> ## Documentation Index > Fetch the complete documentation index at: https://docs.remyx.ai/llms.txt > Use this file to discover all available pages before exploring further. # Feed > Personalized daily recommendations grouped by your Research Interests **Nav:** Discover > Feed | **URL:** [`/papers`](https://engine.remyx.ai/papers) Staying current with AI research is part of the job, but rarely the main job. You have models to ship, pipelines to maintain, and stakeholders asking if you've tried the latest technique they saw online. The result is a persistent backlog of ideas you'll get to "eventually" and relevant work you discover weeks after it would have been useful. The Feed delivers a curated stream of resources (papers, repos, models) matched to what your team is building. You create focused **Research Interests** that each track a different area of your work, with their own recommendation cadence. *** ## Research Interests Research Interests are the context that drives your recommendations. Each interest has its own name, context description, and recommendation cadence. ### Creating an Interest Click **+ New Interest** at the top of the Feed view. | Field | Description | Example | | ------------------------- | --------------------------------------------------------------------------------------------------- | -------------------------------------------- | | **Name** | Short label (appears as a pill tab) | "RAG & Retrieval" | | **Context** | Natural language description, or URLs to HuggingFace models, GitHub repos, arXiv papers, blog posts | `https://huggingface.co/your-org/your-model` | | **Daily Recommendations** | Resources per day (1-10) | 3 | **Richer context = better recommendations.** Link GitHub repos with detailed READMEs, HuggingFace models with comprehensive cards, or specific arXiv papers that define your area. Resources that cite or benchmark against your work get boosted. ### Interest Pill Tabs Your interests appear as **pill tabs** at the top of the Feed: * **All** — Combined feed from all interests * **\[Interest Name]** — Filtered to that interest only, with badge count showing how many new recommendations are available ### Managing Interests Click the **gear icon** (⚙️) next to an interest name to: * Edit the name, context, or daily count * Toggle active/inactive (inactive interests stop generating recommendations but preserve history) * Delete the interest ### Research Interests from a repo Instead of a free-text context, you can create an interest **from a GitHub repo** (pick one from your connected GitHub, or paste a public URL). Remyx extracts the repo's merge history into a structured shipping record in the background, then ranks recommendations against the work your team has actually shipped — not just a topic description. This is the same extracted history that powers a [Project](/platform/manage/projects); a repo-driven interest and a project that links the same repo converge on one history. When you create an interest from a repo, the dialog also offers an **auto-PR** setting that can provision [automated discovery PRs](/platform/discover/outrider) for that repo: | Choice | What happens | | ------------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------- | | **Set it up for me** | Remyx installs the recommendation GitHub Action, opens and merges the setup PR, and fires the first run automatically once recommendations exist. | | **Let me review the setup PR** | Same provisioning, but the setup PR is left open for you to review and merge first. | | **Not now** | No provisioning — turn it on later from the interest's settings. | Editing a repo-sourced interest lets you **curate its extracted history** (include/exclude individual changes), the same way you curate a project's experiment list. Keeping direction-setting work and dropping pure refactors sharpens what gets recommended. *** ## Recommendation Cards Each recommendation shows: | Element | Description | | ------------------- | --------------------------------------------------- | | **Title** | Resource title with link to detail viewer | | **Source type** | Badge indicating `arxiv_paper`, `github_repo`, etc. | | **Relevance score** | How well this matches your interest context | | **Reasoning** | One-line explanation of why this was recommended | | **Interest tag** | Which interest surfaced this resource | Cards are sorted by recency by default. Use the **Sort** control to change ordering. *** ## Refreshing Recommendations Each interest has a **Refresh** button in the toolbar. Click it to trigger an immediate re-ranking — don't wait until the next scheduled run. Refresh runs asynchronously. A progress indicator shows the status. Navigate away and come back; the job continues in the background. Scheduled recommendations refresh daily at **9:30 AM UTC** for all active interests. Use **Refresh** for instant updates, or update your context sources before the daily run. ### How recommendations are ranked For interests backed by a shipping history (any [repo-sourced interest](#research-interests-from-a-repo) or a project), ranking goes beyond matching your context text: * Your team's extracted shipping history is fed into the ranker, so candidates that align with the methodological direction you've actually shipped rank above ones that are merely topically related. * A learned **preference model**, fit over your past experiments, scores candidates and breaks ties behind relevance. Both make the feed sharper as your history grows. *** ## Resource Detail & Chat Click **View** on any recommendation to open the detail page with: * **Details tab** — Full abstract, resource links, citations, Docker availability * **Chat tab** — Ask questions about the resource in natural language with streaming responses See [Search > Resource Detail Viewer](/platform/discover/search#resource-detail-viewer) for full details. *** ## From Feed to Experiment From any resource in the feed, click **Create Experiment** to: 1. Link the resource as the experiment source with `source_type: paper` 2. Pre-fill the resource metadata in `source_ref` 3. Redirect to the [Outcomes](/platform/experiments/outcomes) detail page where a launch context generates automatically This is the natural entry point for the discovery-to-experiment loop: your feed surfaces a relevant technique, you create an experiment to test it, and the results inform future recommendations. *** ## CLI & API You can also access your feed programmatically: ```bash CLI theme={null} # Today's digest grouped by interest remyxai papers digest # Recommendations for one interest remyxai papers list --interest "RAG & Retrieval" --period today # Trigger a refresh remyxai papers refresh --interest "RAG & Retrieval" --wait ``` ```bash API theme={null} # Get digest curl -H "Authorization: Bearer $REMYXAI_API_KEY" \ "https://engine.remyx.ai/api/v1.0/papers/recommended/digest?period=today" # Refresh an interest curl -X POST -H "Authorization: Bearer $REMYXAI_API_KEY" \ -H "Content-Type: application/json" \ -d '{"interest_id": "uuid"}' \ "https://engine.remyx.ai/api/v1.0/papers/recommended/refresh" ``` *** ## Related Let the feed open draft PRs into your repo on a schedule (Outrider) On-demand semantic search across all resources Manage interests and recommendations from the terminal