If you want to do it properly you need to setup an inter-company loan agreement, transfer pricing, you need accountants and tax specialists for both UK and US, ... so the costs will definitely be significant. I don't think there are any shortcuts. Ideally, you find an accountant that is familiar with this setup.
It did make the acquisition process a bit more difficult + US lawyers are more expensive than UK/German lawyers.
It's still worth it IMO given how much easier fundraising gets.
We bet quite early on AG-Grid and it got us very far. We’ve probably built one of the most customised AG-Grid tables out there.
I haven’t checked out Tanstack table much but given that it’s such a core component of the product I would like to build our own eventually. Doing that when you start out is probably not worth it though.
Thanks to the HN community! Everything started with this post from over 5 years ago [1] which gave Taimur and me enough conviction to quit our jobs to work on Causal full-time.
A few months later we launched an Excel sensitivity tool [2]. In 2022 we shared how we scaled our calculation engine to billions of cells [3] and a few months ago we had a successful Show HN of Causal 2.0 [4]
The product is now used by 100s of startups (many YC companies) and we're exited for the next chapter fo faster GTM with Lucanet!
IMO the better paradigm is coming from enterprise applications like Anaplan. Cells are not the right abstraction to work with numbers. Most of the time you work with multi-dimensional quantities (eg revenue by product, geography, month).
We’re working on a more approachable implementation of that paradigm at https://causal.app
IMO this still doesn't change the fact that Excel is a 2D grid. Dealing with multi-dimensional data will always be tricky in that paradigm. Also, you'll still have cell references, no version control, no access control, ...
Excel is an amazing product and I'm sure people will still use it in 10 years. Our thesis is that for financial planning (and various other number-crunching use-cases) our building blocks make more sense.
Great question! Quite often in financial models, you're dealing with "dimensions" (e.g. your revenue is broken down by product, geography, and time). To model this in Excel you have to write complex SUMIF/INDEX/MATCH formulas. Then you have to drag these formulas over 1000s of cells (if you make a mistake you're screwed, see https://eusprig.org/research-info/horror-stories/).
Causal's building blocks are "variables" and "dimensions" which makes it much more powerful to work with dimensional data.
We're using Hypertune at https://causal.app for a few months now and it's been great! We have a few feature flags in there but also some more complex typed data for our onboarding modals.
We've been using it at causal.app for a few weeks now and both engineers and product people love it. Our onboarding content can now be dynamically changed by non-coders.