Summary

iDeer automates the tedious process of monitoring multiple online platforms for research and industry updates. It aggregates content from sources like GitHub, arXiv, and HuggingFace, employing LLMs for intelligent filtering, scoring, and summarization. Personalized digests are delivered via email, transforming repetitive manual searching into efficient, passive content consumption. This tool is invaluable for AI researchers, financial analysts, and academics seeking to quickly identify relevant information and potential research ideas.

How It Works

The system aggregates data from diverse sources including GitHub, HuggingFace, arXiv, PubMed, and Semantic Scholar. LLMs are central to its operation, performing content filtering, scoring, and summarization based on user-defined profiles. Outputs include daily digests, cross-source reports, and research ideas, delivered via email at configurable intervals. A plugin architecture facilitates the addition of new data sources, ensuring flexibility and extensibility.

Quick Start & Requirements

Installation is via pip install ideer or cloning the repository. Essential setup includes initializing the working directory (ideer init) and configuring LLM access (e.g., MODEL_NAME, BASE_URL, API_KEY) in the .env file, supporting OpenAI-compatible APIs and local Ollama. Email delivery requires SMTP server configuration. Basic usage involves commands like ideer run --sources arxiv huggingface. Python 3.10+ is required.

Highlighted Details

• LLM-Powered Curation: Leverages LLMs for intelligent content filtering, scoring, summarization, and research idea generation.
• Cross-Source Correlation: Links updates across GitHub repositories, HuggingFace models, and academic papers.
• Extensive Data Sources: Supports GitHub, HuggingFace, arXiv, PubMed, Semantic Scholar, and X/Twitter, with a plugin system for extensibility.
• Configurable Delivery: Offers scheduled email delivery (daily, weekdays, weekly, monthly) of digests, reports, and ideas.
• AI Assistant Integration: Provides specific skills for Codex and Claude Code to automate daily paper reading and task management.

Maintenance & Community

The README does not detail specific maintenance contributors, sponsorships, or community channels. It encourages users to star the repository as a form of support.

Licensing & Compatibility

Released under the MIT License, which is permissive and generally suitable for commercial use and integration into closed-source projects.

Limitations & Caveats

The README does not explicitly list known limitations or bugs. Users must configure LLM access, which may incur costs or require complex local setups, and SMTP for email delivery. The effectiveness of LLM outputs depends on the configured LLM and user profile quality.