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Deploy Autoscaled Dockerfiles apps

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Deploy Autoscaled Dockerfiles apps

I built a cloud to deploy autoscaled docker apps, being tired of waiting for AWS to apply changes for 30 minutes. It was built to be extremely fast (read sub 30 seconds for any operation). It also has neet features like auto HTTPS, zero downtime rollout and rollbacks, image storing and CNAMEs. It all runs on Kubernetes and I use it to host my own projects. I would love some feedback :) https://usekube.co

Visit productView on Hacker NewsOct 1, 2018
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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: apps, dock · Missing: mac, agents, macos
69%69% 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: io · Missing: https docs, excited, just released
63%63% 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 HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting
48%48% predicted probability of success on Indie Hackers, 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.
TrustMRRLess likely to generate early MRR · Strong signals: apps · Missing: mobile apps, ios, personal
33%33% 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
25%25% 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
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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