Hello everyone

I'm planning a research that analyze data from a couple of website, some of these website provide API, other don't, I'm going to use web scrapping to obtain the data, but I'm worried that I might get into trouble.

Is that a deal breaker or should I proceed anyway?

tldr: some users have more to share when it comes to novel learning resources or interesting projects, they don't spend much time in the comments, it doesn't mean that they don't care about the communities; some users don't want to stare at memes and blog posts all day.

Yesterday r/learnpython removed my post - https://www.reddit.com/r/learnpython/comments/fg9tqg/advanced_pandas_10_github_repo_learn_with_more/

I have been banned from r/learningprogramming today because you can somehow 'spam' a community with linked/textual content that gets upvoted 100+ times, while memes survive.

Last year I was removed from r/datascience because I posted content that got upvoted 300+ times (by there own community members).

They banned me for these 'tainted' posts:

Tainted Posts:

https://www.reddit.com/r/datascience/comments/cgbzos/a_catalogue_of_500_python_machine_learning/

https://www.reddit.com/r/datascience/comments/cf9giw/learn_data_science_through_100_trading_strategies/

https://www.reddit.com/r/datascience/comments/cgqvpd/business_data_science_notebooks_week_13_click_and/

This somehow amounted to spam according to the moderators. While this 'great' content was allowed around the same time:

https://www.reddit.com/r/datascience/comments/cb0gte/the_formatting_struggle/

https://www.reddit.com/r/datascience/comments/cb9wie/its_like_the_notebook_but_with_more_tears/

https://www.reddit.com/r/datascience/comments/ca5x7a/how_do_data_professionals_spend_their_time_on/

I have not once been banned for a reason that didn't make me chuckle. Its all so petty and absurd, over-moderation honestly sucks. But like democracy, Reddit seems to be the least worst form of sharing content.

Hey everyone 👋

I’ve been working on a small side project called TinyGPU - a minimal GPU simulator that executes simple parallel programs (like sorting, vector addition, and reduction) with multiple threads, register files, and synchronization.

It’s inspired by the Tiny8 CPU, but I wanted to build the GPU version of it - something that helps visualize how parallel threads, memory, and barriers actually work in a simplified environment.

🚀 What TinyGPU does

• Simulates parallel threads executing GPU-style instructions (SET, ADD, LD, ST, SYNC, CSWAP, etc.)

• Includes a simple assembler for .tgpu files with labels and branching

• Has a built-in visualizer + GIF exporter to see how memory and registers evolve over time

• Comes with example programs:

  • vector_add.tgpu → element-wise vector addition

  • odd_even_sort.tgpu → parallel sorting with sync barriers

  • reduce_sum.tgpu → parallel reduction to compute total sum

🧠 Why data scientists might care

Most data science tools rely heavily on GPUs (NumPy, TensorFlow, PyTorch).

TinyGPU shows what’s happening behind the scenes - how threads, synchronization, and memory operations actually execute.

🎨 Why I built it

I wanted a visual, simple way to understand GPU concepts like SIMT execution, divergence, and synchronization, without needing an actual GPU or CUDA.

This project was my way of learning and teaching others how a GPU kernel behaves under the hood.

👉 GitHub: TinyGPU

If you find it interesting, please ⭐ star the repo, fork it, and try running the examples or create your own.

I’d love your feedback or suggestions on what to build next (prefix-scan, histogram, etc.)

(Built entirely in Python - for learning, not performance 😅)