My early use of eBay was through WebTV, with both buying and selling, and it largely worked. You could browse webrings and read email from the couch!

Most of all, the dialing music was fantastic and I still listen to it once in a while: https://www.youtube.com/watch?v=brZYWcGgg4Y

When free ad-supported dialup services came around (Juno, Bluelight, NetZero) I alternated using those and WebTV for a while. As pages moved away from simple text/table/image based sites, page rendering quality unsurprisingly degraded. I think the version we owned had some Flash support but it was slow.

Looking back on it, it's impressive how legible text was on a 20" CRT TV in the interface (through S-Video). It was more usable than some modern "smart" TV interfaces.

I've been testing out the multimer (protein complex) mode of Alphafold recently, to see if could predict interactions for a family of proteins where some members in the family are known to form complexes, but others previously were found to not form complexes at least when expressed in vitro rather than in vivo. So far I've found that if you try to throw two completely unrelated proteins together, they won't be modeled with any contacts, but for the ones in the family I'm interested in, there's always at least one (of the five models per run) that has them interacting such that there's something that looks like a real DNA-binding domain. For the latter case, it's presently hard to know based just on Alphafold output if it's a structure that could actually form, or if it's just due to bias in the training data, with perhaps the rest of the structured regions of the protein being conformed in unrealistic ways due to less training information for those parts.

TL;DR Alphafold results are biased by existing experimentally resolved structures, and not based on simulating physics, so proteins- or parts of proteins- that don't have good coverage in existing experimental data are not going to be predicted with high confidence.

I've done quite a few 2hr+ runs (half marathon to marathon) and 100 mile bike rides, and after a while for me it turns into a psychological game.

Trail running, if you haven't tried it, is much more "stimulating" I might say, depending on where you are there's a lot more focus and attention required to stay on the trail, to stay upright (slipping on mud, ending up in a river), to dodge trees and rocks as required, etc. I personally find there's much more of an aspect of being "in the zone" for trail running, and especially in races, when I miss a turn and have to stop and backtrack, it becomes really obvious that I was in some kind of "flow" state and then got pulled out of it.

Alesis also developed an 8-track digital audio recorder based on VHS, ADAT, which used SVHS tapes and could record 20-bit 48khz. ADAT was pretty popular in smaller studios, and was great for the time before multi-gigabyte hard drives. https://en.wikipedia.org/wiki/ADAT

Some examples from scene.org: https://files.scene.org/view/parties/2010/aaa10/music/13_pop... https://files.scene.org/view/music/groups/fusion_music_crew/...

For example, for the Anker power adapters we've tested: https://pqs.app/devices?categoryId=1&search=anker. We're also trying to turn it into a business.

I only just started learning Chinese, so I haven't looked at Pleco yet and can't really compare them.

I'll agree with some other comments that once you get into the area bordering intermediate and advanced proficiency there are many fewer resources, but by then you have a better idea of what you don't know, and and can use your existing base to sort of plot a path to fill those gaps.

If you've had old Apple systems on the shelf for a long time- or an Apple IIgs which also had a battery-backed RTC- get those original batteries out!

There's also more need for integrating "multi-omics" data, where you have data from multiple assays (gene expression, phospho-proteomics, lipidomics, epigenetics, small RNA expression, etc etc) with the goal of somehow combining all these different assay results from various levels of gene regulation, to get closer to figuring out actual mechanism for complex processes. Building on that, we can also do single-cell multi-omics to some extent- where you have results from different sequencing-based assays on the level of the same individual cell. This is still pretty limited, but it's exciting and advancing pretty quickly. This will eventually be combined with things like spatial transcriptomics, which is useful for mapping out what's going on in heterogeneous tissue samples like tumors, for example, so we'll end up with spatial single-cell multi-omics, at which point you're looking at 1) some quantitative trait for multiple genes/loci/molecules, and often 10k+ of such features at the same time per assay, 2) multiple assays, such as DNA accessibility and gene expression, in 3) single-cells, of which you might have 10k of in a single sample, 4) across a physical tissue sample where individual cells are spatially mapped, and where you probably want to figure out how cells might influence the state of those around them, and 5) in multiple different samples, where you might want to compare disease vs control, or look for correlation to heterogeneity of results within one group.

There's a lot of public data already available for single-cell gene expression projects if you want to get a feel for how these things are structured and how (passable but not amazing) the existing tooling is- one of the main repositories for this data is the NCBI's SRA https://www.ncbi.nlm.nih.gov/sra but you'll quickly note that searching and browsing is not as easy as you might think it would be- because one of the main limiting factors in bioinformatics is how bad everyone is at keeping terminology consistent. For many bioinformaticians, a majority of time is spent in the data cleaning phase. It's awful. Sometimes the experimental parameters make it into SRA or GEO, but sometimes you have to read through the associated paper to pull that out. Often it's only large consortium projects like the The Cancer Genome Atlas (TCGA) or the Genotype-Tissue Expression project (GTEx) - which have enough funding for staff dedicated to data management- end up publishing datasets that are easy to "consume" without having to jump through a whole bunch of hurdles to figure out how the data was produced.

I have a BS/MS in bioinformatics and I'm presently a PhD candidate in genetics and computational biology defending in February.

Similar to why people might knowingly buy knock-off "designer" items.