There are various home assistant solutions out there but all of them transfer your voice to some server for processing. This is a no-go for sane people although the technology is interesting and could be useful. There are various open source tools out there to achieve the same result but there is no turn key open source self hosted solution. The goal of this project is to implement a way to have a home assistant running locally - ideally with one command.

The project that is closer to the desired result is Mycroft (https://mycroft.ai/). It is very easy to run the client side components using one docker command but their backend is running remotely. All the tools they use though are open source so it only needs one to do the work and package them in a nice little docker-compose file (https://mycroft-ai.gitbook.io/docs/about-mycroft-ai/faq#can-mycroft-run-completely-offline-can-i-self-host-everything).

Extension:

If time allows, a new skill will be developed to wrap the cf cli. E.g. "Hey mycroft, what apps do I have running?" or "Hey mycroft, redeploy myapp"

Links

  • https://mycroft.ai/
  • https://mycroft.ai/initiatives/
  • https://fosspost.org/lists/open-source-speech-recognition-speech-to-text
  • https://mycroft-ai.gitbook.io/docs/about-mycroft-ai/faq#can-mycroft-run-completely-offline-can-i-self-host-everything
  • https://market.mycroft.ai/skills

Looking for hackers with the skills:

bash python3 python docker docker-compose kubernetes

This project is part of:

Hack Week 19

Activity

  • almost 6 years ago: DKarakasilis added keyword "bash" to this project.
  • almost 6 years ago: DKarakasilis added keyword "python3" to this project.
  • almost 6 years ago: DKarakasilis added keyword "python" to this project.
  • almost 6 years ago: DKarakasilis added keyword "docker" to this project.
  • almost 6 years ago: DKarakasilis added keyword "docker-compose" to this project.
  • almost 6 years ago: DKarakasilis added keyword "kubernetes" to this project.
  • almost 6 years ago: keichwa liked this project.
  • almost 6 years ago: EDiGiacinto liked this project.
  • almost 6 years ago: DKarakasilis started this project.
  • almost 6 years ago: DKarakasilis originated this project.

  • Comments

    • DKarakasilis
      almost 6 years ago by DKarakasilis | Reply

      Work happens here: https://github.com/jimmykarily/mycroft_offline

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      • https://www.cncf.io/wp-content/uploads/2025/11/cncfreporttechradar_111025a.pdf
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      • https://developers.googleblog.com/en/a2a-a-new-era-of-agent-interoperability/


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    We know AI can write "Hello World." and also moderately complex programs from a green field. But can it rebase a 3-month-old PR with conflicts in rancher/rancher? I want to find the breaking point of current AI agents to determine if and how they can help us to reduce our technical debt, work faster and better. At the same time, find out about pitfalls and shortcomings.

    The CONCLUSION!!!

    A add-emoji State of the Union add-emoji document was compiled to summarize lessons learned this week. For more gory details, just read on the diary below! add-emoji


    Kubernetes-Based ML Lifecycle Automation by lmiranda

    Description

    This project aims to build a complete end-to-end Machine Learning pipeline running entirely on Kubernetes, using Go, and containerized ML components.

    The pipeline will automate the lifecycle of a machine learning model, including:

    • Data ingestion/collection
    • Model training as a Kubernetes Job
    • Model artifact storage in an S3-compatible registry (e.g. Minio)
    • A Go-based deployment controller that automatically deploys new model versions to Kubernetes using Rancher
    • A lightweight inference service that loads and serves the latest model
    • Monitoring of model performance and service health through Prometheus/Grafana

    The outcome is a working prototype of an MLOps workflow that demonstrates how AI workloads can be trained, versioned, deployed, and monitored using the Kubernetes ecosystem.

    Goals

    By the end of Hack Week, the project should:

    1. Produce a fully functional ML pipeline running on Kubernetes with:

      • Data collection job
      • Training job container
      • Storage and versioning of trained models
      • Automated deployment of new model versions
      • Model inference API service
      • Basic monitoring dashboards
    2. Showcase a Go-based deployment automation component, which scans the model registry and automatically generates & applies Kubernetes manifests for new model versions.

    3. Enable continuous improvement by making the system modular and extensible (e.g., additional models, metrics, autoscaling, or drift detection can be added later).

    4. Prepare a short demo explaining the end-to-end process and how new models flow through the system.

    Resources

    Project Repository

    Updates

    1. Training pipeline and datasets
    2. Inference Service py