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I launched my DevOps PostgreSQL platform today

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I launched my DevOps PostgreSQL platform today

My name is Elliott. For the last three years, I’ve been building a DevOps platform on the best-in-class open source platforms (Kubernetes, Elixir, PostgreSQL, Grafana, etc.). The goal is to give engineering teams access to a modern DevOps infrastructure without needing to have a full SRE/DevOps team dedicated. It’s also open source /fair source - all the source code is here → https://github.com/batteries-included/batteries-included I shipped a public beta today and would love to hear initial reactions, thoughts, and feedback. Here are details of the platform: * The platform features a user-friendly suggestion-based interface that guides users on topics like PostgreSQL cluster memory/CPU ratios, serverless web hosting, and secure secret sharing. Advanced users can quickly access complete control over their data. * It’s an Elixir-based UI on a database-driven, self-hosted Kubernetes platform. It can automatically deploy a scalable cloud installation (currently on AWS, with more options to follow) without inflicting YAML or Terraform configurations. Alternatively, it can set up a development instance using Kind and Docker or Podman, facilitating a smooth transition from local to production environments. * The platform supports easy AI project hosting for various workloads. Use Ollama embedding models for text embedding, eliminating OpenAI costs and data leakage risks. With PGVector and Cloud Native PG for vector databases, you can achieve near-state-of-the-art performance without exposing your data to third-party APIs. Experiment with Jupyter Notebooks, featuring optional Nvidia Plugin batteries for no DevOps-required experimentation. * Single Sign-On is streamlined via Keycloak, Istio Ingress, and OAuth Proxy, and it is securely hosted on your machine or cloud account. We've simplified security with full mTLS, Istio, SSL generation, and automated routing with Let's Encrypt and Acme for HTTP2. Istio Ingress services are seamlessly configured down to the contents of config maps. Grafana and Victoria Metrics can be auto-configured with just a few clicks for easy installation. Here’s also a look at the demo of the database deploy https://www.youtube.com/watch?v=YbvkWja3VIQ The platform follows all the best practices learned for configuring and running a maintainable system without Kubenete's GitOps pain. If you want to check it out, here are links to docs, site, repo, and signup to try it out: * https://www.batteriesincl.com/ * https://home.batteriesincl.com/signup * https://github.com/batteries-included * https://www.batteriesincl.com/docs

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Product HuntOn track for Day 1 leaderboard · Strong signals: mac, model, user · Missing: agents, macos, agent
93%93% 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 · Strong signals: supports, ios · Missing: reddit linkedin, podcasting, created
86%86% 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, llama, ide · Missing: https docs, excited, just released
84%84% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoStrong fit for a featured deal · Strong signals: platform, host, friendly · Missing: plus, intuitive, reviews
63%63% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: ios, users · Missing: mobile apps, personal, entrepreneurs
31%31% 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
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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