Betaman is a rudimentary rails app to manage a set of beta tests and its testers. Try betaman yourself at betaman.suse.de
- Evaluate Novell login to check for employee permissions instead of just using openID
Integrate mailmech to also manage mailing lists easily
Gemify mailmech and integrate into Betaman
Add user groups, e.g. to manage invitation lists
Cleanup some parts of the code, write more tests
Betaman is live (only internal for now)
This project is part of:
Hack Week 10
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Recipes catalog and calculator in Rails 8 by gfilippetti
My wife needs a website to catalog and sell the products of her upcoming bakery, and I need to learn and practice modern Rails. So I'm using this Hack Week to build a modern store using the latest Ruby on Rails best practices, ideally up to the deployment.
TO DO
- Index page
- Product page
- Admin area -- Supplies calculator based on orders -- Orders notification
- Authentication
- Payment
- Deployment
Day 1
As my Rails knowledge was pretty outdated and I had 0 experience with Turbo (wich I want to use in the app), I started following a turbo-rails course. I completed 5 of 11 chapters.
Day 2
Continued the course until chapter 8 and added live updates & an empty state to the app. I should finish the course on day 3 and start my own project with the knowledge from it.
Hackweek 24
For this Hackweek I'll continue this project, focusing on a Catalog/Calculator for my wife's recipes so she can use for her Café.
Day 1
Use local/private LLM for semantic knowledge search by digitaltomm
Description
Use a local LLM, based on SUSE AI (ollama, openwebui) to power geeko search (public instance: https://geeko.port0.org/).
Goals
Build a SUSE internal instance of https://geeko.port0.org/ that can operate on internal resources, crawling confluence.suse.com, gitlab.suse.de, etc.
Resources
Repo: https://github.com/digitaltom/semantic-knowledge-search
Public instance: https://geeko.port0.org/
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Internal instance:
I have an internal test instance running which has indexed a couple of internal wiki pages from the SCC team. It's using the ollama (llama3.1:8b) backend of suse-ai.openplatform.suse.com to create embedding vectors for indexed resources and to create a chat response. The semantic search for documents is done with a vector search inside of sqlite, using sqlite-vec.