whakim

7 ms·

31.
by whakim 1y ago
At 1M embeddings I'd think pgvector would do just fine assuming a sufficiently powerful database.
32.
by whakim 1y ago
It depends on scale. If you're storing a small number of embeddings (hundreds of thousands, millions) and don't have complicated filters, then absolutely the convenience factor of pgvector will win out. Beyond that, you'll ne
33.
by whakim 1y ago
> It's not like there's some secret sauce here in most of these implementation details. IME the implementation of ANN + metadata filtering is often the "secret sauce" behind many vector database implementations.
34.
by whakim 1y ago
Regardless of the merits of this argument, dedicated vector databases are all running on top of AWS/GCP/Azure infrastructure anyways.
35.
by whakim 1y ago
Elasticsearch and Vespa both fit the bill for this, if your scale grows beyond the purpose-built vector stores.
36.
by whakim 1y ago
Fair enough - agreed there's lots of interesting innovations here - but my point is that semantic search and its associated issues don't really differ that much from other types of search problems at scale, and I therefore don&#x2
37.
by whakim 1y ago
* Filterable ANN certainly decomposes into pre- and post-filtering, and there is definitely a lot of interesting innovation occurring around filterable ANN. But large-scale search systems currently do a pretty good job with pre-filtering, f
38.
by whakim 1y ago
Fair; my question was mostly in the context of ANN, since that was the discussion point - I have to assume ES (as a search engine) would not necessarily be the right tool for data warehousing types of workloads.
39.
by whakim 1y ago
Would you mind putting aside the snark? I have a couple questions. How large is the corpus? I am also curious about the use-case for top- k ANN, k > 10000?
40.
by whakim 1y ago
I do not think data stores are a bottleneck for serving embedding search. I think the raft of new-fangled vector db services (or pgvector or whatever) can be a bottleneck because they are mostly optimized around the long tail of pretty s
41.
Fast, Free, and Fun: Introducing 'Feeds'(halcyon.io)
9 points by whakim 1y ago|0 comments
42.
by whakim 1y ago
The Romans were actually quite smart after Cannae; they had lost a bunch of pitched battles, so they decided to shadow Hannibal's army to make his foraging logistics much more complicated (and forcing him to stay close to Southern Ital
43.
by whakim 1y ago
As the article points out, the difference in cost between these two routes is pretty small in the grand scheme of things; more than two-thirds of the costs associated with the project are at either end getting in and out of Los Angeles&#x2F
44.
by whakim 2y ago
Looks like there's a mistake here - it was Juvenal, not Horace, who coined the phrase "bread and circuses." While we're at it, Juvenal's point was not that the provision of bread and circuses was detrimental to th
45.
by whakim 2y ago
Thanks for sharing! Yes, getting good performance out of pgvector with even a trivial amount of data requires a bit of understanding of how Postgres works.
46.
by whakim 2y ago
We've used Pinecone for a while (and it definitely "just worked" up to a certain point), but we're now actively exploring Google's Vertex Search as a potential alternative if some of the pain points can't be mi
47.
by whakim 2y ago
> Yes, I experienced this too. I from 1536 to 256 and did not try more values than I'd have liked because spinning up a new database and recreating the embeddings simply took too long. I’m glad it worked well enough for me, but with
48.
by whakim 2y ago
I was the first employee at a company which uses RAG (Halcyon), and I’ve been working through issues with various vector store providers for almost two years now. We’ve gone from tens of thousands to billions of embeddings in that timeframe
49.
Climbing the AI Layer Cake(halcyon.eco)
7 points by whakim 2y ago|0 comments
50.
Docket Profiles(halcyon.eco)
6 points by whakim 2y ago|0 comments
51.
by whakim 2y ago
I enjoyed this article but the assertion that Work Simplification was the direct cause of high levels of trust in government in the decades after WWII strains credulity. I would have expected some mention of the twin crises of the Depressio
52.
by whakim 2y ago
Airbnb will always side with the host if possible, because hosts are much more valuable to their platform than guests. I had a similar situation a couple years ago when Santa Cruz was hit with catastrophic flooding; despite providing them w
53.
by whakim 2y ago
> All we do in SF is make car driving worse, we almost never make public transit better. At least NYC has a plenty good enough train system. Except that SF public transit is actually pretty good. East-West transit works extremely well vi
54.
by whakim 2y ago
> I don't accept your definition of "intelligence" if you think that makes sense. Systems must be able to know things in the way that humans (or at least living creatures) do, because intelligence is exactly the ability to
55.
by whakim 2y ago
I’m not really sure what you’re trying to say here - that LLMs don’t work like human brains? We don’t need to conduct any analyses to know that LLMs don’t “know” anything in the way humans “know” things because we know how LLMs work. That
56.
by whakim 2y ago
I think it’s pretty clear that the comparison doesn’t hold if you interrogate it. It seems to me, at least, that humans are not only functions that act as processors of sense-data; that there does exist the inner life that the author discus
57.
by whakim 2y ago
At present, it seems pretty clear they’d get dumber (for at least some definition of “dumber”) based on the outcome of experiments with using synthetic data in model training. I agree that I’m not clear on the relevance to the AGI debate, t
58.
by whakim 2y ago
No matter how much budget you give to an LLM to perform “reasoning” it is simply sampling tokens from a probability distribution. There is no “thinking” there; anything that approximates thinking is a post-hoc outcome of this statistical pr
59.
by whakim 2y ago
Perhaps you’d consider clarifying for me rather than simply making a snarky comment?
60.
by whakim 2y ago
I understand the point being made - that LLMs lack any "inner life" and that by ignoring this aspect of what makes us human we've really moved the goalposts on what counts as AGI. However, I don't think mirrors and LLMs

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