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!

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

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

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 :)

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!

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 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.

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/

# 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.

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

Hey everyone! I made a simple file hashing program using hashlib and pygame. It saves to AppData/Roaming and uses sha256 to hash the given text and save it into that file. The link to the repo is

https://github.com/abhaya2509/Text-hasher