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- Python Weekly (Issue 753 July 9 2026)
Python Weekly (Issue 753 July 9 2026)
Rahul Chaudhary
July 09, 2026
In partnership with
Welcome to issue 753 of Python Weekly. Let's get straight to the links this week.
200+ Claude Prompts Top Professionals Actually Use at Work
Claude can be your analyst, editor, and strategist.
But most professionals are using it to fix grammar.
These 200+ Claude prompts take it from grammar tool to your most powerful AI work assistant.
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Superhuman AI newsletter (4 min daily) so you keep learning new AI tools and skills to stay ahead in your career — the prompts are just the beginning
Articles, Tutorials and Talks
This tutorial builds on the coding agent you implemented in the tutorial “Write a coding agent from first principles”. In this tutorial, you'll take your agent and improve its capabilities by implementing the text edit and bash command tools that Anthropic provides.
How Reddit accidentally leaked its spamurai system.
The post explains the path from rejected PEP 416 and stalled PEP 603 to accepted PEP 814, which adds a built-in frozendict type to Python 3.15. It covers how frozendict works, why it is hashable only when values are hashable, where the standard library now supports it, and what C/Python code may need to update.
The post explains that PyPI Trusted Publishing is an authentication mechanism for machine-to-machine trust between CI/CD workflows and package registries, not a signal that a package is safe or high quality. It shows how Trusted Publishing reduces long-lived credential risk, while warning that users should not treat it like a “green checkmark” because malicious or low-quality packages can still be published through it.
The post gives three practical steps for publishing Python packages to PyPI more securely from GitHub Actions: run zizmor, use Trusted Publishing, and require manual approval through GitHub environments. It focuses on reducing credential risk by removing long-lived PyPI tokens, pinning actions, limiting workflow permissions, disabling persisted checkout credentials, and adding a human approval gate before release.
The post explains why running Celery on AWS ECS can cause lost, delayed, duplicated, or never-completed tasks due to ECS shutdown behavior, Celery defaults, prefetching, and timeout edge cases. It recommends safer production settings, shorter/idempotent task design, fan-out or batching, RabbitMQ on Amazon MQ, and Redis-based locking to make Celery workers more reliable on ECS.
A practical guide to FastAPI's new app.frontend(), SPA fallback, API route priority, and a complete mini dashboard example.
The post explains Hamiltonian Neural Networks through differential geometry, showing why a normal MLP can fit motion data but still invent or lose energy over long rollouts. It shows that by learning a scalar Hamiltonian and deriving motion through the symplectic gradient, HNNs make energy conservation structurally unavoidable instead of hoping the network learns it from data.
The post explains why autonomous AI agents should run in isolated sandboxes instead of directly on a developer’s laptop, especially as agents gain auto-approval, long-running tasks, file access, and network access. It compares sandbox architectures, from tool-backend setups to agents living fully inside the sandbox, and explains the tradeoffs across security, secrets, streaming, performance, cost, and isolation strength.
Miles is an open source PyTorch-native framework for large-scale LLM reinforcement-learning post-training, combining SGLang for rollouts, Megatron-LM for training, Ray for orchestration, and PyTorch for extensibility. The post explains how Miles handles the hard systems problems behind frontier RL, including async rollout/training, fast weight sync, MoE alignment, low-precision recipes, observability, and fault tolerance.
Interesting Projects, Tools, and Libraries
Give Claude the ability to watch any video. /watch downloads, extracts frames, transcribes, hands it all to Claude.
An experimental Python 3.14 implementation: a native compiler and runtime written in Rust that compiles Python “to metal” via JIT and AoT using a Cranelift backend.
Stealth Chromium engine that stops scrapers and browser agents from getting blocked, with one line of code change.
IP lists full of bad IPs - Updated every 2H.
A Python library for building AI agents that leverage the full power of Google Antigravity.
A collection of skills and MCP systems to enable users of CLI, VSCode, Claude to operate over Microsoft Fabric.
Persistent file-based planning for AI coding agents and long-running agentic tasks. Crash-proof markdown plans that survive context loss and /clear, plus a deterministic completion gate and multi-agent shared state on disk. Manus-style. Works with Claude Code, Codex CLI, Cursor, Kiro, OpenCode and 60+ agents via the SKILL.md standard.
An AI Gateway, registry, and proxy that sits in front of any MCP, A2A, or REST/gRPC APIs, exposing a unified endpoint with centralized discovery, guardrails and management. Optimizes Agent & Tool calling, and supports plugins.
TabFM (Tabular Foundation Model) is a scikit-learn compatible tabular foundation model. It allows you to perform zero-shot classification and regression on tabular datasets with mixed column types out-of-the-box.
A GPU-Accelerated Framework for Simulated Humanoids.
New Releases
JupyterLab 4.6 and Notebook 7.6 add many usability improvements, including recently edited cell navigation, better file browser controls, debugger updates, shortcut editing, accessibility fixes, and richer interface customization. Notebook 7.6 also adds a Scratchpad console for quick experiments on the same kernel, while extension authors get faster builds through the move from webpack to Rspack.
Django 6.0.7 and 5.2.16 fix three low-severity security issues involving cached Set-Cookie responses, GDALRaster heap buffer over-read, and DomainNameValidator accepting newlines. The Django team recommends upgrading supported versions as soon as possible, with patches applied to main, 6.1 beta, 6.0, and 5.2 branches.
Upcoming Events and Webinars
There will be following talks
Empowering Coding Agents: Automated High-Performance Distributed Computing with Exasol Python UDFs
What Actually Changed? Causal Thinking Across Attribution, Segmentation, and Growth
There will be a workshop, Building with Coding Agents — Ship a Python Streamlit dashboard.
There will be following talks
My Model Works Locally. Why Is Production Lying to Me?
Speeding Up Clinical Trial Analysis with Python
There will be a talk, Context Driven Code Analysis with Python AST.
There will be a talk, How Much Energy Do Pruned LLMs Actually Save?
There will be following talks
Automation in Action with PlotSense: Transforming Data into Insights with Explainable AI
Agentic AI in Practice: Building Reliable Financial Workflows with Python
Evolving to Agentic AI: Moving Beyond Static Rule Engines in Python
There will be a talk, PySteak: Simulation, Calibration, and Optimization for a High-Steaks Culinary Problem.
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