Aggy 🐊

Your personal content curator. A self-hostable, personalized content aggregator where your preferences are what matter most. Imagine your own private Reddit, but without the clutter, focused solely on what you care about.

Why Use Aggy?

Let’s say you’re fascinated by alligators. You hop between platforms to follow alligator-related creators and subreddits. Every once in a while, you see a post about crocodiles, Argh! Aggy solves this problem by bringing all your content into one place and sorting it based on how relevant it is to you. When you're in the mood for alligator facts, you won’t have to sift through crocodile content—Aggy does the filtering for you.

Aggy works for any type of content. Whether it’s specific animals, hobbies, news, or entertainment, Aggy curates your feed to match your interests.

How does Aggy work?

Bringing all your content into one place

Aggy currently supports:

• RSS sources via templates powered by RSS-Bridge

Upcoming integrations:

• Email newsletters (Sign up for your favorite newsletters using a custom Aggy email and filter out the noise)
• Podcasts (Currently available only via RSS; direct search support is on the way)

When Aggy finds new content, it analyzes metadata and determines how relevant it is based on your past interactions, making sure the most meaningful content rises to the top.

Understanding your preferences

Aggy uses content embeddings to understand the types of content you enjoy. As you browse, like, filter, or provide feedback, Aggy fine-tunes your feed so you get more of what you love without relying on generic algorithms or trends.

Features

Available now:

• RSS source support with predefined templates for faster setup
• Embedding generation for text to improve content relevance

In the works:

• URL remapping (e.g., YouTube to Invidious)
• Embedding generation for images
• User feedback-based model training (Aggy gets smarter with your input)
• Duplicate post detection (Recognizes similar content across different sources and only shows the best)
• Alternate sorting options (Best, Worst, Newest, Oldest)
• Recommendation transparency (Learn why a piece of content was recommended to you)
• Semantic filters (E.g., hide political content)
• NSFW content controls (Blur or block inappropriate images)
• meta feeds Combine one or more aggy feeds into a single feed
• export feed as rss allow an aggy feed to be read by your favorite rss reader

Contributing

We’re still working on a contribution guide, but if you have ideas, we’d love your help!

License

LICENSE (Apache 2.0)

TODO

• generate embeddings for images
• train models on user data + embeddings
• use embeddings to help decide which image would be the best preview image
• fix reddit albums getting very low res thumbnails
• de-duplicate posts where possible (same title, picture(maybe based on embeddings?), link after redirects)

Database Design

flowchart TB
    subgraph Legend
        direction LR
        set{{set}}
        sorted_set([sortered set])
        string[string]
        hash>hash]
        list[[list]]
        hash --linked by key-->string
        hash -.linked implicitly.-> sorted_set
    end

    sources_to_ingest([SOURCE-KEYS-TO-INGEST]) --source key--> source

    subgraph User Space ALL keys prefixed with USER:username_hash:
        user>USER:username_hash]
        user--password--> password_hash[password hash]
        user--username--> username[username]
        user--key-->misc_user_settings[misc user settings]

        feeds([FEEDS])
        feed>FEED:uuid]
        feed_embeddings_model[feed embeddings model]
        feed_name[feed name]
        feed_sources{{FEED:uuid:SOURCES}}
        feed_items([FEED:uuid:ITEMS])
        source>FEED:uuid:SOURCE:name_hash]
        source_url[url]
        source_name[name]
        items([SOURCE:name_hash:ITEMS])


        feeds --> feed

        feed --embeddings model--> feed_embeddings_model
        feed --name--> feed_name
        feed -.-> feed_items
        feed -.-> feed_sources
        feed_sources --> source
        source --url--> source_url
        source --name--> source_name
        source -.-> items

        items -.-> item_state[FEED:hash:ITEM:hash:ITEM_STATE]

    end

    item[ITEM:url_hash]
    item_embeddings>ITEM:url_hash:EMBEDDINGS]
    item_embedding[item embedding]

    items-.->item
    item -.-> item_state
    feed_items -.-> item
    feed_items -.-> item_embeddings
    item_embeddings --model_name--> item_embedding
    item -.-> item_embeddings
    feed_embeddings_model -.-> item_embedding
    users{{USERS}} --> user
    source_templates{{SOURCE_TEMPLATES}} --> source_template[SOURCE_TEMPLATE:hash]

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