Free Hosted JupyerLab with GPU

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Hi, This is Dan and Genevieve from Burstable AI. We've iterated and made a 45 degree pivot, taking what we learned from developing burst ( https://news.ycombinator.com/item?id=28191459 ) to introduce a cloud service that provides access to a GPU-enabled machine using Jupyterlab to provide notebooks, shell access, and a code/text editor. GPU access is measured and the first 50 hours are free. This is *not* a platform to do crypto mining or run weeks of model training for free. We are focused on the R & D phase of modern AI/ML, where developers/scientists are iterating, testing, and measuring results. We are running a beta program with a few seats left to get feedback and constructive directions for how to make Cloudburst a killer environment for modern exploratory AI/ML research, whether you are a high-school student hacking on the weekend, a small team at a startup, or a consultant building ML pipelines for SmB's (or some other user story we haven't thought of yet). Presently, signup is through your github account. If you have questions, please email us at beta@cloudburst.host. Thanks to all our beta folks!

Visit product View on Hacker NewsMar 27, 2022

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

80%80% 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 · Missing: supports, reddit linkedin, podcasting

78%78% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

69%69% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

46%46% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

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

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

18%18% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: crypto, introduce · Missing: web3, chat, cryptocurrency

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

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