Journal July 2024: Helix, dprint, ClickHouse and tree shapes

A few quick-fire notes which might be of interest to others now, and myself in the future.

After I started to use it a year ago, I wasn’t sure how long I’d continue to use the Helix editor. And yet here I am writing this post in Helix, and still using it at work. It’s crashed four or five times — in a year — but has overall proven very stable and capable. I think it’s dev progress is a bit slower than I’d like, but really, I’m very happy with the editor. It starts instantly, LSP+tree-sitter still proves a winning combination, and the improvements that have arrived are solid.

One thing I’ve been searching for is a fast formatter for web languages, specifically the ones used in Hugo and Jekyll sites. Markdown, templated HTML and CSS in the main. Tools like Prettier tend to be noticably slow in kicking in to format if one isn’t willing to pay the price of a constantly running server. I’ve been using deno fmt for a while for Markdown, but it doesn’t do CSS or HTML. So now I’m trying dprint, which has inbuilt formatting for all three languages I wanted. It turns out that deno fmt actually uses some dprint formatters under-the-hood, specifically Markdown. I like finally having a CSS formatter, although since I moved to Tailwind this has been less important. (I still really like Tailwind).

In August 2023’s journal, I mentioned using ClickHouse in a PoC. That PoC became production recently, and we now have over 100TB of data stored in ClickHouse after our pre-production ramp up. We ingest more than a billion rows a day. Throughout our build out, ClickHouse has continued to impress me, coping with each bump in data volume smoothly. Querying has remained efficient. We may need to bump our hardware a bit as we start using it more in earnest, but the simple, vertically-scaled, replicated architecture we are using seems solid 🤞

We went for a walk in a small piece of woodland near Bristol today, Leigh Woods. I loved the shapes within the branches of this tree:

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A neat way of thinking about generative AI for your products
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Evidence that LLMs are reaching a point of diminishing returns