Kata1yst

Dishonor on you! Dishonor on your cow!

these toy OSs don’t solve day to day problems the way that Windows, Mac, and Linux did when they first came to market.

Yes, this is the exact point I made in my first post. And in depth in my response.

Disingenuous how? You don’t think Linux solved a real day to day need of it’s first users?

Sure, from Torvald’s perspective, it was a project specifically to solve a small problem he had. He wanted to develop for a nix platform, but Minix wouldn’t work on his hardware, and the other *Nixs were out of reach.

And this was generally true in the market as well. Linux arrived just in time and was “good enough” to address a real gap, where Minix was limited in scope to basically just education, Hurd was in political development hell, and the other Nixs were targeted at massive servers and mainframes. Linux filled the “*Nix for the rest of us, inexpensively” niche, eventually growing in scope to displace its predecessors, despite their decades of additional professionalism and maturity.

That niche is now filled, the gap no longer exists. A “New Linux” wouldn’t displace Linux, because the original already suits the needs we have well enough. This is precisely why the BSDs and Solaris were “too little, too late”. They were in many ways better than Linux, but the problems they solve compared to Linux are tiny and highly debatable. Linux addressed a huge, day to day need of people who were motivated to help.

Yeah exactly. Toy OSs have only increased in scope, scale, and number. And the public is still completely unaware, because these toy OSs don’t solve day to day problems the way that Windows, Mac, and Linux did when they first came to market.

Newegg came under new management years ago and has gone into the “drop-shipping for random shady merchants” business.

Do not use modern Newegg.

My favorite city builder in decades. A few notes.

Pros:

• Easy mode is relaxing and quite easy.
• Medium mode is a fun challenge at first, eventually becoming fairly chill as you advance in skill and confidence.
• Hard mode is always fairly hard, especially on harder maps.
• There are many resources to manage, but none that feel burdensome.
• The game is extremely thematic, it feels alive with charm.
• Graphics are excellent, though sometimes graphical glitches can still be encountered.
• The water. It’s so hard to explain to someone who hasn’t encountered this system before, but water is life in this game, and it’s both beautiful graphically, and extremely well simulated by physics. Learning to control the water, and see the shortest paths to end water scarcity with beaver engineering is an amazingly fun and unique aspect of the game.
• Mods are well supported and the community is vibrant.

Cons:

• Not a ton of content. They’ve been very good about adding new mechanics (badwater, extract, etc) but there’s still just 2 races of beaver and a dozen or so maps.
• No directed experience. In similar games I’ve enjoyed a campaign, challenge maps/scenarios, weekly challenges, a deeper progression system, just… Something to optionally set your goals. There’s nothing of the sort in the vanilla game. It’s fully open ended and there’s only one unlock outside of your progress though the resource tree in a map.

All in all, I highly recommend it, especially at the modest asking price. If you love city builders, charming and beautiful art, thematic settings, dynamic challenge, and solution engineering, this is a fantastic game for you.

Other games I’ve enjoyed that scratch similar itches:

• KSP
• Cities: Skylines (but Timberborn has been far more compelling)
• Factorio
• Mindustry
• Planet Zoo (Timberborn has less of a directed experience, but is otherwise completely superior)
• Gnomoria
• Banished
• Tropico series (though I view this as more casual)

Get it and have fun is my recommendation.

Seriously. This guy thinks that regulators would have stepped in to stop OpenAI or Microsoft from acquiring a no-name 2 year old startup with two rounds of funding?

Please.

Apparently that wasn’t one of his MBOs, so we can infer the board is a bunch of dumbasses.

You say “no one knows coffee better than he does”, while blatantly disagreeing with his entirely empirical points in his video on decaf, that it can be made by several processes, all of them are fairly good, and the result can be masterful?

I live in a hockey capitol. That makes me nothing like an expert. Same for you.

Okay, so you make brilliant decaf. That means your point in this thread is moot?

Funny thing on that “subjectivity” is when you disagree with other people in this thread, you’ve plainly said they’re just entirely wrong.

When someone disagrees with you, you hide behind “subjectivity”.

I encourage you to introspect.

You sincerely think you have a better grasp on coffee than James Hoffmann?

Much more likely you haven’t tried good decaf from a good roaster, tried a blind tasting, or your preparation is seriously flawed.

Yeah, well for many of us it’s decaf or no coffee due to health issues. You acting like it’s a foolish, childish thing is just tribalism/elitism.

And for what it’s worth, I’d put my decaf vs your coffee in a heartbeat. A good roaster with quality beans is great coffee, decaf or no. Just like Hoffman said.

Development is happening in the dev’s branches. Branches are generally kept local until submitted for a PR. You can easily see this in the origin branches and open PRs.

Honestly I’m not sure if you’re trolling, don’t understand git development, or if you really think that a project needs to iterate main multiple times per month to be your definition of “healthy open source”, but I’m tired of shooting down such lazy attacks and won’t be responding further.

Have a nice day.

What obscure location? Codeberg?

All the activity is open on Codeberg. You can see every member of that team actively merging and reviewing requests.

Why do you assume that? Why is your way of open source the right way?

All open source projects are run by a small team of people reviewing and accepting, rejecting, and prioritizing work. What part of this project’s methodology bothers you?

He did though. And honestly the website has come very far in a short period of time, I really don’t understand the concerns and whining in this thread…

From codeberg-

Core Team

• ernest
• szsz
• cooperaj
• rideranton
• AnonymousLlama

https://codeberg.org/org/Kbin/teams

Design Team

• cody

Rofl. I just imagine OP furiously updating LinkedIn with “AI Programmer”.

A notebook and crayons? I think you’d just get back stick figure-esque drawings of cybertrucks with notes like “bulletproof” and “anti-gas attack”.

Just like the poor Tesla design team.

Just chip a couple bucks to your local instance owner! Basically the same thing, without the glitz.

Hard disagree on them being the same thing. LLMs are an entirely different beast from traditional machine learning models. The architecture and logic are worlds apart.

Machine Learning models are "just"statistics. Powerful, yes. And with tons of useful applications, but really just statistics, generally using just 1 to 10 variables in useful models to predict a handful of other variables.

LLMs are an entirely different thing, built using word vector matrices with hundreds or even thousands of variables, which are then fed into dozens or hundreds of layers of algorithms that each modify the matrix slightly, adding context and nudging the word vectors towards new outcomes.

Think of it like this: a word is given a massive chain of numbers to represent both the word and the “thoughts” associated with it, like the subject, tense, location, etc. This let’s the model do math like: Budapest + Rome = Constantinople.

The only thing they share in common is that the computer gives you new insights.

You’re talking about two very different technologies though, but both are confusingly called “AI” by overzealous marketing departments. The basic language recognition and regressive model algorithms they ship today are “Machine Learning”, and fairly simple machine learning at that. This is generally the kind of thing we’re running on simple CPUs in realtime, so long as the model is optimized and pre-trained. What we’re talking about here is a Large Language Model, a form of neural network, the kind of thing that generally brings datacenter GPUs to their knees and generally has hundreds of parameters being processed by tens of thousands of worker neurons in hundreds of sequential layers.

It sounds like they’ve managed to simplify the network’s complexity and have done some tricks with caching while still keeping fair performance and accuracy. Not earth shaking, but a good trick.