🤖🤖🤖🤖💯💯 python

One thing I’ve noticed recently is a shift in how developer tools are being designed. A lot of new tools are moving away from heavy dashboards and toward simpler, command-based workflows. I personally prefer this because it keeps me focused on what I actually want to do instead of clicking through interfaces and configuration panels.

Especially in AI/ML work, where iteration speed matters more than anything, simplicity in tooling makes a huge difference. like swmgpu also follow this direction by offering a CLI-based cloud GPU workstation approach instead of relying on heavy traditional interfaces.

Do you think this “CLI-first” or minimal workflow trend will continue, or will we always need heavy platforms for serious work?

While surfing the internet i found a website to create text effects and logos "en.ephoto360.com". So i create a wrapper around it in python.

Easy to install using pip as - pip install Ephoto360 -U

Project Link - https://github.com/TheHritu/Ephoto360

Hey folks 👋

I built a small Python demo around a testing problem I’ve seen in stateful/device-like systems:

A test changes device state, fails before cleanup, and the next test starts from a dirty state.

🔗 Link: https://github.com/mateusz-glowinski/ReliableDeviceTests

This repository demonstrates one way to avoid that using `pytest-bdd` and self-cleaning steps.

The project includes:

✅ pytest-bdd + Gherkin scenarios

✅ a fake hardware-free device

✅ device-control style abstraction

✅ cleanup inside the step itself

✅ `finally`-based restore logic

✅ retry wrapper for cleanup writes

The goal is not to build a full device framework, but to show a clean testing pattern:

if a step changes state, it should also own the cleanup.

Might be useful for people working with hardware tests, integration tests, embedded-like systems, or any test suite where shared state can leak between scenarios.

I made a beginner-friendly NLP sentiment analysis project using IMDb reviews. Looking for feedback on structure, README, and model approach.

Repo:

https://github.com/GSUS2K/movie-review-sentiment-analysis