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.
I built a personal finance app (https://tender.run) in the style of mailbox (swiping, keyboard shortcut, inbox-based workflow for reviewing transactions).
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.
Originally this was a privacy angle - the data is primarily stored on your device, with backend storage that’s treated like backup and sync only. I had plans for e2ee that built on this but it didn’t turn out to be a big differentiating factor.
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.
Sort of relatedly, I’ve been fighting Safari bug for years that feels like it has to be related to dates in js.
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.
I’ve been building a personal finance app that runs fully in the browser (using the automerge crdt and sqlite) for over a year now at https://tender.run.
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
It's sad to hear that the new app encrypts everything.
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.
As much as I've have cited, loved, and recommended sourcegraph (even going so far as to help run the open source version at a previous co), I never paid a cent for the product.
I'm curious about the line of thinking in leaving open source behind, but it seems somewhat unsurprising in that lens.
why would consumers who can afford to use credit cards with rewards switch to fednow payments? are there equivalents for chargebacks, fraud protection, and rewards percentage?
I just tried to do this with chase, capital one, and citi. The first two only let you pay up to 10% more than your balance (balance, not credit limit) and citi only 7.5% - nowhere near 10x your credit limit.
I run a personal finance app that's built as a PWA (https://tender.run, my email is in my profile).
I would say running as a PWA has been a mixed bag. There are quite a few missing features across web platforms (for starters, background sync, full featured push notifications, haptics) that make it hard to be competitive with native apps. Every WWDC, we watch in hopes that Safari gets more PWA features, but it's a thin drip. There's a reason so many apps repackage into electron/cordova/whatever shells.
As other folks in thread have pointed out, it's probably worth thinking through why a product works particularly as a PWA vs alternative distribution methods.
It doesn't seem like it will i.e. _delete_ your username/password, but the behavior is actually automatic without user prompt.
See 1:10-ish[0] for the demo - I found this at least somewhat surprising, though I submit this as a passkey advocate (for a while, my side business only-supported passkeys, but we backed away from that).
I am actually currently working on something like this - inbox zero style (don't have swipe gestures built yet) finances tool where you check/set the category. What's old is new I guess.
Is basil still around? Would be interested in taking a look.
Sorry, to be fully clear - i had an internship offer from apple that i declined, and i think i also declined to do the interview process at sonos before getting to the offer stage.
A bit light on the technical details perhaps, but I recall getting stuck on getting the right airplay parameters, learning how byte endianness works... happy to try to answer any other questions as best I can remember.
Consuming a lot of literature on how different systems work helped me develop intuitions around how you might take something apart. Then it's a matter of trying things and banging your head against the wall a lot, e.g. at some point I was interested in how compilers worked so I tried hacking typescript syntax support into babel (circa 2017 maybe) - I got pretty far! and got a lot better sense of how compilers work.
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.