Give me a summary of `Array.from(document.querySelectorAll('[indent="0"]')).map(a => a.parentNode.querySelector('.commtext').innerText).filter(a => a.length > 10 && a.length < 550).map(a => a.replace(/reply$/, '')).join('\n');`
«These predictions discuss various expected events and developments in the year 2023. These include the rise of robotic beings and the end of human rule, changes in language and animal treatment, the rise in enrollment for history classes, the use of artificial intelligence in video game development, the continuation of the COVID-19 pandemic, changes in political leadership, the decline of single-page applications and client-side JavaScript, the potential for conflict between humans and artificial intelligence, and infrastructure changes within the technology industry. There are also predictions of changes in social media platforms, the adoption of Rust programming language, the decline of Tesla, and the end of the recession. Some of the predictions are cautious or negative, while others are more optimistic. It is not clear if these predictions have been accurately fulfilled or not.»
No idea why it gets the "tense" of the predictions wrong.
I had to ask it which were the most positive/negative predictions, and of course a short story in the style of William Shakespeare about how one prediction could be improved by linked data. This is what it came up with:
ChatGPT: --- 8<
Pessimistic Prediction: The potential for conflict between humans and artificial intelligence
Short Story:
Upon the rise of artificial intelligence,
A fear did grip the hearts of humankind;
The fear that machines would one day seek to rise
Against their creators, and all order unbind.
But in this dark and troubled time,
A beacon shone forth, a light divine -
The light of Linked Data, pure and bright,
A tool to bring all beings into one design.
For with the power of Linked Data's link,
The humans and the machines did intertwine,
Creating bonds of understanding and trust,
And peace did reign where once there was only mine.
No idea why it gets the "tense" of the predictions wrong.
edit: larger sample