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Python Weekly - Python Weekly - Issue 665

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Python Weekly
Welcome to issue 665 of Python Weekly. Let's get straight to the links this week.
Articles, Tutorials and Talks

Quick and Easy Rate Limiting for FastAPI 
If you don’t want your API to crash due to too many requests, use rate limiting! This video covers how rate limiting works, what types of rate limiting you can do, how to implement it in FastAPI, and what you need to be mindful of.

Elixir basics for Pythonistas
The article provides a comprehensive introduction to Elixir for Python developers, covering key concepts and syntax differences between the two languages. It explores Elixir's functional programming paradigm, pattern matching, concurrency model, and other distinctive features, offering practical examples and comparisons to help Pythonistas transition to Elixir programming.

New LLM Pre-training and Post-training Paradigms
A Look at How Modern LLMs Are Trained.

CPython Compiler Hardening
The article details the author's Google Summer of Code project to implement compiler hardening options for CPython, following recommendations from the OpenSSF Memory Safety SIG. It covers the process of selecting and testing compiler options, addressing challenges with benchmarking and cross-platform compatibility, and developing tooling to track and manage warnings generated by the new compiler flags.

How Pytorch Powers Training Inference 
Learn about new PyTorch advancements for LLMs and how PyTorch is enhancing every aspect of the LLM lifecycle.

Build a One-Product Shop With the Python Django Framework and Htmx
This is the first of a two-part series using Django, htmx, and Stripe to create a one-product e-commerce website. In this part, we'll start our Django project and integrate it with htmx.

How to Build Chatbots With LangChain
Discover how LangChain lets chatbots interact with LLMs, and follow this guide to build a context-aware chatbot that delivers accurate, relevant responses.

Layman's Guide to Python Built-in Functions
This is a plain language guide to every built-in function in Python, paired with a simple example that shows each function in action.
    
NTLM Credential Theft in Python Windows Applications
The article discloses vulnerabilities in popular Python frameworks (Gradio, Jupyter Server, and Streamlit) that can lead to NTLMv2 hash theft on Windows systems through exploitation of file system operations. It explains how these vulnerabilities can be exploited, potentially leading to credential theft or remote code execution, and emphasizes the increased risk as Python applications become more prevalent on Windows in the AI era.


Interesting Projects, Tools and Libraries

LaunchFlow
Deploy production grade application to AWS and GCP in minutes.

graphiti
Build dynamic, temporally-aware knowledge graphs. 

Sapiens
High-resolution models for human tasks.

picows
Fast websocket client and server for asyncio.

MicroRabbit
A lightweight, asynchronous Python framework for RabbitMQ that simplifies building microservices and distributed systems. Features include easy message routing, plugin support, and intuitive client configuration.

LitServe
Lightning-fast serving engine for AI models. Flexible. Easy. Enterprise-scale.

AdalFlow
The Library to Build and Auto-optimize LLM Applications. 

Liger-Kernel
Efficient Triton Kernels for LLM Training.

Gigi
A framework for rapid prototyping and development of real-time rendering techniques.

django-cotton
Bringing component based design to Django templates.

csv_trimming
Python package to remove common ugliness from a csv-like file


New Releases

PyPy v7.3.17 release
This release includes a new RISC-V JIT backend, an improved REPL based on work by the CPython team, and better JIT optimizations of integer operations.


Upcoming Events and Webinars

PyData London Meetup September 2024
There will be following talks
  • The Power of Connection
  • Deep Dive into Merging Large Language Models 

Hybrid: Michigan Python Meetup September 2024
There will be a talk, Visualize Data with Dash Plotly.

PyData Tallinn Meetup September 2024
There will be following talks
  • 22k models for the Engagement Score: scalability and performance
  • Fraud attack prevention: Anomaly Detection Algorithms in practice
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