Elasticsearch: https://www.elastic.co/platform
272 karma · joined July 13, 2022
There are some leaders who simply can't function in a remote environment. However, this is a flaw of their leadership and the organizational culture, not a problem with employees or their preferences. It is a problem with personal fear, inflexibility, and lack of vision more than anything else. My experience is that the older people are and the longer they have worked at a company, the more issues they have with remote work.
We proved during COVID that we can function just fine remotely. It benefits the employees in a hundred different ways. An organization that wants people to come to work at their detriment is one that doesn't value their people, and this is how they are expressing it.
It is sometimes worth it to take people who are unhappy in their job and/or not performing well and give them a package when they are available. I would guess there are 20 of these people at any company of that size, not just WOTC.
In my experience, remote workers are significantly more productive than on-site employees IF the culture is one of employee empowerment and trust. If you expect people to be mature, communicate effectively, and get things done AND you give them the tools and support to do it then being remote is absolutely no barrier. It just gives people an extra hour or two a day to think about things and do a little extra work because they are not commuting.
The missing piece is that leaders need to plan, execute, and communicate effectively. They need to empathize with their teams and work with people as individuals, which means extra time and thought and attention. Face-to-face work covers up for a lot of incompetence because that person is standing in front of you. Remote employees can't hide behind a herd and remote leaders can't hide behind an org chart. You don't need to spend millions of dollars on a building to cover up for poor leadership and weak culture.
For reference, I run a team of 10 remote employees and am responsible for highly technical engineers that support sales for a team that generates 8-figures of annual revenue on SaaS and on-prem software.
Psychological safety is by far the most important requirement for group participation. You could be doing everything right for people in the middle of the bell curve and still have this person struggling because of something very specific to their childhood or personal experience.
Sorry to hear that happened, that sucks.
For example, the text "chocolate milk" is all the same characters as "milk chocolate", but likely have very different usage within the context of retailers or cooks. So, their vectors should be very different.
Word2Vec is NLP that uses a neural net to build these vectors: https://en.wikipedia.org/wiki/Word2vec
I also use a scale that does body mass measurements (consumer grade). It says:
1. My fat-free body weight went from 148.6 down to 143.2.
2. My muscle mass is down from 142 down to 136 (see below)
3. I have had no bone loss of bone mass.
As I was walking, I seemed to consistently lose about 25% of the weight in muscle. My weight loss stopped as I started hitting the weights, but I started putting on about a pound per month in muscle, which is very visible on my core and arms.
Peter Attia has a nuanced medical take on the drug, based on interactions with his patients (short video https://www.youtube.com/watch?v=T6fhFLv2ERs). His take was that the drug works and there are very few side effects. He also uses it as a last resort, as he has seen his patients lose far more bone mass and muscle than he would like. However, it is a lifesaver for people who don't have success with other methods.
Personally, I don't want to use it, but don't judge others for making the right medical decisions for themselves.
> Return True if the string ends with the specified suffix, otherwise return False. suffix can also be a tuple of suffixes to look for. With optional start, test beginning at that position. With optional end, stop comparing at that position.
... without an example is NOT easier to use than an example for people learning Python. It uses language specific jargon (suffix, tuple), unexpected capitalization, and is needlessly terse.
How about ...
> Check if a string ends with a certain ending (or endings) and return True if it does, or False if it doesn't. You can specify one ending or multiple endings in a tuple. You can also choose where to start and stop checking within the string.
.. along with an example of code that can be easily copied? That is the value the other sites provide. Readability and usability.
Also, a great idea to use pixelated emoji to provide a body of existing data that is already labeled.
Disney can take the gambling dollars, dillute the ESPN brand, then send it on its merry way and keep its reputation long-term.
Even an LLM enabled senior engineer was 100x better, the situation has to be the right one where all 100x can be applied productively.
An analogy -- if I am an athlete who runs a mile 100x faster than my competition, does that mean that my country will win 100x more medals at the Olympics? Not unless I compete in every competition.
Now, if you can make a system that consistently creates or recruits (or even just prevents your competition from finding) more productive developers you can get a real advantage. And, as soon as you do it you will have people imitating your strategy and tactics and poaching the people who are most important to building the system. It is way more complex than providing an LLM.
There were some very cool things that came out of the last strike (like Dr Horrible's Sing Along Blog) but I would guess that at least 75% of you have never heard of it.
Once I got "into" langchain and how it did things my life as a developer got infinitely easier. It is true that it is doing a lot of things that you "could" do elsewhere, but that is kind of the point of a library. For example, it makes it incredibly easy to switch between vector datastores or embeddings, with just a tiny code change. I love that.
Look at how much code it takes to actually get something done. It makes it trival to take a file (or a number of files), chunk them, and load them into a vector store. Sure, I could write and maintain the code to do that, but why?
While I did find it challenging to get started with Langchain, it was more a lack of understanding of the ecosystem than anything else. Great abstractions aren't going to shield me from that without restricting choice. The documentation has improved noticeably in the last few weeks.
Great work, it is very much appreciated by the non-HN crowd. Don't let this feedback get you down.
Why would I want to be constrained to plain HTML when there are pre-made toolkits out there that do it for me? I don't want to figure out how to do rounded corners in CSS, but I would like to have a GUI that shows me how it looks on a page.
https://www.just-auto.com/features/toyota-bets-big-on-hydrog...
More than 10000 people had to return to their country of origin on very short notice, because of decisions they had no control over. They likely lost money having to pay for travel costs, break leases and contracts, and will be without income. That is sad.
How do I get to that number? First, lets trust the 90% number in the headline. Because the top 30 H-1B employers laid of 85,000 visa holders in 2022 and 2023. So, it is not a stretch to say that there would be at least 10,000 people overall.
https://www.epi.org/blog/tech-and-outsourcing-companies-cont...
Fine-tuned Models: Imagine you have a super-smart robot that can talk about anything. But you want it to be really good at talking about, say, dinosaurs. So, you teach it more about dinosaurs specifically. That's what fine-tuning is – you're teaching the robot (or model) to be really good at a specific topic.
Vector Databases and Embeddings with LLM: This might be a little tricky, but let's think of it this way. Imagine you have a huge library of books and you want to find information on a specific topic, say, ancient Egypt. Now, instead of reading every book, you have a magical index that can tell you which books talk about ancient Egypt. This index is created by magically converting each book into a "summary dot" (that's the embedding). When you ask about ancient Egypt, your question is also converted into a "summary dot". Then, the magical index finds the books (or "summary dots") that are most similar to your question. That's how the vector database and embeddings work.
So, if you want your super-smart robot to be really good at one specific topic, you use fine-tuning. But if you want it to quickly find information from a huge library of knowledge, you use vector databases and embeddings. Sometimes, you might even use both for different parts of the same task!
For example, I lived in Tucson, AZ and there was a road nearby called "Silverlake" that at one time led to a silver lake with enough power to support a flour mill (see https://tucson.com/news/blogs/streetsmarts/street-smarts-sil...). Now it leads to apartments and houses and an Air Force base.
More people + less water = hotter environment, even before climate change is factored in. A huge amount of the water in the area goes to agriculture as well.
So, coming myself from a database background but working in search, the SELECT statement (and joins) probably aren't the best way to get your head wrapped around things. I would think of the vector as a unique key for a record, and only using a LIKE statement for all my queries, but one that will return a probability of a match instead of an actual match.
A great use case is to think about similarity, where we want the things that are closest to what we want to see, but there isn't an exact match.
For example; a user gives me a sentence that says, "How long do I have to be with the company before I get a 401K match?". My vector store has a bunch of vectors including "A new employee will be eligible for 401K after 6 months." ,and, "The 401K program is run by <MEGACORP X>."
I would like to be able to see that the first vector is a closer match to the user sentence than the second, and by how much. I would also like to do this without having to change my code much based on the structure of the text. Luckily, there is a very simple algorithm for doing this (cosine similarity) that doesn't change regardless of the sentence structure or the question answered. Also, it doesn't matter what kind of question/answer you do as long as it can be vectorized, so you could even give me a vector representing an image and I can give you an image that is most similar.
Here is the most interesting thing about vectors -- with very little effort they turn the english language into a programming language.
Instead of typing "SELECT document_id, document_name, document_body FROM documents WHERE (document_body LIKE '%401K%' AND document_body LIKE '%match%' AND document_body LIKE '%existing employee%') FROM documents" I can just ask, "How long do I have to be with the company before I get a 401K match?" and I will get back a result and a match probability. How I change my text will change the matches, and can do so in ways that are profound and unexpected. Note that the SQL query I gave would not return any values because I didn't have any documents that had the term "existing" in them. Building the correct SQL query could be quite complex it comparison to just using the text.
This is pretty great for long-tailed search, q&a, image search, recommendations, classification, etc.
BTW, I am biased, I work for Elastic (makers of Elasticsearch) and we have been doing traditional search forever, and vector/hybrid search for the last few years.