It is currently faster than the stock Candle / MPS shaders it replaces on MacOS/ARM64, and IIRC a bit slower than OpenVINO/CPU on my old Windows laptop, where I never got OpenVINO/GPU to compute correctly. Candle didn't have support for GPUs on MacOS/Intel, and OpenVINO ceased to be supported there.
Compared to OpenVINO (I tried ONNX runtime too, but never got it produce correct outputs with my quantized models) it is very nice to be able to build the exact kernels I need, at the quantization settings and precision that works for the models I have and with the custom operators required (speech models do a lot of non-standard stuff), run from a single set of sources, and not have to ship a hefty third-party DLL, and having to deal with their memory leaks and other stability issues.
I was a big fan of Nushell for some time but has the same problems as unix shells. I have this CLI tool that manages a lot of things for me (a big one is downloading and cataloging arxiv papers, it downloads them and tags them with metadata and gives each PDF a proper filename based on the author's name and publication year). I tried building this in Nushell but it became eventually so painful.
He was planning to go into academia, but ended up abandoning his PhD ABD because he got exposed to the negative interpersonal politics that can happen in academia and realized that was not for him.
So he went to go work a dozen years at a hedge fund and then retire to be a stay at home dad.
His primary research interest was logical frameworks, constructive mathematics, etc. His PhD topic was on using linear logic to verify concurrent programs.
Interesting, but I'm not sure how it's different from Observable Framework ( https://observablehq.github.io/framework/ ) for dashboards as code. I like that one because it's Markdown and JS instead of a language defined in YAML + templates
The use case that immediately came to mind for me was to use dbt charts as a rendering method for an MCP App.
You don't really want to give a LLM client code execution environment like Observable Framework does. dbt charts appear to validate the entire yaml input including the SQL being sent to the data source.
So I'm getting: a data vis layer that can be dynamically defined and rendered via LLM/MCP client without the headache of sandboxing a JS Runtime environment.
I like the observable framework too - very flexible, you can nail very specific visualization details by dropping down to JS/html.
Maybe this is targeted to pure SQL folks that can get lost in UI details.
PS: one issue that I have with observable framework is authentication and authorization , it requires some system to be built on top of it to handle authn.
Maybe it's because I'm not an RBAC expert, but I had a hard time understanding the data model for folders. Are they a subject? And then there's a layered CASL where layers overwrite each other? And the user is an actor?
Sure, but the point is (1) that you don’t need a package manager to install software, which the comment I replied to seemed to assume, and (2) that for something you’re developing against like SQLite, installing it via package manager really isn’t that important. It doesn’t need to integrate with the rest of your system the way typical apps might.
What seems to be happening here is that people have learned that package managers are the right way to install software, but they don’t really understand the reasons, or where and how exceptions might apply. IMO your comment does that as well.
Even for a personal project such as the one in the blog post, I'd still rather use a package manager to install SQLite.
It's POSSIBLE to build it from source, and manually re-download the source and C compiler (and any dependencies, though I'm not sure SQLite has any) when I want to update and instruct any collaborators to do the same thing... But I'd still rather automate it all with a package manager.
Especially because I'm probably also handling other dependencies in the project and I value the uniform simplicity of using a package manager for all of them
reply