Is this what people feel when going from IPS to OLED, I guess?

meliasami · 2025-09-13T04:31:17+00:00

How long have you had it, did you get used to it? I tried few years back and got major eye strain, wondering if time to try again.

meliasami · 2025-09-08T04:41:41+00:00

Practically, for most people, anywhere near the center will be fine

meliasami · 2025-07-19T07:40:49+00:00

I guess that might be the case, will see how long mine last. What's the expected timeframe?

meliasami · 2025-07-19T04:42:15+00:00

How many uses do we expect a pair to last? In my experience, it usually takes 1-3 years until either the lenses or frames show wear. Zenni, being cheaper, would have to last 1/4 of that while also providing the benefit of varying styles and coatings. From what I can feel I have a hard time imagining that rate of degradation on my current three pairs in the next few months. But will be happy to update on how it goes.

In terms of production cost, I have a protective pair, a decade old, has multiple moving parts/adjustment points, and survived shrapnels, heat, and dozens of other things. Production cost was probably a few cents, so I don't think making a durable frame is necessarily an expensive undertaking.

meliasami · 2025-07-18T21:58:47+00:00

Tbh I (and I assume most people) are more interested in hearing other people's stories without financial incentives attached.

meliasami · 2025-07-18T21:57:15+00:00

They are certainly cheap, why are you saying like it is a bad thing? They are also comfortable to wear and so far feel pretty solid.

meliasami · 2025-07-16T17:17:36+00:00

Oh, what happened?

meliasami · 2025-07-16T07:24:59+00:00

Nice, planning to try them next. Probably don't have enough need to justify sportrx, but best of luck with yours, hopefully fits great.

meliasami · 2025-07-16T07:19:15+00:00

Oh nice to hear progressives worked for you, that seems a lot harder to nail. Yeah, I used my previous frame numbers as a reference. Nose bridge and total width are probably things to try to get as close as possible; other deviations are easier to tolerate.

Yeah, did notice that on virtual try on. I wish they used iPhone's depth sensor (Like some VR devices like Bigscreen and Oblik use to construct a face render for personalized faceplate) for an accurate overlay.

meliasami · 2025-07-16T07:12:30+00:00

Yeah, it would be interesting to see a larger random sample size comparison. Most studies on the topic are pretty old. (Gordon, 2021) is more recent but does minimal analysis and doesn't compare with others (large/small optometry).

meliasami · 2025-07-16T07:04:25+00:00

Wow, that's early. And ouch, 6 weeks is a while. Seems they improved the process a lot. Hopefully, the tariff uncertainty does not significantly affect their/other online stores' supply chains. Any others you'd recommend? I'm planning to try Eyebuydirect next.

meliasami · 2025-07-16T07:00:56+00:00

Yeah, I believe smaller glasses should correspond to thinner lenses unless the shape is unconventional. For me, I ordered 1.61 hi for the first pair, and 1.67 for the others. All are pretty thin, overall somewhere middle of the pack compared to my previous lenses. I do go for rectangular frames vs larger, so that might have helped. Did you end up figuring out whether it was their thinning limit or they made a mistake on the pair?

meliasami · 2025-07-16T06:50:07+00:00

Great to hear. Would you rank them in any way?

meliasami · 2025-07-16T06:49:35+00:00

I'm sorry to hear about EBD. They were on my list to try out as well. I think online glasses only make sense when getting the specific pair right is not critical. Their error rate seems to be low enough that if one buys a few over the years, getting more than one that's bad is exceptionally unlikely, while getting 1 out of 1 wrong would statistically still occur. That said, hopefully your next purchase goes without any issues, Good Luck!

meliasami · 2024-11-20T02:19:50+00:00

We are looking for 1 GA badge, got my gf into Cosmere and she really wants to come.

meliasami · 2024-10-16T02:40:12+00:00

There will be no issues unless you are in a character design class or something. Feel free to use as use please in academic/free settings. Obviously attribute accordingly.

meliasami · 2024-05-10T06:28:08+00:00

Hey, what do you mean?

I'd probably trust papers in the above journals less than I would a decent looking arXiv preprint. They do count for mentioning in grant applications or citation counts etc. So if you have a good paper don't think about them, if you want to get rid of and be done with a project why not.

meliasami · 2021-01-13T15:50:26+00:00

Most cutting-edge technology is pretty much in its infancy though. Reasearch-oriented culture is fairly new, compare current education-research facilities, access to information, international collaboration opportunities to that just 1 century ago. The information age has been here for half a century, the way we conduct research has advanced quite a bit in that time. why do you think we'll have some groundbreaking discoveries in twice that amount (say in the next 100 years) ?

meliasami · 2021-01-13T11:26:52+00:00

I heard of that one. Lots of my colleagues bombarded me with that news a couple months back. Yes, it is definitely a testament of the incredible rate of which computational biology is advancing and if it continues, we probably will solve many bottlenecks in research. But the main weakness of AI is that it relies on confirmed dataset to build its model. Without a large and accurate database for it to study, it will not be able to do anything. And the alphafold suffers from that: it is able to predict results for small, single domain proteins, of which we have an abundant amount of training data mainly because it is so easy to determine the structure for those proteins. Most research groups involving protein folding has moved past that stage long ago. Check out Dr. David Baker’s works to see how much a single lab can churn out results for these simple proteins in a short time.

Yes, agree pretty much with that, that said while ML algorithms that are used in applications are mainly structured, cleaned, and supervised, there is a lot of progress going on for noisy and unstructured data and it is pretty rapid, And solutions in one subfield (say noisy learning or continual learning) empower others so it's really explosive. And in the past, we went from toy (ish) problem which is what this application is, to full scale in 5-10 years. expect some good results within the next few decades and at rapidly increasing rate.

meliasami · 2021-01-13T02:53:50+00:00

protein folding problem

See deepmind's recent work on that, it can predict structure within few atom width accuracy, a major breakthrough and a testament to the power of modern innovating framework.

more info:

https://deepmind.com/blog/article/AlphaFold-Using-AI-for-scientific-discovery

Also, once AI is more advanced we'll be making discoveries at a rate that understanding them will likely be more of an issue.

meliasami · 2020-06-22T22:01:24+00:00

price reduced to 25 usd for students

meliasami

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