My biggest problem is remembering what I did yesterday. It's starting to become hard to remember what I have going on right now. Multitasking is a killer.
> Search is so bad and LLM so good that it's faster and easier to re-implement than reuse.
This is a scary level of AI mania.
Even if you're being assisted by the best frontier model, there is no way that reimplementing a PDF engine from scratch is faster than just using the one that's already there. AI-driven coding still takes time and tokens. A web search takes maybe 60 seconds to search and find the existing implementations?
You could even tell the LLM to configure (or even fork) one of the existing implementations for you, and that would still be way faster than a from-scratch implementation.
LLMs will miss a lot of edge cases. This is critical for a library that parses PDFs. Ask it to review the code for issues and by the 20th time you ask, it still will find issues. How's that faster (or safer)?
I use AI heavily but it's not this omnipresent deity that knows everything at all times.
Am I alone in thinking type hints are largely a waste of time in Python and can be actively harmful?
I had claude run ty via this new "uv check" on a moderately sized Python package (cli+tui to a multi-transport messaging system) and it found 67 things. Claude checked each finding for bugginess and found 1 is marginally worth fixing. A large fraction of the findings were ty guessing the type and being wrong. Hinting would I guess remove those wrong guesses but hinting is a PITA and greatly increases code size, both of which is bad for human and LLM.
Do people really get benefit from going all-in with type hinting?
Type annotations are generally great, if you've never had the displeasure of working with Python pre type-annotations, you'd know that tracking down "wtf type even is this" was a big part of just working in Python. The extra text/tokens spent on annotations are 100 % worth it, both for humans and LLM.
An issue you run into these days is that even if you have excellent type checking on your own code, some libraries don't. You of course want to avoid such libraries, or see if you can find type stubs. Gradual typing can suck, but well-typed Python is a bliss.
I work with Python today without type annotations, don't find that kind of type tracking difficult at all, nor do I find the annotations worth the effort. The questions I've seen over the years, meanwhile, from people trying to force the annotation system to do strange things, have suggested to me that people are creating far too much complexity for themselves. Incidentally, IMX, ChatGPT usually writes un-annotated new code unless you specifically ask for annotations. And it can easily refactor that un-annotated code, too.
Annotations are ignored by the compiler (except insofar as they require adding some attributes to objects) and do not guide the bytecode-compilation process or empower any kind of optimization (without a dedicated third-party system like mypyc). Nor are they relevant to PyPy. They are not even free abstractions: as mentioned some objects gain additional attributes (in case you want to introspect something at runtime, but you usually don't), and e.g. calls to `typing.cast` still have to be looked up and made even though it's explicitly intended as a no-op. Plus, expecting annotations to reflect types that can be statically checked gets in the way of other interesting uses of annotations (and the original PEP explicitly designed with other use cases in mind).
I have been using Python for over 20 years. If anything, my attitude has become further against type annotations in the last few.
I routinely run into issues, where Claude will try to add functionality to a dict-heavy API and messup the keys, I just do an initial pass converting everything to dataclasses, and it can iterate on it's own with the typechecker to fix everything on it's own, but otherwise I have to do a bunch of manual iterations.
The other great thing about type annotations is that you dont have to use them. Coming from strongly typed language background its life changing for me to sometimes rely on them sometimes just ignore.
I take issue with "understand". I agree QM can not be understood in terms of everyday experience. One must build experience, language and proper metaphor for the understanding. For example, in the intro Ross uses the example of explaining a waterfall to someone who never saw it. He says that can be done but he relies on implicitly the listener knowing what people on Earth may take for granted. That understanding of a waterfall can only happen if the listener already has some understanding of fluid flow and gravity. We do, but say a newly found alien friend from a dry environment that had never experienced fluid flow would not have the mental building blocks to understand it. Feynman gave a famous interview where he said, essentially, "no, I can't explain it to you because you are too ignorant" (he said it more politely). Going the other direction, Physics steeped enough in quantum theory do understand QM enough to have meaningful discussions free (at times) of rigorous math. They do this because they have a shared language, vocabulary, metaphor. Sure, they must rely on math and experiment to ground that understanding in truth but I guarantee they do not communicate understanding (solely) in math and measurement data.
That’s not the meaning of “understand” that was being used. This article points out that nobody understands QM well enough, not even the experts. (Do note the author is an expert!) By “I Don’t Understand”, what he actually means is that QM is incomplete in the same way that Newtonian Mechanics is incomplete; it doesn’t explain physics well enough to understand or explain what’s really there. This is, of course, why there’s an ongoing debate among the experts about what QM even means and how to interpret it.
"I really can't do a good job, any job, of explaining magnetic force in terms of something else you're more familiar with, because I don't understand it in terms of anything else that you're more familiar with"
His point was that fundamental forces cannot be understood as "like" everyday objects, because we don't "understand" everyday objects except as applications of fundamental forces. Using those things to explain fundamental forces would just be circular. He can tell you that a stretching rubber band is due to the electric field, but he can't then turn around and "explain" that the electric field is like a stretching rubber band!
Fundamental forces just are, and you understand them by studying them.
Yes, this sounds like the interview that I was remembering.
>Fundamental forces just are, and you understand them by studying them.
This is largely the point I was trying to make. One must understand QM using non-everyday ideas developed with math and measurement. But once those ideas are held, similes/metaphor (sometimes actual isomorphs) can be useful parts of that understanding and its communication between those that share the basis for that understanding.
A useful analogy might be understanding quantum mechanics versus understanding a computer. In the early days of computing, an engineer might plausibly have known every component in a computer, understood how these components worked, and understood why each one was there. As computers got more complicated, no longer could anyone understand the entire system on that level. But still, as an individual engineer, you can understand the system at a high-level, and for each lower-level component, you can have an approximate idea of why it's there, what it does, and how to learn it in more detail if you ever have to. Any "why" question that you might have about it has a straightforward answer that can be stated in a few sentences.
With quantum mechanics, there's a lot that you have to accept when you learn the subject without understanding why. Why complex numbers? Why infinite-dimensional Hilbert spaces? Why non-commutative algebra? You can kind-of, with great effort, make arguments that all of these are at least reasonable. But nobody really understands "why" at a level beyond "because the theory works".
You can imagine waterfall of sand - the fluidity here comes from all those small sand particles moving simultaneously.
>>>Physics steeped enough in quantum theory do understand QM enough to have meaningful discussions free (at times) of rigorous math.
That sounds a lot like gate keeping and has no actual backing - to understand QM you still need to understand Physics, as mechanics of both of them are explaining each other - the scale is the only difference.
The idea is that you can discuss using hand waving while drinking coffee, but after that you must write the complete mathematical calculation, so there is no gate keeping.
Most of the times the formal version with equations agree with the hand waving, but sometimes you have nasty surprises and must learn to hand wave better.
Dude, the number of people who disable javascript is a rounding error compared to those who don't. Even in the corpus of 'Hacker News readers', I'd expect it to be miniscule.
It might be one of the things you personally care deeply about, but that doesn't mean any site owner needs to. At all.
Or, wrong by design. There is something about nudging users to use the claude CLI from which Anthropic extracts value. Otherwise they would not charge extra for "claude -p" or ACP access.
The tmux send-keys + terminal scrape is the LLM version of the analog hole of DRM'ed audio.
Wait, is `claude -p` usage counted differently? I've built something of a multi-agent daemon system and use `-p` extensively. Now I'm wondering if I'm burning my credits faster.
If you are paying for the API anyways, I don't think it makes a difference. What is different is if you are on a subscription then you need to pay more to use `claude -p`. But then Anthropic started adding free credits, at least for Team and above plans, to offset the cost. Drug dealer pricing.
But don't worry, I'm sure next week it'll all change in some new and confusing way. And not for the benefit of the customer.
Money for the Genesis Mission is coming out of science budgets. Given how damaging this funding for Artificial Intelligence as been for US science, can't wait to see what Super Intelligence funding does to it.
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