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One click deploy from your IDE – AI and Code Analysis to handle setup

Hacker News

One click deploy from your IDE – AI and Code Analysis to handle setup

Alpha release of an IDE extension that deploys full stack apps directly from your editor. We combine AI with code analysis in the backend to eliminate the need for CLI commands, config files, or infrastructure setup — just click and deploy. Supports Next.js (FastAPI & Express coming soon) No cold starts - deployments are up for 48h, no sleep cycles Planned: - Runtime debugging assistant - Permanent deployments - Custom domains - Scaling options Try it: Cursor: cursor:extension/oneclickdevtools.one-click-deploy VS Code: vscode:extension/oneclickdevtools.one-click-deploy Looking for raw feedback — would you use this for your next project? If not, why?

Visit productView on Hacker NewsMar 18, 2025
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Actual performance

3points
Did not reach leaderboard

Launch Intel predictions

Analyze your own launch →
Product HuntOn track for Day 1 leaderboard · Strong signals: cursor, apps, code · Missing: mac, agents, macos
87%87% 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 · Strong signals: supports · Missing: reddit linkedin, podcasting, created
56%56% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: soon · Missing: plus, platform, intuitive
32%32% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released
30%30% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Strong signals: apps · Missing: mobile apps, ios, personal
23%23% 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
21%21% 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
2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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