stpn
Joined 12 years ago
Joined 12 years ago
It's built on the automerge CRDT and sqlite running in the browser, which has been really fun to work with. I'd like to keep going, though honestly I've struggled with the marketing side (growth has been slow) and it's a pretty competitive space.
Working in local-first turns out to be really nice for making the app feel super snappy. The responsiveness you see in the demo is the performance you can expect in day to day usage.
Safari’s saved credit card support works pretty well, but when I switch between US and Asia timezones the expiration date will shift by a month in one direction. My best guess is that they store the expiration as midnight local time on the 1st of the month in a js Date which can shift when you travel outside of that timezone.
Recently I’ve been taking more of being able to flexibly run sql against this data, and this past week I’ve been working with d3 to make fancy sankey graphs to show income/expense flows. Quick preview here: https://demo.tender.run/reports/sankey
A long time ago I worked on hacking airplay support for sonos speakers and it wouldn't have been possible without inspecting a lot of plaintext wireshark traffic.
I was curious about this since it kind of makes sense, but I offer a few reasons why I think this isn't the case:
- In the 10% noise case at least, the second descent eventually finds a minima that's better than the original local minima which suggests to me the model really is finding a better fit rather than just reducing itself to a similar smaller model
- If it were the case, I think we might also expect the error for larger models to converge to the performance of smaller models? But instead they converge lower and better
- I checked the logged gradient histograms I had for a the runs. While I'm still learning how to interpret the results, I didn't see signs of vanishing gradients where dead neurons later in the model prevented earlier layers from learning. Gradients do get smaller over time but that seems expected and we don't have big waves of neurons dying which is what I'd expect to have the larger network converge on the size of the smaller one.