I'm curious - what's the medication? Someone I know gets pretty regular migraines. She takes sumatriptan when they occur, and also has cut alcohol due to being a potential cause.
Congrats! I've been watching this space for a while, having built a couple multiplayer sync systems in the past in private codebases, including a "redux-pubsub" library with rebasing and server canonicity that is (IIUC?) TCR-like. There's a lot to like about this model, and I find the linked article quite clear - thank you for writing and releasing this!
1. You wrote "For example, schema validation and migrations just sort of fall out of the design for free." - very curious to read about what you've found to work well for migrations! I feel like there's a lot of nice stuff you can build here (tracking schema version as part of the doc, pushing migration functions into clients, and then clients can live-update) but I never got the chance to build that.
2. Do you have a recommendation for use-cases that involve substantial shared text editing in a TCR system? I'd usually default to Yjs and Tiptap/Prosemirror here (and am watching Automerge Prosemirror with interest). The best idea I've come up with is running two data stores in parallel: a CRDT doc that is a flat key/value identifying a set of text docs keyed by UUID, and a TCR doc representing the data, which occasionally mentions CRDT text UUIDs.
The “third part” of the post starts with “I’ve benchmarked cola against 3 other CRDTs implemented in Rust: diamond-types, automerge and yrs.” This cola library appears to perform favorably in operation speed.
Congrats on the release! Having built several LLM apps in the past months and embarking on a couple new ones, I’m excited to take a look at Langfuse.
Are there any alternatives you’d also suggest evaluating, and any particular strengths/weaknesses we should consider?
I’m also curious about doing quality metrics, benchmarking, regression testing, and skew measurement. I’ll dig further into Langfuse documentation (just watched the video so far) but I’d love any additional recommendations base on that.
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Together with my business partner, we have advised founders/leaders and built LLM products across education (scalable personalized content authoring, AI tutoring/coaching, AI evaluation and feedback), general productivity, marketing tech, and healthcare.
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For JavaScript, I suggest folks check out fast-check [0] and this introduction to property-based testing that uses fast-check [1].
This is broadly useful, but one specific place I've found it helpful was to check redux reducers against generated lists of actions to find unchecked edge cases and data assumptions.
