KubeAI – Open AI on Kubernetes

Hacker News

The open source AI ecosystem is progressing at a pace that is hard to keep up with. Our team is building KubeAI to make it easy to quickly operationalize OSS models and tools within Kubernetes. The goal is to serve as a drop-in replacement for proprietary platforms like OpenAI while also integrating well with existing cloud infrastructure (i.e. messaging systems, databases, etc). Design decisions are being made with an adherence to simplicity as the guiding principle (no dependency on Istio, etc). We currently have implemented support for scale-from-zero inferencing (built-in vLLM & Ollama operator). Next up, we are are going to focus on end-to-end training-through-serving support for LoRA adaptors next! Would love to hear feedback and ideas!

Visit product View on Hacker NewsAug 26, 2024

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4points

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

82%82% 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: exist, open source, existing · 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.

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

63%63% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

48%48% 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

33%33% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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

31%31% predicted probability of success on AppSumo, 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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