LSTM trained on HN

1) My Get All Hacker News Submissions script is somewhat obsolete since all HN data is now on BigQuery (will add a note to the README today)

2) There is a massive, massive selection bias in terms of the quality of the selected generated texts. If you look at the sample output (https://github.com/danhon/deep-hackernews/blob/master/sample...), there is a large disparity in quality between the chosen submissions for the blog post and a typical generated submission. (this is expected, although the need for human curation damages the "Turing test" suggestions proposed by other commenters)

Compare with the sample output at similar temperature from my 128-cell LSTM network (https://github.com/minimaxir/textgenrnn/blob/master/outputs/...), which is several orders smaller (OP uses a 512-cell network), and many magnitudes lower amount of training data. (I am curious how long OP spent training the network.)

Some pretty hilarious and/or thought-provoking headlines! LSTMs capture so much about the input (compared to N-grams, say), it can be almost eerie.

fucking lost

# I like Self-Driving Cars Using Docker Components

# Elon Musk says he will be more than a secret artificial intelligence

# Show HN: A web browser for harassment in 2017

# Slack is the future of the world

# China has died

But yes, reading through the titles was eerily realistic.

Brilliant.