"In fact, people have actually made spatial pattern generators that allow you to input the frequency profile that you want, and get the corresponding point pattern out. It’s really quite neat, and I highly recommend reading this paper so you can see some other possible noise parameters, like anisotropy."
I had to hunt this 'custom colour' noise paper out since that link was dead. It is quite neat. Here it is on ACM in case anyone else is interested:
+1 for human presence. I agree with the commenter upthread that it feels off-puttingly impersonal. Show me the faces!
Otherwise, I totally get the problem you're trying to solve so I poked it and quite liked it.
As someone with 2091 years worth of Slack convos (and Slack wanting me to upgrade to export them) can you let me know how you handle imports and exports?
You can install the Struct bot in Slack: https://struct.ai/install-slack -- This would by default pick up the last 3 months of conversations (we do this to decrease the unpaid load on GPT). You can ping us and we can help sync it from the beginning of whatever history Slack shows.
We don't have an exporter yet, but can surely put one together which provides a SQL / CSV formatted output (or whatever works best for users). We would never charge for exports.
CEO of Figma here. Most of the original insights around vector networks were in 2013, though we continued to polish the implementation over time. We didn't exit stealth and ship the closed beta of Figma until December 2015 which is why there isn't blog content before then.
At first glance, this thesis looks super neat! I'm excited to check it out! I don't believe I've seen it before which is surprising given the overlap.
