Project Info
Inspiration
When I say I bookmark everything, I mean everything. If I ever find something shocking, awesome, hilarious, drippy, or what have you, I almost always have the primal urge to bottle that moment and try to save it for later. As a result of these tendencies, however, I have a bottomless store of bookmarks and screenshots that have sat collecting dust for nearly a decade. I always want to go through and relive those moments, but the task of sorting through everything has always been so daunting, which is why I bring you today's product.
What it does
Organic Clustering (OG) brings an automatic bookmark and image organization tool, while having the adaptability of learning from user input simultaneously. It will categorize items as it sees fit, until the user makes a move. Each change the user makes is directly logged in the model's reward system, and this immediate learning allows it to instantly adapt and start clustering items in a way the user prefers.
How we built it
At its core, it is a contextual bandit algorithm that has each existing cluster (and the option of a new one) as its arms. When a user changes some cluster/category assignment, the model is penalized and learns this change. It also comes with preexisting configurations to make intelligent similarity-based clustering right out of the box. Each image and webpage is embedded through the CLIP embedding model, and this is served to the user through an interactive react flow UI.
Challenges we ran into
Many, namely deployment related issues. I'll certainly tackle these after the hackathon though!
Analysis
View
Metric
- 85
Figures cover GitHub contributors during the hackathon window. A co-authored commit counts in full for each author, so per-member totals add up to more than the whole-team figures.
Technology
- CSSIn code
- HTMLIn code
- PythonIn code
- ReactIn code
- TypeScriptIn code
- VercelClaimed
5 of 6 appear in the indexed code. 1 claimed on Devpost could not be matched to code, which may simply mean the tool leaves no trace in the repository.
AI coding agents
No AI coding agent signals were found in this repository.
Detected from committed agent config files and commit authorship. Absence of a signal is not proof an agent was unused.
Codebase size
Source size
491 KB
Source files
25
Counts recognized source files only; vendored directories, binaries and lockfiles are excluded, so this is smaller than the repository on disk.
Repository
Kalamojo/organize_stuff
56 files · 4.2 MB · @ 00006cf
Structure
Interface
1 file · 2%Screens, components and styles rendered to the user.
Application logic
23 files · 41%Domain rules, services and shared utilities.
+2 moreBackground jobs
6 files · 11%Work run outside a request: tasks, workers and schedules.
Supporting
Layers are inferred from where files sit in the tree, not from reading the code. A project that names its directories unconventionally will read oddly here — open the file browser to check anything the diagram implies.
Languages
- TypeScript88%
- Python11%
- CSS0%
- Shell0%
- HTML0%
- Markdown0%
Share of indexed source by file size. Binary and vendored files are excluded.
Dependencies
frontend/package.json
npm · 9- axios
- react
- react-dom
- reactflow
- +5 more
backend/requirements.txt
pypi · 8- beautifulsoup4
- fastapi[standard-no-fastapi-cloud-cli]
- huggingface-hub
- numpy
- onnxruntime
- pillow
- requests
- tokenizers
ts-worker/package.json
npm · 6- @vowpalwabbit/vowpalwabbit
- +5 more
worker/pyproject.toml
pypi · 2- numpy
- vowpal-wabbit-next
Declared in the repository’s manifests at the indexed commit. A declared package is not proof it is used, and runtime dependencies are listed first.
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