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The Many Ways to Deploy a Model There are many ways to deploy models and perform inference. Here, we share our decision rubric for model deployments using LLM inference as an example. https://outerbounds.com/blog/the-many-ways-to-deploy-a-model

HypoFuzz Open source smart fuzzing for Python's best testing workflow. https://github.com/Zac-HD/hypofuzz

Why AI has a Python Problem Artificial Intelligence (AI) has propelled Python to unprecedented popularity, making it the go-to language for developers and researchers worldwide. Yet, beneath the surface, a significant challenge looms. Let's get into the specific difficulties Python poses for AI's evolution, backed by real-world examples and technical insights. https://www.youtube.com/watch?v=cGgTvMmtzNU

Toolong A terminal application to view, tail, merge, and search log files (plus JSONL). https://github.com/textualize/toolong

Counting CPU Instructions in Python Did you know it takes about 17,000 CPU instructions to print("Hello") in Python? And that it takes ~2 billion of them to import seaborn? https://blog.mattstuchlik.com/2024/02/08/counting-cpu-instructions-in-python.html

TriOTP The OTP framework for Python Trio. https://github.com/linkdd/triotp

modguard A Python tool to enforce a modular, decoupled package architecture. https://github.com/Never-Over/modguard

Ask HN: Which Python type checker should I use? https://news.ycombinator.com/item?id=39416443

ml-mgie Apple's new open-source AI model that can edit images based on natural language instructions. https://github.com/apple/ml-mgie

Meta loves Python Meta engineers discuss their contributions to Python 3.12, including new features such as custom JIT hooks, Immortal Objects, type system improvements, and faster comprehensions, highlighting their collaboration with the Python community and the company's support for open source https://engineering.fb.com/2024/02/12/developer-tools/meta-loves-python/

Adopt-ruff: find uncobfigured rules easily Adopt ruff faster into your existing repo: find the easiest rules to start with https://github.com/ScDor/adopt-ruff

Engineering Practices for LLM Application Development LLM engineering involves much more than just prompt design or prompt engineering. In this article, we share a set of engineering practices that helped us deliver a prototype LLM application rapidly and reliably in a recent project. We'll share techniques for automated testing and adversarial testing of LLM applications, refactoring, as well as considerations for architecting LLM applicat... https://martinfowler.com/articles/engineering-practices-llm.html

Recording and visualising the 20k system calls it takes to "import seaborn" https://www.reddit.com/r/Python/comments/1asjcnm/recording_and_visualising_the_20k_system_calls_it/

Codium-ai / AlphaCodium Official implementation for the paper: "Code Generation with AlphaCodium: From Prompt Engineering to Flow Engineering"" https://github.com/Codium-ai/AlphaCodium

uv: Python Packaging in Rust https://astral.sh/blog/uv

instld The simplest package management. https://github.com/pomponchik/instld