trees
go-live
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trees
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Ask HN: Show me your Half Baked project
I've always wanted my own gradient boosting machine implementation to compete with Xgboost and LightGBM. There's a huge space of unexplored tricks around regularization, randomization, tree structure, etc., that few people are exploring because neural nets are exploding.
So far I've roughly caught up in speed and accuracy with a few original tricks (and 1/20 of the features), but no real breakthroughs: https://github.com/benpastel/trees
On the plus side
go-live
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Ask HN: Show me your Half Baked project
Created a shell utility in Go, called go-live. The idea is that you start it in a directory, and then those files are immediately hosted on the network.
The core idea is to be as lightweight and performant as possible, and to do one thing only and well - Unix style.
https://github.com/antsankov/go-live
Looking for contributors and feedback on it.
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Announcing the 1.0 release of go-live, an ultra lightweight/performant (4mb compiled) static-site and file server.
Checkout: https://github.com/antsankov/go-live#todo-help-wanted
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1.0 release of go-live: An ultra light (4mb compiled) Go site and file server
Linux: ```snap install go-live```
Checkout the Github for more info on how to install it: https://github.com/antsankov/go-live#install and interesting use cases.
Any feedback is appreciated, since this is the first open-source Unix utility I've worked on! Also need some help on profiling it.
What are some alternatives?
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