@kmelve

helping developers get more out of @sanity_io \n (he/him) \n ask me about real-time text-fields fields in front of delightful JSON \n advisor @heavybit

Oakland, CA
Joined February 2007
this.
*deep breath* people think I'm joking when I tell them that to develop taste for software they need to have brunch with their friends, watch old movies and find old records, walk around metropolitan cities and observe how the building styles lend to the personality of the people around them, but I'm so fkin dead serious. where else is this mythical "taste" going to come from? what are you doing to parse the human condition? mf you can barely decide what discomfort you're subjecting yourself to in finding out what you like/dislike, and I'm supposed to trust you to make decisions for millions of people? go do some pottery and come back to me and describe the joy you felt without faffing about curves or whatever, I need you to feel the clay under your fingernails and fall in love with mud. even steve had the decency to drop acid on a sweaty beach in goa to find himself, the least you can do is listen to your friends talk about their lives over a meal without getting distracted by your phone. *exhale*
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Jev is exciting because you can make agentic experiences so-much-faster. John shows you how you can do it with some real-life stuff.
Looking at agents "thinking" feels like having to wait for an image to load pixel by pixel in the early days of the web. John on our team got a docs agent down to a 0.5s median answer by moving decisions from the LLM to @typesafeai's Jev: → Pick the right knowledge base entry (282ms vs. 1,328ms with Haiku) → Guess what the user needs while they're still typing → Send questions to a bigger model only when they need it → Choose the retrieval method Timings and build patterns on our engineering blog: sanity.io/engineering/the-0-…
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Cool! We posted about how to do this kind of stuff with @typesafeai's Jev in practice on the @sanity_io eng blog yesterday. sanity.io/engineering/the-0-…
"JEV-as-a-Judge: Accept When Confident, Escalate When Unsure" This paper shows you can just use JEV for every evaluation instead of expensive LLM. JEV basically acts as a cheap first-pass judge, returning both a verdict and how confident it is. When confidence is high, keep the answer. When it’s low, escalate to a stronger LLM. This simple routing keeps ~99% of GPT-6’s accuracy while reducing evaluation cost by a lot. alphaxiv.org/abs/2609.26550
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Just re-read this blog post I wrote and I must say I’m pretty happy with it. “I have noticed engineers getting “slop-fatigued,” and frankly, I can relate too. No wonder, as we spend our days reading AI output. In our ai-chatter channel, I have even seen my teammates choosing models based on communication style, and not coding evals.” sanity.io/engineering/announ…
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You’re directionally correct.
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Remember Jev?
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another instant classic on the sanity engineering blog
Are you unable to use Safari because of dizzying wobbly spinners? Me too! 😤🤌 Start using the new CSS `round()` function and fix your app (like we did sanity.io/engineering/we-fix…) so we can all use Safari again (and not just Ash!) 😮‍💨🏖️
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in the spirit of Jev week, I will show up to meetings and only answer "yes" or "no"
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yes, @typesafeai writes "Jev" but for some reason the crowd goes "JEV"... os what is it?
75%Jev
25%JEV
12 votes • Final results
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Now I think all the other coding harnesses should follow suit and support CLAUDE.md.
We're adding support for AGENTS.md to Claude Code. Starting today in version 2.1.277, if there is no CLAUDE.md in a folder, Claude will check for and use AGENTS.md. You can toggle this behavior in /config.
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Jev gets the hype because it doesn't come with python notebooks and pdf.
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We have launched Knowledge Bases for Sanity Context. Now you can enable MCPs on top of your content that let agents: → Query structured content with GROQ, BM25, and semantic similarity (turn on embeddings on your dataset) → Look up facts across your content from a precompiled "wiki" Knowledge Bases also lets you upload PDFs and add URLs alongside content in your dataset. It will flag internal contradictions so you can fix it upstream or add rules to make sure what goes in is correct. I think this is the governance feedback loop and "secret sauce" that you want for teams and orgs.
The idea of the “LLM knowledge base” is powerful, but how do you bring it into organizational contexts?
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tested out @typesafeai Jev for some real-time content linting inside of @sanity_io studio. pretty cool stuff!
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let's see... am i doing this right, @typesafeai?
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anyone tested "is it a biking pelican or not" with @typefaceai's Jev yet?
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knut retweeted
Introducing a new knowledge governance feature in beta: Knowledge Bases. Turns out, it's difficult for agents to find facts across your content. They don't know where to look and stumble on contradictions. Knowledge Bases, part of Sanity Context, pulls the facts into one place organized by topic and flags the contradictions for you to resolve, so an agent asked "Does this case fit the iPhone Duo?" finds the answer in one lookup. Go to your Dashboard > Manage > Apps to enable Sanity Context and create your first Knowledge Base.
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