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The modern API client that lives in your terminal. - darrenburns/posting

Reinforcement Learning (RL) is a type of machine learning. It trains an agent to make decisions by interacting with an environment. This article covers the basic concepts of RL. These include states, actions, rewards, policies, and the Markov Decision Process (MDP). By the end, you will understand how RL works. You will also learn how to implement it in Python. Key

⏰ Modern datetime library for Python, written in Rust - ariebovenberg/whenever

This is Graph and I have a super quick tutorial showing how to create a fully local chatbot with Langchain, Graph RAG and GPT-4o to make a…

Posted by Natalia Bidart on July 9, 2024

There are various mathematical tools that can be used to predict the near future based on a current state. One of the most widely used are Markov chains. Markov chains allow you to predict the uncertainty of future events under certain conditions. For this reason, it is widely used in

Using FastAPI, Jinja2 and DaisyUI.

In this tutorial, you'll learn the basics of working with Python's numerous built-in functions. You'll explore how to use these predefined functions to perform common tasks and operations, such as mathematical calculations, data type conversions, and string manipulations.

There’s huge pressure on Python at the moment to get faster, ideally without changing at all. One increasingly–popular way of achieving that impossible task is to push the performance critical code down into C, C++, or Rust. And this week we’re focussing on the Python route, as we take a look at PyO3. David Hewitt’s the principal committer to PyO3, and he joins us to go through the easy parts, the hard parts, and the works in progress, giving us an insight into how Python and Rust work under the hood, and quite how much work it takes to make them work as one. – Become a Supporter on Patreon:

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PyO3 User Guide:

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Polars:

Tokio:

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Robyn:

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-- 0:00 Intro 3:09 How David Got Involved in PyO3 7:01 So Starting With Python To C... 9:20 Taking That To Rust 17:37 Calling Rust from Python 20:54 Understanding PyO3's Place In The Stack 21:52 Memory Safety 30:37 Rust's Lifetimes 35:07 Applying Lifetimes to Python Code 38:24 Let's Get Into Error Handling 55:55 The Performance Cost 1:01:19 Threading 1:16:05 David's Motivations 1:19:22 Supporting Python's Sub-Interpreters 1:24:14 The Limits of Compile-Time Guarantees 1:27:32 Getting Started with PyO3 1:33:01 Outro