Caspar David Friedrich (German, 1774–1840)

A guide to designing wheelchair using characters!

I hope this helps anyone who's trying to design their oc using a wheelchair, it's not a complete guide but I tried my best! deffo do more research if you're writing them as a character

Ngl i prefer the 2016 version purple on the right.

ily, menswear guy

Not my meme but figured I'd share for those about to ride out the storm

Edit bc I'm seeing a lot of confused international reblogs: this was posted because of Hurricane Milton absolutely demolishing Florida this week after Helene went through and because US insurance agencies are kind of assholes. Stay safe out there and thanks for signal boosting!

Painted an aquarium dress to go with the terrarium dress~ 🐟🐚🌊

Gonna paint a planetarium dress next ahahaha

Here's a short timelapse. The full hours long video recording, HD Image, and PSD file will be DMed on my Patreon on Feb 5th

the aforementioned terrarium dress

I slept in and just woke up, so here's what I've been able to figure out while sipping coffee:

Twitter has officially rebranded to X just a day or two after the move was announced.

The official branding is that a tweet is now called "an X", for which there are too many jokes to make.

The official account is still @twitter because someone else owns @X and they didn't reclaim the username first.

The logo is 𝕏 which is the Unicode character Unicode U+1D54F so the logo cannot be copyrighted and it is highly likely that it cannot be protected as a trademark.

Outside the visual logo, the trademark for the use of the name "X" in social media is held by Meta/Facebook, while the trademark for "X" in finance/commerce is owned by Microsoft.

The rebranding has been stopped in Japan as the term "X Japan" is trademarked by the band X JAPAN.

Elon had workers taking down the "Twitter" name from the side of the building. He did not have any permits to do this. The building owner called the cops who stopped the crew midway through so the sign just says "er".

He still plans to call his streaming and media hosting branch of the company as "Xvideo". Nobody tell him.

This man wants you to give him control over all of your financial information.

Edit to add further developments:

Yes, this is all real. Check the notes and people have pictures. I understand the skepticism because it feels like a joke, but to the best of my knowledge, everything in the above is accurate.

Microsoft also owns the trademark on X for chatting and gaming because, y'know, X-box.

The logo came from a random podcaster who tweeted it at Musk.

The act of sending a tweet is now known as "Xeet". They even added a guide for how to Xeet.

The branding change is inconsistent. Some icons have changed, some have not, and the words "tweet" and "Twitter" are still all over the place on the site.

TweetDeck is currently unaffected and I hope it's because they forgot that it exists again. The complete negligence toward that tool and just leaving it the hell alone is the only thing that makes the site usable (and some of us are stuck on there for work).

This is likely because Musk was forced out of PayPal due to a failed credit line project and because he wanted to rename the site to "X-Paypal" and eventually just to "X".

This became a big deal behind the scenes as Musk paid over $1 million for the domain X.com and wanted to rebrand the company that already had the brand awareness people were using it as a verb to "pay online" (as in "I'll paypal you the money")

X.com is not currently owned by Musk. It is held by a domain registrar (I believe GoDaddy but I'm not entirely sure). Meaning as long as he's hung onto this idea of making X Corp a thing, he couldn't be arsed to pay the $15/year domain renewal.

Bloomberg estimates the rebranding wiped between $4 to $20 billion from the valuation of Twitter due to the loss of brand awareness.

The company was already worth less than half of the $44 billion Musk paid for it in the first place, meaning this may end up a worse deal than when Yahoo bought Tumblr.

One estimation (though this is with a grain of salt) said that Twitter is three months from defaulting on its loans taken out to buy the site. Those loans were secured with Tesla stock. Meaning the bank will seize that stock and, since it won't be enough to pay the debt (since it's worth around 50-75% of what it was at the time of the loan), they can start seizing personal assets of Elon Musk including the Twitter company itself and his interest in SpaceX.

Sesame Street's official accounts mocked the rebranding.

Your Essential Guide to Python Libraries for Data Analysis

Here’s an essential guide to some of the most popular Python libraries for data analysis:

 1. Pandas

- Overview: A powerful library for data manipulation and analysis, offering data structures like Series and DataFrames.

- Key Features:

  - Easy handling of missing data

  - Flexible reshaping and pivoting of datasets

  - Label-based slicing, indexing, and subsetting of large datasets

  - Support for reading and writing data in various formats (CSV, Excel, SQL, etc.)

 2. NumPy

- Overview: The foundational package for numerical computing in Python. It provides support for large multi-dimensional arrays and matrices.

- Key Features:

  - Powerful n-dimensional array object

  - Broadcasting functions to perform operations on arrays of different shapes

  - Comprehensive mathematical functions for array operations

 3. Matplotlib

- Overview: A plotting library for creating static, animated, and interactive visualizations in Python.

- Key Features:

  - Extensive range of plots (line, bar, scatter, histogram, etc.)

  - Customization options for fonts, colors, and styles

  - Integration with Jupyter notebooks for inline plotting

 4. Seaborn

- Overview: Built on top of Matplotlib, Seaborn provides a high-level interface for drawing attractive statistical graphics.

- Key Features:

  - Simplified syntax for complex visualizations

  - Beautiful default themes for visualizations

  - Support for statistical functions and data exploration

 5. SciPy

- Overview: A library that builds on NumPy and provides a collection of algorithms and high-level commands for mathematical and scientific computing.

- Key Features:

  - Modules for optimization, integration, interpolation, eigenvalue problems, and more

  - Tools for working with linear algebra, Fourier transforms, and signal processing

 6. Scikit-learn

- Overview: A machine learning library that provides simple and efficient tools for data mining and data analysis.

- Key Features:

  - Easy-to-use interface for various algorithms (classification, regression, clustering)

  - Support for model evaluation and selection

  - Preprocessing tools for transforming data

 7. Statsmodels

- Overview: A library that provides classes and functions for estimating and interpreting statistical models.

- Key Features:

  - Support for linear regression, logistic regression, time series analysis, and more

  - Tools for statistical tests and hypothesis testing

  - Comprehensive output for model diagnostics

 8. Dask

- Overview: A flexible parallel computing library for analytics that enables larger-than-memory computing.

- Key Features:

  - Parallel computation across multiple cores or distributed systems

  - Integrates seamlessly with Pandas and NumPy

  - Lazy evaluation for optimized performance

 9. Vaex

- Overview: A library designed for out-of-core DataFrames that allows you to work with large datasets (billions of rows) efficiently.

- Key Features:

  - Fast exploration of big data without loading it into memory

  - Support for filtering, aggregating, and joining large datasets

 10. PySpark

- Overview: The Python API for Apache Spark, allowing you to leverage the capabilities of distributed computing for big data processing.

- Key Features:

  - Fast processing of large datasets

  - Built-in support for SQL, streaming data, and machine learning

 Conclusion

These libraries form a robust ecosystem for data analysis in Python. Depending on your specific needs—be it data manipulation, statistical analysis, or visualization—you can choose the right combination of libraries to effectively analyze and visualize your data. As you explore these libraries, practice with real datasets to reinforce your understanding and improve your data analysis skills!

I keep hate-reading plague literature from the medieval era, but as depressed as it makes me there is always one historical tidbit that makes me feel a little bittersweet and I like to revisit it. That’s the story of the village of Eyam.