Lex Fridman - Chris Lattner

1) Chris discusses the use of emojis in programming. He suggests that file extensions are essentially metadata and that using emojis in their place could be a more visually appealing and colorful alternative. Although not fully supported in all programming languages,  says that some languages such as Swift already have partial support for it.  also explains that his company Modular is driving the development of Mojo, an AI-first programming language designed to tackle problems in AI infrastructure. While Mojo is AI-first,  notes that it is a fully general-purpose programming language that simplifies coding by optimizing one thing.

2) Chris and Lex discuss the benefits and drawbacks of Python as a programming language.  highlights several reasons why he loves Python, including its intuitive and natural language-like syntax, condensed structure, and extensive package ecosystem. Additionally, Python's popularity and usability have contributed to its growth as a universal connector for various systems. However,  also acknowledges Python's commonly cited issue of being slow. The discussion also delves into a tangent on the design decision of tabs versus curly braces and how 's programming language, Mojo, is a superset of Python with different syntax options.

3) Chris discusses the challenges of building a compiler for the Mojo language, which includes a new approach to the design of the compiler, and how it takes the standard model of programming languages and unifies it with the runtime model. He also talks about the meta-programming features of many programming languages, including Lisp, and how it simplifies programming and makes it more composable and consistent. Additionally, he explains how auto-tuning and adaptive compilation are significant features of the Mojo language, and how they remove the burden of having humans figure out the complicated multi-dimensional space of compute problems by allowing computers to do the work.

4) Chris discusses how ultimate performance is important for enterprises looking to save cost and compute and how better performance leads to more efficiency in terms of the environment. He explains how his Mojo technology can provide over a 35,000X speed up over Python by moving to a compiler and removing the interpreter, and also optimizing the code with more modern techniques. He further explains how moving the memory representation of objects from the heap out of a box and into the registers can add a 10X speed up.

5) Chris discusses the basics of Mojo and its role in using modern hardware to speed up programming. He explains how modern computers can perform multiple operations at once using vectors, which Python does not expose. Mojo allows programmers to adopt vectors, threads, and other hardware features, and enables parallelization and control of memory hierarchy.  also talks about Mojo's compatibility with Python and its progressive approach to types, stating that they are not the right or wrong thing but a very useful thing. He argues against the cultural norm that there is only one right way to program, suggesting that Python can achieve objectives such as performance and predictability without feeling guilty about not using strict typing.

6) Chris discusses the challenges faced by companies who have 400 million lines of Python code, and how Mojo aims to address these challenges by allowing people to use optional typing while still being fully compatible with Python. He also explains how Python's approach to types is more of a hint than a requirement, which led to the fragmentation of the ecosystem and difficulties in utilizing types for performance optimization. Mojo, on the other hand, enforces type declarations and provides a more standardized approach to types, making it easier for developers to reason about interfaces and avoid bugs at scale. Chris also explains the basic types in Mojo, which are more strictly defined than in Python.

7) Chris discusses the challenges and benefits of open-source software development and community involvement. He explains that with over 10,000 people on Mojo's Discord, it can be frustrating to manage the differing expectations and desires of such a large group. However, he believes that working with the community results in something way better and that it's important to take responsibility for doing it right and solving long-standing problems. He also talks about value semantics and immutability, which are useful for knowing whether something can change out from underneath you, thereby avoiding bugs.

8) Chris discusses two design patterns that are commonly used in programming languages to avoid bugs. The first is a defensive copy inside the database where every record added gets copied to avoid breaking the code. The other approach is through value semantics, where a logically independent copy is provided, and no copy is made unless a change is made to the record. This approach defines away the bugs and reduces the number of copies in practice. He also discusses the ownership system, which tracks who owns the code, and is used in systems programming to avoid weird types of data like atomic numbers, mutex, or uniquely-owned databases.

9) Chris argues that the machine learning industry has not taken a step back to re-think the complexity of the software and hardware, and as a result, progress has been slowed down. Modulabs aims to solve this by creating software that can handle the complexity and innovation in the industry and scale with products and applications over time.

10) Chris discusses the challenges of integrating new hardware into the module framework of AI programming languages like TensorFlow and PyTorch. With an ever-increasing number of hardware options available for AI, Lattner believes that Hardware Innovation is essential, but he also acknowledges the difficulty in incorporating exotic new generation compilers necessary to enable this innovation. Lattner's philosophy is that there is no one right solution to address the hardware diversity, and as Moore's Law ends, specialization is inevitable and essential.

11) Chris discusses how the industry has been trying to convert the problem of writing specialized kernels for different hardware devices into a compiler problem and how this has been useful for the industry in generating faster operators. However, not everyone can or should be a compiler person and not everyone is excluded from the new wave of technology that has emerged as a result of this. Lattner discusses how the modular stack brings programmability back into this world by allowing researchers and hardware innovators to extend the stack without having to hack the compiler itself to extend the stack on the algorithm side or hardware side.

12) Chris talks about the challenges of coordinating multiple hardware blocks for different processes in devices like smartphones and laptops, and how machine learning is helping to mitigate this complexity by moving to data flow graphs and higher levels of abstraction. He discusses the need for a way for all these devices to communicate with each other and the importance of hierarchal parallelism and asynchronous communication. Lattner also shares an algorithm for optimizing the use of different specialized systems in a device and explains how this can be turned into a machine learning problem using genetic algorithms and reinforcement learning.

13) Chris discusses how to make machine learning tasks faster, particularly in Python, where the problem is bottlenecked by memory. Lattner explains that specialized accelerators pose a bottleneck when data needs to be sent back and forth to memory, whether local or distant, as model training sizes get larger. While developers previously hand-tuned and built models that worked well but did not generalize, the solution to avoiding bottleneck lies in building a hybrid kernel numerical algorithm that keeps things in the accelerator, called kernel fusion.

14) Chris discusses the goal of the Mojo programming language, which is to become a full superset of Python. While Mojo will continue to grow and add features, the goal is to be able to bring over arbitrary Python code that will just work in Mojo. The downside of this is that the existing Python packages won't benefit from the performance benefits of Mojo. To address this issue, an integration layer has been built to load all the existing C Python packages and libraries, which then provides a migration path to move to Mojo code incrementally.

15) Chris discusses the challenge of making a language a superset of another language. He uses the example of Python, which was not designed to be a superset, but its amazing dynamic meta-programming features translate to beautiful static meta-programming features, making it scale elegantly in the space. Lattner compares it to C and C++, where building a superset means you get stuck with the design decisions of the subset, making it even more complicated. Additionally, Lattner shares a war story about building the standard-compliant C and C++ parser, Clang, and how he had to consider long-tail compatibility, even if it meant implementing design mistakes and Legacy features that most engineers find distasteful, but doing hard work as an engineer than going to talk to C programmers.

16) Chris discusses his conversations with Guido van Rossum, the creator of Python, regarding his new language, Mojo. Lattner sought van Rossum's feedback and input to ensure that Mojo would be a good member of the Python family without fragmenting the community. They discussed the lessons learned from the migration from Objective C to Swift and how they can apply those lessons to Mojo and Python. Lattner believes that Mojo could be seen as the evolution to Python 4.0, allowing Python to go places it's never been before and to have an even greater impact on the world.

17) Chris Lattner reflects on his experience with Google's TensorFlow and how it wasn't set up for eager mode at the time. Despite the project not working out, Lattner thinks the technology and the ideas went on to have an influence on other systems like PyTorch. He also discusses the evolution of programming language in the context of machine learning and AI, and how it could be a new programming language like Julia, which he says has major differences with what he is doing with his new language, Mojo, which is designed for Python programmers without requiring them to relearn syntax. Python is the most popular programming language in the world, and Lattner cites its low compile time, the fact that it integrates into notebooks easily, its simple object representation, and expressive and beautiful APIs as some factors that make it a great language.

18) Lattner discusses how the popularity of machine learning is growing and there is a reputation of prestige with the field. Python has become the main language of teaching software engineering schools now, due to its ease of learning and its pervasiveness. He notes that there are growth cycles, and Python has done a really good job of building those growth loops and helping to propel the ecosystem. The viral growth loop is to switch people to Unicode file extensions, and Lattner is betting on it. He hopes that Mojo, a low-level language for performance-sensitive code, will be useful to people but if not, then that's fine as well. He suggests that most people don't like to rewrite their code, but they do like learning new things, so the dopamine hit of 10x faster with Mojo will be enticing to some.

19) Lattner talks about how his experience in building languages and compilers through LLVM has influenced his work on building Mojo, a new programming language designed for the future of computing including AI. He explains that Mojo is currently available through a workbook on the Mojo Playground in the cloud with plans to allow for local downloads. Lattner also reflects on the lessons learned from the launch of Swift and the importance of not rushing launches to avoid bugs and technical debt. By setting expectations and iterating through the development process, he hopes to avoid the tension and friction that occurred during the early days of Swift.

20) Chris talks about the excitement around his company’s release. He says their team's approach is to build the world’s best version of a product, and even if it takes extra time to do it well, the benefits will be worth it in helping to solve deep problems in the Python ecosystem. People have been excited about ownership and stepping beyond Rust, as well as increasing the speed of things like Game of Life. Lattner credits machine learning expert Jeremy Howard with convincing him to pursue this kind of work. Howard, who has a desire for making AI more accessible, has been pushing for this for years and is one of the reasons Lattner embarked on the project.

21) Chris discusses the potential for a specialized search and discovery interface for GitHub, but admits he doesn't know the right answer for improving GitHub's searchability. He emphasizes the importance of having usable, accessible interfaces for people of different skill levels and the benefits of having standards in the programming ecosystem for building next-level infrastructure. Lattner also speculates about why Python doesn't have function overloading, citing dynamic language's use of a dictionary and the challenge of dispatching that could arise in a type language.

22) Chris discusses the differences between the def and FN methods of defining a function in Python. The FN method is a stricter version of def that enforces the declaration of variables before they are used, providing more predictability and error checking. However, the def method is still popular for those who enjoy hacking around and doing research. Lattner also talks about the trade-off with superset languages such as Python, which require more time for compatibility with existing code and may lock in decisions of the past. He mentions the importance of testing and the use of asserts and exceptions to catch bugs and errors.

23) Chris discusses zero cost exception handling and how it has impacted the design of APIs in C++. He explains that zero cost exceptions are not actually zero cost, as they can massively blow out your code and binary, as well as reduce the number of optimizations. However, newer languages like Swift, Rust, and Go have implemented throwing errors as the same as returning a variant in order to treat errors as fast as returning something interesting. This has a huge impact on API design since developers won't have to fork APIs, and this feature works well on accelerators and GPUs. Lattner also explains the challenges of exceptions in code compilation and how they work during compilation.

24) Chris talks about the challenges he faces in recruiting specialized individuals from Big Tech companies for his new company. Lattner believes that building an inclusive culture is key to scaling his company and attracting the right people. He also stresses the importance of having a clear vision, understanding the customer's problems, and working backward to find solutions. Drawing from his experience at Apple, Lattner learned that speaking to customers about their problems and understanding their pain points is far more significant than building technology that they don't find useful.

25) Chris discusses the challenges of building a remote first company and the importance of periodically bringing the team together in-person to plan and execute work as there is an intimacy in in-person brainstorming that cannot be wholly replicated in a virtual environment. While remote work has many pros, like being able to hire from anywhere in the world and attract amazing talent, it also has its cons, such as time zone differences and the potential for a lack of understanding among team members.

26) Chris discusses the liberating feeling of building a product that directly solves people’s problems, unlike his past experiences of building technology used as enabling infrastructure. He explains how this makes it easier to make decisions and justify expenses, like getting a t-shirt for the community. Moving on to the topic of large language models (LLMs), Lattner expresses his optimism about how LLMs could help automate mechanical coding tasks and scale productivity, but he does not believe this will replace coding entirely. He also highlights the importance of working with people to understand the problem and build the product, which cannot be solely achieved through LLMs.

27) Chris discusses the potential for LLMs (large language models) to generate code and how they can help make programming languages more suitable for machines. Lattner posits that LLMS, because they are an intermediate representation between humans and computers, will not have any issues learning any programming language. He also notes that LLMs can help solve the problem of compilers being particular and needing correct format, as well as increase productivity. While Lattner acknowledges the potential dangers of AI, he does not believe that AGI will be solved in the immediate future and therefore has a zen-like calm about the situation.

28) Chris discusses the potential future of AI and its impact on human civilization, acknowledging that while AI moves quickly, humans adapt slowly, implying that it may take longer to roll out than many expect. Lattner also argues that breaking down the barriers to AI use is important, allowing more practical application of AI techniques by broadening participation to more than the few people who have the money and skills to do so. He believes that the future of programming will involve reducing complexity and making AI tools accessible to more people, which will ultimately transform many aspects of our lives in optimistic ways.