btel

Since I left X, I follow more sources and tend to verify them more often.

The alternatives are numerous. Starting from other social media with real governance (like mastodon), through RSS feeds, to forums/newsletters etc.

I especially like the idea of RSS feeds that allow to content creators to control completely their distribution channels. There are many good RSS aggregators such as innoreader of feedflow (for android).

Also connecting with people in real world is great.

It's an interesting read and a thought provoking idea. The authors definitely make a good point, but it's not really a scientific piece of work as they lack the quantitative analysis and even fail to describe the method used for jabberyfication.

They also tend to overextend this idea to human intelligence:

"We think a more promising approach lies in studying how our pattern-matching minds are extended by cognitive prostheses which allow us to formulate and manipulate progressively more abstract and larger patterns."

I would put it the other way around: how our cognitive system can delegate some of the processing to simple pattern matching prostheses? When I think of concepts such as LLMs there is more than pattern matching of all that I read about them, but there is a vague idea of what they are, how they are build and how I feel about them, and the name "LLM" is just a simple tag for this concept. I am wondering what cognitive scientist would make of this paper.

SEEKING WORK | Python dev/data scientist | Paris or remote

I have 10+ years of experience in Python / data analysis / machine learning / deep learning. I can work on proof-of-concepts or implementing data processing pipelines in production.

I am a strong advocate of agile practices in programming and data science (version control, unit-testing, CI, code reviews).

Stack:

* Programming: Python, C, Javascript

* Backend: Django + rest-framework, Flask

* Databases: PostgreSQL, DynamoDB, MongoDB

* Platforms: AWS (EC2, Kinesis, DynamoDB), GCP (CPU/GPU instances),

* Data science: jupyter (contributor), scikit-learn (contributor), tensorflow, pandas, matplotlib (contributor), numpy (contributor)

Contact:

* Email: [email protected]

* LinkedIn: https://www.linkedin.com/in/bartosz-telenczuk

* Github: https://www.github.com/btel

* Website: https://datascience.telenczuk.pl

There is also a jupyter notebook to accompany it: http://nbviewer.jupyter.org/github/btel/streams_with_amazon_...

> * the last one (`return None`) make me very dubious

simply `return` would be enough

According to the pep ensurepip was rejected: http://www.python.org/dev/peps/pep-0453/#including-ensurepip...