The software is a rails app with an Angular.js frontend using the gphoto2 library to trigger a Nikon D60 camera.
Features: take pictures, browse pictures, automatic upload to a gallery (tumblr, flickr, owncloud), qr code for download, image post-processing, using the gpio ports for the trigger button, leds for states.
Github page: https://github.com/digitaltom/photobooth
Looking for hackers with the skills:
This project is part of:
Hack Week 13 Hack Week 17
Activity
Comments
Similar Projects
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As my Rails knowledge was pretty outdated and I had 0 experience with Turbo (wich I want to use in the app), I started following a turbo-rails course.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/
Results
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.