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Newest success stories

Using Python to build a solution for instant tokenized real estate redemptions

Python programmability on Algorand makes the entire development lifecycle easier and means more affordable and efficient maintenance and upgrades going forward. Read more

Zama Concrete ML: Simplifying Homomorphic Encryption for Python Machine Learning

To simplify the adoption of FHE, which involves a complex and resource-intensive technological stack, Zama developed tools that streamline the integration of FHE into applications. Since Python is the de facto standard for building machine learning (ML) applications, it was an obvious choice to create an open-source FHE library in Python. Read more

Building Robust Codebases with Python's Type Annotations

Maintaining our ever-evolving Python codebase poses an intricate challenge: how do we make updates to reflect the changing rules and regulations of 200+ global markets without compromising access to the systems that our engineers and traders use on a daily basis? While an inner layer of shared business logic enables coherency in our codebase performance, it also means small regulatory changes can impact many systems. In this article, Python Engineer John Lekberg details how we use Python type annotations to minimize the time and risk involved in manual verification. Read more

Building a Dependency Graph of Our Python Codebase

Prioritizing cutting-edge speed and supporting the rapid growth of Hudson River Trading’s codebase can have unintended effects that require innovative solutions. For those working on our Python codebase, this means addressing “code tangling,” the coupling of unrelated code through unintuitive import cycles. In this article, George Farcasiu, Noah Kim, Jacob Brugh, and Jiahao Li discuss how they mitigate the cost and time burden of this issue by creating new tools to analyze and untangle dependencies efficiently. Read more

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