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Run AI apps and dev environments with dstack

Hacker News

Run AI apps and dev environments with dstack

Hi everyone, I'm the creator of dstack, a tool that makes it easier to train models in the cloud. Our tool allows extending it with custom providers to support different languages, frameworks, etc. All the built-in providers are also open-source: https://github.com/dstackai/dstack Today, we've released a new update that extends the capabilities of dstack beyond training models, and now also allows users to quickly build and share apps with Streamlit, Gradio, and FastAPI in the cloud – in just a few clicks. Also, similar to apps, it's possible to run dev environments with the required hardware and data access also in one command from the Terminal. All you have to do is to link your own AWS account to run commands. Currently, we support VS Code, and JupyterLab but plan to support more. Invite everyone to give it a try and share their thoughts. Happy to discuss the approach and what would be great to have in terms of other features! Blog post: https://blog.dstack.ai/introducing-apps-and-dev-environments P.S.: Currently, it's possible to run models and apps only in the configured cloud. If you'd like the tool to also allow you to run it locally, and if you would like this part to be open-source too, please leave comments!

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Product HuntOn track for Day 1 leaderboard · Strong signals: model, apps, user · Missing: mac, agents, macos
94%94% 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: ide, io · Missing: https docs, excited, just released
70%70% 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: users · Missing: plus, platform, intuitive
46%46% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: apps, users · Missing: mobile apps, ios, personal
42%42% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: training · Missing: arr, mrr, revenue
20%20% 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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