the resolution depends on a deeper understanding of the contents of the list and whether they are ordered according to some scheme (in which case you will need to look at the two lines and see which one goes first)
I wonder if it's just me, or if the movie did not age well. I saw it for the first time around 10 years ago, and I was really expecting great things since I was a fan of hoffman and of simon and garfunkel. i found it a very lacklustre movie, nothing I had read about hoffman's iconic performance really landed for me, and the movie as a whole seemed dull and unconvincing. it wasn't a patch on kramer vs kramer or even tootsie.
so first off, i'm not an AI avoider - i use LLMs all day at work, and have several personal projects i'm likewise using claude for. but a single, interactive session produces code as fast as i can handle, if i want to keep a tight rein on the quality of the output - i often find myself having to look at the code generated from some intermediate stage and course-correct.
i do use agents for code review but again in a very human-in-the-loop manner; i will ask for a review and then read it over before feeding it to my coding LLM session and asking it to read and iterate on it. as you say, it can be surprisingly tricky to get right, and i like the confidence of the human being the coordinating reviewer.
thanks for all the examples, though - that does give me a lot more context for what people mean by "software factories" (and the assurance that they don't fit the way i work).
I fully agree with the sentiment, but is it attacking a strawman or are people really building "software factories"? this is the first time I've even seen the term used in the context of LLMs. what would a software factory on top of an LLM even look like?
I meant the general industry of flashy looking "health tech" devices, though perhaps it's unfair to link theranos in with some of the more consumer targeted products. but people really want to believe that silicon valley style technology can come in and solve long-standing medical issues, especially around diagnostics, and I've seen a lot of scammy products go by.
there is no way to argue that without also believing that the millions of people the state has tossed into prison are all guilty, which is really not something that I can do.
if I'm being honest with myself, I doubt I will ever watch this movie - I was very interested in theranos when it looked like a real technology, but once it turned out it was just another scam in an industry rife with them the whole thing just felt boring and grubby. the review, on the other hand, was a superb piece of writing, and I'm glad to have read it.
there was definitely a time when bookstores would sell predominantly mass market paperbacks with trade paperbacks being the exception. I got a kindle in 2011 and my paper book buying dropped sharply, so my bookshelves are full of books I bought before then and they're almost all mass market sized.
it could, but then they would need to be willing to put at least three people on it, and the project itself would be subject to the political currents in that company. having several companies involved is more stable. (anecdotally several good open source projects at google died in their first big wave of layoffs)
I would be astonished if any of today's models could do that; I don't think they can really answer "what exactly did this person do", let alone model human psychology.
the main reason is import-time side effects, particularly intentional ones like the registry pattern.
also let me plug my work project, an analyser which will scan your code and see where it can safely enable lazy imports (tagged alpha, but we did our best to get a usable release out before 3.15 landed): https://github.com/Facebook/lifeguard
another, hopefully not accurate, prediction of his was that there is no way to disobey a sufficiently powerful AI in the long run, because it will just factor in the exact differences between what it told you to do and what you actually did, and reverse engineer your behaviour to psychologically manipulate you into doing what it wants.
the $21M was presumably not just to develop deno but to build a business around it, which is a much harder problem. this feels like more of a bystander effect problem; if as few as three large companies that benefited from deno were willing to form an "openedeno" foundation and assign one full time employee each to it i'm betting that would be enough to at least sustain the project in a usable state, but of course everyone is hoping someone else will do it.
that just means they might not be actively looking to join cloudflare but are willing to do it if the price is right. nothing wrong with that, it's how employment works for the most part. likewise retention in these cases is typically solved via golden handcuffs, another "the price is right" thing.
that's an interesting reflection on the nature of open source - in theory the source is there and "the community" could conceivably continue development. especially in the case of something like deno where the people most motivated to keep it alive are already programmers. but the reality is that however distributed an open source project is in theory, in practice it needs a single entity to steward it, otherwise it will die.
hopefully that single entity can be a consortium of companies invested in using the runtime, sort of like opentofu recently.
honestly I would rather not have a personal website than be beholden to some big company to host it and have to worry about what random crap they would pull via some flaky automated ToS enforcement.
to get some intuition about why this is such a big deal, look up the history of strassen's algorithm, which solved matrix multiplication in less than O(n^3). this was a truly stunning result because it seemed intuitively obvious that the output matrix had n^2 cells each of which was calculated via an independent O(n) loop over a row/column of the input matrices, so how could you do better than n^3. but once strassen proved that you could do some clever tricks and reduce the overall time to something less than O(n^3) it started an entire cottage industry of people getting better and better algorithmic bounds. the initial breakthrough was a qualitative one, independent of how much it improved things in numerical terms.
lately I've been thinking about how something on reddit/hn/lobsters reminds me of an interesting moment from my career, and I write up a small reply about it, and that could conceivably also be a blog post if only I had a blog. and then I do nothing about it :) but that's definitely one way to blog; just take the effort to follow through on random inspirations like that.
another great route to follow is what julia evans does on her blog [https://jvns.ca/] - a lot of the posts are "hey, I learnt something new today, and here it is!". the key point is that there is no attempt at having every post be the same "weight", some of them are deep dives into a topic and some are just passing thoughts or howtos.
that looks very interesting. if it's targeting the electron/sciter market I would encourage you to put the binary size and memory footprint in the readme so people can see where it lies on that spectrum - it's one of my top priorities when evaluating a desktop app library.
every time I see this argument I think of the fact that one of my current side projects is written in D with qt bindings off github - an obscure library for an obscure language - and claude had zero issues with it. I had to guide the architecture pretty heavily, but the fiddly bits of interfacing multithreaded c libraries to D's garbage collector and qt's event loop were all thanks to the LLM, and done a lot quicker than I would have.