Shade – A desktop app for local AWS development

Hacker News

Hi HN, I’m a solo developer who has been developing serverless applications using AWS for a few years now and I always found working locally with them to be tedious. So I have created a desktop application (Electron, yes I know!) that provides some much needed UX to the existing local AWS experience. Its built with LocalStack in mind which provides an existing mock implementation of many AWS services on a local machine. Shade just calls the AWS SDK and provides a UI in front of it. The main goal is to reduce the friction of deploying and setting up the main components of AWS serverless locally, and to just get started tinkering with them right away. I hope some people will find it as useful as I have, and any feedback is greatly appreciated. Harry

Visit product View on Hacker NewsJul 23, 2024

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Product HuntOn track for Day 1 leaderboard · Strong signals: mac, using · Missing: agents, macos, agent

75%75% 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.

Hacker NewsStrong engagement from HN community · Strong signals: exist, existing, ide · Missing: https docs, excited, just released

64%64% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Indie HackersFits the IH revenue-focused audience · Strong signals: created, started · Missing: supports, reddit linkedin, podcasting

51%51% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: calls · Missing: plus, platform, intuitive

43%43% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal

31%31% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: arr · Missing: mrr, revenue, profit

12%12% 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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