Tool to manage on-demand cloud development environments

Hacker News

Hey HN, I started building this tool for myself for several reasons: 1. My 2020 M1 MBP with 16Gb of RAM started to become frustratingly slow when I had to run the local development stack 2. Sometimes I needed to switch between my Linux desktop and MBP, and continue the work from where I left it 3. At some point I was traveling and tried to work from a train, where the mobile internet was very unstable and things like `docker compose up` and `npm install` would take a lot of time and retries before successfully finishing First, I researched various options. Most of them felt too cumbersome, and pretty much all of them were requiring a single GitHub repo to start with. Gitpod seemed the most convenient out of all of them, but I quickly started to struggle with its limitations. At least some docker images weren’t starting at all. And after ditching the idea of using JetBrains Gateway for writing code, I couldn’t make my Gitpod instance to not start it every time I needed to do some coding. Got frustrated and unsatisfied, so as an engineer, I decided to just build it myself. I already had something of my own running on digital ocean, and I experimented with automating it via their api, so I went with building something that I could use from my terminal, where I already spend the most of my time, and something that didn’t require a server hosted and running 24/7. Eventually, I traveled to Greece for a month of workation, and this tool has been a life saver for me. I built features, fixed bugs, improved performance of the app I’m working on. All this would’ve been much more difficult and much less productive without this tool. Would love to hear your feedback and thoughts.

Visit product View on Hacker NewsAug 25, 2023

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Actual performance

4points

Did not reach leaderboard

Launch Intel predictions

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

91%91% 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: dock, single, using · Missing: mac, agents, macos

91%91% 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

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.

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

40%40% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: host · 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 · Missing: arr, mrr, revenue

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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