TyrianMollusk

@TyrianMollusk@infosec.pub

• ⁨Comment⁩ on ⁨Steam Owner Valve Faces $900 Million Lawsuit Over PC Monopoly Claims, Following UK Tribunal Ruling - IGN⁩ ⁨⁨2⁩ ⁨days⁩ ago⁩:

  Yeah, Steam Input could have been huge for the entire gaming industry, but instead it’s only for Steam and so only can get fixed by Valve, who just doesn’t really care about coming back to things and keeping them working after initially building something. Frustrating to see something almost so good just kinda limp along, accumulating bugs no one will fix because Valve doesn’t really care beyond the simple button mapping use.

  Just like how dynamic collections could have been pretty great, but Valve got a rudimentary version working, patted themselves on the back, and left forever without even implementing the most basic tools anyone would need to actually use them (boolean combinations, actually using the tags you set on games, etc). It could even have been a slick new interface to Steam’s tagging (imagine if you set a collection specifically as a tag, and Steam took your manually adding and removing games there as tag votes) that might’ve helped ease some of the dumb problems tags have (there’d be a lot more info for Steam to draw on than just the people actually updating tags on the store page).

  I’m kind of impressed no one makes a better gaming social-launch client than Steam, but then Steam’s own client has a massive lock in advantage so you basically can’t make something that wholly replaces it, and Valve doesn’t care to play nice when they want that obvious Steam-game vs non-Steam-game divide.

• ⁨Comment⁩ on ⁨Spent more time looking for a game than actually playing one? Help me test Gamescovery, a recommendation engine built for your actual taste.⁩ ⁨⁨3⁩ ⁨days⁩ ago⁩:

  I also saw a solution for normalizing the scores of every person to battle this bias. This is used in Criticker system (recommendation for movies

  Sounds good. Doesn’t actually work :/ Sure, if everyone gave a statistically valid data spread covering every rating point, then you could probably normalize them so it doesn’t matter what numbers an individual used. But people don’t do that. Maybe someone only rates 8-10, but is that because they like everything, because they don’t rate anything they didn’t like, because they think an 8 is bad, because they just lump everything they don’t like in the “8 or below” group, or some other random thing? They don’t know, and what about the obvious fact that most everyone watches more movies they rate good than bad, so ratings have a huge implicit skew to the distribution? They don’t know that either, but they scale the ratings anyway, and that’s some of why they don’t really work if you get down to it. The rest is just that their analysis concept is broken.

  I actually use criticker for my movie rating, and it doesn’t really do me any good (but it’d be a pain to move everything, so I haven’t :). Their system still falls prey to the usual issues, just not as obviously as say Steam which basically just always throws the most popular candidate it can shoehorn into a rec. If you have weird taste, you get grouped with rating profiles that happen to agree enough on something, but that don’t actually have real connection to your taste. Eg, if I like some movies everyone likes (and let’s face it, we pretty much all have some close-enough-to-universally appreciated likes), my “close rating” users will be focused on people who also liked those movies, and a lot of meaningful stuff becomes noise, but one’s taste is much more in the noise data than in the big obvious strokes. Alternatively, if I watch and like some fringe thing no one sees, suddenly anyone else who did is closer to me, mainly because there’s so little data in common between us to go on.

  Criticker is convinced I love esoteric foreign drama (I really don’t), because I scour deep into horror during part of the year and occasionally find a gem that gets a good rating, often from some dark corner of Asia. They also think my 50 is 77th percentile, probably for the same reason (ie I do have a lot of low ratings, because I’m watching things just because they are horror). A 50 is where I put “pretty decent/not really that good” stuff, which seems a lot lower than 77 to me, but I can’t tell Criticker that because of their “helpful” scaling. After my partner (who watches basically everything I do and has very similar taste), the next closest TCI (their code for how close your normalized ratings are to someone else’s, and the basis for their rating prediction) comes at thirty. That basically says they’re useless, which is more accurate than any given rating prediction they generate for me, with my mere 1,845 ratings to go by ;)

  I really think one needs to find and minimize the “common” elements to focus on the uncommon in rating analysis, and in prediction. Eg if people tend to like X but I don’t, that actually means a lot more than if I also like X. And recommending I rush out to watch The Godfather (thanks again, Criticker, never heard that one…), doesn’t do me any good, because everyone already knows it. It’s an “easy” rec, but it’s not a good rec.

  If Criticker used the 3-4-3 system for their ratings instead of telling us it will just work out, that would lead me to apply my numbers differently, which on its own is kinda telling for improving their data. I didn’t make up the 3-4-3 thing, BTW. I was working on a related web/database project, and that was passed on to me as studied and statistically well-proven for producing better survey results (and that was someone from an industry that definitely cares about that). Does make a lot of sense, though. It’s nice when something has a clear right answer like that… except you get a little frustrated seeing nothing actually use it ;)

• ⁨Comment⁩ on ⁨Spent more time looking for a game than actually playing one? Help me test Gamescovery, a recommendation engine built for your actual taste.⁩ ⁨⁨3⁩ ⁨days⁩ ago⁩:

  I have one more question, if you don’t mind - what is your feeling about game recommendations after you rated 3-4 games? Were recommendations lean towards predicable “correct” way, or were they completely random and off?

  I didn’t rate any games, just looked at what it would take and had some quick feedback to offer. Part of the issue with Itch is that to rate games, you have to first find things on itch, as well as find things that’d be representative so you might see how recs do. For testing something that isn’t going to do much right now, that’s a fair bit of trouble, especially since my key interest would be whether recommendations really take taste into account or use one of the usual shortcuts that either lump you into categories or fall prey to the “well everyone likes X so X” syndrome. Either of those would take a fair bit of data for me to put in, and a rather surprising amount of data for you to already have at such an early stage.

• ⁨Comment⁩ on ⁨Spent more time looking for a game than actually playing one? Help me test Gamescovery, a recommendation engine built for your actual taste.⁩ ⁨⁨3⁩ ⁨days⁩ ago⁩:

  I notice you are using a nineteen point rating scale, going from 1 to 10 with halves in between and a slider. You will get better ratings if you use a more standard scale that’s compatible with other sites and a better method for inputting ratings.

  You’ll want to link your rating data to other sites (eg, backloggd) if you have any hope of this being used, so that’s why compatibility is valuable. Mapping a backloggd 10 point scale to a 19 point scale is a silly wrench to throw in, and how will you translate your users’ 19 point scale to backloggd’s 10? You need to be able to keep users from entering scores over again to survive at all.

  As to entry, something almost everything gets wrong is you actually get better data if you present ratings with the right number of points to the scale and use a tiered grouping (visually, not as in requiring a series of questions for a single rating). There’s basically a right answer here, and its 10 points grouped 3-4-3. The grouping helps cognitively because you’re basically picking high-mid-low twice instead of analyzing a 10 point spread. People are significantly statistically worse at using a wide, flat rating scale, and the two-tier version corrects that and gives you richer and more accurate data, especially if you label the tiers, to help reduce individual bias about how they apply their feelings to numbers (eg the modern 6/10=bad syndrome).

  We need better rating analyzers than we have, but it will never work without connecting to other rating systems and processing games outside itch.io. And if you keep your recommendation mechanism under wraps with only manual rating entries, especially limited to itch.io games, you’re asking far too much from someone to see if it’s potentially relevant to them, both in the sense of effort and the sense of trust (“non-biased, community driven”).

• ⁨Comment⁩ on ⁨Augmented Steam browser plugin added AI features from VaporLens⁩ ⁨⁨3⁩ ⁨weeks⁩ ago⁩:

  It’s just displaying review summaries from VaporLens next to the reviews on the store page. There’s no AI boogeymen in the plugin itself, and you can easily disable the feature.

• ⁨Comment⁩ on ⁨Ars Technica’s Top 20 video games of 2025⁩ ⁨⁨4⁩ ⁨weeks⁩ ago⁩:

  Sure, I knew which game you meant. It was just an oddly dismissive and mildly inaccurate way to refer to a legit top-tier game.

  I play a lot of twin-sticks, so Robotron was a real curve to throw in. From my perspective, those are pretty different ;P

• ⁨Comment⁩ on ⁨Ars Technica’s Top 20 video games of 2025⁩ ⁨⁨4⁩ ⁨weeks⁩ ago⁩:

  geometry wars meets robotron type deal.

  That makes no sense. Both of those are just arcade twin-stick shooters, and Sektori is no more Robotron than Geometry Wars was. Also, while Sektori very obviously draws a lot on Geometry Wars, it’s an amazingly good arcade twin-stick that improves so much on what GW did, and really deserves recognition. It’s niche, but it’s genuinely a top game in that niche, and I mean best in ten years top game.

• ⁨Comment⁩ on ⁨Any games I missed in the last 21 months?⁩ ⁨⁨1⁩ ⁨month⁩ ago⁩:

  If you like arcade twin-stick shoot-em-ups, Sektori is one of the best the genre has seen. Hard though: if one doesn’t know the genre, play Waves and Assault Android Cactus first.

• ⁨Comment⁩ on ⁨Steam: Updates to User Review Scores Based on Language⁩ ⁨⁨5⁩ ⁨months⁩ ago⁩:

  They don’t want to hurt some dev’s numbers, but they don’t like the game either.

  Or they just can’t handle giving the thumbs down. A lot of people like that nowadays. Only likes are allowed.

• ⁨Comment⁩ on ⁨Steam Summer Sale 2025 has begun!⁩ ⁨⁨6⁩ ⁨months⁩ ago⁩:

  Have you looked at Zero-K? They have some TA history.

• ⁨Comment⁩ on ⁨RimWorld - Odyssey will bring spaceship building, exploration and a lot more content⁩ ⁨⁨7⁩ ⁨months⁩ ago⁩:

  Ouch, Stardeus is not going to be happy to see that news.

• ⁨Comment⁩ on ⁨⁩ ⁨⁨7⁩ ⁨months⁩ ago⁩:

  The app requires an existing login to use, right? I tried the app since the website’s email/password registration screen just returns “An unexpected error occurred” every time I try it, but didn’t see a new registration option for the app, just the login option.

• ⁨Comment⁩ on ⁨lemm.ee is shutting down at the end of this month⁩ ⁨⁨7⁩ ⁨months⁩ ago⁩:

  How do we prevent this from happening to other instances?

  We don’t, and we can’t. What we need is to stop acting like Actors/People/Groups are single points homed on a single instance. They should be a ring of mirrored instance entities, so the “source” Actor and content are still there when one instance drops out of the ring. Everything is already getting copied around, we’re just missing out on the biggest value of that: RAID for identities.

  Ie, if I make my accounts on three sites, those should all be me, and it doesn’t matter to the fediverse which one I use at any given moment. Same for communities.

  That’s real federation, not this ramshackle heap of points of failure where we just hope we don’t individually get bit too often by shutdowns, even though shutdowns are completely inevitable.