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I bet you do, or you will. In my case, I had been getting movie recommendations from friends and also randomly. I'd look up the flick on IMDB, metacritic, and rotten tomatoes and I'd guess whether I would like it. I wanted something more 'algorithmic' - and more accurate. So me and Claude build an sbcl-based Film Recommendation system. Type in a new film name, it goes to open film database OMDB, grabs the scores, and then, using films I have already rated and with a built-in tiny Neural Net, gives me a personal recommendation. It uses the OMDB data and an algo that weights those with my personal values (in half a dozen areas: overall, acting, cinematography, etc), along with a 'comments' box to note for friends / others. The code is very well-done CL, heavily commented. The film & ratings database is stored as S-exprs, naturally, all in one file. I don't use a database as I only have a few hundred films, but maybe later I will. And that's the point -- this is a living chunk of code that I from time to time bolt in new features, or try new things (e.g. the UI is localhost:8080) I fretted about having a 'real' project to really dig into CL for a long time, and finally, found a meaty-enough project that ends up being really useful & quite a learning (continuing learning) experience. I start it up inside emacs/sly like this: (ql:quickload '(:hunchentoot :dexador :yason))
(load "s:/filmrec.lisp")
(filmrec:start)
I have to set the omdb key: (setf filmrec:*omdb-api-key* "fxxxx70")
Then, every so often, I retrain the NN: (filmrec:retrain)
Good Luck, you'll find a project. And with an LLM buddy, you'll succeed. |