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- Python Weekly (Issue 739 April 2 2026)
Python Weekly (Issue 739 April 2 2026)
Rahul Chaudhary
April 02, 2026
Welcome to issue 739 of Python Weekly. Let's get straight to the links this week.
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News
The OX Research team exploited a known Redash sandbox escape vulnerability that allows remote code execution when the Python data source is enabled, leading to full server compromise
Articles, Tutorials and Talks
Learn how attackers hide malware inside WAV audio files using steganography, based on the real-world TeamPCP supply chain campaign.
Scaling a monolith to one million lines of code requires prioritizing modularity and “seams” over premature microservices to maintain developer velocity and architectural clarity. The 113 pragmatic lessons show that success at this scale depends on rigorous automated testing, consistent coding standards, and treating the monolith as a set of well defined internal services.
This video is about how code that looks clean can still hide a bad design, and why overusing tiny abstractions can make a program harder to understand and change. It refactors a Python reporting example by simplifying the structure, making the pipeline explicit, and focusing on cohesion over smallness.
NumPy can be used as a real time sound synthesis engine, generating all audio directly from mathematical functions like waves, noise, and filters without any pre recorded samples. The broader idea is that powerful general purpose tools like NumPy can be pushed far beyond their intended use, enabling complex systems like music generation through pure computation.
Polars is a high-performance query engine for DataFrame workloads, written in Rust. Over the last two years, the Polars team has built a novel streaming engine that is becoming the default backbone for all lazy processing. As the optimizer increasingly rewrites and transforms query plans, the physical execution can diverge significantly from what users originally wrote, making profiling and query insights more important than ever. This talk will explore how Polars tackles that challenge and gives users visibility into what their queries actually do.
Learn how Quansight team built Jupyter Everywhere, a JupyterLite-based application that runs Python and R notebooks entirely in the browser for high school students and educators, without requiring any servers, accounts, or installation.
Including digital attestations in pylock.toml allows developers to verify the origin and integrity of dependencies, not just their versions and hashes, improving protection against supply chain attacks. The broader point is that modern package security requires provenance, not just reproducibility, so lock files are evolving from “what to install” into “what can be trusted to install.”
A hands-on guide to implementing CFD with NumPy, from discretization to airflow simulation around a bird's wing
Building a production RAG system is far more about data pipelines, indexing strategy, and infrastructure tradeoffs than model choice, with most failures coming from scaling, retrieval quality, and compute constraints. The key lesson is that RAG success depends on iterative engineering and system design discipline, not just plugging in an LLM, with real-world performance shaped by bottlenecks like GPU limits, chunking, and retrieval accuracy.
A security audit of LangChain reveals multiple critical vulnerabilities where prompt injection and insecure defaults allow attackers to bypass safeguards and potentially execute arbitrary code. The analysis shows that high level AI abstractions can introduce hidden data access paths, making it essential to treat LLM generated tool calls as untrusted input.
Reservoir sampling lets you pick a sample from an unlimited stream of events; learn how it works, and a new variant useful for profilers.
How does PyTorch autograd deal with mutation? In particular, what happens when a mutation occurs on a view, which aliases with some other tensor? In 2017, Sam Gross implemented support for in-place operations on views, but the details of which have never been described in plain English… until now.
Interesting Projects, Tools, and Libraries
A visual, example-driven guide to Claude Code - from basic concepts to advanced agents, with copy-paste templates that bring immediate value.
Hindsight is an agent memory system built to create smarter agents that learn over time. Most agent memory systems focus on recalling conversation history. Hindsight is focused on making agents that learn, not just remember.
Make Your Agents: Smarter, Low-Cost, Self-Evolving.
Modern REST framework for Django with types and async support!
Utility package for comparing polars data frames.
A Python CAD programming library.
Adaptive Test-time Learning and Autonomous Specialization.
A developer tool that captures outgoing HTTP requests from your code and displays them in a local web dashboard.
Upcoming Events and Webinars
There will be following talks
Query federation in modern OLAP databases
How I Learned to Stop Worrying and Let Claude Code Write the Python
There will be a talk, Google Apps Script: connecting Google services to fit your needs.
There will be a talk, Debunking the myths of Quantum AI.
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