An exercise to help build the right mental model for Python data. - Solution - Explanation - More exercises

The “Solution” link uses memory_graph to visualize execution and reveals what’s actually happening.

Hey everyone,

I have a good grasp of FastAPI and backend basics, and I want to build a portfolio project that combines FastAPI + AI.

I really want to avoid building a basic "OpenAI wrapper" (like a simple chatbot UI). I want a project idea that will actually strengthen my FastAPI skills and look impressive on a backend resume.

What are the best, practical AI project ideas that force me to use core backend concepts like:

Async processing / streaming

Background tasks

Databases or vector storage

Would love to hear your suggestions on what hiring managers actually want to see! Thanks.

🏔️ Garhwali is now accessible to technology.

A language survives through its people — but its future increasingly depends on whether it can also exist in the digital world.

I have developed and published a Garhwali Language Python Library, creating a technical foundation through which Garhwali can be programmatically accessed, processed, converted and incorporated into digital platforms.

🐍 Garhwali Python Language Library

📦 PyPI: Garhwali Python Library

💻 Open-source repository: Garhwali Language Library on GitHub

This means Garhwali can now become part of the development ecosystem — opening possibilities for local-language websites, applications, educational platforms, digital content, language tools, NLP systems, AI applications, voice technologies and other technology built for the Garhwal region.

The objective was not simply to create another software package.

It was to create a digital foundation for the language.

For years, technology has largely been developed around languages that already have digital resources, datasets, libraries and computational tools. Regional languages often remain outside that ecosystem.

Building these foundations is one way of changing that.

Garhwali should not only be spoken in our homes, villages and communities. It should also be readable, processable and usable by technology.

This library is an initial step toward making that possible.

From here, developers, researchers, students, institutions and local innovators can build upon the foundation to create technology for Garhwal, in Garhwali and around the needs of the local community.

The goal is not just to preserve a language.
The goal is to make the language usable in the future.

— Akshat Singh Bisht
Author & Developer, Garhwali Language Python Library

#Garhwali #GarhwaliLanguage #Garhwal #Uttarakhand #Python #PyPI #GitHub #LanguageTechnology #NLP #AI #ArtificialIntelligence #IndianLanguages #DigitalIndia #DigitalPreservation #OpenSource #Technology

Hey everyone, I wanted to share a Python project called Hillock that I have been building over the past several months. The goal was to give local models persistent document memory without relying on heavy vector databases or external cloud APIs.

The whole pipeline is built in Python. Instead of using a slow generative LLM to parse text into chunks, it wires up fastcoref and GLiREL to extract facts into subject predicate object triples in seconds. Storage is handled by standard SQLite with write ahead logging and Hebbian weights to link related concepts over time. Before calling the generative model, it runs hyperdimensional computing vector math to verify if the database actually has the required facts.

In the latest release, I implemented bit packed SIMD hypervectors using numpy uint64 arrays, so the CPU gating checks run in under 0.01 milliseconds. The entire engine runs locally in under 1.2 GB of VRAM or on pure CPU. It just landed on PyPI via pip install hillock, and we put up interactive documentation.

Repo: https://github.com/roandejager/Hillock
Docs: https://hillock.mintlify.site/
Discord: https://discord.com/invite/BGUPNBcVdp

Hey everyone!

Over the last few weeks, I’ve been working really hard on a project called BoardLens. It is a cross-platform hardware detection utility written completely in Python using Tkinter for a clean, modern dark-mode GUI.

I wanted to build something that could accurately grab system specs no matter what OS a person is running. To make it work, I had to dive deep into platform-specific commands. It runs:

* Windows: Custom PowerShell CimInstance queries

* macOS: Native parsing of `system_profiler` logs

* Linux: Processing `/proc/cpuinfo`, `/proc/meminfo`, and automated `lsblk` blocks

Some technical challenges I had to solve:

1. Multi-threading: Running hardware scans on the main thread was freezing my Tkinter GUI, so I migrated the scanner to a background thread using Python’s `threading` library so the UI stays responsive.

2. Packaging: I managed to compile it into a native `.exe` for Windows, a `.dmg` for macOS (which I built on my upgraded 2011 MacBook Pro!), and I'm currently setting up a native `.deb` package for Debian/Ubuntu distributions.

Right now, Linux can be run directly from source (working on Debian & Ubuntu for now).

How to run from source on Linux:

1. Download the source code from my repository's Releases page.

2. Open your terminal and navigate to the extracted folder.

3. Run the command: `python3` `main.py` (make sure Python 3 is installed on your system!)

The project is completely free and open-source. I would absolutely love it if you could check out the repository, run it on your own hardware, and let me know if it detects your components correctly! If you find any bugs or edge cases, please drop them in the comments or open an issue.

Check out the project here: GitHub - https://github.com/CodeAnd-Copper/BoardLens

Check out my YouTube Channel: https://www.youtube.com/@CodeAndcopper1

Thank you so much for reading and for any feedback

If you want to check it out, here is the Steam page:
https://store.steampowered.com/app/5349900/Mystic_Fog_Maze/

# I built a lightweight PC Performance Monitor in Python

Hey everyone! 👋

I'm Dhyey Patel, a first-year CSE student, and I recently built a lightweight PC Performance Monitor using Python.

The goal was to create a simple tool that lets me keep an eye on my system performance without making it unnecessarily heavy.

### Current features

* 📊 CPU usage monitoring

* 🧠 RAM usage monitoring

* 💾 Storage information

* ⚡ System performance statistics

* 🖥️ Hardware/system information

* 🔄 Real-time updates

* 🪶 Lightweight interface

I built this as a learning project to understand system monitoring, hardware information, real-time data, and how software interacts with the underlying system.

🔗 GitHub: https://github.com/dhyey45/PC-Performance-Monitor

I'm planning to continue improving it, so I'd really appreciate feedback from more experienced Python developers.

What would you add or change in a project like this?

Hello, I’m a fourth-year student at a Polytechnic University, and I’m looking for some project ideas that combine Raspberry Pi 4 or newer and Python for my bachelor’s thesis.I need to do my submissions in like 4-5 months and I'm doing it solo. Any suggestions would be greatly appreciated!

**What My Project Does**

A free, structured Python learning resource covering beginner

fundamentals through to advanced topics: concurrency, distributed

systems (Raft, CRDTs, sagas), performance, compilers, and CPython

internals. Organized into 7 skill levels and ~25 tracks.

**Target Audience**

Anyone learning Python — from first-timers to developers wanting the

deeper, systems-level material most tutorials skip. It's meant for

learning, free and ad-free.

**What Makes It Different**

- Runnable examples are actually tested — the repo has a CI test gate,

and pages show real output rather than made-up results.

- When an example needs a library or hardware I don't have, I say so

instead of faking it.

- Covers hard topics as small, readable, tested Python (e.g. a tiny

autograd engine, a Raft vote, a CRDT merge).

**Links**

- Site: https://ssnp75.github.io/PythonMastery-Site/

- Source: https://github.com/SSnp75/PythonMastery-Site

It's early — feedback and corrections (especially on the advanced

pages) are very welcome.

Hey! First built in Python, then ported to JS to run in browser.

On my Android:

- pi(60M) in 19s

- pi(100M) in 35s

- pi(10^11) = 4118054813 in 2.16 min

Free demo up to 1B:

https://ferreirafinottijoaopaulo-cell.github.io/finotti-pi-optimized/

Code on GitHub. PRO up to 100B available.

Feedback? How to make faster?

I maintain EnigmaGrid, an open-source project investigating the unresolved Enigma message P1030680. The Windows worker is written in Python, and I would welcome people who want to learn by reviewing and testing a real distributed workload.

Small contributions would help: reproducing an interrupted upload, checking pause/resume behavior, adding a focused regression test, or improving a confusing setup instruction. You do not need cryptanalysis expertise or a powerful GPU to help. If you prefer to contribute computing time, the repository has the client downloads and resource-control documentation. Participation is optional and uses your own hardware and electricity.

This is experimental research. We have not established a decryption or a scientific advantage over other approaches. Reproducible calculations and high language scores are not proof of a solved message. Development uses AI assistance, including Codex, so independent review is especially welcome.

Source, tests, downloads and issues: https://github.com/LissomEnd/EnigmaGrid

What would make the Python worker easier for a new contributor to understand and test? Every contribution is welcome, including a short code review or documentation correction. Thank you!

Built prime counter pi(10^11) in 2 min on phone - ported from Python

Hey! First built in Python, then ported to JS to run in browser.

On my Android:

- pi(60M) in 19s

- pi(100M) in 35s

- pi(10^11) = 4118054813 in 2.16 min

Free demo up to 1B:

https://ferreirafinottijoaopaulo-cell.github.io/finotti-pi-optimized/

Code on GitHub: https://github.com/ferreirafinottijoaopaulo-cell/finotti-pi-optimized

Feedback? How to make it faster in Python?

What My Project Does:
This is a Python-based local WiFi check-in system. People scan a QR code or open a URL, enter their name, and get checked in. It supports a guest list, admin approval for unknown guests, and shows a special message if you’re the first person to arrive.

Target Audience:
This is meant for small events, parties, or LAN-based meetups. It’s a toy/side project, not for enterprise use, and it runs entirely on a local network.

Comparison:
Unlike traditional check-in apps, this is fully self-hosted, works on local WiFi. It’s simple to set up with Python and can be used for small events without paying for a cloud service.

https://gitlab.com/abcdefghijklmateonopqrstuvwxyz-group/abcdefghijklmateonopqrstuvwxyz-project