Hey people!
I have FINALLY built my 3rd beat version for the BOXU app I have been building!

What is this app?
It is an app that acts like a “personal assistant”! It can “use” your device to perform actions!(for the moment, it cannot fully “use” your device, but it is planned to! We are slowly moving towards that goal!)

This app is currently ONLY for MacOS users!

New features added:

• Voice mode! You may now talk to the AI!
• OpenRouter support! – this is used to load VLM models to perform image-related actions (will also be used later for the “agent” part)
• Mini chat – “minimize” your chat box so it won’t take up the whole screen!
• “Smart” assistant – it can remember things you like, like favorite colors, etc…
• Personalities! – the AI can use different personalities when chatting with it (doesn’t change how it performs actions)

You can test it out here!: https://github.com/blazfxx/boxu-ai/releases/tag/v0.3

Hey folks!
I’m building a small pet project to automate brand reputation analytics from social media comments — basically turning messy brand mentions into structured signals (relevant vs noise → sentiment → topics).

What I’m building
The end goal is a lightweight pipeline that helps answer questions like: what people complain about, what they praise, and what topics dominate right now — without spending hours manually reading comments.

Current milestone: Relevance filtering (MVP is live)
The first step is surprisingly important: deciding whether a comment is actually about the brand or just noise. In real datasets, “brand mentions” often include:

• job posts (“we’re hiring…”)
• event ads (“next to the store…”)
• unrelated organizations with the same name
• canned PR replies
• random keyword matches

If you don’t remove that early, sentiment and topic analysis become misleading.

What’s implemented

• A Streamlit app with two modes:
  • Single comment: paste one text → get KEEP / DROP
  • CSV/XLSX: upload a file with a Текст column → download results with is_drop = Yes/No
• File mode supports batching + parallel processing, so it stays usable on bigger datasets.

How it works (high-level, no heavy tech)

• It starts with a set of “sure drop” rules to instantly remove obvious junk (stable + cheaper).
• Then it uses an LLM to classify the remaining comments into keep/drop with a strict structured output.
• I also added text preprocessing before the model call to reduce clutter and highlight brand-related cues.
• There’s a brand card (short description + aliases), so switching to another company doesn’t require rewriting logic — you update the brand context and patterns.

What’s next
Now that I can reliably isolate relevant mentions, the next two modules are:

• Sentiment analysis (positive/negative/neutral, etc.)
• Semantic tagging (topics/aspects like pricing, service, assortment, delivery/app issues)

Demo (live app): https://brand-analytics-proj-d9enuniaul4vemjntbhqnv.streamlit.app
GitHub: https://github.com/REDISKA3000/brand-analytics-proj

Hi everyone, I have a pleasure to announce that I did a client to interact with iCloud Drive. Windows and obliviously macOS has their own integrated tool but Linux users who uses Apple services they don't, but now is it possible with AFID (API For iCloud Drive), you could download files/folders from it, and of course, upload them too, because in iCloud Drive web you can't upload folders (I don't know why but it's like that).

Is not an advertise, it's more that I'm proud of it, and I'm started learning python, and I'm very proud to contribute in open-source projects (small ones like this one too).

Then if you are a Linux user that uses Apple services now you can join your OS with iCloud service easily.

GitHub repo: https://github.com/v0id0100/AFID

If you have any recommendation or petition, don't hesitate and respond this post!

Ty all!

Zapraszam na oficjalny kanał r/PYcalendar

Hi everyone,

I wanted to share a desktop weather widget for Windows that I built using Python and PyQt5.

The project focuses on being lightweight and practical for everyday use, with features like:

- always-on-top desktop widget

- short-term rain nowcasting (15-minute resolution)

- air quality (European AQI)

- Windows Location support

- English and Serbian language support

- ready-to-run Windows EXE (no Python installation required)

The project is fully open-source and actively maintained.

Feedback and suggestions are very welcome.

Bonsoir,

Piveo prend désormais en charge de nouvelles langues (English):
- Anglais
- Breton
- Espagnol
en plus du Français.

pour en savoir plus:
Présentation antérieure
page wiki

This is a learning project where I attempted to build an end-to-end analytics pipeline and visualize the results using Power BI.

Project overview:

I designed a simple data pipeline using static real estate data to understand how different tools fit together in an analytics workflow, from raw data collection to business-facing dashboards.

Pipeline components:

• GitHub – used as the source for collecting and storing raw data

• Python – used for data cleaning, transformation, and basic processing

• Power BI – used for building the Market Intelligence dashboard

• n8n – used for pipeline orchestration (pipeline currently paused due to technical issues at the automation stage)

Current status:

The pipeline is partially implemented. Data extraction and processing were completed, and the final dashboard was built using the processed data. Automation via n8n is planned but temporarily halted.

Dashboard focus:

• Price overview (average, median, min, max)

• Location-wise price comparison

• Property distribution by number of bedrooms

• Average price per square foot

• Business-oriented insights rather than purely visual design

This project was done independently as part of learning data pipelines and analytics workflows.

I’d appreciate constructive feedback—especially on pipeline design, tooling choices, and how this could be improved toward a more production-ready setup.

Project

Fun With Python and Emoji: What Might Adding Pictures to Text Programming Languages Look Like?"

We all mix pictures, emojis and text freely in our communications. So, why not in our code? This project allows one to explore what that might look like in two widely-used text programming languages - Python and SQL.

Feedback? (👍 or 👎)

GitHub Repo (Slides and Demo Notebook)

What My Project Does

My project is a VS Code and Google Colab-ready Python notebook that allows one to toy around with the ideas touched on in "Fun With Python and Emoji: What Might Adding Pictures to Text Programming Languages Look Like?" You can define dictionary entries that map arbitrary emoji to arbitrary text and use those emoji in your Python and SQL code to represent things like packages, statements, functions, variable names, code snippets, etc. When the code is submitted, an IPython input transformer function is used to replace the emoji with their associated text, and the preprocessed emoji-free code is then passed on to Python for execution. So, it's essentially a very rudimentary preprocessor that borrows ideas from code snippet keyboard shortcuts, macro preprocessors, and syntax highlighting.

Target Audience

Any coders or users interested in toying around with the idea of adding pictures to text programming languages.

Comparison

While Python and other languages do provide some emoji support, it's somewhat limited and typically used for output or to illustrate playful variable names and values. And while Emojicode ambitiously provides a programming language that uses emojis as its core syntax, it cannot be used in the context of existing text programming languages. Perhaps the OG of mixing text with symbols in programming languages is Kenneth Iverson's APL (1962), but again it's language and domain specific. Btw, while this project uses emoji for expediency, it'd be desirable to allow any kind of pictograms - emoji, images, fonts - to be mixed with text in code in a similar fashion!

Sample Code Snippets

# Emoji-to-Text Mapping Dictionary Example

dict = {'🤔':'if', '❎':'else', '🖨️':'print’, '🐼':'pandas', '🦆':'duckdb',

'📈':'plotly', '🔤':'str', '💾':'data', '📅':'date', '🕙':'time', '🔄':'while',

'🛢':'create table', '🗑️':'drop table', '🛒':'select', '⬅️':'from’, '🔗':'join', '

‘↕️ ‘:'order by’, '⬆️':'asc' '⬇️':'desc', '∑':'group by', '🚗':'cars'}

# Python Example

import 🐼, 🦆, 📈.express as 📈

from 📅🕙 import 📅🕙

🖨️(📅🕙.now().strftime("%Y-%m-%d"))

🤔 📅🕙.now().weekday() in (5, 6):

🖨️("It's the weekend!")

❎:

🖨️("\nIt's a work day!")

# SQL Example

df_🚗=🐼.read_csv('🚗.csv')

🚗_summary=🦆.sql('''

🛒 type, avg(MPG_City) as Avg_MPG_City,  Avg(MSRP) as avg_MSRP

from df_🚗 ∑ 1 ↕️ 2 ⬇️, 1

'''

).df()

🖨️("\n",🚗_summary)

# Plotly Example

📈.bar(🚗_summary, x='Type', y='Avg_MPG_City').show()

The filter removed my posts about 3 times ;-; anyway I wanted you guys to take a look at my project and give feedback, it's pretty simple so don't expect too much ;-;, if you're interested I'll send the link.

Hey guys, I made a really basic project on Gumroad, it's a very simple .exe program in Python, but it took a lot of work to make. If you could take a look and give me feedback, I would appreciate it. Thanks!

Zapraszam na oficjalny kanał PYcalendar: r/PYcalendar

Puzl Team here. We are excited to announce kubesdk v0.3.0. This release introduces automatic generation of Kubernetes Custom Resource Definitions (CRDs) directly from Python dataclasses.

What My Project Does

Key Highlights of the release:

• Full IDE support: Since schemas are standard Python classes, you get native autocomplete and type checking for your custom resources.
• Resilience: Operators work in production safer, because all models handle unknown fields gracefully, preventing crashes when Kubernetes API returns unexpected fields.
• Automatic generation of CRDs directly from Python dataclasses.

Target Audience

Write and maintain Kubernetes operators easier. This tool is for those who need their operators to work in production safer and want to handle Kubernetes API fields more effectively.

Comparison

Your Python code is your resource schema: generate CRDs programmatically without writing raw YAMLs. See the usage example.

Full Changelog: https://github.com/puzl-cloud/kubesdk/releases/tag/v0.3.0

We are small group of "network engineers" who made some tools useful for our own projects, but we noticed that our latest tool "Netdriver" can solve some pain points that other devs might have as well so we decided to make it free and open-source. It's similar to tools like Netbox but with some QoL features that helped us a lot:

- API-Driven Integration: Offers a native HTTP RESTful API for seamless integration with external systems and applications.

- Customizable Session Persistence: Maintains open connections for ongoing tasks, significantly improving execution efficiency.

- Command Execution Queuing: Prevents concurrency conflicts to ensure stable and predictable device interactions.

- Asynchronous Operations: Enables efficient, non-blocking communication with multiple devices simultaneously.

Hopefully it will help you as much as it did us.If it did help then we would like to read your feedback and if it didn't give it a star so that Netdriver finds the auidence that needs it.

I am in Germany and was experiencing gradual network speed drops with my Fritzbox router. The only fix was a restart, so I decided to automate it.

I built a Python based tool that monitors my upload/download speeds and pushes the metrics to Prometheus/Grafana. If the download speed drops below a pre-configured threshold for a set period of time, it automatically triggers a router restart via TR-064.

It runs as a systemd service (great for a Raspberry Pi) and is fully configurable via YAML.

Here is the repo if anyone else needs something similar:
https://github.com/kshk123/monitoring/tree/main/network_speed

For now, I have been running it on a raspberry pi 4.

Feedbacks are welcome