No chance Waymo can operate in many parts of NYC. Good luck getting through double parked cars in Astoria and elsewhere.
Maybe not immediately but gathering the data on those areas will eventually lead to their ability to drive there.
Readit Newsstarlust2 commented on Waymo granted permit to begin testing in New York City cnbc.com/2025/08/22/waymo... · Posted by u/achristmascarl
starlust2 commented on Waymo granted permit to begin testing in New York City cnbc.com/2025/08/22/waymo... · Posted by u/achristmascarl
starlust2 commented on Why LLMs can't really build software zed.dev/blog/why-llms-can... · Posted by u/srid
The way it works for me at least is I can fit a huge amount of context in my head. This works because the text is utterly irrelevant and gets discarded immediately.
Instead, my brain parses code into something like an AST which then is represented as a spatial graph. I model the program as a logical structure instead of a textual one. When you look past the language, you can work on the program. The two are utterly disjoint.
I think LLMs fail at software because they're focused on text and can't build a mental model of the program logic. It take a huge amount of effort and brainpower to truly architect something and understand large swathes of the system. LLMs just don't have that type of abstract reasoning.
It's not that they can't build a mental model, it's that they don't attempt to build one. LLMs jump straight from text to code with little to no time spent trying to architect the system.
It is hard to accept as a premise because the premise is questionable from the beginning.
Google already reported several breakthroughs as a direct result of AI, using processes that almost certainly include LLMs, including a new solution in math, improved chip designs, etc. DeepMind has AI that predicted millions of protein folds which are already being used in drugs among many other things they do, though yes, not an LLM per se. There is certainly the probability that companies won’t announce things given that the direct LLM output isn’t copyrightable/patentable, so a human-in-the-loop solves the issue by claiming the human made said breakthrough with AI/LLM assistance. There isn’t much benefit to announcing how much AI helped with a breakthrough unless you’re engaged in basically selling AI.
As for “why aren’t LLMs creating breakthroughs by themselves regularly”, that answer is pretty obvious… they just don’t really have that capacity in a meaningful way based on how they work. The closest example is Google’s algorithmic breakthrough absolutely was created by a coding LLM, which was effectively achieved through brute force in a well established domain, but that doesn’t mean it wasn’t a breakthrough. That alone casts doubt on the underlying premise of the post.
> through brute force
The same is true of humanity in aggregate. We attribute discoveries to an individual or group of researchers but to claim humans are efficient at novel research is a form of survivorship bias. We ignore the numerous researchers who failed to achieve the same discoveries.
starlust2 commented on LLMs are mirrors of operator skill ghuntley.com/mirrors/... · Posted by u/ghuntley
Every time I ask an LLM to write some UI and model for SwiftUI I have to specify to use @Observable macro (is the new way), which they normally do, after asking for it.
The LLM tells me that they prefere the "older way" because it's more broadly compatible, that's ok if you are aiming for that. But If the programmer doesn't know about that they will be stuck with the LLM calling the shots for them.
A thing people miss is that there are many different right ways to solve a problem. A legacy system might need the compatibility or it might be a greenfield. If you leave a technical requirement out of the prompt you are letting the LLM decide. Maybe that will agree with your nuanced view of things, but maybe not.
We're not yet at a point where LLM coders will learn all your idiosyncrasies automatically, but those feedback loops are well within our technical ability. LLM's are roughly a knowledgeable but naïve junior dev; you must train them!
Hint: add that requirement to your system/app prompt and be done with it.
starlust2 commented on My AI skeptic friends are all nuts fly.io/blog/youre-all-nut... · Posted by u/tabletcorry
Efficiency to corporate knowledge? Absolutely not, no way. My coworkers are beginning to use AI to write PR descriptions and git commits.
I notice, because the amount of text has been increased tenfold while the amount of information has stayed exactly the same.
This is a torrent of shit coming down on us, that we are all going to have to deal with it. The vibe coders will be gleefully putting up PRs with 12 paragraphs of "descriptive" text. Thanks no thanks!
Well I'm certainly not saying that AI should generate more corporate spam. That's part of them problem! And also a strawman argument!
starlust2 commented on My AI skeptic friends are all nuts fly.io/blog/youre-all-nut... · Posted by u/tabletcorry
> But often prompting is more complex than programming something.
I'd challenge this one; is it more complex, or is all the thinking and decision making concentrated into a single sentence or paragraph? For me, programming something is taking a big high over problem and breaking it down into smaller and smaller sections until it's a line of code; the lines of code are relatively low effort / cost little brain power. But in my experience, the problem itself and its nuances are only defined once all code is written. If you have to prompt an AI to write it, you need to define the problem beforehand.
It's more design and more thinking upfront, which is something the development community has moved away from in the past ~20 years with the rise of agile development and open source. Techniques like TDD have shifted more of the problem definition forwards as you have to think about your desired outcomes before writing code, but I'm pretty sure (I have no figures) it's only a minority of developers that have the self-discipline to practice test-driven development consistently.
(disclaimer: I don't use AI much, and my employer isn't yet looking into or paying for agentic coding, so it's chat style or inline code suggestions)
A big challenge is that programmers all have unique ever changing personal style and vision that they've never had to communicate before. As well they generally "bikeshed" and add undefined unrequested requirements, because you know someday we might need to support 10000x more users than we have. This is all well and good when the programmer implements something themselves but falls apart when it must be communicated to an LLM. Most projects/systems/orgs don't have the necessary level of detail in their documentation, documentation is fragmented across git/jira/confluence/etc/etc/etc., and it's a hodge podge of technologies without a semblance of consistency.
I think we'll find that over the next few years the first really big win will be AI tearing down the mountain of tech & documentation debt. Bringing efficiency to corporate knowledge is likely a key element to AI working within them.
starlust2 commented on Robin: A multi-agent system for automating scientific discovery arxiv.org/abs/2505.13400... · Posted by u/nopinsight
Not my subject area, but at least one other group looked at ABCA1, and judging from this abstract, it has been linked via GWAS already, and furthermore concludes it doesn’t play a role (I haven’t looked at the data though).
I don’t know, but if we were to reframe this as some software to take a hit from a GWAS, look up the small molecule inhibitor/activator for it, and then do some RNA-seq on it, I doubt it would gain any interest.
https://iovs.arvojournals.org/article.aspx?articleid=2788418
Wouldn't the fact that another group researched ABCA1 validate that the assistant did find a reasonable topic to research?
Ultimately we want effective treatments but the goal of the assistant isn't to perfectly predict solutions. Rather it's to reduce the overall cost and time to a solution through automation.
What about brownfield development though? What about vague requirements or cases with multiple potential paths or cases where some technical choices might have important business consequences that shareholders might need to know about? Can we please stop pretending that software engineering happens in a vacuum?
The thing with vague requirements is that the real problem is that making decisions is hard. There are always tradeoffs and consequences. Rarely is there a truly clear and objective decision. In the end either you or the LLM are guessing what the best option is.
starlust2 commented on The Northeast is becoming fire country newyorker.com/news/the-le... · Posted by u/gregorymichael
Humidity can still hit 100% though, yea?
I'm not sure about that but in that scenario polar regions would be 75-85F and life would be very harsh just about everywhere else. The one period in time where temperatures were that high, the Paleocene–Eocene Thermal Maximum, diversity plummeted, mammals were smaller and migrated to higher latitudes.