At the time I was using both inline completions and the chat function (at least the inline chat, not the agent capabilities, which didn't exist yet).

Interesting, thanks for sharing. I didn't know about this. Tbh I mostly did this exercise to test out the gender and region prediction package. But thought the results might be interesting. Nice to know that there is a resource directly from RePEc.

Check out my blog post for a detailed explanation of my methodology and findings: www.paulbose.com/thisandthat/...

I used the "nametrace" package (github.com/parobo/namet...) to predict gender and region of origin based on author names rather than checking each author's actual gender or background. Therefore, take the results with a grain of salt.

The rise in female representation in the top 5% appears to be a global phenomenon, with increases observed in both North America/Europe and the "rest of the world." The trend is slightly stronger in North America and Europe, but these regions also had more ground to cover in terms of catching up.

The growth for regions other than US/Canada/Europe is primarily driven by scholars from Eastern Asia, Southern Asia and South America. Unfortunately, Africa remains extremely underrepresented.

While these numbers are super low, there's a small of positive change, ... a slow one.
- The share of women has edged up from roughly 9% to 12% over the past 12 years. Progress, but the pace is too slow!
- Representation from "the rest of the world" has increased from 16% to 25% in 2025.

#econsky #polisky

Our results suggest that refugee influx caused a sharp but short lived spike in salience of refugees. People who remained active tweeters on the topic started to show more opposition of refugees after a while however.
We combine the analysis with extremely local voting data and find similar results.

As an example I show how to analyze the sentiment of the last 100 posts of the reddit CEOs spez and kn0thing in mere seconds.

BONUS: use ollama to interact with the models from python and receive structured responses. This is super helpful for classification or structured outputs for e.g. Text summary.

FYi, in case you might care, here is an explanation how the models moderate internally (i.e. without using the API, but using system prompts).
www.lesswrong.com/posts/jGuXSZ...

I seems the abliterated versions are not censored but the the base model seems to be:
bsky.app/profile/paul...

Interesting point, your screenshots do somewhat indicate hard coded instructions in the model, but it could still be only on the API version. Would like to see this for the local model.