
feeds.fun
News reader with tags, scoring, and AI
Stars: 68

Feeds Fun is a self-hosted news reader tool that automatically assigns tags to news entries. Users can create rules to score news based on tags, filter and sort news as needed, and track read news. The tool offers multi/single-user support, feeds management, and various features for personalized news consumption. Users can access the tool's backend as the ffun package on PyPI and the frontend as the feeds-fun package on NPM. Feeds Fun requires setting up OpenAI or Gemini API keys for full tag generation capabilities. The tool uses tag processors to detect tags for news entries, with options for simple and complex processors. Feeds Fun primarily relies on LLM tag processors from OpenAI and Google for tag generation.
README:
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 with curated collections of feeds that are tagged for free.
Blog: blog.feeds.fun
- 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.
- A lot of other features are comming.
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.
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.
- Backend is accessible as ffun package on PyPI.
- Frontend is accessible as feeds-fun package on NPM.
- Use the same versions for front and back.
Alternatively, you can install from tags in this repo.
There are no official docker images yet. Feeds
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.
Format of environment variables:
- For backend:
FFUN_<component>_<option>
orFFUN_<component>_<option>__<suboption>
. - For frontend:
VITE_FFUN_<component>_<option>
orVITE_FFUN_<component>_<option>__<suboption>
— must be set on build time!
For example:
FFUN_AUTH_MODE="supertokens"
FFUN_LIBRARIAN_OPENAI_GENERAL_PROCESSOR__ENABLED="True"
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.
You can find an example of configuration in the code.
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. 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 withupper-case-title
tag.
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 the processor's config.
DANGER!!! You can set the "general API key" in the processor's config; in this case, the processor will use it to process ALL news. It may be convenient if you self-host the service and fully control who has access to it.
You can set custom URLs as entry points for OpenAi and Gemini API by setting nthe ext environment variables:
FFUN_OPENAI_API_ENTRY_POINT="<your url>"
FFUN_GOOGLE_GEMINI_API_ENTRY_POINT="<your url>"
That will allow you to use any compatible API provider.
pip install ffun
# 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="True"
# Set if you want multi-user setup.
FFUN_ENABLE_SUPERTOKENS="True"
FFUN_API_PORT="443"
FFUN_APP_DOMAIN=...
FFUN_APP_PORT="443"
FFUN_AUTH_MODE: "supertokens"
FFUN_AUTH_SUPERTOKENS__COOKIE_SECURE="True"
FFUN_AUTH_SUPERTOKENS__API_KEY=...
FFUN_AUTH_SUPERTOKENS__CONNECTION_URI=...
# Has default value for development environment.
# I strongly recommend to redefine it because of potential security issues.
FFUN_USER_SETTINGS_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.
If you find this approach too strange, just use tags frontend-<version>
.
npm init -y
npm install feeds-fun
npm install --prefix ./node_modules/feeds-fun
# Set environment variables before next step!!!
# Build static content.
npm run build-only --prefix ./node_modules/feeds-fun
cp -r ./node_modules/feeds-fun/dist ./wherever-you-place-static-content
The minimal configuration for the frontend:
VITE_FFUN_AUTH_MODE="supertokens" # or "single_user"
VITE_FFUN_APP_DOMAIN=...
VITE_FFUN_APP_PORT=...
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.
Simply loads & parses feeds.
Can use HTTP proxies, see configuration options
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.
git clone [email protected]:Tiendil/feeds.fun.git
cd ./feeds.fun
Build some docker images
./bin/build-local-containers.sh
Start the API server and frontend:
docker compose up -d
The site will be accessible at http://localhost:5173/
Start workers:
./bin/backend-utils.sh poetry run ffun workers --librarian --loader
List all backend utils:
./bin/backend-utils.sh poetry run ffun --help
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.
You should always keep versions of the backend and frontend in sync.
Open CHANGELOG 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.
To profile a cli command, run py-spy record -o profile.svg -- python ./ffun/cli/application.py <command name>
For Tasks:
Click tags to check more tools for each tasksFor Jobs:
Alternative AI tools for feeds.fun
Similar Open Source Tools

feeds.fun
Feeds Fun is a self-hosted news reader tool that automatically assigns tags to news entries. Users can create rules to score news based on tags, filter and sort news as needed, and track read news. The tool offers multi/single-user support, feeds management, and various features for personalized news consumption. Users can access the tool's backend as the ffun package on PyPI and the frontend as the feeds-fun package on NPM. Feeds Fun requires setting up OpenAI or Gemini API keys for full tag generation capabilities. The tool uses tag processors to detect tags for news entries, with options for simple and complex processors. Feeds Fun primarily relies on LLM tag processors from OpenAI and Google for tag generation.

ai-town
AI Town is a virtual town where AI characters live, chat, and socialize. This project provides a deployable starter kit for building and customizing your own version of AI Town. It features a game engine, database, vector search, auth, text model, deployment, pixel art generation, background music generation, and local inference. You can customize your own simulation by creating characters and stories, updating spritesheets, changing the background, and modifying the background music.

seer
Seer is a service that provides AI capabilities to Sentry by running inference on Sentry issues and providing user insights. It is currently in early development and not yet compatible with self-hosted Sentry instances. The tool requires access to internal Sentry resources and is intended for internal Sentry employees. Users can set up the environment, download model artifacts, integrate with local Sentry, run evaluations for Autofix AI agent, and deploy to a sandbox staging environment. Development commands include applying database migrations, creating new migrations, running tests, and more. The tool also supports VCRs for recording and replaying HTTP requests.

WebCraftifyAI
WebCraftifyAI is a software aid that makes it easy to create and build web pages and content. It is designed to be user-friendly and accessible to people of all skill levels. With WebCraftifyAI, you can quickly and easily create professional-looking websites without having to learn complex coding or design skills.

CLI
Bito CLI provides a command line interface to the Bito AI chat functionality, allowing users to interact with the AI through commands. It supports complex automation and workflows, with features like long prompts and slash commands. Users can install Bito CLI on Mac, Linux, and Windows systems using various methods. The tool also offers configuration options for AI model type, access key management, and output language customization. Bito CLI is designed to enhance user experience in querying AI models and automating tasks through the command line interface.

AIOStreams
AIOStreams is a versatile tool that combines streams from various addons into one platform, offering extensive customization options. Users can change result formats, filter results by various criteria, remove duplicates, prioritize services, sort results, specify size limits, and more. The tool scrapes results from selected addons, applies user configurations, and presents the results in a unified manner. It simplifies the process of finding and accessing desired content from multiple sources, enhancing user experience and efficiency.

ai-voice-cloning
This repository provides a tool for AI voice cloning, allowing users to generate synthetic speech that closely resembles a target speaker's voice. The tool is designed to be user-friendly and accessible, with a graphical user interface that guides users through the process of training a voice model and generating synthetic speech. The tool also includes a variety of features that allow users to customize the generated speech, such as the pitch, volume, and speaking rate. Overall, this tool is a valuable resource for anyone interested in creating realistic and engaging synthetic speech.

airbyte_serverless
AirbyteServerless is a lightweight tool designed to simplify the management of Airbyte connectors. It offers a serverless mode for running connectors, allowing users to easily move data from any source to their data warehouse. Unlike the full Airbyte-Open-Source-Platform, AirbyteServerless focuses solely on the Extract-Load process without a UI, database, or transform layer. It provides a CLI tool, 'abs', for managing connectors, creating connections, running jobs, selecting specific data streams, handling secrets securely, and scheduling remote runs. The tool is scalable, allowing independent deployment of multiple connectors. It aims to streamline the connector management process and provide a more agile alternative to the comprehensive Airbyte platform.

gpt-subtrans
GPT-Subtrans is an open-source subtitle translator that utilizes large language models (LLMs) as translation services. It supports translation between any language pairs that the language model supports. Note that GPT-Subtrans requires an active internet connection, as subtitles are sent to the provider's servers for translation, and their privacy policy applies.

qb
QANTA is a system and dataset for question answering tasks. It provides a script to download datasets, preprocesses questions, and matches them with Wikipedia pages. The system includes various datasets, training, dev, and test data in JSON and SQLite formats. Dependencies include Python 3.6, `click`, and NLTK models. Elastic Search 5.6 is needed for the Guesser component. Configuration is managed through environment variables and YAML files. QANTA supports multiple guesser implementations that can be enabled/disabled. Running QANTA involves using `cli.py` and Luigi pipelines. The system accesses raw Wikipedia dumps for data processing. The QANTA ID numbering scheme categorizes datasets based on events and competitions.

openui
OpenUI is a tool designed to simplify the process of building UI components by allowing users to describe UI using their imagination and see it rendered live. It supports converting HTML to React, Svelte, Web Components, etc. The tool is open source and aims to make UI development fun, fast, and flexible. It integrates with various AI services like OpenAI, Groq, Gemini, Anthropic, Cohere, and Mistral, providing users with the flexibility to use different models. OpenUI also supports LiteLLM for connecting to various LLM services and allows users to create custom proxy configs. The tool can be run locally using Docker or Python, and it offers a development environment for quick setup and testing.

MiniSearch
MiniSearch is a minimalist search engine with integrated browser-based AI. It is privacy-focused, easy to use, cross-platform, integrated, time-saving, efficient, optimized, and open-source. MiniSearch can be used for a variety of tasks, including searching the web, finding files on your computer, and getting answers to questions. It is a great tool for anyone who wants a fast, private, and easy-to-use search engine.

redbox-copilot
Redbox Copilot is a retrieval augmented generation (RAG) app that uses GenAI to chat with and summarise civil service documents. It increases organisational memory by indexing documents and can summarise reports read months ago, supplement them with current work, and produce a first draft that lets civil servants focus on what they do best. The project uses a microservice architecture with each microservice running in its own container defined by a Dockerfile. Dependencies are managed using Python Poetry. Contributions are welcome, and the project is licensed under the MIT License.

polis
Polis is an AI powered sentiment gathering platform that offers a more organic approach than surveys and requires less effort than focus groups. It provides a comprehensive wiki, main deployment at https://pol.is, discussions, issue tracking, and project board for users. Polis can be set up using Docker infrastructure and offers various commands for building and running containers. Users can test their instance, update the system, and deploy Polis for production. The tool also provides developer conveniences for code reloading, type checking, and database connections. Additionally, Polis supports end-to-end browser testing using Cypress and offers troubleshooting tips for common Docker and npm issues.

reai-ghidra
The RevEng.AI Ghidra Plugin by RevEng.ai allows users to interact with their API within Ghidra for Binary Code Similarity analysis to aid in Reverse Engineering stripped binaries. Users can upload binaries, rename functions above a confidence threshold, and view similar functions for a selected function.

dravid
Dravid (DRD) is an advanced, AI-powered CLI coding framework designed to follow user instructions until the job is completed, including fixing errors. It can generate code, fix errors, handle image queries, manage file operations, integrate with external APIs, and provide a development server with error handling. Dravid is extensible and requires Python 3.7+ and CLAUDE_API_KEY. Users can interact with Dravid through CLI commands for various tasks like creating projects, asking questions, generating content, handling metadata, and file-specific queries. It supports use cases like Next.js project development, working with existing projects, exploring new languages, Ruby on Rails project development, and Python project development. Dravid's project structure includes directories for source code, CLI modules, API interaction, utility functions, AI prompt templates, metadata management, and tests. Contributions are welcome, and development setup involves cloning the repository, installing dependencies with Poetry, setting up environment variables, and using Dravid for project enhancements.
For similar tasks

feeds.fun
Feeds Fun is a self-hosted news reader tool that automatically assigns tags to news entries. Users can create rules to score news based on tags, filter and sort news as needed, and track read news. The tool offers multi/single-user support, feeds management, and various features for personalized news consumption. Users can access the tool's backend as the ffun package on PyPI and the frontend as the feeds-fun package on NPM. Feeds Fun requires setting up OpenAI or Gemini API keys for full tag generation capabilities. The tool uses tag processors to detect tags for news entries, with options for simple and complex processors. Feeds Fun primarily relies on LLM tag processors from OpenAI and Google for tag generation.

rss-can
RSS Can is a tool designed to simplify and improve RSS feed management. It supports various systems and architectures, including Linux and macOS. Users can download the binary from the GitHub release page or use the Docker image for easy deployment. The tool provides CLI parameters and environment variables for customization. It offers features such as memory and Redis cache services, web service configuration, and rule directory settings. The project aims to support RSS pipeline flow, NLP tasks, integration with open-source software rules, and tools like a quick RSS rules generator.
For similar jobs

book
Podwise is an AI knowledge management app designed specifically for podcast listeners. With the Podwise platform, you only need to follow your favorite podcasts, such as "Hardcore Hackers". When a program is released, Podwise will use AI to transcribe, extract, summarize, and analyze the podcast content, helping you to break down the hard-core podcast knowledge. At the same time, it is connected to platforms such as Notion, Obsidian, Logseq, and Readwise, embedded in your knowledge management workflow, and integrated with content from other channels including news, newsletters, and blogs, helping you to improve your second brain 🧠.

extractor
Extractor is an AI-powered data extraction library for Laravel that leverages OpenAI's capabilities to effortlessly extract structured data from various sources, including images, PDFs, and emails. It features a convenient wrapper around OpenAI Chat and Completion endpoints, supports multiple input formats, includes a flexible Field Extractor for arbitrary data extraction, and integrates with Textract for OCR functionality. Extractor utilizes JSON Mode from the latest GPT-3.5 and GPT-4 models, providing accurate and efficient data extraction.

Scrapegraph-ai
ScrapeGraphAI is a Python library that uses Large Language Models (LLMs) and direct graph logic to create web scraping pipelines for websites, documents, and XML files. It allows users to extract specific information from web pages by providing a prompt describing the desired data. ScrapeGraphAI supports various LLMs, including Ollama, OpenAI, Gemini, and Docker, enabling users to choose the most suitable model for their needs. The library provides a user-friendly interface through its `SmartScraper` class, which simplifies the process of building and executing scraping pipelines. ScrapeGraphAI is open-source and available on GitHub, with extensive documentation and examples to guide users. It is particularly useful for researchers and data scientists who need to extract structured data from web pages for analysis and exploration.

databerry
Chaindesk is a no-code platform that allows users to easily set up a semantic search system for personal data without technical knowledge. It supports loading data from various sources such as raw text, web pages, files (Word, Excel, PowerPoint, PDF, Markdown, Plain Text), and upcoming support for web sites, Notion, and Airtable. The platform offers a user-friendly interface for managing datastores, querying data via a secure API endpoint, and auto-generating ChatGPT Plugins for each datastore. Chaindesk utilizes a Vector Database (Qdrant), Openai's text-embedding-ada-002 for embeddings, and has a chunk size of 1024 tokens. The technology stack includes Next.js, Joy UI, LangchainJS, PostgreSQL, Prisma, and Qdrant, inspired by the ChatGPT Retrieval Plugin.

auto-news
Auto-News is an automatic news aggregator tool that utilizes Large Language Models (LLM) to pull information from various sources such as Tweets, RSS feeds, YouTube videos, web articles, Reddit, and journal notes. The tool aims to help users efficiently read and filter content based on personal interests, providing a unified reading experience and organizing information effectively. It features feed aggregation with summarization, transcript generation for videos and articles, noise reduction, task organization, and deep dive topic exploration. The tool supports multiple LLM backends, offers weekly top-k aggregations, and can be deployed on Linux/MacOS using docker-compose or Kubernetes.

SemanticFinder
SemanticFinder is a frontend-only live semantic search tool that calculates embeddings and cosine similarity client-side using transformers.js and SOTA embedding models from Huggingface. It allows users to search through large texts like books with pre-indexed examples, customize search parameters, and offers data privacy by keeping input text in the browser. The tool can be used for basic search tasks, analyzing texts for recurring themes, and has potential integrations with various applications like wikis, chat apps, and personal history search. It also provides options for building browser extensions and future ideas for further enhancements and integrations.

1filellm
1filellm is a command-line data aggregation tool designed for LLM ingestion. It aggregates and preprocesses data from various sources into a single text file, facilitating the creation of information-dense prompts for large language models. The tool supports automatic source type detection, handling of multiple file formats, web crawling functionality, integration with Sci-Hub for research paper downloads, text preprocessing, and token count reporting. Users can input local files, directories, GitHub repositories, pull requests, issues, ArXiv papers, YouTube transcripts, web pages, Sci-Hub papers via DOI or PMID. The tool provides uncompressed and compressed text outputs, with the uncompressed text automatically copied to the clipboard for easy pasting into LLMs.

Agently-Daily-News-Collector
Agently Daily News Collector is an open-source project showcasing a workflow powered by the Agent ly AI application development framework. It allows users to generate news collections on various topics by inputting the field topic. The AI agents automatically perform the necessary tasks to generate a high-quality news collection saved in a markdown file. Users can edit settings in the YAML file, install Python and required packages, input their topic idea, and wait for the news collection to be generated. The process involves tasks like outlining, searching, summarizing, and preparing column data. The project dependencies include Agently AI Development Framework, duckduckgo-search, BeautifulSoup4, and PyYAM.