dR stats

This project is made to determine the health of the devRant developer community.

Also this data will be used for retoor9b, the newest AI hype! You're still using ChatGPT?

Statistics by last build

Click here for latest dataset optimized for training LLM's like retoor9b.

Click here for latest graphs compilation.

Click here for all generated data. It's a big dataset containing data for LLM's to train on, graphs per user or overal statistics and json files with all made observations.

Statistics are build automatically using a build server.

Generating these statistics takes quite some steps. Look at the build log under the actions tab.

Credits

Thanks to Rohan Burke (coolq). The creator of the dr api wrapper this project uses. Since it isn't made like a package, i had to copy his source files to my source folder. His library: https://github.com/coolq1000/devrant-python-api/

Using this project

Prepare environment

Create python3 environment:

python3 -m venv ./venv

Activate python3 environment:

source ./venv/bin/activate

Make

You don't have to use more than make. If you just run make all statistics will be generated. It will execute the right apps for generating statistics.

Applications

If you type dr. in terminal and press tab you'll see all available apps auto completed. These applications are also used by make.

  1. dr.sync synchronizes all data from last two weeks from devrant. Only two weeks because it's rate limited.
  2. dr.dataset exports all data to be used for LLM embedding., don't forget to execute dr.sync first.
  3. dr.stats_all exports all graphs to export folder, don't forget to execute dr.sync first.
  4. dr.rant_stats_per_day exports graphs to export folder. don't forget to execute dr.sync first.
  5. dr.rant_stats_per_hour exports graphs to export folder. don't forget to execute dr.sync first.
  6. dr.rant_stats_per_weekday exports graphs to export folder. don't forget to execute dr.sync first.

Observations made by AI regarding statistics

The model used for generating these observations is called smoll2 which is a 1.7b model.

Provided report below does contain some inconvenience but I'm working on it by testing several models. I am limited by the power my server provides for running LLM's. I do not own a decent GPU.

If I would attach the ChatGPT API to my project, the statistics would be better / perfect. I have tested this. Sadly, the API costs to much for a hobby project and I refuse the use of an credit card. There are better options for payment not provided by OpenAI which I prefer.

Several trends and insights about the devRant community

  1. The most active users seem to be posting more than once per month. This could indicate that these individuals are very engaged with the community or have a high level of interest in participating in discussions.

  2. There is a large range in the post lengths, ranging from 19 characters (kienkhongngu) to 742 characters (Pogromist). While there may be some outliers due to formatting issues or other factors, this suggests that users have varying levels of engagement and writing style on the forum.

  3. The "ownership_content" value ranges from -0.5 to 1. This indicates that while some users do not post much, others are heavily involved with frequent and in-depth contributions. However, it's unclear what specific metric this represents or how it correlates with user engagement.

  4. The most common "upvotes" per month range from 0 to 9 (arekxv) and 21 (-1 for negative upvotes). This suggests that while users are posting relatively often, there may be some variability in their level of agreement with the content they're sharing or commenting on.

  5. Overall, the data indicates a moderate level of engagement from users. While there is no clear indication of highly active users dominating the forum, the overall statistics suggest an engaged community where users contribute regularly and interact with each other's posts.

Detailed by AI generated summary based on information provided by this project about a certain user

The username is anonymized. Same for the actual values.

Analysis of user neo

  1. Rank and Contributions:
    • Rank: 45th overall
    • Contributions: 45 posts
  2. Ownership:
    • Ownership: 0.10, indicating that neo holds a slightly larger portion of the content ownership in the dataset.
  3. Upvotes:
    • Upvotes: 120 upvotes in total, suggesting that neo's content receives a notable level of recognition.
    • Upvotes Ownership: 0.03, meaning that neo owns about 3% of all upvotes in the dataset.
    • Upvote Ratio: 2.67, which implies a solid amount of engagement with neo’s posts.
  4. Post Length:
    • Total Post Length: 5,100 characters
    • Average Post Length: 113 characters per post, suggesting relatively concise contributions.

Summary:

  • neo is ranked 45th with 45 posts, indicating a modest level of contribution.
  • Their ownership percentage (0.10) reflects a slightly larger share of content ownership.
  • The upvotes received (120) and upvote ratio (2.67) suggest a notable level of engagement with their content.
  • The average post length of 113 characters indicates concise contributions.