Victor Coatalem

I developed this project in early 2023, as I was getting fed up of all the manual work    that represents appartment search.

Signing up alarms on different platforms (

Filling in (yet another) form with my personnal info.

This toolsuite benefits from the openness of the contact form on the french website "

". It makes it possible to contact an agency through a simple, unprotected 

 request. As a result, there is no Puppeteer magic nor scrapping shenanigans happening there.

Sign up an email address to a bunch of alarms.

Program a job that does a daily scrapping of all the SeLoger annonces in the mail, and contact agencies through the 

Keep your phone nearby for when agency people call you, as they like your profile for the appartment.

As a result, RoofR inverts the power balance between appartment seekers and agencies.

This project was a good opportunity to work on 2 subjects:

. A subject I was deeply interested in at the time. It allows me to treat all the external depedencies (mail service provider, agency, cache system) through an abstract interface.

The core domain module is essentially a url scanner for mail content (plain text, html), which sends a contact demand for urls that are identified as agency annonces.

RoofR appartment search automation

Nicolas Caron

Emilien Gaury

As the final project of a 6 month C/Unix coding bootcamp, I worked on the implementation of a chess AI, that is able to compete online using the UCI protocol.

implementation of the rules of the game of chess. Chessboard representation, pieces, allowed moves, state, etc.

This is the engine used locally to test our AI. It is critical that it is

1. functional. So that our AI plays proper chess.

2. efficient. Our AI involves a lot of chessboard replication to test all possibilities. All the operations related to the chessboard need to be as light as possible on our CPU and memory.

The bitboard representation is a very efficient way to represent a chessboard. Each piece family (type + color) is modeled as a single 

. As a result we only need 12 integers to represent our board (instead of the 64 we would have with the naive representation of a chess board)

implementation of a decision AI that searches for the best move given a board state

 a board state, and searches for the best move it.

we define a heuristic function. This heuristic is critical to the good behaviour of our AI, and tries to answer the question "given this board situation, am I in a good position ?". Our heuristic computes the sum of the value of our pieces remaining, as well as their position. For instance, a pawn that is 1 tile away from being upgraded to a queen, should be worth more than a pawn that is somewhere on the middle of the board. This heuristic is subject to change though, and remains a major route to improve the AI.

The AI performs a lot of chessboard duplication: when searching for my best move, I have to project what chessboard would emerge from performing such move. Then, I have to see what is my opponent's best move on the resulting chessboard. And so and so on. We are limited by the memory of our computer in this case, and have to introduce a "depth" parameter, that indicates how many steps we can predict in advance. Ultimately, the performance of our AI will wildly depend on the depth we allow it to use.

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