jeromechoo

This actually looks 100% playable in the office.

I’ve had 3 SD cards fail on me in the last year. I now avoid using them as serious long term storage.

To be fair, these SD cards were exposed to fairly extreme Texas temperatures. One in a car dashcam, the other in an outdoor camera.

We maintain a list of around 1000 major publishers across the world and we crawl it every 15 minutes. For every other publisher (smaller blogs, etc..), they come through our global crawl.

The list itself isn’t particularly hard to maintain. What’s hard are the myriad of rules and configurations required to crawl and scrape each publisher. We built a model that extracts article data and it does a good job figuring out headlines, images, authors, and text.

Scraping rules are very self-manageable if you're planning on crawling just a few publishers. But jt gets exponentially more difficult to crawl hundreds.

Back when I managed a small coworking space, one of our most loyal members paid the premium for 1 of 2 private offices we had where he would show up to every day and shut the door for 8 hours working in the dark. He liked our rowdiness outside but hated being in the middle of it.

People know what they need to be productive. Everything else just gets in the way.

Ultimately this has very little to do with “the right office layout” and far more to do with how much trust your employer affords to its employees to manage themselves by default.

I don't mind it. But as others have already said, a single website can easily break a web ring. Why not a simple "I'm Feeling Lucky" button that takes you to a random website in the ring?

Nice! Yes, coreference resolution is surprisingly absent even in enterprise NLP.

Depends on what you mean by "better". With more accuracy?

Not a paper, but NLP extraction into knowledge graph representations do exist and are in use today. For example, here's a general purpose NLP model that links organization and people entities (among others) to each other based on factual relationships described in the text — https://demo.nl.diffbot.com

This semantic extraction can be extrapolated to most any trainable context. A useful one I've worked with involved mapping supplier-partner relationships. A well built supply chain graph can identify every layer of risk in a single supply chain and provide the provenance to back it up.

IMO, the biggest blocker to more mainstream use of Knowledge Graphs (even in the commercial world) is an actually intuitive interface for knowledge exploration. The real market innovation behind GPT isn't its 175 billion parameters, its the prompt interface that makes ChatGPT so universally accessible.

I actually do this for every service I put my email down for. It’s been about 2 years since I started.

Fortunately (unfortunately?) my email has only been sold once, and it wasn’t as egregious as you might think.

Amplitude, the user analytics company, sold my address to at least 3 companies who simply started emailing me as if I’ve always been a subscriber to their newsletter.

I do use their free plan though so I’m not mad about it.

This is awesome. Noticed you mentioned building an accurate product graph. Do you use a knowledge graph behind the scenes to find lookalikes?

Good find! Worth a follow up to get more!

Thanks! The test drive should show what kind of example data to expect without getting too deep. Are you looking for the JSON output specifically?

Hey there, I’m sorry to hear that, but I’d love to help. There’s a help button on the page that’ll reach me directly.

Or we could just troubleshoot here - What seems to be the issue?