131–140 of 297 posts
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Copilot is a game-changer and very underrated IMO. GPT4 is smart but not really used in production yet. Copilot is reportedly generating 50% of new code and I can't imagine going without it.
Where do you get that 50% number? Do you mean 50% of all new code in the industry? That seems beyond extremely unlikely.
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Either way, you’re sending your companys biggest asset to another company, aren’t you? I’ll try these tools when they start being able to run locally
No or no company would be able to use it. As you type fragments of code are sent and discarded after use. You need to trust Microsoft to actually do the discarding but contractually they do and you can sue them if they accidentally or deliberately keep your code around or otherwise mismanage it.
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But you can't determine if a statement is true by simply reading more words. It's also not efficient for doing higher level work. There was a time before we had algebra where people were still expressing the same ideas but the notation wasn't there. Mathematics was expressed in "plain language." It's extremely difficult to read for us. For mathematician's of the time there was no other way to explain algorithms or ex…
Arguably reading code can’t lead to definitive conclusions about its bug-free-ness
If we want more programs that are correct with respect to their specifications we need to write better, precise specifications… not wave our hands around.
However for a lot of line-of-business tasks we’re generally fine with ambiguous, informal specifications. We’re not certain our programs are correct with respect to the specifications, if we had written them out formally, but it’s good enough.
I think most businesses that are writing software that needs to be reliable and precise are not going to benefit from these kinds of tools for some time.
* The tests are written in BDD style "it('should xyz')", which programmers do in code like this for convenience. But if we're automating their creation, then actual human-readable Cucumber clauses would be more useful. Maybe the tests can be transpiled. This isn't the AI's fault, but more of a symptom of how the original spirit of BDD as a means for nonprogrammers to test business logic seems to have been lost.
* React hooks and Redux syntax are somewhat contrived/derivative. The underlying concepts like functional reactive programming and reducers are great, but the syntax is often repetitive or verbose, with a lot of boilerplate to accomplish things that might be one-liners in other languages/frameworks. This is more of a critique of the state of web programming than of the AI's performance.
* MVVM is a fine pattern, but at the end of the day, it's an awful lot of handwaving to accomplish limited functionality. What do I mean by that? Mainly that I question whether the frontend needs models, routes, controllers (which I realize are MVC), etc. I mourn that we lost the idempotent #nocode HTML of the 90s and are back to manually writing app interfaces by hand in Javascript (like we did for native desktop apps in the C++ OOP days) when custom elements/components would have been so much easier. HTMX combined with some kind of distributed serverless lambda functions (that are actually as simple as they should be) would reduce pages of code to a WYSIWYG document that nonprogrammers could edit.
What I'm really getting at is that I envisioned programming going a different direction back in the late 90s. We got GPUs/TensorFlow and Docker and WebAssembly and Rust etc etc etc. And these things are all fine, but they're contrived/derivative too. More formal systems might look like multicore/multimemory transputers (or Lisp machines), native virtual machines with full sandboxing built in so anything can run anywhere, immutable and auto-parallelized languages like HigherOrderCO/HVM or true vector processing with GNU Octave (MATLAB) so that we don't have to manually manage vertex buffers or free memory, etc.
I've had architectures in mind for better hardware and programming languages for about 25 years (that's why I got my computer engineering degree) but I will simply never have time to implement them. All I do is work and cope. I just keep watching as everyone reinvents the same imperative programming wheel over and over again. And honestly it's gone on so long that I almost don't even care anymore. It feels more appealing in middle age to maybe just go be a hermit, get out of tech. I've always known that someday I'd have to choose between programming and my life.
Anyway, now that I'm way too old to begin the training, I wonder if AI might help to rapidly prototype truly innovating tools. Maybe more like J.A.R.V.I.S. where it's just on all of the time and can iterate on ideas at a superhuman rate to assist humans in their self-actualization.
Then again, once we have that, it becomes trivial to implement the stuff that I rant about. Maybe we only have about 5-10 years until all of the problems are solved. I mean all of them, everywhere, in physics/chemistry/biology/etc. Rather than automating creative acts and play as AI is doing now. If the Singularity arrives in 2030 instead of 2040, that also seems like a strong incentive to go be a hermit.
Does any of this resonate with anyone? That somehow everything has gone terribly wrong, but it's more of a hiccup than a crisis? That maybe the most impactful thing that any of us can do is.. wait for things to get better?
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> But it's not just like humans. For one thing it's built differently I'm referring to the behaviour, not the inner nature. > in fact extant AIs are notorious for doubling down instead of accepting correction. My experience suggests ChatGPT is better than, say, humans on Twitter. I've had the misfortune of several IRL humans who were also much, much worse; but the problem was much rarer outside social media. > Even m…
> I'm referring to the behaviour, not the inner nature. Since the inner nature does affect behavior, that's a non sequitur . > we had to invent counter-intuitive maths to make most of our modern technological wonders. Indeed, and that's worth considering, but we shouldn't pretend it's the common case. In the common case, the machine's lack of real-world context is a disadvantage. Ditto for the absence of any actual u…
I would say the reverse: we humans exhibit diverse behaviour despite similar inner nature, and likewise clusters of AI with similar nature to each other display diverse behaviour.
So from my point of view, that I can draw clusters — based on similarities of failures — that encompasses both humans and AI, makes it a non sequitur to point to the internal differences.
> The ability to form a coherent - even if novel - theory and an experiment to test it is key to that kind of progress, and it's something these models are fundamentally incapable of doing.
Sure.
But, again, this is something most humans demonstrate they can't get right.
IMO, most people act like science is a list of facts, not a method, and also most people mix up correlation and causation.
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One thing that really bothers me is that I want it to use best practices and it doesn't really know which ones I'm talking about, and then I realize they are _my_ set of best practices, made from others' nameless best practices. So I have to decide if it's just a matter of manually converting the 5-10 little things like using `env bash` in the header, etc. Or do I ask it to remember that and proceed to the next layer…
I think that Copilot is much better/more promising for this kind of thing because it's looking at the code you've already written without you having to constantly prompt it. I had a lot of the same hangups as you when I had played around with ChatGPT. How do I get it to handle the monotonous stuff without me having to spend all my time teaching it? I finally tried Copilot the other day and it was stunning. I had a ha…
This would make copilot the better tool in 90% of cases, but I’ve been using GPT-4 to script a lot of things I previously would never have scripted at all. It reduces the cost to where even one-off scripts for a twenty minute job are usually worth writing.
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Either way, you’re sending your companys biggest asset to another company, aren’t you? I’ll try these tools when they start being able to run locally
No or no company would be able to use it. As you type fragments of code are sent and discarded after use. You need to trust Microsoft to actually do the discarding but contractually they do and you can sue them if they accidentally or deliberately keep your code around or otherwise mismanage it.
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For my side projects, copilot easily generates 80% of the code. It snoops around the local filesystem and picks up my naming schemes and style to help recommend better. It makes me so much more productive. For work projects, I tried it on some throwaway work because we're still not allowed to use it for IP reasons, but it is very good at finding small utility functions to help with DRY, and can help with step by step…
> It snoops around the local filesystem and picks up my naming schemes and style to help recommend better. Are you sure about this? It doesn't seem to work on my machine. I think it will infer things that might be in other modules, but only based on the name. I'm basing this on the fact it assumes my code has an API shape that's popular but that I don't write (eg free functions vs methods).
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Absolutely. People will quickly realize that for coding, the natural language part of LLMs is a distraction. Copilot is much better for someone actually writing code, but unfortunately doesn't get as emphasized due to the narrative surrounding LLMs right now.
Has the Copilot backend been updated to use anything more advanced yet? I tried it out when it was new and free for a while and it really struggled with anything that wasn't incredibly common. GPT 4 in its chat form works a whole lot better for niche stuff than that one did.