Collaboration slows things down but going fast at all cost leads to tech fragmentation, tech debt, and product debt.
fwiw I'm not in the camp of "we must have everything done in one consistent way" but there are places, for example a public API, where having 4 different names for the same concept, 3 different response format/url path styles, etc. starts to look really sloppy.
In my company we have the best of both worlds - things are slow as hell and there's shitload of tech debt. This is direct result of "collaboration" aka "nobody actually owns anything". I put forward a proposal to standardize various things across our software, and in response my manager called a meeting of 10 people. No proposal can survive a meeting of 10 people at once.
IMO the best way is to split your organization into units that nicely map with technological/business boundaries, and then give each unit the responsibility to own something tanglible. The problem is, if the organization is full of idiots, everyone tries to do the opposite, in order to diffuse the responsibility.
Yeah, the first few times I thought maybe I was just imagining it; but I went from STATE=7280398215952, tried to merge my 64s into the left corner and instead ended up in STATE=7280398025476; my 64s have become 4s.
Can't reproduce it exactly as it happened, but if I run `STATE=7280398215952 bash 2048.bash` and press a 8 times, the 128 always becomes a 2.
I'm certainly not qualified to speak on this but I heard this story recently about an effective treatment for alcoholism that's been available for decades but for complicated reasons hasn't been widely accessible: https://www.reflector.show/p/how-to-listen-to-episode-1-the-...
This is a cool toy, I've been wanting to do something similar with my flipper zero to make a BT morse keyboard.
This also reminds me of TapXR, which I would totally buy if did morse, instead of inventing their own encoding. I get it, theirs is probably way faster but fluency is morse is more general purpose.
Just how general purpose is it these days? I learned it for amateur radio (a couple years ago), which is probably the only “common” place to use Morse. And even there it’s all but dead
Continuous Wave / Morse is definitely not "all but dead". In fact, it's in literally continuous use, 24/7, worldwide. If you turn on an HF radio (and have an antenna up) and tune to an open band, you will hear morse code.
It's getting more and more popular within amateur radio. If I look on the Parks On The Air spots page, there are currently 20 people in a park across the US doing Morse, and I know that when I go to a park I can knock out 60 Morse contacts in about an hour on one band since there are so many hunters.
Clubs like Long Island CW have thousands of members and run classes all day to teach people Morse and help with their operating skills. Just this morning I joined the weekly CWOps mini contest which is so popular they have it in 4 x 1 hour sessions. And that's on top of the 3 medium speed sessions on Mondays, and 2 slow speed ones.
There might not be as much ragchew activity but between contests, DXers, and POTA, there's CW activity all over the bands.
There was a downswing awhile ago because the macro users switched to using digital modes. People who want to make handmade CW contacts are still having fun and that is attracting some people to the space.
Also, knowing Morse has been my escape room superpower. Escape room designers love Morse.
all (RF) navigational aids in aviation have a more code associated. It's actually required to identify them (by listening to the morse code/looking at the decoded morse code in your panel) to be able to use them for instrument navigation. So that means a big part of all GA flights and pretty much any commercial flight at any point in time in the world are using morse code.
I’ve been pretty happy with web-ext, I’m curious what abstractions and configurations Extensions.js avoids comparatively. I’m assuming you’ll still need a manifest.json, and it looks like both use npm/package.json for dependencies.
Yes, the manifest.json is a required file for all extensions, and the package.json file provides necessary package metadata.
web-ext is excellent, but it seems there are no plans for the project to support more browsers than it currently does. On the other hand, Extension.js plans to support all major vendors.
Except for Firefox support, which is in progress, I believe Extension.js offers parity with all core functionalities of web-ext, but it goes further by providing built-in support for React and TypeScript. All you need to do is add the correct dependencies to get up and running.
Additionally, Extension.js provides comprehensive extension reload support, including changes to the manifest.json file and the service_worker background. This feature sets it apart from similar tools, including web-ext, which do not offer this capability.
I must be rare in that the longer I use tiktok, the less relevant the recommendations feel. Maybe because I compulsively watch videos until the end even if I don't like them.
> Maybe because I compulsively watch videos until the end even if I don't like them.
That would definitely do it, basically destroying their most important signal.
TikTok is best in class for recommendeding content and I personally haven't see a dip in quality. Aka I never get trashy videos or anything cringe, just a consistent stream of science/tech, local Toronto restaurant reviews, cat videos, etc
? Do you have any proof things aren't working out for TikToks algorithm besides a few people whining on HN who probably weren't the target market in the first place? Because they seem to be doing just fine.
GP didn't say anything about TikTok's algorithm not working out for people in general, but I'll bite.
The selling point behind TikTok is that by using the app normally (i.e. watching what you want) TikTok can figure out what stuff you want to watch.
But pretty much every suggestion that comes out on HN for people who complain about the algorithm on sites like this is to do unnatural things to "train" the algorithm: be very careful about how long you watch videos, click on videos that you think are relevant even if you don't want to watch them, watch things you're not interested in to prevent falling into a rut, avoid click on things you're curious about if you don't think you want to watch 100s of them to avoid tainting your recommendations, etc.
If that's "working" I think I'd agree that TikTok was mostly hype.
Yep. It's almost like tiktok targets users who are only using system 1. If you just go with lizard brain reactions you don't care about any of that. But once system 2 is in play you start doing "crazy" things like watch a video forensically even if you don't want to be fed similar videos on the regular or skip videos even if they are about things you like, and you need some control over what you watch
A good example here is YouTube, which started recommending me a bunch of really short (5-second) meme videos lately which I really don’t want polluting my feed. Even if I really want to click and watch them just out of dumb curiousity… (I don’t go to youtube for 5 second meme videos, I prefer longer form content.)
I spent some time disliking every 5-second video being recommended to me, and the problem went away. It was easy.
I'm another one who's tried TikTok on a number of occasions and it's always failed badly with me, worse than any other site I can think of. It never improved despite people telling me it eventually would.
My impression is that it was making too many assumptions about me based on the wrong signals or something.
But to your point: wouldn't there be a survival bias of sorts with assessing recommendation performance? I didn't like it and left, so presumably the people who remained liked it.
It's obviously popular so there's that, but it seems circular to say it's working for the people who like it and stay. You're losing information about opportunity costs of lost users.
I mean there is an upvote/downvote in that there's a like button and a not interested button. If tiktok shows you something you really don't like just long press on the screen and hit "not interested"
They used to have options for like "don't show me videos with this sound" or "don't show me videos from this user" but now it's just a general purpose button.
>The lack of up/downvote is what destroys their signal, I think.
They don't need up/downvotes. They have signals such as liking, sharing, watching multiple times, commenting, looking at comments, saving, skipping, looking at creators account and other videos by the creator. If you want to focus on up and downvotes, you end up with reddit.
I want to know what portion of the algorithm is responsible for, when you are given a new blank slate user, "tries" certain categories
like, let's present this user travel or cooking material, that's usually safe
then, let's try things like certain genres of music, we'll see what they like/don't like
what i don't get is... how does that first recommendation on the #foryoupage or discover or whatever it's called, starts recommending you the sex workers who try to post as close to NSFW material as possible, get you to land on their profile, in their bio is a link to their Instagram or Linktree, and then from there it's an OnlyFans link
does the system try to recommend a soft entry into this content and then just pivot away if the user doesn't like it?
TikTok likely has enough information about others that it can begin to build a profile about you from the moment you login.
Let's use a hypothetical scenario: Someone states that they identify as a man, they're in the 20-25 year old age range, and based on phone location you can gather that they live in Texas. Now you're labeled as a 20-25yo Texas Man. Then you can look at others who fall in the "20-25yo Texas Man" category and show things you'd expect that group to like because chances are, you're more similar to others in the group than being a true outlier. If other people in the "20-25yo Texas Man" group have expressed interest in Apples, NSFW material, and lawn mowing videos, then since you're in that group, it's going to start off with that same material.
disclaimer: i've never signed up for tiktok and have no clue if this is how they do it.
100% it is. I'm a 34 year old woman and have to be pretty aggressive about not wanting the mommy and wifey shit. I want cats, watching things explode, and the Zoomers' digital Dadaism.
The classic terminology for this in AI/ML is "explore vs exploit", i.e. striking a balance between trying new things (in hopes of finding a new favorite) vs going back to the tried-and-true.
I have noticed these types of videos slip into Facebook’s Reels late at night. It’ll switch from showing me people making candy and doing home improvement stuff - videos with tens of thousands of views and likes, then it will cut to a video with almost 0 views/likes that are basically the beginning of the Onlyfans sales funnel. Never when the sun is up!
> It probably just recommends a bunch of stuff that is popular at the moment for new users.
I get that, but I feel like it starts with "known safe/neutral" material like cooking/traveling/photography/whatever
How can it detect "hey, this person might like if we introduce softcore porn into their timeline"? Like, do they have softcore porn identified on a scale and they introduce the really "safe" stuff and then gradually crank it up? Why are they presenting softcore porn at all? The Apple App Store is cool with that ToC wise?
It's not trying to start with "safe" stuff, it's not trying to "gently introduce" softcore porn.
It's going "This video got a billion views in the last 30 minutes, people must love it, let's keep amplifying it to any account that hasn't explicitly rejected this category of content"
Presumably blank slate accounts are treated as open to anything, until people start curating.
At the risk of going down a rabbit hole for no real reason, I don't use tiktok but when I speak to those that do I've not yet heard this softcore porn/sex worker thing.
For example, in my mind, not all ASMR content might lead to sexualized recommendations, but a girl in a bikini top with cat ears doing ASMR might generate both recommendations for ASMR and other more cam-girl like content. So I guess my question is, when you're starting off in tiktok seeing cooking videos, do you trend towards ones that feature 'sexier' hosts? They might not be sex workers to you, but they might be making tiktok think you're interested.
Also, what does tiktok know about you to start? What info do you have to give it to start an account?
so you agree that tiktok is able to classify "cooking videos" and "cooking videos with slightly sexualized hosts"? and that they "willingly" "try to push in recommendations" posts with higher "sexuality" attached content?
No, again, my assumption is that the user would trend towards that content. You don't need to push people towards it if you have a nuanced enough profile of each video.
(all things made up for this example)
cookinglady39 does a beach bbq recipe tiktok, in a bathing suit. You watch it. They give you another cookinglady39 video where she's back in the kitchen, you skip it, they give you a new cooking host also female, also dressed in summer attire cooking outside. You watch til the end. It gives you a man cooking outside, you skip. Nothing you've seen so far has been sexual, but tiktok is probably picking up on some trends that might lead them to give you more and more things done by women, then women in a certain setting, dressed a certain way and so on.
TikTok gives you the content you enjoy. When someone complains about TikTok content I basically assume they don't understand how good the algorithm is and that you just like that kind of stuff. I don't care whether you do or not but TikTok thinks you do because of the feedback you are giving the app. I mean, you clicked their profile and followed their links all the way to onlyfans. They have to assume you like it.
Assuming we're starting with a blank slate, and a heteronormative male user that would happen to enjoy consuming that content on TikTok:
In the initial set of recommendations based only on overall popularity, there might be a video that's popular that incidentally contains a pretty woman. If the user skips most videos after barely a few seconds, but watches that one fully 3 times through, then the recommendation engine probably looks at users it does know more about that exhibit similar behavior and have higher engagement. It will then recommend videos that those users would probably watch a lot. Now the recommendations are shifted in the direction from "generally popular" to "contains pretty women". You repeat this enough times and the user ends up navigating the space of recommendations until they're maximally engaged (in theory). That means they might end up at softcore porn. Goodness knows that porn is popular if nothing else.
The recommendation engine doesn't even have to know anything about the content of the video. Just know what already high-engagement users that watched that video a lot also watched a lot.
That's at it's most basic really, I'm sure there's additional cleverness on top in practice.
The point of the original poster was that those videos have zero views or low rating - not popular by any means - and they appear out of place in the stream.
Youtube does it too, and my best guess is that it is a form of supervised training and the real question is who's being trained.
This is a critical step to get rid of bad recommendations, and the algorithm seems to treat it as a very strong signal—after 1-a few “not interested” labels you won’t see that content for weeks if ever.
That's because on the inverse of algorithm recommendations, everyone forgets that the platform is primarily built around an ad system that generates money... There is always going to be a conflict with recommendations because they need to placate and accommodate paying users of all kinds, even users with low quality content and outright commercials. Pretty much all of these social platforms work in this manner now. As a result everyone is seeing undesired (ad) boosted content on top of the other ads (that are marked as ads).
They are probably trying to save GPU power on already hooked users. This is a common trick in Recommendation Systems. You want to spend the most resources / run your most expensive model on users that are just checking out your platform.
A bit like how Poker sites give you better cards in the beginning.
It's less idiotic if you assume the bias goes the other way. People who stick with online poker had better cards in the beginning leading to them early winning and continuing to play. They then experience a reversion to the mean. Those who lose too often too early lose interest and sign off.
They say the worst result to get on your first roulette spin is to hit your number.
Obviously, these are just trends among people. Some gambling addicts never won and skilled poker players won't go on tilt after 20 bad hands.
Eh, it’s bad, but not as bad as the “companies intentionally leak false information to make their upcoming products look unappealing just so the real announcement is a pleasant surprise” 8D chess players.
This can happen to me if it gets stuck down a avenue that it thought I was interested in. But the next day or even a few hours later, it seems to correct itself.
fwiw I'm not in the camp of "we must have everything done in one consistent way" but there are places, for example a public API, where having 4 different names for the same concept, 3 different response format/url path styles, etc. starts to look really sloppy.