kmelve
Joined 9 years ago
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349
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79
Member since
Nov 14
working with the JSON community over at sanity.io
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Comment · 1 month ago
Editor of the Sanity engineering blog here. Sharing this post from our SRE team about how we reduced the hosting cost for Grafana Mimir while keeping redundancy for zonal failures.
Happy to answer questions, and I'll pull in the author for anything I can't.
Comment · 7 months ago
Author here. I work at Sanity, so I'm obviously looking at this through our lens, but I tried to write it as general advice for anyone building developer tools and haven't thought that much about agents yet.
The thing that prompted this: I kept having conversations with teams where the first question was "how do we write better skills files?" and I kept finding that the issues were upstream in our error messages, CLI surface, and so on. I increasingly feel that Skill files is your "last resort."
The Workbench experiment in the post is admittedly simplified to unpack the mental model; you would probably set up evals etc to actually test it, which we have started doing at Sanity as well.
Happy to answer questions and channel more of our experience with agents (heh) and designing for them.
Comment · 8 months ago
I work with Rosti at Sanity and helped get the word out on this. The core problem is that logos vary wildly in aspect ratio, pixel density, and visual weight, so lining them up at the same height or width never looks right.
Rosti's approach uses three things: proportional normalization based on aspect ratio, pixel density analysis (so a dense wordmark doesn't visually overpower a thin logomark), and a center-of-mass calculation for optical alignment. The perceptual sizing math builds on Dan Paquette's earlier work.
The core normalization logic is plain JS with no React dependency: https://github.com/sanity-labs/react-logo-soup/blob/main/src...
Someone already forked it into a framework-agnostic version: https://github.com/auroris/logo-soup
Rosti is around if there are questions about the density compensation or optical alignment math.
Comment · 9 months ago
Author's colleague here. Rosti wrote this after we kept running into the same problem across customer sites.
The core formula is Dan Paquette's proportional normalization (aspect ratio raised to a scale factor), but the density compensation and visual center-of-mass calculation are the interesting additions.
The visual center algorithm weights each pixel by sqrt(colorDistance) * (alpha / 255) to find where the "ink" actually lives vs. the geometric center. It's a subtle effect but makes a noticeable difference with asymmetric logos.
Library is MIT licensed: https://github.com/sanity-labs/react-logo-soup
Live Storybook: https://react-logo-soup.sanity.dev
Working on getting Rosti signed up to HN for questions you might have!