Thanks for the heads up! App error it seems, tried to clean it up.

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Absolutely, but also the attorney from Devil’s Attorney

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Not op but https://www.cbc.ca/news/politics/poilievre-to-run-in-alberta-byelection-1.7525104

Basically he asked a con in the highest % of con votes to step down to trigger a by-election. It’s an area where the other parties don’t even campaign, they just hand it to the cons.

There are already talks of “liberals rigged the election” so that he can deflect and not make it a personal failing that he lost a riding that’s historically always been conservative and lost a 25 point lead in the polls in a few months.

Interesting points, maybe a book I’ll have to give a read to. I’ve long thought that information overload on its own leads to a kind of subjective compression and that we’re seeing the consequences of this, plus late stage capitalism.

Basically, if we only know about 100 people and 10 events and 20 things, we have much more capacity to form nuanced opinions, like a vector with lots of values. We don’t just have an opinion about the person, our opinion toward them is the sum of opinions about what we know about them and how those relate to us.

Without enough information, you think in very concrete ways. You don’t build up much nuance, and you have clear, at least self-evident logic for your opinions that you can point at.

Hit a sweet spot, and you can form nuanced opinions based on varied experiences.

Hit too much, and now you have to compress the nuances to make room for more coarse comparisons. Now you aren’t looking at the many nuances and merits, you’re abstracting things. Necessary simulacrum.

I’ve wondered if this is where we’ve seen so much social regression, or at least being public about it. There are so many things to care about, to know, to attend to, that the only way to approach it is to apply a compression, and everyone’s worldview is their compression algorithm. What features does a person classify on?

I feel like we just aren’t equipped to handle the global information age yet, and we need specific ways of being to handle it. It really is a brand new thing for our species.

Do we need to see enough of the world to learn the nuances, then transition to tighter community focus? Do we need strong family ties early with lower outside influence, then melting pot? Are there times in our development when social bubbling is more ideal or more harmful than otherwise? I’m really curious.

Anecdotally, I feel like I benefitted a lot from tight-knit, largely anonymous online communities growing up. Learning from groups of people from all over the world of different ages and beliefs, engaging in shared hobbies and learning about different ways of life, but eventually the neurons aren’t as flexible for breadth and depth becomes the drive.

My guess was that they knew gaming was niche and were willing to invest less in this headset and more in spreading the widespread idea that “Spatial Computing” is the next paradigm for work.

I VR a decent amount, and I really do like it a lot for watching TV and YouTube, and am toying with using it a bit for work-from-home where the shift in environment is surprisingly helpful.

It’s just limited. Streaming apps aren’t very good, there’s no great source for 3D movies (which are great, when Bigscreen had them anyways), they’re still a bit too hot and heavy for long-term use, the game library isn’t very broad and there haven’t been many killer app games/products that distinct it from other modalities, and it’s going to need a critical amount of adoption to get used in remote meetings.

I really do think it’s huge for given a sense of remote presence, and I’d love to research how VR presence affects remote collaboration, but there are so many factors keeping it tough to buy into.

They did try, though, and I think they’re on the right track. Facial capture for remote presence and hybrid meetings, extending the monitors to give more privacy and flexibility to laptops, strong AR to reduce the need to take the headset off - but they’re first selling the idea, and then maybe there will be a break. I’ll admit the industry is moving much slower than I’d anticipated back in 2012 when I was starting VR research.

I think he’s basically saying that it’s racist to “artificially” integrate communities, because (I think he’s saying) if they need to be integrated, then that’s the same as saying that black folks are necessarily inferior. I don’t think he’s trying to say they’re inferior, but that laws forcing integration are based on that assumption. So he can be well educated and successful because he isn’t inherently inferior, therefore there is no need for forced integration.

… Which is such a weird stretch of naturalism in a direction I wasn’t ready for. Naturalist BS is usually, “X deserves fewer rights because they are naturally inferior”, whereas this is “We should ignore historical circumstances because X is not naturally inferior”.

Start a game of monopoly after three other players have already gone around the board 10 times and created lots of rules explicitly preventing you from playing how they did and see how much the argument of “well, to give you any kind of advantage here would just be stating you’re inferior, and we can’t do that.”

Man probably got angry at his golf handicap making him feel inferior and took things too far. Among other things.

Yeah, I may be wrong but I think it usually comes down to a very specific kind of precision needed. It’s not meant to be hostile, I think, but meant to provide a domain-specific explanation clearly to those who need to interpret it in a specific way. In law, specific jargon infers very specific behaviour, so it’s meant to be precise in its own way (not a law major, can’t say for sure), but it can seem completely meaningless if you aren’t prepped for it.

Same thing in other fields. I had a professor who was very pedantic about {braces} vs [brackets] vs (parentheses), and it seemed totally unnecessary to be so corrective in discussions, but when explaining where things went wrong with a student’s work it was vital to be able to quickly differentiate them in their work so they could review the right areas or understand things faster during a lecture later down the line.

But that noise takes longer to teach through, so if it is important, it needs it’s own time to learn, and it will make it inaccessible to anyone who didn’t get that time to learn and digest it.

Absolutely! One of the difficulties that I have with my intro courses is working out when to introduce the vocabulary correctly, because it is important to be able to engage with the industry and the literature, but it adds a lot of noise to learning the underlying concepts and some assessments end up losing sight of the concept and go straight to recalling the vocab.

Knowing the terms can help you self-learn, but a textbook glossary could do the same thing.

There was a lovely computer science book for kids I can’t remember the name of, and it was all about the evil jargon trying to prevent people from mastering the magical skills of programming and algorithms. I love these approaches. I grew up in an extremely non/anti-academic environment, and I learned to explain things in non-academic ways, and it’s really helped me as an intro lecturer.

Jargon is the mind killer. Shorthands are for the people who have enough expertise to really feel the depths of that shorthand and use it to tickle the old familiar neurons they represent without needing to do the whole dance. It’s easy to forget that to a newcomer, the symbol is just a symbol.

Functional programming is much more math oriented and I think works well here, as it likes to violate a lot of these rules as a rule. I think it’s what makes it so challenging and so obvious for different folks.

I really want to see if worker owned cooperatives plus AI could do help democratize running companies (where appropriate). Not just LLMs, but a mix of techniques for different purposes (e.g., hierarchial task networks to help with operations and pipelining, LLM for assembling/disseminating information to workers).

It is real, you just have to have sufficient funds already to be able to pay someone else to do the active part of the income and make sure they are earning less than their worth so that you can pick up the excess. Most effective if there are many layers in between, so that the income becomes increasingly passive as you move up the chain, so that those under you have something to strive for, because you don’t want to be in charge of hiring all of those people, so you hire people to hire those people, each taking a cut of the value along the way.

But don’t worry, the American Dream™ is that, as long as you keep working about 10 layers deep in value cuts, eventually you might be able to get into layer 3 or 4 and get your kid into the job early so that they can get to layer 5 or 6, and maybe they’ll have enough money to get their kid to 6 or 7.

C could just be a blank and you have to bit blit the arrow on yourself.

I don’t know why, but I still can’t open a core file without going I’m in. I don’t do QA, though, and so tinkering with final breath of my program frozen in time maintains some novelty.

My two cents, after years of Markdown (and md to PDF solutions) and LaTeX and a full two years of trying to commit to bashing my head against Word for work purposes, I’m really enjoying Typst. It didn’t take long to convert my themes, having docs I can import which are basically just variables to share across documents in a folder has been really helpful. Haven’t gone too deep into it but I’m excited to give it a deeper test run over the next little bit.

Canadians still feeling scorned after eliminating FPTP was a big election point, only to have it fizzle away when third parties clearly started garnering too much support (not that I think it was ever really in the cards regardless) and concerns about proportional representation being too supported by the other parties.

Lots of immediate hate for AI, but I’m all for local AI if they keep that direction. Small models are getting really impressive, and if they have smaller, fine-tuned, specific-purpose AI over the “general purpose” LLMs, they’d be much more efficient at their jobs. I’ve been rocking local LLMs for a while and they’ve been great as a small compliment to language processing tasks in my coding.

Good text-to-speech, page summarization, contextual content blocking, translation, bias/sentiment detection, click bait detection, article re-titling, I’m sure there’s many great use cases. And purely speculation,but many traditional non-llm techniques might be able to included here that were overlooked because nobody cared about AI features, that could be super lightweight and still helpful.

If it goes fully remote AI, it loses a lot of privacy cred, and positions itself really similarly to where everyone else is. From a financial perspective, bandwagoning on AI in the browser but “we won’t send your data anywhere” seems like a trendy, but potentially helpful and effective way to bring in a demographic interested in it without sacrificing principles.

But there’s a lot of speculation in this comment. Mozilla’s done a lot for FOSS, and I get they need monetization outside of Google, but hopefully it doesn’t lead things astray too hard.