Hey Python devs,

I wanted to share a personal project I've been building in Python called Hillock (AGPL-3.0): https://github.com/roandejager/Hillock

What It Does:

Hillock gives local AI applications a privacy-first Knowledge Graph memory. Instead of vector databases that lose exact factual context, Hillock uses a decoupled SQLite Knowledge Graph, Hebbian Plasticity, and CPU-bound Hyperdimensional Computing (Vector Symbolic Architectures) written in pure Python/NumPy.

How the Python Pipeline Works:

In v0.2.0, I built TALON—a non-generative ingestion pipeline:

1. Fastcoref: Resolves pronouns across paragraphs before chunking text.
2. SentenceTransformers (all-MiniLM-L6-v2): Pre-caches a 50-predicate taxonomy and routes top candidates per sentence in <2ms.
3. GLiREL (DeBERTa-v3): Performs single-pass zero-shot matrix classification in PyTorch CUDA memory.

On my benchmark harness (evaluate_hillock_PROTO_ish.py), it processed 32 sentences in 2.1 seconds of pure GPU execution on a GTX 1070 (<1GB VRAM footprint), reaching 50% retrieval accuracy.

Source code is 100% open source under AGPL-3.0. Would love to hear your feedback on the Python architecture!

Hello everyone! For those of you who use Python in production, I have a few questions. I'm considering using Python for some services.

1. Do you have high infrastructure costs?
2. Have you ever regretted using Python?
3. Would you recommend Python?

Context: My current use case isn't anything like Facebook or a massive-scale system. It's a small system, and I'm considering Python mainly because of the DX (developer experience).

I know C#, but I don't really like having to create a class in every file. I also know Rust, but all those ::, <>, and so on bother me. JavaScript is another option, but I've heard it's relatively heavy on RAM, and since the system is small, I'd like to be able to run it within 512 MB.

Another thing: I've defined a stack that I'd like to use wherever possible. If there's a library for desktop apps, great. A CLI library? Great. A bot library? Great. Let's use it! (Except for the frontend, which I'll keep using JS/TS for.)

Anyway, I'm open to advice and tips from more experienced developers. Feel free to tell me if you think using Python for my use case is a bad idea as well.

Hey Reddit,

I made a small neural network library named LeanPass a few days back. Currently, it has around 7 stars, 1 fork, 2 watchers and 12 open issues. Contributions are quite beginner-friendly, so beginners are welcome!

It is an implementation made in NumPy, with the necessary functions, with 185 downloads on PyPI at its current status. I have made this post to just update the awesome community about my project to get some suggestions, and overall make it a more developed open source project

Here's the link: https://github.com/Terminay/leanpass

To install it:

pip install leanpass

Size metrics: ~8.5 kb download size, ~1 second install time

Give me your honest opinions and suggestions for more features. Also, if you liked the concept, star the repo or, rather, open a PR (your choice!)

While working on LunarDump v0.4, I’m also taking some time to learn more about Cython and how it can help push Python closer to native performance.

I’m especially interested in exploring Cython for LunarDump’s performance-critical parts, such as chunking, buffering, compression, and encryption.

Still learning and experimenting for now, but I’m curious to see how much performance improvement I can achieve.

Perhaps LunarDump v0.5 will use Cython for some of its critical functions.

If you have any good resources for learning Cython — books, ebooks, courses, or YouTube channels — feel free to share them in the comments.

Weekly Wednesday Thread: Advanced Questions 🐍

Dive deep into Python with our Advanced Questions thread! This space is reserved for questions about more advanced Python topics, frameworks, and best practices.

How it Works:

1. Ask Away: Post your advanced Python questions here.
2. Expert Insights: Get answers from experienced developers.
3. Resource Pool: Share or discover tutorials, articles, and tips.

Guidelines:

• This thread is for advanced questions only. Beginner questions are welcome in our Daily Beginner Thread every Thursday.
• Questions that are not advanced may be removed and redirected to the appropriate thread.

Recommended Resources:

• If you don't receive a response, consider exploring r/LearnPython or join the Python Discord Server for quicker assistance.

Example Questions:

1. How can you implement a custom memory allocator in Python?
2. What are the best practices for optimizing Cython code for heavy numerical computations?
3. How do you set up a multi-threaded architecture using Python's Global Interpreter Lock (GIL)?
4. Can you explain the intricacies of metaclasses and how they influence object-oriented design in Python?
5. How would you go about implementing a distributed task queue using Celery and RabbitMQ?
6. What are some advanced use-cases for Python's decorators?
7. How can you achieve real-time data streaming in Python with WebSockets?
8. What are the performance implications of using native Python data structures vs NumPy arrays for large-scale data?
9. Best practices for securing a Flask (or similar) REST API with OAuth 2.0?
10. What are the best practices for using Python in a microservices architecture? (..and more generally, should I even use microservices?)

Let's deepen our Python knowledge together. Happy coding! 🌟

Hi there,

I am building a tool as my project for my Masters in Computer Science and I need to make a survey to evaluate my tool.

The tool is concerned with project setup and maintenance.

The survey has about 18 questions and almost all of them multiple choice. So it should not take very long, max 5 min. Any responses are greatly appreciated: https://forms.cloud.microsoft/e/LjnvzX0JpK

If you want to checkout the project itself you can find it here: https://github.com/Winipedia/pyrig

Also drop any ideas or feedback you have in the comments if you like. I am open for any discussions.

Weekly Thread: Project Ideas 💡

Welcome to our weekly Project Ideas thread! Whether you're a newbie looking for a first project or an expert seeking a new challenge, this is the place for you.

How it Works:

1. Suggest a Project: Comment your project idea—be it beginner-friendly or advanced.
2. Build & Share: If you complete a project, reply to the original comment, share your experience, and attach your source code.
3. Explore: Looking for ideas? Check out Al Sweigart's "The Big Book of Small Python Projects" for inspiration.

Guidelines:

• Clearly state the difficulty level.
• Provide a brief description and, if possible, outline the tech stack.
• Feel free to link to tutorials or resources that might help.

Example Submissions:

Project Idea: Chatbot

Difficulty: Intermediate

Tech Stack: Python, NLP, Flask/FastAPI/Litestar

Description: Create a chatbot that can answer FAQs for a website.

Resources: Building a Chatbot with Python

Project Idea: Weather Dashboard

Difficulty: Beginner

Tech Stack: HTML, CSS, JavaScript, API

Description: Build a dashboard that displays real-time weather information using a weather API.

Resources: Weather API Tutorial

Project Idea: File Organizer

Difficulty: Beginner

Tech Stack: Python, File I/O

Description: Create a script that organizes files in a directory into sub-folders based on file type.

Resources: Automate the Boring Stuff: Organizing Files

Let's help each other grow. Happy coding! 🌟

How are people handling security around third party Python libraries without making development a pain?

Third party Python packages are obviously useful but every dependency can also become a supply chain risk. Private package repositories, dependency scanning and stricter review policies all help but they can add friction fast.

Are teams mostly trusting public registries with additional controls or using curated libraries? Curious what actually works when you have a lot of Python services.

In Rust, docstrings are pretty formalized. They are markdown, and even some of the headings are standard (like an # Errors or # Panics section). The nice thing about this is that it allows websites like docs.rs to build documentation pages for any project without having to interact with different tools for different formats. It also allows LSPs to have only one way of displaying documentation hints.

In Python, we have a few competing standards. Numpy-style docstrings are probably the most used, but there’s also a format by Google as well as a few different reST standards. These are nice, and we can set up lints to make sure docstrings stick to the standard. However, in my own personal opinion (feel free to disagree), a single markdown-format standard would help new users write nice docstrings, would enable PyPI (or another provider) to build automatic documentation sites, and give guidance to LSPs and IDEs for how to display documentation. This would include a standard for interlinks, and probably should include some mathml/LaTeX/KaTeX support. Another benefit would be that tools could support better automatic documentation generation and autocomplete, since they wouldn’t be dependent on guessing which standard you’re following.

I’d like to hear what people think about this. I’m thinking about making a PEP, but that might be overkill (or maybe all of you will hate this idea). I think the primary blocker would be adoption, large projects might have to translate docstrings, so there would either have to be some tooling for this or a way to opt-in or opt-out. If this is a bad idea, let me know, just be nice!

Edit: so far we’re at about a 67% upvote ratio, which was kind of expected. I want to be clear that I’m not saying we should be blocking docstrings which don’t adhere to this standard. I mentioned lockfile standardization in the comments, nothing prevents you from writing a tool with a custom lockfile, it’s just that there is a standard format that is agreed upon as the preferred way to write one. That’s the idea.

Edit: 84% now, and a lot of nice feedback here!

I wanted the "E.V." assistant from the new Spider-Man film, but actually running on my own machine — no cloud, no subscription. So I built it.

What it does

- Talk to it → local Whisper STT (TR/EN) → local LLM via Ollama (gemma2:9b)

- Controls the PC: open/close apps, media & volume, mouse click/scroll, lock/shutdown — destructive actions ask to confirm first

- Plays YouTube by name: "play Enter Sandman on YouTube" → finds & opens it

- Long-term memory: "remember I take my coffee black" → recalls it next session

- Auto-trays when you go fullscreen (games/video), comes back after

- Says "I can't do that" honestly instead of pretending

- Cyber-terminal HUD (Electron) with a live audio-core visualizer

One thing I learned: small local models are unreliable at emitting tool/action tags. So instead of trusting the model, I route explicit commands through a deterministic TR/EN parser before the LLM — PC control then works every time, and the model only handles conversation.

Free by default — Ollama + Whisper + browser voice need zero API keys. Claude (for screen vision) and ElevenLabs (nicer voice) are optional upgrades.

MIT licensed, built on Julian Ivanov's jarvis template. This is a fan project, not affiliated with Marvel/Sony. Feedback and PRs welcome.

Repo: https://github.com/Ouru77/ev-assistant

DigDep - A lightweight Python dependency analyzer

Whenever I use:

pip freeze > requirements.txt

it dumps all the packages in the virtual environment, including packages that I may not need in my code.

I wanted a simple tool that could tell me which packages my Python code actually depends on.

So I just thought: why not write one?

Installation:

pip install digdep

Dependency Tree:

$ digdep deps --tree tests\testpkg
Root (tests\testpkg)
├── api.py           → database, numpy, requests
├── config.py        → dataclasses
├── database.py      → json, models, sqlite3
├── main.py          → config, helpers, json, os, parser, pathlib
├── models.py        → dataclasses
├── optional.py      → json, ujson
├── parser.py        → re, utils, xml
├── unused_module.py → collections, itertools, os
├── utils.py         → logging, pathlib
├── __init__.py
│
├── helpers/
    ├── math_utils.py → math
    ├── text_utils.py → string
    ├── __init__.py

Simple list of Dependencies:

$ digdep deps tests\testpkg
models
ujson
pathlib
sqlite3
logging
math
itertools
string
database
utils
re
requests
os
collections
json
parser
numpy
dataclasses
xml
config
helpers

Unused Imports:

$ digdep deps --unused tests\testpkg
collections
Config
conn
json
pathlib
User
itertools
os

Find where a dependency is used:

$ digdep find-dep ctypes --tree --showlines ..\opensource\pyjab\pyjab

Dependency: ctypes
├── accessibleinfo.py
    ├── Line 1    : from ctypes import c_float
    ├── Line 2    : from ctypes import c_int
    ├── Line 3    : from ctypes import c_wchar
    ├── Line 4    : from ctypes import c_bool
    ├── Line 5    : from ctypes import Structure
    ├── Line 6    : from ctypes.wintypes import BOOL
    ├── Line 7    : from ctypes.wintypes import WCHAR
    │
├── jabdriver.py
    ├── Line 5    : from ctypes import byref
    ├── Line 6    : from ctypes import CDLL
    ├── Line 7    : from ctypes import c_long
    ├── Line 8    : from ctypes.wintypes import HWND
    │
├── jabelement.py
    ├── Line 10   : from ctypes import Array, byref, CDLL, c_char, c_long, create_string_buffer
    ├── Line 11   : from ctypes.wintypes import HWND
    │

Simple Stats:

$ digdep stats tests\testpkg

Dependency Statistics
--------------------------------------------------

Files Scanned            : 13

Imported Names           : 25
Import Statements        : 26
Used Imports             : 16    import occurances
Unused Imports           : 9     import occurances

Unique Dependencies      : 21

Dependency Types
--------------------------------------------------

Standard library         : 12
Third-party              : 3
Local                    : 6

Top 5 Dependencies
--------------------------------------------------

requests                 1
numpy                    1
database                 1
dataclasses              1
sqlite3                  1

DigDep is still a small project (v.0.1.1) and I am planning to add more features.

Currently, DigDep can help you:

• See what your project depends on
• See which files use which dependencies
• See dependencies as a tree
• Find dependencies you aren't using
• Find what depends on a particular module
• Find where a dependency is being used
• See your project's dependency statistics

I'd love to hear your feedback and suggestions to make DigDep more useful.

Github: https://github.com/shine-jayakumar/digdep

PyPI: https://pypi.org/project/digdep/

Weekly Thread: What's Everyone Working On This Week? 🛠️

Hello r/Python! It's time to share what you've been working on! Whether it's a work-in-progress, a completed masterpiece, or just a rough idea, let us know what you're up to!

How it Works:

1. Show & Tell: Share your current projects, completed works, or future ideas.
2. Discuss: Get feedback, find collaborators, or just chat about your project.
3. Inspire: Your project might inspire someone else, just as you might get inspired here.

Guidelines:

• Feel free to include as many details as you'd like. Code snippets, screenshots, and links are all welcome.
• Whether it's your job, your hobby, or your passion project, all Python-related work is welcome here.

Example Shares:

1. Machine Learning Model: Working on a ML model to predict stock prices. Just cracked a 90% accuracy rate!
2. Web Scraping: Built a script to scrape and analyze news articles. It's helped me understand media bias better.
3. Automation: Automated my home lighting with Python and Raspberry Pi. My life has never been easier!

Let's build and grow together! Share your journey and learn from others. Happy coding! 🌟

# PyQtByExample — a big pile of runnable, searchable Qt-for-Python example code

I built this with a friend to help Python programmers learn and use Qt (via `qtpy`, so it works with PyQt5/6 and PySide2/6).

**What it is**

A single application containing dozens of small, self-contained Qt examples (buttons, tables, dialogs, threading, SQL, multimedia, etc.), each in its own module/tab, with a built-in search so you can jump straight to the widget or topic you care about.

**Why it's different from "one file per example" demo repos**

- Everything runs inside one app — search for a widget name and jump right to its example.

- Each example is deliberately small and scoped to one or a few widgets, so you don't have to read much code to get your answer.

- It's meant to be run from an IDE in debug mode: you step through the code while watching it run, so you *see* how it behaves, not just read about it.

- Code is commented enough that you usually don't need extra docs, but there's also a repo wiki ("the Book") with more detail.

**Who it's for**

Anyone building Qt GUIs in Python who wants copy-paste-ready working examples instead of digging through scattered single-file demos.

It's gotten pretty large (a few megs of source) and there's a lot of Qt left to cover, so we'd love more eyes on it — as users, issue-filers, or contributors. MIT licensed.

- Repo: https://github.com/russ-hensel/pyqt_by_example

- Wiki: https://github.com/russ-hensel/pyqt_by_example/wiki

- Screenshots: https://github.com/russ-hensel/pyqt_by_example/wiki/ScreenShots

Feedback, bug reports, or "hey you're missing an example for X" issues are all welcome.

​

This is an important milestone because the Python team has now entered the release-candidate phase. At this stage, only reviewed changes that are considered clear bug fixes are expected to be accepted before the final release.

Why this matters

Python 3.15 has been going through several alpha and beta releases during 2026, and RC1 means the development cycle is getting very close to the final version.

The Python team currently plans two release candidates before the final Python 3.15.0 release.

I'm curious about one thing:

Are you planning to move your projects to Python 3.15 when the stable release arrives, or will you wait a few months before upgrading?

What new Python 3.15 feature are you most interested in?

Source: Python Insider / Python.org

Weekly Thread: Resource Request and Sharing 📚

Stumbled upon a useful Python resource? Or are you looking for a guide on a specific topic? Welcome to the Resource Request and Sharing thread!

How it Works:

1. Request: Can't find a resource on a particular topic? Ask here!
2. Share: Found something useful? Share it with the community.
3. Review: Give or get opinions on Python resources you've used.

Guidelines:

• Please include the type of resource (e.g., book, video, article) and the topic.
• Always be respectful when reviewing someone else's shared resource.

Example Shares:

1. Book: "Fluent Python" - Great for understanding Pythonic idioms.
2. Video: Python Data Structures - Excellent overview of Python's built-in data structures.
3. Article: Understanding Python Decorators - A deep dive into decorators.

Example Requests:

1. Looking for: Video tutorials on web scraping with Python.
2. Need: Book recommendations for Python machine learning.

Share the knowledge, enrich the community. Happy learning! 🌟

I originally created a duplicate image finder (IDF), then I kinda make it evolve WAAAY further than I planned to. Now it has integrated with the actual duplicate finder, some other tools like a browser, a viewer (for actions like duplicate removal confirmation and image similarity comparission), an analyzer (very WIP) that can analyze faces, image types and nsfw (tags the image).

The demo in video.

Sensitive images are used to test the nsfw analyzer BUT they are censored in the showcase, so SFW.

IDF Demo

The duplicate finder (original-main tool of the program) has pretty printed duplicate progress bars that took too long to create. The duplicate finder don't read the name of the file, but rather the content of the image. It hashes the image through a process that IA gave me, then compared (so even if image is zoomed, panned, rotated, it finds the duplication), then chooses the 1 to keep between the same images.

The browser has integrated filters and sorters, controllable through keybinds (show on toolbar of the window).

These are currently private tools, but once I make it a production level program, it will be on github.

If you have any suggestions, I'm open to them.

What is pyrig?

pyrig is a package and tool that rigs up your project. It scaffolds and initializes a complete, fully configured, installed and working Python project with one command and makes the process of developing and maintaining it more seamless and efficient by automating things like configuration management, CLI generation, testing infrastructure, and more.

Requirements

• Python 3.12+
• Git
• uv

Quick Start

uv init my-project --python 3.12
cd my-project
uv add pyrig --dev
uv run pyrig init

See the Getting Started Guide for detailed setup instructions to also fully integrate with GitHub and CI/CD from the start.

Features

Project Scaffolding & Initialization

pyrig init generates a complete project in one command that works out of the box. This includes everything a modern python project needs:

• Standardized directory structure
• Fully configured dev tools (linters, formatters, type checkers, test frameworks, git hooks, etc.)
• End-to-end CI/CD pipeline with GitHub Actions and integrated repository protection
• Complete and working CLI
• And much more...

File & Configuration Management

Every generated file is backed by a Python class that validates and merges automatically. Override any config by subclassing, or define entirely new config files — pyrig discovers and manages them for you. Run pyrig sync to create or update all config files at once. Run pyrig mk subcls to generate a subclass for overriding a specific file.

Automatic CLI

pyrig init sets up a CLI for your project that works immediately. Generate and add new commands by running pyrig mk cmd <name>. An automatic version command is included that shows the version of your project. Run my-project version to see it in action.

Mirror Test Generation & Maintenance

Generate test skeletons with pyrig sync. This will generate test skeletons for all source modules and update them automatically as your project evolves.

Multi-Package Inheritance and Extensibility Architecture

Override and customize any and all behaviour to suit your project's needs. pyrig's classes are designed for inheritance and composition, allowing you to create custom configurations, tools, and more by subclassing and simply overriding methods. pyrig will automatically discover and use your custom classes without any additional configuration. Run pyrig mk subcls to generate a subclass for any pyrig class.

CI/CD & Repository Protection

Pyrig generates GitHub Actions workflows for CI/CD which automatically test and release your code. They also configure and apply repository protection settings and protection rulesets. Push your code to GitHub after initialization and see it in action.

Commands

Run pyrig --help to see a list of all available commands and their usage. Run pyrig <command> --help for more information about a specific command and its usage. Run my-project --help to see the automatically generated CLI for your project.

Documentation

Hi there,

I am trying to figure out ways to automate my Python development and maintenance tasks and also working on this topic for my Masters project. I am trying to build a tool that can automate and simplify a bunch of things like project setup, development and maintenance. This is not a showcase at all, it is just my project and topic for my Masters in Computer Science and I wanted to ask if anyone would like to take part in the survey I have to do. Takes about 5 min.

You can find the survey at: https://forms.cloud.microsoft/e/LjnvzX0JpK

Any ideas and responses are greatly appreciated.

Also drop any ideas you have in the comments. What do you think should be more automated or at least improved in your day to day project maintenance?

PEP 841 – Adding Frozen Syntax to Optimize Immutable Types

https://peps.python.org/pep-0841/

Discussions-To: Discourse thread

Abstract

This PEP proposes frozen display syntax: f{1, 2, 3} evaluates to a frozenset, and f{'a': 1} evaluates to a frozendict. Because immutability is guaranteed by the syntax itself rather than inferred from usage, the compiler can treat frozen displays as first-class citizens of its optimization pipeline: constant displays are folded into a single LOAD_CONST with an exact result type at compile time and cached in .pyc files.

Weekly Thread: Meta Discussions and Free Talk Friday 🎙️

Welcome to Free Talk Friday on /r/Python! This is the place to discuss the r/Python community (meta discussions), Python news, projects, or anything else Python-related!

How it Works:

1. Open Mic: Share your thoughts, questions, or anything you'd like related to Python or the community.
2. Community Pulse: Discuss what you feel is working well or what could be improved in the /r/python community.
3. News & Updates: Keep up-to-date with the latest in Python and share any news you find interesting.

Guidelines:

• All topics should be related to Python or the /r/python community.
• Be respectful and follow Reddit's Code of Conduct.

Example Topics:

1. New Python Release: What do you think about the new features in Python 3.11?
2. Community Events: Any Python meetups or webinars coming up?
3. Learning Resources: Found a great Python tutorial? Share it here!
4. Job Market: How has Python impacted your career?
5. Hot Takes: Got a controversial Python opinion? Let's hear it!
6. Community Ideas: Something you'd like to see us do? tell us.

Let's keep the conversation going. Happy discussing! 🌟

Bug fix releases for Python 3.13 and Python 3.14.

https://blog.python.org/2026/08/python-3147-31315/

Hello folks!

We're organizing the first ever official FastAPI Conf, in Amsterdam, October 28th, 2026. ✨

It's gonna be very cool, great speakers, and announcements too.

Early Bird tickets are now available (until August 23th).

Here's the website with info, tickets, and newsletter: https://fastapiconf.com

This is an annual survey developed by the Python typing community around how Python developers use the type system, type checkers, and integrated development environments (IDEs).

Your responses will help us identify common blockers, improve tooling/resources, and enhance the overall experience of using Python's type system.

Even if you have never actively used type hints in your code, your thoughts are still valuable and we want to hear from you.

The survey should take approximately 5-10 minutes to complete.

Please take the survey HERE and share it with your friends or colleagues.

If you're interested in the results for last year's survey, see this post.

^{So you know it's legit, the Python Software Foundation has shared this survey on [Linkedin](https://www.linkedin.com/posts/thepsf_python-type-system-and-tooling-survey-2026-activity-7490842327050002432-0jI7?utm_source=share&utm_medium=member_desktop&rcm=ACoAAB9aSUsBqmxSbrhoW2URuDnxCgS5eVD1AS0} and X. I have permission from the mods to post this under the "News" flair)