pbourke

Most banks with a large retail deposit base are immune from bank runs. SVB is unique in that it didn’t have a large retail deposit base yet also had a bimodal customer distribution (financially sophisticated VCs and later-stage startups/enterprises and early stage startups which have the financial sophistication of your local auto mechanic).

No acquiring bank will want to touch SVB because of its lack of retail deposits - the risk of account holders fleeing at the earliest opportunity is very high. That forecloses one of the FDIC’s main tools for resolving a bank collapse - especially one of this magnitude.

So we have a very unique situation here: a large mostly commercial bank that, unlike most large commercial banks, has a bunch of mom-and-pop level customers.

I don’t know what the solution is, but hopefully it will go in the direction of minimizing impact on these small businesses.

https://news.ycombinator.com/item?id=35104326

Some languages have a good integrated tool chain (Rust, Go) but are not as approachable and forgiving.

Some are approachable but lack the friction-free story for locating and installing 3rd party dependencies.

  # pip install faiss-cpu sentence-transformers
  from sentence_transformers import SentenceTransformer
  import faiss
  
  # replace with own texts - this is a bad example since it contains only single words
  with open("/usr/share/dict/words", mode="r") as infile:
      corpus = { num: s.strip() for num, s in enumerate(infile.readlines()) }
  
  # encode the corpus using a good sentence transformer model - will be slow if no GPU
  model = SentenceTransformer("all-mpnet-base-v2")
  corpus_vectors = model.encode(sentences=list(corpus.values()))
  
  # construct a faiss kNN index
  num_vectors, num_dimensions = corpus_vectors.shape
  index = faiss.index_factory(num_dimensions, "L2norm,Flat")
  index.add(corpus_vectors)
  # optional: save index for reuse
  faiss.write_index(index, "/tmp/corpus_index.bin")  
  # index = faiss.read_index("/tmp/corpus_index.bin")
  
  # encode target text and find 10 nearest neighbors in index
  target_vector = model.encode(sentences=["apples"])
  distances, nearest_indexes = index.search(target_vector, 10)
  print(list(zip([corpus[i] for i in nearest_indexes[0]], distances[0])))
  # [('apples', 4.382169e-13), ('fruits', 0.47413948), ('fruit', 0.57227534), ...

OpenAI and the Pinecone database are not really needed for this task. A simple SBERT encoding of the product texts, followed by storing the vectors in a dense numpy array or faiss index would be more than sufficient. Especially if one is operating in batch mode, the locality and simplicity can’t be beat and you can easily scale to 100k-1M texts in your corpus on commodity hardware/VPS (though NVME disk will see a nice performance gain over regular SSD)

Why do you immediately separate eduction by wealth?

I suspect you mean that the purpose of eduction is to ensure that an individual can be gainfully employed and not be a burden on the state. One of your aims would be heading off any poor students pursuing a less lucrative degree (liberal arts).

This seems like a rather sad view of education.

As usual, we don’t. The question is, do you want the power to be in a condo in the Bahamas or in a building with the company’s logo on it, within walking distance of the SEC and SDNY?

Usually it’s more along the lines of “for my own understanding, why can’t we just ship things as they’re ready instead of waiting for everything in the order to go in one box?”

It certainly seems like you wouldn't have any bias when discussing this topic as well.

> They had statements in their articles such as "the majority of the funding for the protests came from Canada" when the actual number was 54% came from Canadian sources. Maybe from a strict mathematical definition that is still a "majority" but it's certainly not what anyone imagines when they hear the word.

What non-strict, non-mathematical definition of the word majority do you propose?

I say this while sipping my morning coffee, browsing HN and waiting for an amount of computation to finish on a single machine that required either a supercomputer or multi-million dollar cluster 20 years ago.

I’ve seen a lot of “hey you just got your spurs” back slapping when someone gets a check.

This was a coaster - hard to get more minimis than that.

The measured intervention was not the colonoscopy, it was the invitation to screen. Only 42% of invited patients actually got a colonoscopy. This is far more persuasive to me:

> "When the investigators compared just the 42% of participants in the invited group who actually showed up for a colonoscopy to the control group, they saw about a 30% reduction in colon cancer risk and a 50% reduction in colon cancer death. “That adds to a bunch of observational study data that suggests exposing people to colonoscopy can reduce risk of developing and dying of colon cancer,” Gupta said."

As a member of the public, I don't really care about invitation to screen, but do care about the efficacy of colonoscopy. I can see invitation to screen being an important concern from a public health standpoint.

“Superior” is a value judgement. I think “preferred” is closer to the mark. People like single-family homes and they go to great lengths to buy them and deal with the externalities of living in them (commuting, maintenance). As the externalities lessened (broader acceptance of working remotely), SFH became more valuable in the last two years.

> It’s not true in New York.

There are great swathes of lower-density housing in New York and its surrounding region. NYC is an outlier on account of its geography and place in the economy.

> Perhaps we can engage with the material without insulting the people behind it. It’s not exactly a compelling argument.

I think it was an appropriate way to engage with an article that takes “people would rather live in mixed-density neighborhoods than single family homes” as its premise. The revealed preference of the entire post-WW2 era in North America is mostly the opposite of this assertion. In general, as society has gotten richer more people have looked to own single family homes.