crisbal_

I use the following for feeding into AI

   find . -print -exec cat {} \; -exec echo \;

Which will return for each file (and subfolders) the filename and then the content of the file.

Then `| pbcopy` to copy to clipboard and paste it into ChatGPT or similar.

> big company

Isn't it the Swiss post?

Well, apparently there is. Just press export and you will find it costs 8$.

How does this compare to Voila from the Jupyter team? ( https://voila.readthedocs.io/en/stable/index.html )

A few days ago I discovered this initiative that I found very interesting and fun: during the month on November you have to develop a program to generate a novel of around 50k words. This is the 7th edition of the event.

I started reading the dev-logs (GitHub issues) of the past editions and discovered some great novels both in term of technology involved and also content.

Some gems:

* MARYSUE (https://github.com/catseye/MARYSUE), which aims to create an interesting novel with a plot and everything else (and the related write-up "A story compiler" (https://git.catseye.tc/MARYSUE/blob/master/doc/Overview%20of...)

* LIFE OF THE AZAR (https://github.com/NaNoGenMo/2017/issues/39), an archive/enciclopedia of some sort of an imaginary city of a couple of thousands of citizens describing the people, their relationships, events that happens in the city and much more

* THE DESERT OF THE WEST (https://github.com/dariusk/NaNoGenMo-2015/issues/156), a generated guide of imaginary worlds together with a map generation alghoritm with rivers and erosion (http://mewo2.com/notes/terrain/) and a generator for city names based on natural language theories (http://mewo2.com/notes/naming-language/)

* MEOW (https://github.com/dariusk/NaNoGenMo-2014/issues/50) meow meow meeeooow mew

Some projects are based on Neural Networks, some on Markov Chains, some on simulations, some on Tracery grammars (https://tracery.io/), some on Prolog, some on "plain" text processing and some on a mix of all of these. (I found an overview of the approaches for the 2016 edition here https://github.com/NaNoGenMo/2016/issues/154)

I find all of this very fascinating and might start my own adventure with text generation in the next days.

Beware, since your main div has the "newsletter-subscribe" class, it gets gidden by uBlock Origin and the website is unusable.

It's just planned maintenance.

As someone who is doing his bachelor thesis on Reinforcement Learning this is some useful information.

OT (but not really) question: does anyone here use Reinforcement Learning techniques at work? For the thesis I am working on black-box optimization of 2 variable functions with Reinforcement Learning (and comparing it with Bayesian Optimization techniques).

As someone else suggested the Sutton & Barto book is really great knowledge but I would also like to suggest these lessons (https://www.youtube.com/watch?v=2pWv7GOvuf0) by David Silver (who worked on AlphaGo)

There is not a single reason to try this in production, or even for a side project.

Hello, author here. Sorry if I am posting from a new account but I forgot the password to my old one (crisbal) and I didn't set a recovery email.

Let me know if you have any question.