Feeds Fun
News reader with tags, scoring, and AI.
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Fetched · June 5, 2026 ★ 371 Updated: June 4, 2026
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# Feeds Fun News reader with tags & AI. Self-hosted, if it is your way. - Reader automatically assigns tags to news. - You create rules to score news by tags. - Filter and sort news how you want ⇒ read only what you need. Site: [feeds.fun](https://feeds.fun) with curated collections of feeds that are tagged for free. Blog: [feeds.fun/blog](https://feeds.fun/blog) Roadmap: [long-term plans](https://github.com/users/Tiendil/projects/1/views/1?pane=info) # Screenshots  # Features - Multi-/single-user. - Feeds management. - Automatic tag assignment for every news entry. - Rules to score news by tags. - Filter news: exclude news by tags, show only news with tags. - Sort news by score, date, etc. - Track news you've read already. - Improved support for major feed sources like Reddit, GitHub, YouTube, ArXiv, Hacker News. - A lot of other features are comming. # Motivation I've subscribed to a lot of news feeds and want to read only the most interesting & important from them. I did not find an open-source solution that suited my needs => decided to create my own. # Official site The last stable version is always available at https://feeds.fun/ It is free and should be stable: no database resets, minimal downtime, etc. Just do not forget to set up your OpenAI or Gemini API key to access the full power of tags generation. # Fastest way to try locally Check instructions in the [docs/examples/single-user](docs/examples/single-user) directory. # Self-hosted version Instructions for docker-based installation: - [single-user setup](docs/examples/single-user) - [multi-user setup](docs/examples/multi-user) - [how to use third-party LLM models](docs/examples/third-party-models) Also: - Backend is accessible as [ffun](https://pypi.org/project/ffun/) package on PyPI. - Frontend is accessible as [feeds-fun](https://www.npmjs.com/package/feeds-fun) package on NPM. - Use the same versions for front and back. Alternatively, you can install from tags in this repo. ## Configuration All configs can be redefined via environment variables or `.env` file in the working directory. You can print actual backend config values with: ``` ffun print-configs ``` The output is not as pretty and ready for copying as it should be, but I'll improve it later. All actual frontend configs can be found [here](site/src/logic/settings.ts). Format of environment variables: - For backend: `FFUN_<component>_<option>` or `FFUN_<component>_<option>__<suboption>`. - For frontend: `VITE_FFUN_<component>_<option>` or `VITE_FFUN_<component>_<option>__<suboption>` — must be set on build time! For example: ``` # general application setting FFUN_ENABLE_SENTRY="True" # component (auth) setting FFUN_AUTH_HEADER_USER_ID="X-FFun-User-Id" # component (librarian) subsetting (openai_general_processor.enabled) FFUN_LIBRARIAN_OPENAI_GENERAL_PROCESSOR__ENABLED="True" ``` ### Configure tag processors Feeds Fun uses different tag processors to detect tags for news entries. Some of them are simple, like `set domain as tag`, some of them are more complex, like `use LLM to detect all possible tags`. - Processors are configured via a separate configuration file. - Each processor defines explicit routes that decide which entries it can handle. - When multiple routes can handle an entry, the dispatcher uses the first acceptable route, so route order in the processor config is important. - LLM routes can also carry a configured API key; routes without a configured key use user API keys. - Routes can additionally opt in to processor quality checks with `allowed_for_quality_tests`. You can find an example of configuration [in the code](./ffun/ffun/librarian/fixtures/tag_processors.toml). To pass your own configuration, set `FFUN_LIBRARIAN_TAG_PROCESSORS_CONFIG` to the path to your configuration file. To configure LLM processors, you may be interested in configuring models. You can find an example of it [in the code](./ffun/ffun/llms_framework/fixtures/models.toml). It mostly the slice of info from the official OpenAI/Google documentation. To pass your own configuration, set `FFUN_LLMS_FRAMEWORK_MODELS_CONFIG` to the path to your configuration file. Currently implemented processors: - `domain` — extracts domain and subdomains from URL and saves them as tags. - `native_tags` — saves tags that are received with the feed entry. - `llm_general` — asks ChatGPT/GeminiGPT to detect tags. Currently, it is the most powerful processor. Must-have if you want to use Feed Fun in full power. - `upper_case_title` — detects news with uppercase titles and marks them with `upper-case-title` tag. #### LLM processors LLM tag processors are the primary source of tags for Feeds Fun. Currently, we support two API providers: OpenAI (ChatGPT) and Google (Gemini). In the future, there will be more, including self-hosted. By default, LLM processors will skip feeds from default collections and use user API keys to process their news. You can set the API key for collections in a processor route. **DANGER!!!** You can allow a route with a configured API key to process user feeds; in this case, the processor will use it to process **ALL** news matched by that route. It may be convenient if you self-host the service and fully control who has access to it. ### Use third-party LLM models Check our example on [how to use third-party LLM models](docs/examples/third-party-models). You can use any LLM model via a provider that has an OpenAI-compatible API or a Gemini-compatible API. You just need to: 1. Set an API entry point. 2. Add the used model to the models configuration file. 3. Optionally, configure the token estimation calculator for the model. #### Set API entry point You can set custom URLs as entry points for OpenAI and Gemini API by setting the following environment variables: ``` FFUN_OPENAI_API_ENTRY_POINT="<your url>" FFUN_GOOGLE_GEMINI_API_ENTRY_POINT="<your url>" ``` Example: `FFUN_OPENAI_API_ENTRY_POINT="http://host.docker.internal:1234/v1"` #### Add used model to the models configuration file You can find an example of the models configuration file [in the code](./ffun/ffun/llms_framework/fixtures/models.toml). To pass your own configuration, set `FFUN_LLMS_FRAMEWORK_MODELS_CONFIG` to the path to your configuration file. #### Configure the token estimation Most likely, you don't need that. However, if you need to have really precise token usage estimation (they are used to split long texts into chunks for LLM processing), you can do so. However, we have fallback logic that should work for any model, even without explicit configuration. For an OpenAI-compatible API, you should use the `tiktoken_ext` plugin mechanism to register your `Encoding` objects with tiktoken. See [tiktoken README](https://github.com/openai/tiktoken) for details. We do not support custom token estimation for Gemini-compatible models at the moment. ### Configure tag normalizers Tag processors produce what we call "raw tags". They are relevant to the text, but may lack consistency. It is especially true for LLM-generated tags. It makes it difficult to use them in rules and filters. Thus, we have a chain of tag normalizers that receive raw tags and produce normalized tags. For example: - We remove articles `the`, `a`, `an` from the tags, and tags like `book-review`, `a-book-review`, `the-book-review` will become a single tag `book-review`. - We also split tags by various parts like `-vs-`, so `google-vs-microsoft` will become two tags: `google` and `microsoft`. **Tags normalization is a complex topic, and it is in active development, so we really appreciate your feedback.** Normalizers are configured via a separate configuration file. You can find an example of configuration [in the code](./ffun/ffun/tags/fixtures/tag_normalizers.toml). To pass your own configuration, set `FFUN_TAGS_NORMALIZERS_CONFIG` to the path to your configuration file. Currently implemented processors: - `part_blacklist` — removes blacklisted parts from tags: `a-book`, `the-book` ⇒ `book`. - `part_replacer` — replaces parts in tags: `e-mail` ⇒ `email`. - `splitter` — splits tags by specified parts: `google-vs-microsoft` ⇒ `google`, `microsoft`. ### Configure integrations with new sources Feeds Fun allows the creation of plugins to improve interaction with specific feed sources, such as YouTube, Reddit, etc. You may find examples of such plugins in the [./ffun/ffun/integrations/plugins](./ffun/ffun/integrations/plugins) directory. Plugins can: - Define own logic of discovering feeds from the source. For example, when the source has feeds but doesn't provide links to them in the standard way, as GitHub does. - Define own logic of postprocessing news entries to improve formatting and extract additional information. The list of plugins can be specified via the environment variable `FFUN_INTEGRATIONS_INTEGRATIONS`. See [settings definition](./ffun/ffun/integrations/settings.py) for details. ## Backend ``` pip install ffun # download SpaCy model (if you want to run librarian worker with default configs) python -m spacy download en_core_web_lg # run DB migrations ffun migrate # run API server uvicorn ffun.application.application:app --host 0.0.0.0 --port 8000 --workers 1 # run workers ffun workers --librarian --loader ``` The minimal configuration for the backend: ``` # DB connection parameters have default values, # but it is better to redefine them FFUN_POSTGRESQL__HOST=... FFUN_POSTGRESQL__USER=... FFUN_POSTGRESQL__PASSWORD=... FFUN_POSTGRESQL__DATABASE=... FFUN_ENVIRONMENT="prod" # Required for API server. FFUN_ENABLE_API_SPA="True" # If you want Feeds Fun in a multi-user mode, you need a more complex setup, check examples mentioned above. # Set fixed identity provider & user id for single-user setup. # Remove these settings for multi-user setup. FFUN_AUTH_FORCE_EXTERNAL_USER_ID="dev-user" FFUN_AUTH_FORCE_EXTERNAL_IDENTITY_PROVIDER_ID="single_user" FFUN_APP_DOMAIN="your.domain.com" # Has default value for development environment. # I strongly recommend to redefine it because of potential security issues. FFUN_USER_SETTINGS_SECRET_KEY="your-super-secret-key" ``` If you want to periodically clean your database from old entries, add the call `ffun cleaner clean` to your cron tasks. **It is recommended.** More details see in the architecture section. ## Frontend If you find this approach too weird, just checkout the tag `release-<version>` and build the frontend from the sources in the `./site` directory. ``` npm init -y npm install feeds-fun npm install --prefix ./node_modules/feeds-fun # You may want to setup environment variables here (before the build step). # Check them in the ./site/src/logic/settings.ts file. # Hovewer, there are no required variables for the minimal setup. # Build static content. npm run build-only --prefix ./node_modules/feeds-fun cp -r ./node_modules/feeds-fun/dist ./wherever-you-place-static-content ``` # Architecture ASGI application, which you run with `uvicorn` (in the example) provides only HTTP API to access the data and change user-related properties. All actual work is done by workers, which you run with `ffun workers` command. ## Loader worker Simply loads & parses feeds. Can use HTTP proxies, see [configuration options](ffun/ffun/loader/settings.py) ## Librarian worker Analyse feeds' entries and assign tags to them. All logic is split between tag processors. Each processor implements a single approach to produce tags that can be enabled/disabled via configuration. # Development Feeds Fun is developed with the help of [Donna](https://github.com/Tiendil/donna) — a tool that makes coding agents predictable and reliable. You may want to install it if you plan to contribute to the project, since some important specifications and workflows (such as auto-fixing lint errors and running tests) are implemented with the help of Donna. ## Preparations To use less hacks in dev configuration and be more consistent with production setup, we use custom domains `feeds.fun.local` & `idp.feeds.fun.local` for local development. - `feeds.fun.local` — Feeds Fun service; - `idp.feeds.fun.local` — identity provider, such as Keycloak, for multi-user mode. Add the following line to your `/etc/hosts` file: ``` 127.0.0.1 feeds.fun.local 127.0.0.1 idp.feeds.fun.local ``` Then, you can access the site at http://feeds.fun.local/ **We use self-signed TLS certificates for local development.** You need to accept them in your browser. ## Run ``` git clone git@github.com:Tiendil/feeds.fun.git cd ./feeds.fun ``` Build some docker images ``` ./bin/build-dev-containers.sh ``` Start the API server and frontend: ``` # single-user mode docker compose --profile single-user up -d # multi-user mode # docker compose --profile multi-user up -d ``` The site will be accessible at http://feeds.fun.local/ Start workers: ``` ./bin/backend-utils.sh poetry run ffun workers --librarian --loader ``` ## Utils List all backend utils: ``` ./bin/backend-utils.sh poetry run ffun --help ``` ## DB migrations Apply migrations: ``` ./bin/backend-utils.sh poetry run ffun migrate ``` Create new migration: ``` ./bin/backend-utils.sh poetry run yoyo new --message "what you want to do" ./ffun/<component>/migrations/ ``` Pay attention. There are different directories layouts in the repository and in the docker containers => paths for migrations should be with only a single `ffun` directory. ## Upgrade to new versions You should always keep versions of the backend and frontend in sync. Open [CHANGELOG](CHANGELOG.md) and look at which versions require DB migrations. You should upgrade to the first of them, run migrations and only after that upgrade to the next version. Algorithm: - Stop services. - Install the next version. - Run `ffun migrate`. - Start services. You can skip this step if you plan to upgrade to the next version immediately. Also, pay attention to breaking changes and notes in the CHANGELOG. ## Profiling To profile a cli command, run `py-spy record -o profile.svg -- python ./ffun/cli/application.py <command name>`