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:
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
Showcase a Go-based deployment automation component, which scans the model registry and automatically generates & applies Kubernetes manifests for new model versions.
Enable continuous improvement by making the system modular and extensible (e.g., additional models, metrics, autoscaling, or drift detection can be added later).
Prepare a short demo explaining the end-to-end process and how new models flow through the system.
Resources
Looking for hackers with the skills:
This project is part of:
Hack Week 25
Activity
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Self-Scaling LLM Infrastructure Powered by Rancher
Description
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Project Repository: github.com/alexander-demicev/llmserverless
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A complete, self-scaling LLM infrastructure that:
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A combination of open source tools working together:
Flow:
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A CLI for Harvester by mohamed.belgaied
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Project Description
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Github Repo for Harvester CLI: https://github.com/belgaied2/harvester-cli
Done in previous Hackweeks
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Some nice additions might be: * Improve handling of namespaced objects * Add features, such as network management or Load Balancer creation ? * Add more unit tests and, why not, e2e tests * Improve CI * Improve the overall code quality * Test the program and create issues for it
Issue list is here: https://github.com/belgaied2/harvester-cli/issues
Resources
The project is written in Go, and using client-go the Kubernetes Go Client libraries to communicate with the Harvester API (which is Kubernetes in fact).
Welcome contributions are:
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Harvester CLI might be interesting to you if you want to learn more about:
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- Go programming language
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Advent of Code: The Diaries by amanzini
Description
It was the Night Before Compile Time ...
Hackweek 25 (December 1-5) perfectly coincides with the first five days of Advent of Code 2025. This project will leverage this overlap to participate in the event in real-time.
To add a layer of challenge and exploration (in the true spirit of Hackweek), the puzzles will be solved using a non-mainstream, modern language like Ruby, D, Crystal, Gleam or Zig.
The primary project intent is not just simply to solve the puzzles, but to exercise result sharing and documentation. I'd create a public-facing repository documenting the process. This involves treating each day's puzzle as a mini-project: solving it, then documenting the solution with detailed write-ups, analysis of the language's performance and ergonomics, and visualizations.
|
\ ' /
-- (*) --
>*<
>0<@<
>>>@<<*
>@>*<0<<<
>*>>@<<<@<<
>@>>0<<<*<<@<
>*>>0<<@<<<@<<<
>@>>*<<@<>*<<0<*<
\*/ >0>>*<<@<>0><<*<@<<
___\\U//___ >*>>@><0<<*>>@><*<0<<
|\\ | | \\| >@>>0<*<0>>@<<0<<<*<@<<
| \\| | _(UU)_ >((*))_>0><*<0><@<<<0<*<
|\ \| || / //||.*.*.*.|>>@<<*<<@>><0<<<
|\\_|_|&&_// ||*.*.*.*|_\\db//_
""""|'.'.'.|~~|.*.*.*| ____|_
|'.'.'.| ^^^^^^|____|>>>>>>|
~~~~~~~~ '""""`------'
------------------------------------------------
This ASCII pic can be found at
https://asciiart.website/art/1831
Goals
Code, Docs, and Memes: An AoC Story
Have fun!
Involve more people, play together
Solve Days 1-5: Successfully solve both parts of the Advent of Code 2025 puzzles for Days 1-5 using the chosen non-mainstream language.
Daily Documentation & Language Review: Publish a detailed write-up for each day. This documentation will include the solution analysis, the chosen algorithm, and specific commentary on the language's ergonomics, performance, and standard library for the given task.