71–80 of 99 posts
It seems like an expansion play from their team and their end vision as both a platform (clickhouse + postgres) and product (observability) seems to be pretty good combo that fits hand in hand.
Congratulations to everyone involved, quite remarkable considering Langfuse was only founded as part of YC 23.
Without the purchase price, it is unclear whether this deserves congratulations or condolences. Two years in the LLM race will have definitely depleted their seed raise of $4m from 2023, and with no news of additional funds raised it's more than likely this was a fire sale.
> Our goal continues to be building the best LLM engineering platform Interesting headline for a checks notes time series database company.
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Your notes aren't very good. They're not a time series database company, they're a columnar database company. But yeah the LLM bit is weird, database companies _always_ feel like charlatans when it comes to LLMs.
Willing to bet most columnar stores are used for time series.
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Language models are time series models. It’s great when you get this insight as a student of NLP, because suddenly your toolset grows quite a bit.
Could you elaborate? because that sentence made my brow wrinkle with confusion. I have thought to myself before that all business data problems eventually become time series problems. I'd like to understand your point of view on how LLMs fit into that.
E.g, fitting a model to house prices, you don’t care if feature 1 is square meters and feature 2 is time on market, or vice versa, but in a time series, your model changes if you reverse the order of features.
With text, the meaning of word 2 is dependent on the meaning of word 1. With stock prices, you expect the price at time 2 to be dependent on time 1.
Text can be modeled as a time series.
A language model tells you the next character/token/word depending on the previous input.
Language models are time series.
It’s not an audacious claim.
Any student of nlp should have met a paper modeling text as time series before writing their thesis. How could you not meet that?
Earlier quoted context omitted.
Language models are time series models. It’s great when you get this insight as a student of NLP, because suddenly your toolset grows quite a bit.
Could you elaborate? because that sentence made my brow wrinkle with confusion. I have thought to myself before that all business data problems eventually become time series problems. I'd like to understand your point of view on how LLMs fit into that.
[2] https://arxiv.org/abs/2506.02389
[3] https://arxiv.org/html/2402.10835v3
Some links from the top of Google search.
Take a look here, also, it's an important law: https://en.wikipedia.org/wiki/Benford%27s_law
It is possible for LLMs to learn Bernford's law, implicitly. So they will be non-null predictors of time series data, because time series data is also Bernford-law-distributed [4].
[4] https://ui.adsabs.harvard.edu/abs/2017EGUGA..19.2950T/abstra...
Just did a funding round. In a sign of the times clickhouse used to be an interesting DB product, but is now a "database software that companies can use as they develop AI agents " Database technology startup ClickHouse Inc. has raised $400 million in a new funding round that values the company at $15 billion — more than double its valuation less than a year ago. https://www.bloomberg.com/news/articles/2026-01-16/cli…
Investors are finicky creatures, if you've been relying on VC-funding since before, it's hard to stop until you are really successful, and if everyone starts to only look at shiny AI stuff and you still need investors, you end up with not much choice. I wish there was less of it, we'd have better software then, but :/
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Investors are finicky creatures, if you've been relying on VC-funding since before, it's hard to stop until you are really successful, and if everyone starts to only look at shiny AI stuff and you still need investors, you end up with not much choice. I wish there was less of it, we'd have better software then, but :/
What but? If this is the "best" that VC can do with the money, the US government should simply tax it away from them. Absolutely worse way to allocate resources and develop a robust forward looking tech industry, you're just chasing the shiny while fucking over the commons.
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> You can look at places with less funding Yeah, like FOSS which is drastically underfunded since birth, yet continues to put out software that the entire world ends up relying on, instead of relying on whatever VC-pumped companies are putting out. I'm not talking "better software" as in "made a lot of money", I meant "better" as in "had a better impact on the world".
FOSS software is written by people working at companies that likely owe their existence to VC.
I don’t think everything VCs touch is gold, but it’s also not the case that they are pure evil either. It’s almost as if you can’t claim they are all good or all bad.
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FOSS software is written by people working at companies that likely owe their existence to VC.
That sounds like more sign of recent times. FOSS software that many rely on that has been around for a while were non-VC: VCS, Linux / GNU / BSD, web browsers, various programming languages, various databases...
Back then it was very normal to get VC funding and then hire the core committers of your most important open source software and pay them to keep working on it. I worked at Sendmail in the 90s and we had Sendmail committers (obviously) but also BSD core devs and linux core devs on staff. We also had IETF members on staff.
And we weren't unique, this happened a lot.