Ka

Karton is a simple, type-safe RPC and state-syncing framework (OSS,MIT)

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Karton is a simple, type-safe RPC and state-syncing framework (OSS,MIT)

Karton (German for “carton”) is a type-safe and web socket based solution that allows you to define a shared definition of what is synchronized between server and client side (the “Karton contract”): - The type of state that is synced between the server- and client-side (read-only on client-side) - Server procedures that clients can call to make mutations on server-side - Client procedures that servers can call to make queries etc. to the client-side All connections share the same state, and the state is always defined by the server. Deltas are efficiently synced through JSON-style patches (using immer under the hood). We’re Glenn and Julian, and we built this as a tool for our startup (stagewise - YC S25). We needed a simple and type safe solution to sync a CLI app (JS-app) with a browser-based UI-app (we use React). We didn’t find any solutions out there that seemed easy to use, so we simply decided to build our own one. We offer both the server and client in vanilla TS, but also ship a React-specific client that gives you a Zustand-style access to the state (allowing to only select slices of the whole state as well). The API is made to be very easy to use, with just one output type that’s shared between server and client apps, making it a low-overhead solution if you build locally running JS apps that consist of multiple sub-apps. Karton is MIT-licensed, available on npm (@stagewise/karton) and currently part of our bigger OSS monorepo. It’s pretty much in beta state but we already use it in production. GitHub: https://github.com/stagewise-io/stagewise/tree/main/packages... NPM package: https://www.npmjs.com/package/@stagewise/karton Our YC Launch: https://www.ycombinator.com/launches/O3M-stagewise-the-front...

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Indie HackersFits the IH revenue-focused audience · Strong signals: efficiently · Missing: supports, reddit linkedin, podcasting
74%74% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Product HuntOn track for Day 1 leaderboard · Strong signals: apps, using · Missing: mac, agents, macos
54%54% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
52%52% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Strong signals: efficient · Missing: plus, platform, intuitive
47%47% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: apps, way · Missing: mobile apps, ios, personal
40%40% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
14%14% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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