I just scraped data from reddit and other sources so i could build a nsfw classifier and chose to open source the data and the model for general good.
Note that i was a 1 year experienced engineer working solely on this project in my free time, so it was basically impossible for me to review or clear out the few csam images in the 100,000+ images in the dataset.
Although, now i wonder if i should never have open sourced the data. Would have avoided lot of these issues.
Ooh, love the UX, the heat meter and contrarian detection are very nice ideas.
I recently released https://trackernews.app/browse which surfaces out and groups content from hn, reddit .. for user defined topics and extracts user defined structured data along with summaries.
a tangential take on similar problem with focus on grouping posts by topics.
would love to get any feedback on the UX/ understandability of the interface
This holds true in most other countries as well. Gore/ chopping of appendages is happily accepted and enjoyed (in movies, games etc) by all of India, whilst a simple kiss can be a taboo/ issue.
> The 2021 M1 Air is a slightly chunkier fanless machine that runs circles around Intel laptops with desktop-class processors, at lower cost and up to triple/quadruple the battery life. It's unlike any Intel laptop you'll find on the market today.
Second this. I got the cheapest M1 macbook air in december and it really is streets ahead of competetion in terms of performace/ battery life.
The new macbook air is actually decent for gaming.
On the intel macbook airs (and on most pros without gpus) playing CS:GO at full resolution was a bad experience (overheating and <60fps).
I got the cheapest M1 macbook air and it plays CS:GO with no issues and without heating up (even when the ambient temperature in my city is ~38 Celsius)
> In fact, most data science and ML engineers are quite skilled in systems engineering, because you have to do so much work with GPU hardware issues, underlying scientific package management, efficient data transportation, etc.
Unless your data scientists are expected to build the machines they use, they won't be dealing with any hardware issues at all.
Literally every data scientist at big companies use pre-configured vms/notebooks in cloud.
especially, because once x0K words crossed, the output becomes worser.
https://github.com/quilrai/LLMWatcher
made this mac app for the same purpose. any thoughts would be appreciated