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  • Migrating millions of lines of code to TypeScript

    On Sunday March 6, we migrated Stripe’s largest JavaScript codebase (powering the Stripe Dashboard) from Flow to TypeScript. In a single pull request, we converted more than 3.7 million lines of code. The next day, hundreds of engineers came in to start writing TypeScript for their projects. Seriously unreal. I remember a short time ago laughing at the idea of typescript ever landing at Stripe, an

      Migrating millions of lines of code to TypeScript
    • A Practitioner's Guide to Wide Events | Jeremy Morrell

      Adopting Wide Event-style instrumentation has been one of the highest-leverage changes I've made in my engineering career. The feedback loop on all my changes tightened and debugging systems became so much easier. Systems that were scary to work on suddenly seemed a lot more manageable. Lately there have been a lot of good blog posts on what "Wide Events" mean and why they are important. Here are

      • Oracle Security Alert Advisory - CVE-2021-44228

        Description This Security Alert addresses CVE-2021-44228, a remote code execution vulnerability in Apache Log4j. It is remotely exploitable without authentication, i.e., may be exploited over a network without the need for a username and password. It also addresses CVE-2021-45046, which arose as an incomplete fix by Apache to CVE-2021-44228. Due to the severity of this vulnerability and the public

        • A Look at dyn* Code Generation

          As I've written about before, an important goal for async Rust is to support async functions everywhere, including in trait objects (dyn Trait). To this end, we are adding a new experimental type called dyn* that will give us more flexibility to support dynamic dispatch for async methods. We now have experimental support for dyn* in nightly Rust now, so we can start to kick the tires and use our e

          • https://deeplearningtheory.com/PDLT.pdf

            The Principles of Deep Learning Theory An Effective Theory Approach to Understanding Neural Networks Daniel A. Roberts and Sho Yaida based on research in collaboration with Boris Hanin drob@mit.edu, shoyaida@fb.com ii Contents Preface vii 0 Initialization 1 0.1 An Effective Theory Approach . . . . . . . . . . . . . . . . . . . . . . . . 2 0.2 The Theoretical Minimum . . . . . . . . . . . . . . . .

            • Do large language models understand us?

              DisclaimerThese are my own views, not necessarily those of my employer. SummaryLarge language models (LLMs) represent a major advance in artificial intelligence (AI), and in particular toward the goal of human-like artificial general intelligence (AGI). It’s sometimes claimed, though, that machine learning is “just statistics”, hence that progress in AI is illusory with regard to this grander ambi

                Do large language models understand us?
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