for those looking to enhance their programming experience.

> Another significant point discussed is the forward-looking perspective on computing evolution. The vision for Mojo is to provide a universal platform that supports the ever-changing landscape of hardware advancements. By aiming to simplify the process of adapting to new technologies and devices, Mojo strives to empower users and foster growth without constant code rewrites.

Mojo playground

> The launch of Mojo Playground is all about cautious experimentation; "we're setting expectations of saying like, okay, well, don't use this for production, right?" By starting with a controlled environment, we can monitor feedback and make improvements without overwhelming ourselves with technical debt, a lesson I learned the hard way with Swift.

> I'm committed to doing things the right way this time around; "if you do it right and it lifts the industry, it doesn't matter if it takes an extra two months." I want to build the best possible product, taking the time needed to create a robust foundation rather than rushing into a release filled with bugs and issues.

Jeremy Howard

> Releasing Mojo has been an overwhelming success. It's exciting to see people so enthusiastic about ownership and performance improvements, especially over things like speeding up Python. The feedback has shown that our belief in solving deep problems in the Python ecosystem is resonating with users, who are thrilled to break away from C++ pain points.

> Jeremy Howard profoundly influenced this journey. Witnessing his innovative approaches and deep understanding of machine learning, as well as his mission to make AI more accessible, inspired significant parts of Mojo's development. His pragmatic, efficiency-driven yet accessible methodology made a lasting impact.

> Addressing Python packaging issues is a critical and complex challenge. Combining Python and C creates major headaches in building, distributing, and managing dependencies. Mojo aims to streamline this by potentially reducing the reliance on C and improving package management systems, thus simplifying the ecosystem for developers.

Function overloading

> Function overloading is a game-changer for type-safe programming; it allows for clearer, more predictable code by letting you express different behaviors based on input types, reminiscent of how complex functions behave in dynamic languages like Python. It feels empowering because it reduces clutter, avoiding the need for multiple function definitions while enhancing clarity.

> Stricter typing, like what fn offers over def, brings a sense of control and predictability that can greatly improve debugging efficiency. It’s fascinating how different programming communities tackle this challenge—Ruby, for instance, elevated unit testing to compensate for their lack of compile-time errors, showing that both strictness and creativity have their unique places in development.

Error vs Exception

> The traditional zero-cost exception handling in languages like C++ incurs significant overhead and can lead to forking APIs due to high costs, impacting how developers design their APIs. Newer languages like Swift, Rust, and Go opt for treating exceptions as fast as returning a variant, enhancing performance and simplifying error handling for programmers.

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> By reimagining the way exceptions are handled during compilation, like in the Mojo language, the process of percolating errors up the call stack is automated, simplifying coding complexities. Nesting exceptions and functions, though seemingly complex, can offer powerful functionality like closures and callback patterns, showcasing the versatility of language design.

Mojo roadmap

> "Mojo is just seven months old, and while we have a lot on our to-do list, what's exciting is that we're building a language with a clear purpose: to address the complexities of AI and high-performance computing."

> "Implementing missing features like traits and lifetime management is critical because it enables safe referencing and categorizing types, which leads to greater flexibility and expressivity in programming."

> "I'm passionate about avoiding unnecessary syntactic sugar; while it can be tempting, we need to focus on solidifying the core abstractions first before getting sidetracked, and I want to ensure we maintain a strong connection to the Python community along the way."

Building a company

> Building Modular has highlighted the importance of creating an "amazing culture" from the beginning; it's about attracting top talent by fostering a collaborative environment, rather than just hiring and hoping for the best. I've learned this crucial lesson from past experiences, understanding that a clear vision forward, combined with an inclusive atmosphere, is key to driving innovation.

> Recruiting is one of the biggest challenges when you're on a mission to build something impactful, especially when "the best all have jobs." You can't just throw money at the problem; you need to connect with candidates' aspirations and demonstrate how their work can solve real-world issues, which ultimately makes the journey worthwhile for everyone involved.

> I've come to appreciate the unique dynamics that come with in-person collaboration. There’s a depth to brainstorming over lunch or a casual moment that you just can't replicate on Zoom. It's these shared experiences that foster trust and excitement, leading to teams that work better together, enabling the creativity needed to tackle the ambitious goals at Modular.

ChatGPT

> Reflecting on the advancements with large language models (LLMs), it's becoming increasingly clear how they challenge my understanding of programming and creativity. The way these models can "predict the kind of code I was about to write so well" prompts me to question my uniqueness in code design. "When you stand on the shoulders of giants, are you really doing anything?" This speaks to the profound impact of LLMs: they can automate much of the rote programming work, allowing us to focus on deeper problem-solving and innovative design.

> Also, the potential for LLMs as collaborative companions is fascinating. I'm excited about the concept of using LLMs to help bridge the gap between human thought and machine logic. "I think the LMS will have no problem learning whatever keyword we pick," showcasing how they can enhance our productivity and creativity in programming languages like Mojo. Ultimately, this synergy could redefine how we approach coding and collaboration in tech.

Danger of AI

> The development of AI, particularly through platforms like Modular and languages like Mojo, has the potential to revolutionize both digital and physical spaces. While there’s immense promise in speeding up AI advancements, the practical deployment of this technology will likely be slower and more complex than anticipated, much like the delayed realization of autonomous vehicles.

> Reflecting on AI’s impact, I'm cautiously optimistic. While there are genuine concerns about job displacement and potential existential threats, I believe in focusing on the positive outcomes AI can bring. This includes automating repetitive tasks and unlocking new capabilities, ultimately aiming to enhance human productivity and improve our lives.

Future of programming

> The future of programming is driven by simplifying complexity to make tools and technologies more accessible to a larger audience, enabling personalized experiences and impactful innovations. Breaking down barriers in AI usage, akin to programming in Python, can lead to practical applications and a broader scope of users tapping into AI techniques.

> In the evolving programming landscape, there may be more programmers, but they may not identify as traditional programmers. Embracing AI as a new programming paradigm could streamline its integration into existing languages and toolchains, making it more user-friendly and versatile for a wider range of applications.

Advice for young people

> You'll find the most success by working on projects that genuinely excite you. Instead of just memorizing concepts, "go build something" and tackle real problems. The thrill of creating something meaningful provides both achievement and an incredible learning experience.

> Don’t be afraid to forge your own path. Just because everyone else is following a trend doesn’t mean you have to. Pursuing interests that others overlook can lead you to unique expertise, making you valuable in ways that the mainstream may not appreciate. “Sometimes it’s pretty cool to go right” when everyone else is going left.