> Haven't typed a line of code or read any code for over 6 months now
> I ask questions to confirm things
Oh my.
As someone who reads the code, I can tell you, asking questions to confirm things is inadequate. The models lie to me, daily.
Every day I have two experiences:
1. I’m blown away by what it can do
2. I say, ”wait, you said this, but the code shows that, so you were just going to leave that endpoint without requiring any authentication??” and I get the “you’re absolutely right, that was my mistake, and you’re right to call it out” song and dance. Daily.
It also adds all kinds of bloat to code, tests, and “documentation”. I’d say I spend ~30% of my dev time picking lines of code or documentation and asking, “why does this exist?” and “what would break if we deleted this line?” and then arguing with it and removing things.
Yeah, I agree, they are far from perfect and make a lot of mistakes.
Truth is, modern software was already quite shit and full of bugs. All major apps had bugs, issues, going down, etc, so users did get used to things not working. I honestly beleive AI coding nowadays, for better or worse, does things better than the average developer.
Yes, it is overly defensive and verbose, but the end result is in general ok and fully functional. Yes, it adds 30 tests and "release gates", and they are not even that useful, most of the times they just act as an extra safety mechanism to make parts of the code immutable, so release fails if the model accidentally changed things.
Another issue with looking at code, is that it's very hard to manually change things anyway. I can't just change a variable from 10 to 20, because I don't know where it is used. I have to ask the model to set that value to 20. It is quite stupid and inefficient, but this is one cost of coding using AI. But, if you do this, things will likely work.
That being said, I've mostly used Astra xhigh since it was released and things just work.
> Another issue with looking at code, is that it's very hard to manually change things anyway. I can't just change a variable from 10 to 20, because I don't know where it is used.
This should be a giant flashing red light. If you can't figure this out, either 1) you're too junior to be effective using AI, or 2) the AI is doing a truly awful job organizing the codebase. In either case, it's a sign to slow things down and understand what's happening before proceeding.
Is that that variable might be used in tests, UI, docs, agenr markdown files, other related projects too.
The design spec could say "always use 10px margin", then in code we have a const with value 10 used everywhere. If we update 10 manually in code to 20, then the design spec is now outdated.
I got it working on my laptop. It has an Intel integrated iGPU and an NVIDIA discrete dGPU. I was able to pass through the NVIDIA GPU and use it for CUDA/etc.
But the NVIDIA dGPU is muxless, so apparently all of the display outputs (laptop screen, HDMI outputs) are effectively hard wired to the iGPU. dGPU can render in a VM, it just has no where it can send it without trying to use Looking Glass or similar approach.
Cloud VM runs the built-in `code serve-web` command, over Tailscale using `tailscale serve`. No SSH. Extensions work. No extensions run locally (with SSH some extensions have to run locally on your machine).
I’m trying to see how little I can run on my local machine. VS Code over SSH is a good step in that direction, but there’s more attack surface if SSH is misconfigured, plus risk of an extension getting compromised.
I expected the added layers (neovim, through an extension, inside VS Code, over the web) to be slow, but so far it works surprisingly well.
Ah, so it’s ”No Silver Bullet” (1986) [1] all over again.
Thinking out loud here...
AI mainly helps reduce accidental complexity. It can help one understand essential complexity, but essential complexity must still be paid.
You describe doing the essential, irreducible part. And that’s specific to your needs, depends on the problem you’ve chosen, understanding reality of your domain, evaluating tradeoffs, and being accountable for the outcome.
> We’ll model it in Ballet on your real systems in a 30-minute working session
You drastically underestimate how bad most systems are. All of the vendors in your examples have good APIs. Those vendors already did the hard part.
All of the systems we need solutions for are moving targets that are breaking constantly. You can’t model those in 30 minutes because the ways in which they break don’t show up until months later.
Absolutely. And scale does not matter. I had issues with top tier systems like docusign, tableau etc in beforeGPT era. Many undocumented issues and when you write support usually they only were pushing to buy top tier support package, but never fix anything.
> I'm very intrigued by AI note takers, but I'm absolutely unwilling to expose me or my clients to this exact problem
Unfortunately it’s mostly not up to you. It’s a weakest-link problem. It doesn’t matter if you don’t use a note taker AI, if even one person on the call uses one. Their tool doesn’t notify you and usually the person doesn’t either.
It also has the reverse impact to the person using the note taker, where people say less around them. Same as if I'm talking to someone with Meta glasses.
I wonder if the people who use these tools know the people they meet with speak less during their meetings, and then all of the participants have a post-meeting call without them to say what they really thought.
> I ask questions to confirm things
Oh my.
As someone who reads the code, I can tell you, asking questions to confirm things is inadequate. The models lie to me, daily.
Every day I have two experiences:
1. I’m blown away by what it can do
2. I say, ”wait, you said this, but the code shows that, so you were just going to leave that endpoint without requiring any authentication??” and I get the “you’re absolutely right, that was my mistake, and you’re right to call it out” song and dance. Daily.
It also adds all kinds of bloat to code, tests, and “documentation”. I’d say I spend ~30% of my dev time picking lines of code or documentation and asking, “why does this exist?” and “what would break if we deleted this line?” and then arguing with it and removing things.
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