Python Weekly (Issue 752 July 2 2026)

Author

Rahul Chaudhary
July 02, 2026

In partnership with

Welcome to issue 752 of Python Weekly. Let's get straight to the links this week.

The 1,700%+ Lesson From SpaceX’s IPO

SpaceX IPO’d at a $1.75T valuation. Three business days later, they were the sixth-most valuable public company.

Those who bought at the opening bell saw a 40% gain. Andreessen Horowitz, who invested back in 2023? 1,700%+. The lesson? Today’s biggest growth comes at the private stage.

It’s why institutions love private-stage opportunities. A similar dynamic’s playing out in lithium, where General Motors backed $1B+ private unicorn EnergyX. Except this time, you can join them.

EnergyX’s patented tech can recover up to 3X more lithium than traditional methods. And with lithium prices up 75% this year and demand projected to grow 5X by 2040, they’re perfectly positioned.

After opening America’s largest lithium production facility of its kind, EnergyX is preparing to unlock up to 13M tons. Become a private-stage EnergyX before the 7/16 deadline.

Energy Exploration Technologies, Inc. (“EnergyX”) has engaged Beehiiv to publish this communication in connection with EnergyX’s ongoing Regulation A offering. Beehiiv has been paid in cash and may receive additional compensation. Beehiiv and/or its affiliates do not currently hold securities of EnergyX.

This compensation and any current or future ownership interest could create a conflict of interest. Please consider this disclosure alongside EnergyX’s offering materials. EnergyX’s Regulation A offering has been qualified by the SEC. Offers and sales may be made only by means of the qualified offering circular. Before investing, carefully review the offering circular, including the risk factors. The offering circular is available at invest.energyx.com/.

Comparisons to other companies are for informational purposes only and should not imply similar results. Past performance is not indicative of future results. Market shortfall are forward‑looking estimates and are subject to substantial uncertainty.


Articles, Tutorials and Talks

Using Open-Weight Models in Local Coding Harnesses as an Alternative to Claude Code and Codex Subscriptions.

Learn about Distributed Data Parallelism (DDP), an important technique for training large-scale AI models. This course provides a hands-on guide for implementing DDP, ensuring your training processes are efficient and scalable. Learn how to overcome memory limitations and train models effectively across multiple GPUs.

A novel Python sparse_range implementation enables memory-efficient iteration over ranges with excluded intervals and patterns. The design is stress-tested using a declarative Sieve of Eratosthenes built entirely from inclusion and exclusion ranges.

An open-source attempt to bring dbt Cloud-style workflows to self-hosted dbt Core, with a web IDE, orchestration, job management, and environment controls. The project highlights the benefits of API-first integrations over fragile CLI wrappers.

A deep dive into Python’s free-threaded future, covering the removal of the GIL, the engineering challenges involved, and the progress made through Python 3.14 and beyond. The talk also explores what developers and extension authors need to know as free threading moves toward mainstream adoption.

Post-quantum cryptography is now built into Python's cryptography library, bringing NIST-standard ML-KEM and ML-DSA algorithms to the broader ecosystem. The article covers implementation details, performance tradeoffs, and the path toward quantum-resistant protocols.

A practical pattern for attaching metadata to Python objects using WeakKeyDictionary instead of custom attributes. The technique avoids namespace conflicts and automatically cleans up cached data when objects are no longer in use.

A guide to setting up Python for serious AI development using professional engineering tools and workflows. The video explains how to move from quick AI prototypes to a production-ready development environment.

Dropbox used DSPy to improve both its AI evaluation judges and the Dash chat agent itself, creating a feedback loop where human-labeled examples calibrate judges, and those judges automatically optimize prompts. The approach led to a 26% reduction in incomplete answers, a 13% reduction in missed key aspects, and lower token usage

What every line of a PyTorch training loop does, why it belongs where it is, and what breaks if you move it.

Three pip-installable mesh boolean libraries on the Thingi10K corpus at 200K–1.5M polygons per operand: trueform, MeshLib, Manifold. trueform is the fastest tested in Python - 18 ms median, 4.9× MeshLib, 6.9× Manifold (geometric mean), fastest on all 1000 pairs.

PEP 832 aims to standardize how Python projects expose virtual environments and workflow tools to editors and automation systems. The proposal seeks to simplify environment discovery and reduce friction for both developers and tooling.


Interesting Projects, Tools, and Libraries

AI coding assistant skill (Claude Code, Codex, OpenCode, Cursor, Gemini CLI, and more). Turn any folder of code, SQL schemas, R scripts, shell scripts, docs, papers, images, or videos into a queryable knowledge graph. App code + database schema + infrastructure in one graph.

ArchUnitPython is an architecture testing library. Specify and ensure architecture rules in your Python app. Easy setup and pipeline integration.

DriverScope automates the analysis of Windows kernel drivers by mapping imports, IOCTL dispatch paths, and privileged kernel capabilities. The tool helps security researchers identify vulnerable drivers that could be abused for privilege escalation or system compromise.

A package and dependency manager for Meson projects, centered on Meson's wrap system.

A lightweight desktop teleprompter.


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