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.
You either buy a datastar license to get their debug tools, or you spend a couple of hours to build your own. I'm sure you can find something on GitHub too.
One of the cool things is how extensible d* is. Everything is a plugin, and you are free to extend it to suite your needs. We've invested a few days building a few specific data-* attributes for our application and it's super convenient.
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.
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