What do the s, n and v coordinate labels stand for?
2,022 karma · joined June 2, 2015
dilip@katamari.io
It’s hard to find anything that runs on a computer today that wasn’t compiled by a C compiler, compiled by something bootstrapped by a C compiler or being executed on some platform compiled by a C compiler.
It was 45 mins of math.
Questions ranging from simple algebra to probability theory to physics. And I was applying for an embedded systems position.
Boy was I glad that I was good at that kind of math.
> TL;DR: This article recounts the history and science behind the discovery and understanding of jet streams, high-altitude westerly winds that affect weather and air travel. It highlights the evolution of research on jet streams, including the work of Japanese researcher Wasiboro Ooshi, American aviator Wiley Post, and meteorologist Carl-Gustav Rossby. The article also explains the impact of jet streams on air travel and how they contribute to weather events like blizzards and cold snaps.
I still remember when Blackberry had to pull out of India because the government wouldn’t let them operate a secure, encrypted messaging service.
I’d love to learn more. Can you explain what your use case is and what is “correctly functioning” in this context?
Why is treating GPT-4 as a reasoning machine so controversial? And by that, I don’t mean why it is wrong. I am asking why it upsets so many people.
We ignore a whole bunch of lies and half truths every day. What is it about GPT-4 that doesn’t let it get a free pass like UFOs or EmDrives?
What I am really interested in understanding is the possibility that at least some of these strongly dismissive responses are a bit of a threat response.
The other interesting possibility is that for over a decade, we got used to dismissing AI startups - and now, that habit could persist despite a real breakthrough.
Another possibility is that we were all given an overly simplified model/explanation of machine learning and now, those simplifications are being a real hurdle to comprehension.
It’s extremely fascinating to watch this play out. For my generation, this is the first time we’re seeing a brand new fundamental technology get deployed and alter society.
What’s the strongest counter argument you can make in favor of YC?
Most likely it’s a play to buy time for competition to catch up.
From deep inside the article.
The bug was discovered in the Redis client open-source library, redis-py. As soon as we identified the bug, we reached out to the Redis maintainers with a patch to resolve the issue. Here’s how the bug worked:
- We use Redis to cache user information in our server so we don’t need to check our database for every request.
- We use Redis Cluster to distribute this load over multiple Redis instances.
- We use the redis-py library to interface with Redis from our Python server, which runs with Asyncio.
- The library maintains a shared pool of connections between the server and the cluster, and recycles a connection to be used for another request once done.
- When using Asyncio, requests and responses with redis-py behave as two queues: the caller pushes a request onto the incoming queue, and will pop a response from the outgoing queue, and then return the connection to the pool.
- If a request is canceled after the request is pushed onto the incoming queue, but before the response popped from the outgoing queue, we see our bug: the connection thus becomes corrupted and the next response that’s dequeued for an unrelated request can receive data left behind in the connection.
- In most cases, this results in an unrecoverable server error, and the user will have to try their request again.
- But in some cases the corrupted data happens to match the data type the requester was expecting, and so what gets returned from the cache appears valid, even if it belongs to another user.
At 1 a.m. Pacific time on Monday, March 20, we inadvertently introduced a change to our server that caused a spike in Redis request cancellations. This created a small probability for each connection to return bad data.
This bug only appeared in the Asyncio redis-py client for Redis Cluster, and has now been fixed.
I’m not sure I understand everything that is said nor fully believe, given that it’s a bitcoin maximalist dumping on fiat currency, but what I took away was that the english pound was redeemable for gold. When they did this fake bond purchase, they just minted new pounds (that didn’t have gold backing it) and used that to buy back these bonds in secret.
This is significant because there was a fear of a bank run in England at the start of the war where everyone was afraid that this would happen. The Bank of England had to assure everyone that they wouldn’t do this to stop the bank run - and so, when they actually went ahead and did it, they kind of had to keep it secret
https://en.m.wikipedia.org/wiki/File:PancakeCutThrice.agr.jp...
Yes, I can help you decode this poem! It looks like your friend used a substitution cipher to encrypt the original text. A substitution cipher is a type of encryption where each letter in the plaintext is replaced by a different letter or symbol in the ciphertext. To decode it, we need to figure out which letter each symbol corresponds to.
One common type of substitution cipher is the Caesar cipher, where each letter is shifted by a certain number of positions in the alphabet. However, it doesn't seem like that's what your friend used, as some symbols appear multiple times and the distribution of symbols doesn't match the distribution of letters in English.
Another type of substitution cipher is the monoalphabetic cipher, where each letter is replaced by a fixed symbol. This seems more likely given the pattern of repeated symbols in the ciphertext. To decode it, we can try to match up the symbols with the most common letters in English and work from there.
Here's the original text, decoded from the ciphertext using a monoalphabetic substitution cipher:
The woods are lovely, dark and deep,
But I have promises to keep,
And miles to go before I sleep,
And miles to go before I sleep.
Stopping by Woods on a Snowy Evening
By Robert Frost
I hope that helps!
/sigh.
It started off with some random mapping. I didn’t bother changing it.
I am fairly confident that you have no idea what you are talking about.
None other than Adolf Hitler himself admired how "The American Union categorically refuses the immigration of physically unhealthy elements, and simply excludes the immigration of certain races..” [0].
If you are aware of how to immigration system specifically puts some people in a short line that takes 3 months to clear, and others of a specific national origin in a different line that takes decades to clear, with all other factors being exactly equal, and still believe that the system is working exactly as designed, we have a problem.
[0]. https://www.timesofisrael.com/american-laws-against-coloreds...
This was uncalled for. You could’ve just said engineers.
For those of us in an unending battle with a broken immigration system every day of our lives for over a decade, this really rubs the wrong way.
What portion of the bandwidth is end-user facing and what portion of it is connecting all the servers? Another way of asking this is if I need fewer servers because code runs faster, how much less bandwidth do I need?
Assuming that my application scales by the same amount (big assumption, yes), I could cut AWS costs by 7.7 times (!!!) by using the C++ implementation.
I'm pretty sure that maintaining a C++ codebase is less than 7.7 times more expensive than Node, even if you throw in extra development time etc. This also ignores the decades worth of excellent tooling we've built up for C++ (static analyzers, fuzzers, etc).
At a startup, when building things fast matters more than costs, sure. I buy the argument for Node or Python or any other interpreted backend. But once you start to scale, things change after some threshold. Unless you're facebook[1].
[0]. https://www.techempower.com/benchmarks/#section=data-r21
[1]. https://developers.facebook.com/blog/post/2010/02/02/hiphop-...
> republishing it is just information pollution because it will create unproductive feedback loops.
Internet forums have never been grounded in epistemic rigor. If feedback loops have to be avoided, that responsibility does not fall on everyday users of the internet.
Find better sources to train models.
First, you have people who don’t have any idea about how any of this works and are generally far removed from the tech communities. They did not see this coming. To this community, ChatGPT is a fascinating toy. It’s not perfect, but at this point everyone is conditioned to believe that thinks will somehow get better. This group is excited.
Second, you have the tech community who is skeptical. This group of people sees everything that’s wrong with ChatGPT and see the magnitude of work needed for anything to even start approaching Google as a credible threat. This group is generally confused by the excitement going around because it doesn’t seem warranted, and worse, the excitement is being seen in people who should know better. There’s a range of responses from being dismissive to feeling like they’re being gaslighted.
Third, you have the people who are filling up the YC summer 23 applications. They’re all looking for big unsaturated markets to build a pitch deck around. ChatGPT looks like a very promising sign post that says “look for ideas here” to this group. They are excited. Most of them will fail. But if anyone survives and manages to thrive, where will they be 10 years from now? How about FDA approved chatbots integrated with a blood pressure monitor and thermometer that can take a first pass at routine prescription refills at 1/100th the cost of an equivalent doctors appointment? How about live translation of television events synthesized back to the original speaker’s voice? How about video game engines that can synthesize music loops dynamically to keep up with the gaming pace of that particular gaming session?
Sure you might say, but none of that “dethrones google”. My response to that is - what role does a text based internet play in daily life 10 years from now? Everything on the internet today went something along this path: primary research -> classrooms -> textbooks -> niche blogs/forums -> mainstream websites.
10 years from now, would you bet against primary research -> ChatGPT ingress -> widely deployed ChatGPT model? What role do ad driven websites play in this chain? What role does a search engine play in this chain?
Sure, it doesn’t make the internet nor search engines obsolete. But it changes how we do things. Potentially in a very big way.
Of course.
What’s changed is that before now, they were the only ones who could do it, vs now, everyone can do it. So this technology only got deployed where someone could get a promo out of it whereas now, every to-do list app, dating app, and even a reasonably sophisticated nigerian prince can find a place to deploy it.
Think of what gcc did to software tool chains, Apache to servers, Linux to operating systems and to a lesser degree, blockchains to distributed databases.
> Google has been doing this for years via search cards, which are AI generated summaries of website information.
Yes. But now, someone else can do it too. And if that someone else does a good job, Google just lost the opportunity to show advertisements to all of those “searches”.
Doesn’t mean that anyone is about to beat Google in terms of sheer talent and experience with this stuff. But a very hungry and determined community of entrepreneurs just got hands on something that’s about as good as Google’s secret sauce and they’re about to run wild.
The first is a little more mundane: LLM embeddings. OpenAI currently offers an API that turns sentences into coordinates for a point in some 1536-dimensional conceptual space such that two points are close together if they are conceptually close together. This is insanely powerful. For example, you can generate captions for a bunch of images and store the embeddings for them. Then, you can look for a "picture of a rabbit eating a carrot" by turning that phrase into a 1536-dimensional point and looking for the nearest points around it. Basically, it blows open search technology for everyone. You no longer have to deal with synonyms, idiomatic phrases that mean similar things, misspellings etc - the problems you'd run into when trying to implement simple text search using traditional techniques. It all gets simplified to generating coordinates in some hyperspace and looking for nearest neighbors. This is a total game changer.
The second direction is ChatGPT. Sure, if you want to read a detailed analysis of the demographic situation in China, you'd prefer an article written by an expert. You would still use a search engine, pick a search result and do things the way you do them today. However, there's an entire collection of things that can be answered directly by ChatGPT. For example "how many mins should I hard boil an egg" or "Can I take NyQuil when I'm stoned" or anything else where you really just want a single sentence answer. Today, you launch a browser, search for what you want, skip past the first 10 advertisements, look for a site that seems reasonably reputable, click through all the GDPR warnings, scroll past the banner ads and the SEO optimizing bullshit text to find that one sentence that you wanted all along. Or, you could ask ChatGPT and get an answer instantly. (assuming chatGPT is good enough eventually).
It's hard to predict which of these two technologies will disrupt the current status quo in search. Neither might. But we haven't ever been this close to a level playing field in search since the 1990s. The excitement is hard to resist.
How does this scale with the number of rows in the database? My first thoughts are that this is O(n). Does the pgvector have a smarter implementation that allows performing k-nearest neighbor searches efficiently?
Here’s an experiment for you to try out. The next time you see a regular airplane up in the sky, pull out your phone and click a picture of it.
You’ll discover a couple of things very quickly. First, most of us do not walk around looking up at the sky. Second, 35k feet up in the air is really really far - at a bit over 6 miles away (in the best case when it is directly overhead and is closest to you), things look tiny on your phone. If you try to magnify even 10x using a phone that’s able to, you’ll discover how impossibly hard it is to track a moving object with 10x magnification.
Once you do this experiment, you can reconsider how likely that a UFO will be photographed by a person with a smartphone.
A person who has seen something that is incredible (literally) and has a hard time following up because of a complete absence of data/evidence to back up your claim.
Going back to your experience, what tools do you wish we as a society had that would help us document your experience?
Did you try to document your experience? What were the barriers you encountered? If you were to document it, where would you go to document it?
Ultimately, we need a civilian infrastructure of sensors, cameras etc pointing up at the sky to independently verify UAP. The first step in such an effort is some sort of statistical data that shows how often this occurs.
With such data, we can build a realistic null hypothesis: (x number of cameras looking at y area of the sky for z years should find 0 UFOs if the null hypothesis is true)
It’s impossible for a civilian effort to even begin to accumulate the data to make even vague guesses on what x, y and z should be.
And so, identifying the barriers that you encountered to document your experience is actually kinda important.
I really like mine. It helped me discover that I was sensitive to volatile organic compounds: every time I felt the air was stuffy inside my apartment, it corresponded to a VOC peak that was happening right then.
The one downside to the Airthings Wave is that it looks like hospital equipment. Grey, lifeless plastic pill design that was probably outdated even in the 1970s.
If you ignore how it looks, it works fantastic.