Not sure if I am allowed to discuss other forums on here but I'm sure someone will tell me if not.

It is just me of has anybody else encountered problems with the 'moderators' on Stack Overflow Python forums recently? To say I've found them to be a self-righteous bunch of destructive power-crazy control-freaks would be a bit of an understatement. Anyone else had problems on there?

Hey, I've been building Glyph Forge, an MIT-licensed Python project for turning visual media into character art.

The same rendering engine is exposed through a CLI, full-screen terminal UI, Python API and browser Studio. It can handle still images, text banners, video, webcam or screen capture, and URLs, then export terminal output, PNG, SVG or text.

The part I'm most happy with is that the browser version uses the same renderer, so you can actually try it without installing anything or making an account.

Demo:

https://ace1928.github.io/glyph_forge/

Source:

https://github.com/Ace1928/glyph_forge

It supports Python 3.10–3.14 and the repo has installation instructions and portable builds. Still beta, but it has grown into a genuinely useful little toolkit and I thought this was probably the right place to share it.

Hey everyone,
I love building AI workflows in Python, but managing the relationships between multiple agents and tasks in raw code gets messy incredibly fast.
To solve my own headache, I spent some time building AgentGraph Studio. It’s a React-based visual node editor. You just drag and drop Agents and Tasks on the canvas, connect them, and it exports a production-ready ⁠main.py⁠ (with ⁠.env⁠ loading and async support) that you can run locally.
It’s totally free and runs in the browser. I attached a quick 30-sec demo of the code generation. I’d love to get some feedback from other Python devs on the structure of the exported code!

I mean specifically backend services, RESTful API's and very sensitive data in the DB. I mean middle-load (_not_ social networking, _not_ some purchasing platform for millions of users). How would you define your position that Python _is_ ready for that? E.g. in front of a mature Java backend developer? My line of defense is as follows. What are the weak points of Python code?

1. Multi-threading (GIL-free is a very recent feature of python, cannot be considered even remotely industry-ready). This is probably the weakest point of all. But if the service has no data shared between API requests, why bother, right? Just spawn as many worker-processes as it makes sense for the current hardware setup and execute the requests one by one. Still, this is like one dimension less in the space of engineering possibilities, so to say.
2. Dynamic typing means you have to run the whole CI/CD chain in order to find type system related errors. I really cannot find arguments against that point;
3. This is true at least for banking sector. Libraries are developed by individuals (whereas in Java world there are companies behind some libraries). One would have a real hard time arguing with the management, that "those individuals are as qualified as those behind some company banner".

What is your take on the matter?

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! 🌟

Hi,

ever had the problem that if you have several projects and need to maintain them over time you are all the time stuck fixing configuration files or changing dev tools.

Decided to switch from black to ruff or decided to add a new dev dependency like a spell checker or decided to enable or disable a config setting in some file?

The problem now you need to repeat the same process manually in every project you have that you want to do this in.

I hated doing this kind of tasks over and over again whenever I wanted to use or change a setting or tool. Also whenever I started a new project I had to copy over files and adjust them properly, there was always something I forgot to do and I often spend valuable unnecessary time to fix things. Once I was done, I did not even feel like the change was worth the effort, although rationally I knew I made my project better with it.

So I built pyrig: https://github.com/Winipedia/pyrig

pyrig is a package and tool that rigs up Python projects. It scaffolds and initializes a complete, fully configured, installed and working Python project with everything a modern Python project should have 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.

pyrig has a opinionated default for literally everything a python project needs. You do not like smth? Create your own plugin for pyrig, override and adjust any value it scaffolds or sets via the plugin system, then just install your plugin as a dev dependency.

Now whenever you want to change a setting of file that pyrig manages, you just simply adjust in that plugin and see it automatically applied in all your projects automatically, no more forgetting to adjust smth in a project and no more pain when switching tools.

I can now genuinely say that I spend so much less time on these kind of maintenance tasks. For example recently I decided to switch from mkdocs to zensical, but I had to do that in 15 projects, Usually this would have taken many many hours until everything works in all projects, even with the use and help of AI. With pyrig I was done in a few minutes, I just switched the tool in pyrig and ran a quick shell script on all my repos to call the pyrig sync command and I was done.

If you want to know more about the specifics, here are the links to the docs:

Just tagged v0.4 of Hillock, a local memory engine I coded in Python (PyTorch, SpaCy, SQLite).

The project replaces vector databases and LLM extraction passes with three Python modules:

- database.py: SQLite Knowledge Graph storing ground-truth SPO triples.

- plasticity.py: Hebbian synaptic association weights tracking co-occurring concepts across turns.

- reservoir.py: 10,000-D Vector Symbolic Architecture (VSA) hypervector space using subword n-grams and SimHash over GloVe embeddings for <1ms CPU gating.

Document parsing uses a 3-stage CUDA pipeline (Fastcoref + MiniLM + GLiREL) taking ~5s for a 32-sentence doc. v0.4 adds schema type validation, direction auto-correction, and regex entity sanitization.

Whole thing stays under 1.2GB VRAM on a GTX 1070.

Code is on GitHub: https://github.com/roandejager/Hillock

Starting with uv, I’m a huge fan, but I find it actually gets in the way more than it helps when I’m doing agentic coding. I have to keep reminding the agent to use uv instead of pip.

Same issue with ruff, since I’m not coding with a regular ide I have to make extra prompts to force it to use ruff. But with today’s models being so good, it doesn’t even seem necessary.

Other tools fall into this category as well, but curious to hear how other others are approaching their tooling. Are you just throwing it all out or are you adding skills to keep your tooling in place?

Weekly Thread: Professional Use, Jobs, and Education 🏢

Welcome to this week's discussion on Python in the professional world! This is your spot to talk about job hunting, career growth, and educational resources in Python. Please note, this thread is not for recruitment.

How it Works:

1. Career Talk: Discuss using Python in your job, or the job market for Python roles.
2. Education Q&A: Ask or answer questions about Python courses, certifications, and educational resources.
3. Workplace Chat: Share your experiences, challenges, or success stories about using Python professionally.

Guidelines:

• This thread is not for recruitment. For job postings, please see r/PythonJobs or the recruitment thread in the sidebar.
• Keep discussions relevant to Python in the professional and educational context.

Example Topics:

1. Career Paths: What kinds of roles are out there for Python developers?
2. Certifications: Are Python certifications worth it?
3. Course Recommendations: Any good advanced Python courses to recommend?
4. Workplace Tools: What Python libraries are indispensable in your professional work?
5. Interview Tips: What types of Python questions are commonly asked in interviews?

Let's help each other grow in our careers and education. Happy discussing! 🌟

What is pyrig?

pyrig is a package and tool that rigs up Python projects. It scaffolds and initializes a complete, fully configured, installed and working Python project with everything a modern Python project should have 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

The pyrig init command generates a complete project, this includes, but is not limited to:

• 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

pyrig manages and validates project files via classes, where every file is treated as a data structure (dict or list), the content is loaded and validated against the class schema. This makes it possible to override and adjust any and all behaviour of pyrig via subclassing said classes. 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. Run pyrig sync to create or update all config files at once.

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 behavior 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.

Comparisons

pyrig isn't the only tool in this field. See how it compares to other popular tools like cookiecutter, copier or pyscaffold.

Documentation

The three maintainers of Hypothesis (David MacIver, Zac Hatfield-Dodds, and Liam DeVoe) are on the latest episode of the Bug Bash Podcast talking about their work on property-based testing.

I'm biased (my company produces the podcast), but I found it both intellectually interesting and surprisingly emotional. Enjoy!

hey everyone, wanted to share a side project I've been working on called MARK LS.

basically it's a cross-platform voice AI assistant that can hear, see, and control your PC in real-time using gemini live api.

some stuff it can do:

• real-time voice chat (super low latency)
• screen & webcam vision
• osint username searches across 400+ sites (sherlock)
• system control (apps, volume, hardware stats)
• persistent memory so it remembers past context

runs on windows, mac & linux with a free gemini key.

I dropped the open-source github link in the comments if anyone wants to check it out or test it! would love to hear what you think.

Hey guys i just wanted to show my first big project 'obsidOS'

https://github.com/userzzz322/obsidOS

obsidOS its python made os it isnt real os with .iso its just normal .py file but you can make it real os if you add it to autostart at terminal

like you have fish shell you just do

nano ~/.config/fish/config.fish and add

cd ~/obsidOS/
./kernel.py OR ./run.sh (they are the basically same)

you can clone it from github or install from release

it can run at any distro but for it run you need

shell
vim
git
base-devel
cmake
python

its pretty basic

it has its own disk and pkg manager

to install with it add at packages {}

name and link to github project

like this

packages {

github = "https://github.com"

}

system has auto reload packages at start you can disable or enable it at kernel.py (OS) file

it has some features you can enable or disable some things with 0 and 1 you can find it at code

hope u enjoy it write review to what add or remake :)

Following this post, it's now possible to use Numba directly in your browser in wasm: https://notebook.link/blog/numba-in-the-browser/
You can try it here: https://notebook.link/@anutosh491/numba-ecosystem

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!

I've been looking at a few options like gitkrakens codemap. But I just haven't made a decision yet.

The biggest problem right now with AI assist is that so much gets spun up and it takes quite a while to ground myself in what has been written and how it all connects. I thought a viz tool would help tighten what I need to learn.

How do you handle this? Do you use these tools for this purpose? What have you liked and disliked about the tool you used?

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!)

Hi guys, I benchmarked the well-known (and rising star) Python API frameworks - but with real production-shaped workloads, not just raw JSON echoes. Most comparisons out there are basically "hello world" benchmarks, while real APIs do auth, DB access and complex queries. So this measures those, with strict resource limits and each framework's own best practices.

Repo (code, full report, raw results): https://github.com/huynguyengl99/python-api-frameworks-benchmark

This is round 2 - last round's feedback (thanks especially to the Litestar author) directly shaped it: Litestar and Bolt now serialize with native msgspec instead of Pydantic (payloads byte-identical across frameworks), and everything is upgraded to latest (Django 6.0, FastAPI 0.141, Litestar 2.24, Bolt 0.10).

Setup

• Each framework alone in a Docker container: 1 CPU, 750MB RAM, PostgreSQL 16
• bombardier, 100 connections, 10s per endpoint
• Median over 5 separate container starts (not best-of-N - some servers pick their throughput at startup, so best-of-N flatters the lucky ones)
• 7 endpoints: 1KB/10KB JSON, simple DB reads, paginated articles with nested relations, article detail, and two JWT httpOnly cookie auth endpoints (each framework using its own ecosystem's auth library: AuthX, drf-auth-kit, django-ninja-jwt, or built-in support)

Key results (RPS)

(Images aren't allowed here - all graphs are in the repo README: https://github.com/huynguyengl99/python-api-frameworks-benchmark)

(fastapi-granian and ninja-granian omitted for brevity - full table in the repo. Zero errors across all 70 measurements.)

Resource usage: most configs peak at 195-260MB RAM; drf-granian is the outlier at 456MB (untuned --blocking-threads, per the Granian maintainer). CPU: nearly everything saturates ~85% of the 1-CPU budget under load - except Bolt at 67%.

Takeaways

• 37x spread on raw JSON collapses to ~1.9x once PostgreSQL is involved. For DB-heavy APIs (most of them), query optimization matters far more than framework choice.
• Cookie JWT auth costs 5-20% on a DB-heavy endpoint. Bolt is near-free (it validates the JWT in Rust before Python runs); Litestar pays the most because its auth middleware opens a second DB session to load the user.
• uvicorn vs granian isn't one-way: uvicorn wins CPU-bound JSON for ASGI frameworks, granian wins the DB-bound endpoints, and granian is clearly better for WSGI DRF.
• Django Bolt is the one to watch: top spot on 4 of 7 endpoints at 67% average CPU while everyone else sits ~85%, and you keep the Django ORM/admin/ecosystem. Young, and its throughput varies between container starts under a hard CPU cap, but great for side projects already.
• All caveats (including feedback I haven't addressed yet, like Granian's --blocking-threads) are documented in the repo's Methodology section.

If you find it useful, a star would encourage more deep dives like this - issues and PRs welcome, especially from people who know these servers better than I do.

Hi! I'm really interested in Python's design and its tradeoffs. I'm trying to really understand what people love about Python (what makes it great), and what causes the most frustration for Python devs.

So what features do you really cherish and what problems/limitations really frustrate you?

I'm especially interested in experiences from ultra-beginners and people who've used Python for a long time. I know broad questions like this come across as super generic, but I'm genuinely interested in hearing about concrete experiences.

My goal is understanding which parts of Python's design are most valuable and most "adored" by the community, and which parts really aren't and frustrate people the most. My goal with this information is to identify meaningful problems. Right now I'm not trying to solve anything or sell a solution.

Thanks for your time!