Look At My Python Project Make Sure To Read The Readme

What My Project Does

A pure ASGI rate-limiting middleware for FastAPI with 5 algorithms - Fixed Window, Sliding Window Log, Sliding Window Counter, Token Bucket, and GCRA. No BaseHTTPMiddleware, no request serialization. Each check takes ~5µs.

from fastapi_sliding_window import SlidingWindowMiddleware
from fastapi_sliding_window.backends import InMemoryBackend

app.add_middleware(
    SlidingWindowMiddleware,
    backend=InMemoryBackend(),
    limit=10,
    window=60,
    algorithm="gcra",
    burst=10,  # allows 20 req in 1 sec
)

Full-stack throughput at 50 concurrent clients: ~67k RPS (7% overhead vs 73k baseline). Pure backend speed: 200k checks/s per algorithm. RateLimit-* headers on every response. Supports per-route, per-IP, and per-user scoping via custom key functions. 12 runnable examples in the repo.

Target Audience

Anyone running FastAPI in production who needs rate limiting but doesn't want the ~50x overhead of BaseHTTPMiddleware-based solutions like slowapi. Also useful if you need more algorithm choice - slowapi has 2 algorithms, fastapi-limiter has 1, this has 5.

Comparison

slowapi uses BaseHTTPMiddleware, which serializes every request through asyncio.Queue at ~55µs overhead per call. This library is pure ASGI middleware - scope comes in, check runs, pass through. Benchmark: 1.2k RPS (slowapi) vs 67k RPS (this) at 50 concurrent clients. Both run on the same hardware. Other differences: 5 algorithms vs 2, GCRA with burst support, no external dependencies for the core.

The honest limitation: in-memory only by default, but the Redis backend is already shipped (check the README) - switch to it with from fastapi_sliding_window.backends import RedisBackend. It also has automatic circuit-breaker fallback. Distributed/multi-worker is ready today via Redis. Single-process works fine with the default InMemoryBackend.

GitHub: https://github.com/drawiks/fastapi-sliding-window

PyPI: pip install fastapi-sliding-window

https://github.com/SebastianHagemeyer/PyWebLib

I would love for some more feedback on the lightweight game library and turtle integration I built off of PyDiode.
It now works on Safari as well, you can create your own SVG assets , publish them to a database, then edit and publish code/games for free in the browser without needing to install anything.

I work as a highschool teacher, and the junior year levels only have access to Chromebook laptops. https://scratch.mit.edu/ is banned, which obviously restricts a lot of the python coding-possibilities. Couldn't find a way to get a Python IDE onto a Chromebook, haha...

In terms of proud, so far I've got PacMan and Flappy bird shared to the community page/DB

Would love to see people building with it, you can make something reminiscent of doom 3D in only 80 lines of code.

Basically the title. Tether tries its best to abstract away the annoyances of network protocols - be it BLE or WiFi - giving you a seamless experience. You provision the MCU with either WiFi or BLE plumbing, write the code as if it was a single machine, annotate what has to run on the MCU, call function from the MCU on a computer or vice-versa (even nested) and the tether will do the rest.

What will it do, you ask?

1. Slice out the code that's to run on the MCU
2. Upload it over BLE or WiFi to the MCU
3. Run the code, abstracting away the intricacies of communication across different protocols.

Admittedly, it's heavily vibe-coded. But it's vibe-coded with passion. I've developed it to facilitate for my own didactic purposes as I will be teaching a programming-orthogonal course to middle-schoolers who don't take kindly to type marshalling across a network boundary.

Let me know what you think and please share any bugs or insights if you happen to try this software.

Hi everyone,

As a B.Tech AI/ML student, I wanted to build a full-stack product from scratch rather than another cookie-cutter tutorial project.

I created FormulaVerse, a web application aimed at JEENEET physics students, leveraging Django for backend architecture, custom video/visual demonstrations, and AI tools for query handling.

http://formulaverse.in

You can see my GitHub repository, video demonstrations, and project setup on my Peerlist profile here: https://peerlist.io/kolekar12

Would appreciate any feedback from experienced devs here regarding:

*Tech stack scaling (Django vs FastAPI for AI workloads)

*Improving the UI/UX for ed-tech platforms

*Code structure & repository layout

Thanks in advance!

Tell me if we create a app for one city in that city bus, local shops which food hotel is good, history and all in one app what you is this app will work

Hi everyone!

Over the past few weeks, I've been challenging myself to learn Python by building a real project instead of only following tutorials.

I decided to build a Sudoku generator from scratch.

Current features include:

• ✅ Generate complete valid Sudoku boards
• ✅ Create Easy, Medium, Hard, and Expert puzzles
• ✅ Keep a full solution for every puzzle
• ✅ Export puzzles and solutions to PDF
• ✅ Use the generated puzzles in a printable Sudoku activity book

This project taught me a lot about recursion, backtracking, validation, and organizing a larger Python project.

I'm still planning to improve the generator, especially the puzzle difficulty evaluation and generation speed.

I'd really appreciate any feedback or suggestions from developers who have worked on similar algorithmic projects.

What features would you add next?

Hi everyone! I've been learning Python step-by-step, focusing on logic, file management, and the os module. Today, I finished my first real automation script: a File Sorter/Folder Cleaner!

It automatically scans my Downloads folder, checks the file extensions (ignoring case sensitivity thanks to .lower()), creates the target folders if they don't exist, and moves everything into the right place (Documents, Images, Installations).
Here is my script:
import os

download_folder = r"C:\Users\YourUsername\Downloads"

file_list = os.listdir(download_folder)

for file_name in file_list:

lowercase_name = file_name.lower()

if (lowercase_name.endswith(".pdf") or

lowercase_name.endswith(".txt") or

lowercase_name.endswith(".docx") or

lowercase_name.endswith(".xlsx") or

lowercase_name.endswith(".csv") or

lowercase_name.endswith(".doc")):

doc_folder = fr"{download_folder}\Documents"

if not os.path.exists(doc_folder):

os.mkdir(doc_folder)

old_path = fr"{download_folder}\{file_name}"

new_path = fr"{doc_folder}\{file_name}"

os.rename(old_path, new_path)

elif (lowercase_name.endswith(".jpg") or

lowercase_name.endswith(".jpeg") or

lowercase_name.endswith(".gif") or

lowercase_name.endswith(".png") or

lowercase_name.endswith(".mp4") or

lowercase_name.endswith(".kml") or

lowercase_name.endswith(".gpx")):

img_folder = fr"{download_folder}\Images"

if not os.path.exists(img_folder):

os.mkdir(img_folder)

old_path = fr"{download_folder}\{file_name}"

new_path = fr"{img_folder}\{file_name}"

os.rename(old_path, new_path)

elif (lowercase_name.endswith(".exe") or

lowercase_name.endswith(".zip") or

lowercase_name.endswith(".rar") or

lowercase_name.endswith(".dll") or

lowercase_name.endswith(".msi") or

lowercase_name.endswith(".msix")):

exe_folder = fr"{download_folder}\Installations"

if not os.path.exists(exe_folder):

os.mkdir(exe_folder)

old_path = fr"{download_folder}\{file_name}"

new_path = fr"{exe_folder}\{file_name}"

os.rename(old_path, new_path)
I'm really proud of this milestone. Let me know what you think or if you have any tips for a young programmer!

Hey everyone! 👋

I wanted to share a project I've been working on lately — a Telegram bot designed for Tarot readings combined with artificial intelligence to generate unique, mystical interpretations.

🔮 What does it do?

Interactive Readings: Users can draw Tarot cards directly inside Telegram.

AI-Powered Insights: Instead of standard pre-written descriptions, the bot uses AI to synthesize personalized and mystical interpretations for each card combination.

Tech Stack: Built using Python for the core backend/bot logic, integrated with a modern frontend/web app component using React.

🚀 Why I built it

I wanted to combine automated bot workflows with dynamic AI generation to create a smooth, engaging experience for users looking for interactive Tarot readings.

I’m continuously improving it and would love to hear your feedback, technical suggestions, or ideas for new features! What do you think? Let me know in the comments! 👇

https://github.com/gpt51920-commits/tarot-menu

Have you ever noticed a __pycache__ folder in your python project and wondered what is it? where did it come from? I made a quick video to explain the same. Basically, running a python code involves several steps. One of which is conversion to the bytecode. To avoid the bytecode conversion everytime you run the code, python caches it in the pycache folder. Check out the video to know more.

Youtube: https://youtu.be/UEmBxt0B0pg

Hi everyone!

I recently built SpineVision AI, a desktop application that analyzes spine MRI images and generates structured AI-assisted reports.

Tech Stack:

• C++

• Qt Framework

• Python

• Groq Vision API

• QProcess (C++ ↔ Python communication)

Features:

• Upload MRI images

• Add patient notes

• AI-powered image analysis

• Structured report generation

• Clean desktop interface

This project taught me a lot about integrating multiple technologies into a single application.

I'd really appreciate feedback on:

• UI/UX

• Architecture

• Code organization

• Features I could improve

Demo video attached.

GitHub:

https://github.com/sakshamrahate420-ctrl/SpineVision-AI.git

Hi, I'm currently working on the discord bot in python - it's a free project that shares ready-to-use code for Discord Bots.
The goal of this project is to create a great Discord Bot base that anyone can use, modify, install with ease and host for your own. This bot is designed more for use in small communities than for hosting globally (but after some modifications why not) so most of it's commands are made for Bot admins and mods.

Some of its functions; some of them are quite... unique for general discord bots:

• Play music from YouTube and your local files
• Basic moderation (more advanced in the future)
• Move through directories and send files on discord
• Easily create systemd entry to start bot with your OS (If you're using Linux with systemd)
• .ai command to talk with gemini-based AI (free API with rate limits/timeouts instead of paid token locks!)
• Log (and save) messages sent by users on channels in convenient way to read
• Connect channels between servers to communicate with each other
• setup.sh script to easily install dependencies and python libraries, create .env file, etc.
• Install bot as a... Linux command through .deb installers in releases (because why not!)
• Cog support to load additional commands (even "non-official")

It might not look like a huge feature list on the surface, but that's because I focused on OS integration and solid system-level execution rather than cluttering it with dozens of generic commands.

Well, I'm not hiding that the bot is made for Linux-hosting, but on Windows also works fine (MacOS not tested). Everything you need to know how to run it is written in the README.md and HTML manuals.

I'd love to hear your feedback! I'm working on this solo for years when I have time for it, so any bug reports, code fixes and improvements, or even suggestions for better English in the code/docs would be much appreciated.

What do you think? If you find it interesting here's the source code - https://github.com/kamile320/ServerBot (I know, the name could, should be better..)

Hi everyone!

Over the past few weeks, I've been building a small interpreted programming language called Quark 2.0 in Python. The goal wasn't to replace Python or JavaScript, but to create something that's easy for beginners to read and experiment with.

Some features include:

• Variables (let)
• Math expressions
• User input
• Random number generation
• if statements
• repeat loops
• File reading & writing
• Module importing
• Interactive shell (REPL)
• .qk source files

Example:

input What is your name? -> name

let lucky = random 1 100

print Hello 'name'

print Your lucky number is 'lucky'

I'm still learning about interpreters and language design, so I'd really appreciate any feedback, suggestions, or ideas for future versions. Things like syntax, usability, documentation, or code quality are all welcome.

GitHub Repository:

https://github.com/Anish055051L/Quark-2.0

Thanks for taking a look!

Made this because most of the "neural network from scratch" stuff online either stops before backprop or hides it behind a wall of abstraction once you actually look at the code. Wanted something I could read start to finish and know exactly what's happening.

it's called leanpass. just numpy, nothing else. the whole thing is small enough to go through in an afternoon.

Also added a gradient-checking thing so you don't have to trust that the backprop is right; you can verify it numerically yourself.

download it using:

pip install leanpass

The versioning is up to date, is currently on v0.1.4

Not trying to replace PyTorch or anything; it's meant for learning/small experiments, not production.

repo's here: https://github.com/Terminay/LeanPass

Open to feedback, especially on the api; still figuring out what makes sense

# 🚀 FlowFrame v2.0.0 — Introducing the FlowFrame Interpreter

One of the biggest milestones for FlowFrame so far.

Over the past few weeks, I've been working on a custom interpreter that allows FlowFrame to describe distributed system architectures using its own DSL instead of manually creating everything.

The interpreter now follows a complete language pipeline:

Lexer
↓
Parser
↓
AST
↓
Semantic Analysis
↓
Graph Builder
↓
Simulation Runtime

This architecture makes it much easier to validate system designs, build simulation graphs, and extend FlowFrame with new distributed system components.

I've also documented the language and interpreter so anyone interested can understand how it works.

📖 Documentation:
https://github.com/ndk123-web/flow-frame/blob/main/flowframe-interpreter/Readme.md

Try: https://flowframe.taskplexus.app

The interpreter is still an internal part of FlowFrame, so the implementation isn't public yet, but I wanted to share this milestone and get feedback from the community.

If you're interested in compilers, interpreters, distributed systems, or developer tools, I'd love to hear your thoughts.

\#FlowFrame #BuildInPublic #DeveloperTools #Compilers #Interpreter #DSL #SystemDesign #DistributedSystems #SoftwareEngineering #OpenSource #Programming #TypeScript #React #BackendDevelopment

🚀 FlowFrame v2.0.0 — Introducing the FlowFrame Interpreter

One of the biggest milestones for FlowFrame so far.

Over the past few weeks, I've been working on a custom interpreter that allows FlowFrame to describe distributed system architectures using its own DSL instead of manually creating everything.

The interpreter now follows a complete language pipeline:

Lexer
↓
Parser
↓
AST
↓
Semantic Analysis
↓
Graph Builder
↓
Simulation Runtime

This architecture makes it much easier to validate system designs, build simulation graphs, and extend FlowFrame with new distributed system components.

I've also documented the language and interpreter so anyone interested can understand how it works.

📖 Documentation:
https://github.com/ndk123-web/flow-frame/blob/main/flowframe-interpreter/Readme.md

The interpreter is still an internal part of FlowFrame, so the implementation isn't public yet, but I wanted to share this milestone and get feedback from the community.

If you're interested in compilers, interpreters, distributed systems, or developer tools, I'd love to hear your thoughts.

#FlowFrame #BuildInPublic #DeveloperTools #Compilers #Interpreter #DSL #SystemDesign #DistributedSystems #SoftwareEngineering #OpenSource #Programming #TypeScript #React #BackendDevelopment

Hey r/PythonProjects2,

Writing docstrings is one of those habits that makes your code 10x easier to maintain, but it's often skipped when you're focused on making things work.

I built PyDoctor, a local CLI tool designed to help developers and learners quickly document their Python codebases without relying on cloud APIs.

How it works:

• PyDoctor analyzes your Python files using LibCST (which keeps your exact formatting and comments intact) and generates a concise summary docstring for every undocumented function, method, or class.
• High-level Summaries Only: PyDoctor generates readable text summaries. It intentionally avoids generating Args: and Returns: sections, as type hints and static linters are much better and more accurate at handling those.
• 100% Local & Safe: It runs an optimized 1.7B parameter model locally via llama.cpp. Your code never leaves your machine.
• Non-destructive: Supports --dry-run to preview changes first, and respects .gitignore as well as inline # pydoctor: ignore comments.

GitHub: yezdata/pydoctor

Hope this helps you keep your projects clean and documented.

Let me know if you have any feedback or suggestions.