Causal's company blog covers financial planning for startups, SaaS metrics, forecasting and engineering work on their spreadsheet-like modeling tool.
HN links it for Show HN launches and finance guides; commenters praise the app as a promising tool for financial modeling and dashboards.
I've had this conversation with a number of financial-savvy friends. It seems that we all have different methods depending on what you're after.
For myself - I hate spreadsheets and updating and categorising data.
I have also tried a number of tools that will "attempt" to automagically present useful reports. But I've found that in 90% of cases - I'm well aware of what I spend on and how much I spend.
The data that really interests me personally is - savings, pensions, asset wroth tracking and scenario simulation.
I find that looking at growth over the past 3,6,12 months motivates me to do even better or look for more efficient ways of organising my finances.
At the moment here's what I do:
- Following a guide like (UK Personal Investing Flowchart)[https://ukpersonal.finance/flowchart/] to judge where I am at and what my short/long term goals are.
- I have automated transfers at salary date (T+3 days to account for variations) to investments and savings.
- I do a monthly scan through of current and credit accounts to make sure nothing unusual occurs as well as monthly credit-rating check.
- For crypto investments I use (Delta)[https://delta.app/en]
- For Stocks and Shares I track current positions via Yahoo Finance.
- Lastly - every now and then I check-in to see how my Pension accounts are doing. So far I've gone through 4 different pension providers and tools - but none of them see to have good forecasting, scenario tools.
As a side topic recently got shown…
(Co)author here - happy to answer any questions :)
- This was a v fun (and challenging, due to Causal's scale!) project to do
- Most optimizations we’ve implemented were (very effective) one-off fixes. But we were also glad to find some rules of thumb that help to keep the app faster by default [1]
- My most interesting discovery was how ineffective Web Workers are. Causal, sometimes, has to do some expensive client-side data parsing. We tried to move data parsing to a web worker (= a separate thread) – but ended up reverting the optimization because it made the app slower. In JS, you can’t just pass a pointer to a data structure between threads; the browser has to clone the data structure [2] (with limited exceptions [3]). In our case, cloning the data between threads ended up being several times slower than parsing it in the main thread.
[1] https://www.causal.app/blog/react-perf#span-class=inline-codeuseselectorspan-vs-span-class=inline-codeusestorespan
[2] https://developer.mozilla.org/en-US/docs/Web/API/Web_Workers_API/Structured_clone_algorithm
[3] https://developer.mozilla.org/en-US/docs/Glossary/Transferable_objects#supported_objects
Causal | Engineering | REMOTE (US, UK, Canada) | Full-time | https://causal.app
Causal is a tool for performing calculations, visualising data, and communicating with numbers. We take the good parts of spreadsheets and combine them with the good parts of programming, to make number-crunching fast, collaborative, and accessible to everyone.
We're a team of 25, raised $25m from top VCs (Coatue, Accel) and angels (Naval, Scott Belsky, and more), and are hiring for a bunch of engineering roles:
- Backend Engineer — Data (https://jobs.lever.co/causal-app/e696335f-c4d8-49ec-b264-c06de71b7099)
- Full-stack Engineer — Product (https://jobs.lever.co/causal-app/4acaafbe-ef3d-45a6-bbe6-687c022d727c)
If this sounds interesting, then we'd love to hear from you: lukas [at] causal [dot] app. Thanks!
Some compelling reasons - I think most of them can be addressed in Excel, but the reality is most people don't know how Excel really works or how to do anything complex in it. Having said that, as your need to batch process numbers goes up, R and other solutions have more value in my opinion.
I'm particularly interested in "half-way" solutions - something between R and Excel. I've been looking at https://www.causal.app/ - no affiliation but I find their approach similar to a Mac app I like called Numi.
I scanned the comments to see if anyone had mentioned Causal[1] yet. I’d describe it as a spreadsheet specifically for modelling.
I used it to build a cost model for the startup I work at, consisting of around 100 different inputs, and it was rather enjoyable.
I was able to (reasonably easily) insert the different AWS costs for (for example) SD vs. HD video transcoding and see how that affected the costs of encoding and storing video 12 months from now.
[1] https://www.causal.app/
Nice demo! It's perhaps a little confusing if you're using uniform probability distributions for the sensitivity analysis inputs, but normal distributions for the uncertainty analysis (shown at https://causal.app/buy-to-rent).
A previous comment mentions you are using SALib in Python, which can (even if it's not documented) use normal probability distributions for the inputs: here's a notebook with an example: https://risk-engineering.org/notebook/sensitivity-analysis.html