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OpenAI ChatGPT App starter DevXP feels like 2010, I built a better one

Hacker News

OpenAI ChatGPT App starter DevXP feels like 2010, I built a better one

Hi HN! Two weeks ago at their Dev Days, OpenAI unveiled ChatGPT apps, interactive widgets that render inside ChatGPT using the Apps SDK and MCP. Building them using their template repository was surprisingly painful: every time you make a change on your React widget, you need to rerun the entire build pipeline to get fresh JS and CSS assets. The dev feedback loop was pretty terrible. That’s why I built a TypeScript starter that gets you up and running in under a minute. Clone, npm install, npm run dev, paste your ngrok URL into ChatGPT, and you're iterating on widgets with Hot Module Reload in the ChatGPT interface, just like building a regular web app. When you're ready to ship, the production build pipeline deploys instantly to Alpic or any other PaaS. What's included: - Vite dev server with Hot Module Reload (HMR) piggy-backed on your MCP server Express server - Skybridge framework: an abstraction layer I built on top of OpenAI's skybridge runtime that maps MCP tool invocations to React widgets, eliminating manual iframe communication and component wiring. - Production build pipeline: one-click deploy to Alpic.ai (with bundling, hosting, auth, MCP-specific analytics included) or elsewhere - No lock-in: uses the official MCP SDK, works with OpenAI's examples Quick start: git clone https://github.com/alpic-ai/apps-sdk-template cd apps-sdk-template pnpm install && pnpm run dev ngrok http 3000 # Add https://your-url.ngrok-free.app/mcp to ChatGPT Settings → Connectors Feedback welcome, especially if you hit rough edges or want specific features! Starter Kit GitHub → https://github.com/alpic-ai/apps-sdk-template Skybridge Github → https://github.com/alpic-ai/skybridge

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Product HuntOn track for Day 1 leaderboard · Strong signals: mcp, apps, chatgpt · Missing: mac, agents, macos
91%91% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
81%81% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: apps, widgets · Missing: mobile apps, ios, personal
52%52% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, pipe, 000 · Missing: https docs, excited, just released
45%45% 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: host, interface · Missing: plus, platform, intuitive
38%38% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: active · Missing: arr, mrr, revenue
19%19% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency
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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