Buffet – A web-based virtual machine manager

Hacker News

Hi HN! I developed Buffet as a part of my bachelors dissertation and would like to show it to all of you today. Buffet is a full-stack, fully-featured web-based virtual machine manager that aims to provide a free, open-source, easy-to-use, secure alternative to other web-based virtual machine managers such as DistroSea and DistroTest. The back-end is implemented in Python using the Flask framework, while the front-end is implemented in React using TypeScript. Due to the closed source nature of these websites, and the fact that DistroTest shut down a few years ago with little to no warning, I decided to take a stab at implementing the same kind of project for myself. I'm very happy to say that I graduated with a First Class distinction with Honours. Since graduating, I have made the source code fully free and open source, licensed under the GNU AGPLv3. Please let me know what you think, and if you have any other feedback. Thanks!

Visit product View on Hacker NewsJun 15, 2024

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

89%89% 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

77%77% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: open source, ide, io · Missing: https docs, excited, just released

75%75% 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 · Missing: plus, platform, intuitive

42%42% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

36%36% 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

3%3% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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