Operating Inside the Interpreted: Offensive Python
The post discusses how Python on Windows (often preinstalled via the Microsoft Store) can be used as a surprisingly effective tool for offensive security and malware operations, thanks to its rich standard library, isolated package management, and low-profile presence in corporate environments. It outlines methods such as malware delivery, offline package installation, and leveraging cty...
https://trustedsec.com/blog/operating-inside-the-interpreted-offensive-python
The post discusses how Python on Windows (often preinstalled via the Microsoft Store) can be used as a surprisingly effective tool for offensive security and malware operations, thanks to its rich standard library, isolated package management, and low-profile presence in corporate environments. It outlines methods such as malware delivery, offline package installation, and leveraging cty...
https://trustedsec.com/blog/operating-inside-the-interpreted-offensive-python
TrustedSec
Operating Inside the Interpreted: Offensive Python
Discover how to use Python for malicious purposes on Windows, leveraging its ease of installation and existing tradecraft to evade detection and deploy…
The Step-by-Step Guide to Python Packaging Tools 2025
A future-ready guide comparing Python packaging tools like uv, Poetry, Hatch, and PDM—perfect for modern developers and teams.
https://www.datumlabs.io/resources/the-step-by-step-guide-to-python-packaging-tools-2025?utm_source=pythonhub&utm_medium=blog&utm_campaign=blog-published
A future-ready guide comparing Python packaging tools like uv, Poetry, Hatch, and PDM—perfect for modern developers and teams.
https://www.datumlabs.io/resources/the-step-by-step-guide-to-python-packaging-tools-2025?utm_source=pythonhub&utm_medium=blog&utm_campaign=blog-published
html-to-markdown
A modern, fully typed Python library for converting HTML to Markdown.
https://github.com/Goldziher/html-to-markdown
A modern, fully typed Python library for converting HTML to Markdown.
https://github.com/Goldziher/html-to-markdown
GitHub
GitHub - Goldziher/html-to-markdown: HTML to markdown converter
HTML to markdown converter. Contribute to Goldziher/html-to-markdown development by creating an account on GitHub.
aiosqlitepool
A resilient, high-performance asynchronous connection pool layer for SQLite, designed for efficient and scalable database operations.
https://github.com/slaily/aiosqlitepool
A resilient, high-performance asynchronous connection pool layer for SQLite, designed for efficient and scalable database operations.
https://github.com/slaily/aiosqlitepool
GitHub
GitHub - slaily/aiosqlitepool: 🛡️A resilient, high-performance asynchronous connection pool layer for SQLite, designed for efficient…
🛡️A resilient, high-performance asynchronous connection pool layer for SQLite, designed for efficient and scalable database operations. - slaily/aiosqlitepool
KubeDiagrams
Generate Kubernetes architecture diagrams from Kubernetes manifest files, kustomization files, Helm charts, helmfiles, and actual cluster state
https://github.com/philippemerle/KubeDiagrams
Generate Kubernetes architecture diagrams from Kubernetes manifest files, kustomization files, Helm charts, helmfiles, and actual cluster state
https://github.com/philippemerle/KubeDiagrams
GitHub
GitHub - philippemerle/KubeDiagrams: Generate Kubernetes architecture diagrams from Kubernetes manifest files, kustomization files…
Generate Kubernetes architecture diagrams from Kubernetes manifest files, kustomization files, Helm charts, helmfiles, and actual cluster state - philippemerle/KubeDiagrams
Inheritance over composition, sometimes
Last time, we built a hybrid concurrent.futures executor using inheritance. Today, we're building it again (twice!) using composition and functions only, to figure out which way is better and why.
https://death.andgravity.com/over-composition
Last time, we built a hybrid concurrent.futures executor using inheritance. Today, we're building it again (twice!) using composition and functions only, to figure out which way is better and why.
https://death.andgravity.com/over-composition
death and gravity
Inheritance over composition, sometimes
Last time, we built a hybrid concurrent.futures executor using inheritance. Today, we're building it again using composition *and* functions only, to figure out which is better and why. Consider this a worked example.
pictex
A powerful Python library to create beautifully styled text images with a simple, fluent API.
https://github.com/francozanardi/pictex
A powerful Python library to create beautifully styled text images with a simple, fluent API.
https://github.com/francozanardi/pictex
GitHub
GitHub - francozanardi/pictex: A powerful Python library to create beautifully styled text images with a simple, fluent API
A powerful Python library to create beautifully styled text images with a simple, fluent API - francozanardi/pictex
Using Lean like an External SMT Solver from Python
The author demonstrates using Lean as an external SMT solver by translating proof goals into SMT queries and reconstructing the results back into valid Lean proofs. He builds a lightweight Python workflow that integrates Lean and SMT solvers seamlessly, enabling automated, proof-generating tactics within Lean.
https://www.philipzucker.com/lean_smt/
The author demonstrates using Lean as an external SMT solver by translating proof goals into SMT queries and reconstructing the results back into valid Lean proofs. He builds a lightweight Python workflow that integrates Lean and SMT solvers seamlessly, enabling automated, proof-generating tactics within Lean.
https://www.philipzucker.com/lean_smt/
Hey There Buddo!
Using Lean like an External SMT Solver from Python
It seems that the only thing I don’t want to use Lean for is it’s main purpose. That tracks.
Building Autonomous Systems: A Guide to Agentic AI Workflows
A complete guide to building autonomous systems with agentic AI workflows—covering core concepts, tools, and real-world applications.
https://www.digitalocean.com/community/conceptual-articles/build-autonomous-systems-agentic-ai
A complete guide to building autonomous systems with agentic AI workflows—covering core concepts, tools, and real-world applications.
https://www.digitalocean.com/community/conceptual-articles/build-autonomous-systems-agentic-ai
Digitalocean
Building Autonomous Systems: A Guide to Agentic AI Workflows | DigitalOcean
A complete guide to building autonomous systems with agentic AI workflows—covering core concepts, tools, and real-world applications.
TextGAN-Researcher
Open Deep Research Agent: Automated Knowledge Discovery with TextGAN.
https://github.com/imbue-bit/TextGAN-Researcher
Open Deep Research Agent: Automated Knowledge Discovery with TextGAN.
https://github.com/imbue-bit/TextGAN-Researcher
GitHub
GitHub - imbue-bit/TextGAN-Researcher: 🧐 Open Deep Research Agent: Automated Knowledge Discovery with TextGAN
🧐 Open Deep Research Agent: Automated Knowledge Discovery with TextGAN - imbue-bit/TextGAN-Researcher
Escaping Contravariance Hell
The post explains how Python's type checkers reject certain Callable and Protocol overrides due to contravariant parameter rules, using a vegetable-peeler analogy to clarify the concept. It then shows how to resolve these issues safely with generics like TypeVar or PEP 695 patterns, avoiding the need to fall back on Any.
https://labs.quansight.org/blog/escaping-contravariance-hell
The post explains how Python's type checkers reject certain Callable and Protocol overrides due to contravariant parameter rules, using a vegetable-peeler analogy to clarify the concept. It then shows how to resolve these issues safely with generics like TypeVar or PEP 695 patterns, avoiding the need to fall back on Any.
https://labs.quansight.org/blog/escaping-contravariance-hell
labs.quansight.org
Escaping Contravariance Hell
Protocols and TypeVars
Python and Make in 2025
The post recommends using GNU Make with Python projects for automation entrypoints, preferring uv for fast interpreter/venv setup and delegating complex logic to Python task runners, keeping the Makefile minimal and focused.
https://onebadbit.com/posts/2025/07/python-and-make-in-2025/
The post recommends using GNU Make with Python projects for automation entrypoints, preferring uv for fast interpreter/venv setup and delegating complex logic to Python task runners, keeping the Makefile minimal and focused.
https://onebadbit.com/posts/2025/07/python-and-make-in-2025/
one bad bit
Python and Make in 2025
Mike Wilkerson writes things here
❤1
PyTorch in One Hour: From Tensors to Training Neural Networks on Multiple GPUs
This tutorial offers a fast-paced introduction to PyTorch, covering key topics like tensors, GPU training, and backpropagation in about one hour. Its goal is to help you quickly start building and training deep neural networks, including large language models.
https://sebastianraschka.com/teaching/pytorch-1h/
This tutorial offers a fast-paced introduction to PyTorch, covering key topics like tensors, GPU training, and backpropagation in about one hour. Its goal is to help you quickly start building and training deep neural networks, including large language models.
https://sebastianraschka.com/teaching/pytorch-1h/
Sebastian Raschka, PhD
PyTorch in One Hour: From Tensors to Training Neural Networks on Multiple GPUs
A curated introduction to PyTorch that gets you up to speed in about an hour.
Python Gotcha: Reusing Generators Returns Nothing
Reusing a Python generator after it’s been consumed yields no results, because generators maintain state and don’t reset once iterated. The fix is straightforward: call the generator function again to produce a fresh iterator before reusing it.<br>
https://andrewwegner.com/python-gotcha-reusing-generator-returns-nothing.html
Reusing a Python generator after it’s been consumed yields no results, because generators maintain state and don’t reset once iterated. The fix is straightforward: call the generator function again to produce a fresh iterator before reusing it.<br>
https://andrewwegner.com/python-gotcha-reusing-generator-returns-nothing.html
Ponderings of an Andy
Python Gotcha: Reusing Generators Returns Nothing
Generators provide lazy evaluation for processing large datasets efficiently. However, once a generator is exhausted through iteration, it cannot be reused or reset. Let's cover this common gotcha that trips up developers new to this Python feature.
Superfunctions: A universal solution against sync/async fragmentation in Python
https://github.com/pomponchik/transfunctions
https://github.com/pomponchik/transfunctions
GitHub
GitHub - pomponchik/transfunctions: Say NO to Python fragmentation on sync and async
Say NO to Python fragmentation on sync and async. Contribute to pomponchik/transfunctions development by creating an account on GitHub.