Python Weekly (Issue 755 July 23 2026)

Author

Rahul Chaudhary
July 23, 2026

In partnership with

Welcome to issue 755 of Python Weekly. Let's get straight to the links this week.

The Ultimate Claude Code Guide to ship like Anthropic engineers

AI will write 90% of code by the end of 2026, and only 10% of developers will stay relevant.

The engineers in that 10% aren't smarter. They just know how to use AI.

We put together the exact playbook Anthropic engineers use to make sure you're in the top 10%.

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News

PyPI now blocks new file uploads to releases more than 14 days old, reducing the risk that compromised publishing credentials could be used to poison stable package versions. The change affects few projects and pushes maintainers to publish a new release when adding support for newer Python versions.


Articles, Tutorials and Talks

Netflix built an in-house LLM serving platform using vLLM and NVIDIA Triton, integrating self-hosted models into its existing production infrastructure through unified gRPC and OpenAI-compatible APIs. The article details production lessons around model packaging, version compatibility, zero-downtime deployments, observability, and scaling constrained decoding by moving bottlenecks from sequential Python processing to batched C++ execution.

The author trained a text-conditioned generative kick-drum model from 13,615 audio samples on a seven-year-old GPU with only 6GB of VRAM, using a VAE, latent diffusion model, and HiFi-GAN vocoder. The project shows that useful generative models can be built with modest hardware and personal datasets by compressing data into latent space, choosing practical constraints, and using serverless GPUs for deployment

This notebook explores how LLVM's optimization pipeline transforms a simple loop, step by step, using llvmlite running entirely in the browser via WebAssembly. We follow one small C++ function through three distinct stages — from source to raw stack-based IR (Intermediate Representation), through SSA form, to a closed-form calculation. After each stage, we inspect the control flow graph (using WASM compiled graphviz), run-time compile using a custom WASM execution engine (included in our llvmlite package distributed on emscripten-forge), and execute it to verify correctness.

A walkthrough of a Databricks App combining Model Serving route optimization, Lakebase, and autoscaling to score transactions in tens of milliseconds, with code and benchmarks at every layer.

Found yourself on a data project and have no idea what they all are talking about? Feel excluded from all the fun discussions in the office kitchen? If only there were a humongous guide going over all the concepts and buzzwords...

Adam Johnson introduces django-crawl, a tool that uses Django’s test client to crawl a site and uncover broken links, exceptions, and other hidden issues without making real HTTP requests. It can run as a management command or automated test, providing a fast safety net that found seven bugs even in a project with 100% test coverage.

A step-by-step guide to connecting AppSignal to your Django app on Fly.io so you can track errors, performance, and host health from a single dashboard.

In this article, you will learn how to build a complete agentic workflow in Python with LangGraph, from a single model call to a tool-using agent with persistent conversation memory.


Interesting Projects, Tools, and Libraries

A curated list of Artificial Intelligence (AI) courses, books, video lectures and papers.

Open-source GenBI: generative BI for AI agents.

An experiment to query Xarray datasets with SQL.

An open-source cost controller for AI agent loops.

Exploratory stats and data-quality diagnostics for pandas and Polars DataFrames. One call each.

A Lightweight, Research-Friendly Multimodal Model Family.

Our inference and training framework to run on the Cosmos Models.

A powerful CLI tool for managing Python virtual environments with ease.

A lightweight Python library to measure the carbon footprint of your code.


Upcoming Events and Webinars

There will be a talk, Okmain: you have an image, but you want a colour.

There will be following talks

  • Running Python fully sandboxed with Wasm

  • Agent Memory Is Not One Thing

There will be following talks

  • What’s Provable, Not Probabilistic: Schema Evolution as a Graph Rewrite

  • Secure Sandboxes For Coding Agents

  • FFIs but better: BAML Bridge

There will be following talks

  • Building data pipelines where Polars and dbt collaborate

  • Zero-Shot Energy: Forecasting Meters Without Training a Single Model

  • Agent testing with Pytest

There will be a workshop, Building Multi-Agent AI Systems on Databricks: Patterns & Pitfalls.


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