Comments URL: https://news.ycombinator.com/item?id=49424532

Points: 3

# Comments: 0

As a side note, thank you for all the work you have done in the Clojure ecosystem! Techascent’s data science packages was a major tipping point in my company’s decision to build our data science ecosystem in Clojure and has been wonderful to work with.

[1] https://github.com/dynamic-alpha/ducktape

Regarding my experience, I have done a fair amount of work in the contact center space with long calls. I used statistical Bayesian approaches which I found to be much more resilient especially on long documents than embeddings/transformers. It also provided a joint modeling foundation for classification with much lower label requirements than BERT or traditional ML.

At the scale of a few hundred to a few thousand documents, especially short documents, there are a few out of the box methods that can yield reasonable results, whether it be embedding clustering or leveraging LLMs for tagging.

However as your (1) datasets gets larger (2) documents expand from tweets and text messages to 30+ minute conversations and (3) you build downstream analytics on top of the learned semantic units, you really start to feel the limitations of LLMs and embedding for reliable annotation. That doesn't even get into the nuances associated with taxonomy management, seasonality, and model drift.

TLDR; this problem solved effectively has a lot of value and is a lot harder than it seems.

Comments URL: https://news.ycombinator.com/item?id=48814503

Points: 37

# Comments: 23

Comments URL: https://news.ycombinator.com/item?id=48349345

Points: 3

# Comments: 0

We started with the second two points: our core technology was a sampler that enables arbitrary hierarchical Bayesian graph models for sparse data, our constraint was cpu bound tractable compute. The piece that took us the longest to discover was the fact that our end products need to be separate from our underlying technology.

We were given that advice in various words from many people even before we started but some lessons need to be lived to be learned.

Comments URL: https://news.ycombinator.com/item?id=47897512

Points: 4

# Comments: 1

Comments URL: https://news.ycombinator.com/item?id=47807285

Points: 6

# Comments: 1

The ecosystem is currently very young. Each additional deployment meaningfully strengthens the ecosystem (ours is only the fifth server) and each additional implementation helps harden the spec (which is soon approaching 1.0).

We wrote a bit more about it in a separate article: https://blog.sturdystatistics.com/posts/roughtime/

Official protocol document: https://datatracker.ietf.org/doc/html/draft-ietf-ntp-roughti...

Comments URL: https://news.ycombinator.com/item?id=47224832

Points: 75

# Comments: 14

As a sidenote: That clock is so cool: I was just mesmerized for multiple minutes!

My company's core technology extends topic models to enable arbitrary hierarchical graphs, with additional branches beyond the topic and word branch. We expose those annotations in a SQL interface. It's an alternative/complementary approach to embeddings/LLMs for working with text data. In this case, the hierarchy broke submissions down into paragraphs added a layer to pool them into submissions, and added one more layer to pool them by year (on the topic branch).

Our word branch is a bit more complicated, but we have some extended documentation on our website if you are interested in digging a bit deeper. Always happy to chat more about the technical details of our topic models if you have any questions!

Overview of Our Technology: https://blog.sturdystatistics.com/posts/technology/

Technical Docs: https://docs.sturdystatistics.com

I do think as a metric for total reach, a static cutoff actually works reasonably well. I think some form of square root normalization over total users is probably the best balance.

I was planning on doing this yearly but the Clawd excitement is definitely worth diving into.

Comments URL: https://news.ycombinator.com/item?id=47039478

Points: 134

# Comments: 34