I genuinely can't understand the thought process of a Yankees fan. If it's just a tradition thing, then sure whatever. But someone who watches them play and goes "yeah that's my team" is just mindblowing. They'll have a batting lineup that costs more than the opponents entire field, knowing full well they are all just hired guns who will be gone the moment the contract is up, and then you watch them in the playoffs against regular teams and it's just visually hilarious at this point. Like watching a bunch of NFL linebackers playing teeball.
Some people want to back a winner, and they don't really get too worked up about the details. Another example would be Ferrari in early 2000s in F1. Biggest budget, most skilled driver, all the dirty tricks at all levels (on-track, technical, political), plenty of fans.
> I genuinely can't understand the thought process of a Yankees fan.
There is very little free agency in American sports fandom. People are (for the most part) fans of the team local to where they grew up. (This kind of bums me out as someone raising kids in New England, which is not where I'm from, and so not whose teams I root for.)
Backing the local team always makes the most sense. In NYC you can choose Mets or Yankees (though where you live in the city affects even that). Choosing a team from some other city means you see your team play much less often and only after much effort. Worse there are less people to talk about the game as nobody has seen your team play and you didn't see their team plan. (except when your team plans the local team)
More capability, less reliability please. I want something that can achieve superhuman results 1 out of 10 times, not something that gives mediocre human results 9 out of 10 times.
All of reality is probabilistic. Expecting that to map deterministically to solving open ended complex problems is absurd. It's vectors all the way down.
Reality is probabilistic yes but it’s not black box. We can improve our systems by understanding and addressing the flaws in our engineering. Do you want probabilistic black-box banking? Flight controls? Insurance?
”It works when it works” is fine when stakes are low and human is in the loop, like artwork for a blog post. And so in a way, I agree with you. AI doesn’t belong in intermediate computer-to-computer interactions, unless the stakes are low. What scares me is that the AI optimists are desperately looking to apply LLMs to domains and tasks where the cost of mistakes are high.
Stability is the bedrock of the evolution of stable systems. LLMs will not democratize software until an average person can get consistently decent and useful results without needing to be a senior engineer capable of a thorough audit.
>Stability is the bedrock of the evolution of stable systems.
So we also thought with AI in general, and spent decades toiling on rules based systems. Until interpretability was thrown out the window and we just started letting deep learning algorithms run wild with endless compute, and looked at the actual results. This will be very similar.
This can be explained easily – there are simply some domains that were hard to model, and those are the ones where AI is outperforming humans. Natural language is the canonical example of this. Just because we focus on those domains now due to the recent advancements, doesn’t mean that AI will be better at every domain, especially the ones we understand exceptionally well. In fact, all evidence suggests that AI excels at some tasks and struggles with others. The null hypothesis should be that it continues to be the case, even as capability improves. Not all computation is the same.
Rules based systems are quite useful, not for interacting with an untrained human, but for getting things done. Deep learning can be good at exploring the edges of a problem space, but when a solution is found, we can actually get to the doing part.
Stability and probability are orthogonal concepts. You can have stable probabilistic systems. Look no further than our own universe, where everything is ultimately probabilistic and not "rules-based".
> Expecting that to map deterministically to solving open ended complex problems is absurd.
TCP creates an abstraction layer with more reliability than what it's built on. If you can detect failure, you can create a retry loop, assuming you can understand the rules of the environment you're operating in.
>If you can detect failure, you can create a retry loop, assuming you can understand the rules of the environment you're operating in
Indeed, this is what makes autonomous agentic tool using systems robust as well. Those retry loops become ad-hoc where needed, and the agent can self correct based on error responses, compared to a defined workflow that would get stuck in said loop if it couldn't figure things out, or just error out the whole process.
Superhuman results 1/10 are, in fact, a very strong reliability guarantee (maybe not up to today's nth 9 decimal standard that we are accustomed to, but probably much higher than any agent in real-world workflow).
I'm a hater of complexity and build systems in general. Following the instructions for building solvespace on Linux worked for me out of the box with zero issues and is not difficult. Just copy some commands:
>I'm a hater of complexity and build systems in general.
But you already have a complex cmake build system in place. Adding a standard Docker image with all the deps for devs to compile on would do nothing but make contributing easier, and would not affect your CI/CD/testing pipeline at all. I followed the readme and spent half an hour trying to get this to build for MacOS before giving up.
If building your project for all supported environments requires anything more than a single one-line command, you're doing it wrong.
>> But you already have a complex cmake build system in place.
I didn't build it :-(
>> Adding a standard Docker image with all the deps for devs to compile on would do nothing but make contributing easier, and would not affect your CI/CD/testing pipeline at all.
I understand, but to me that's just more stuff to maintain and learn. Everyone wants to push their build setup upstream - snap packages, flatpak, now we need docker... And then you and I complain that the build system is complex, partly because it supports so many options. But it looks like the person taking up the AI challenge here is using Docker, so maybe we'll get that as a side effect :-)
"You will need git, XCode tools, CMake and libomp. Git, CMake and libomp can be installed via Homebrew"
That really doesn't seem like much. Was there more to it than this?
Edit: I tried it myself and the cmake configure failed until I ran `brew link --force libomp`, after which it could start to build, but then failed again at:
Alternative perspective: you kids with your Docker builds need to roll up your sleeves and learn how to actually compile a semi-complicated project if you expect to be able to contribute back to said project.
I can see both perspectives! But honestly, making a project easier to build is almost always a good use of time if you'd like new people to contribute.
>"Alternative perspective: you kids with your Docker builds need to roll up your sleeves and learn how to actually compile a semi-complicated project if you expect to be able to contribute back to said project."
Well, that attitude is probably why the issue has been open for 2 years.
It only takes one dictator, then the wishes of the people become irrelevant. Or propagandized; I'm sure the war is quite popular in Russia still despite horrific casualties.
As I understand it, Russia hasn't been able to actually call it a "war" domestically, they did burn through prisoners rather than trained forces until they ran out of people willing to believe the chances of surviving to enjoy early release, and Russian forces have been only partially rather than fully mobilised with their conscripts mostly kept back from the front line for a while now due to domestic concerns.
>I burned though $25 in just 3 hours. Claude code will be great when they can get the cost down. If the cost is like 1/10th of that I’d be using it all the time, but +/- $10 / hour is too much.
I've been trying to figure this out, and I don't think it's malicious, but it's just a matter of incentives. Anthropic devs are certainly not paying retail prices for Claude usage, so their benchmark (or just intuition) of efficiency is probably much different than the average user. Without that hard constraint the incentive just isn't there for them to squeeze out a few more pennies, and it ends up way more expensive than stuff like Cline or Cursor.
There's something so quaint and comforting in revisiting the world of peak post-modernist "sarcastic irony" that infused everything in this era. It was just so damned sure of itself.
reply