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Nav: Discover > Feed | URL: /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. There’s no cap on how many interests you keep.

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Research Interests

Research Interests are the context that drives your recommendations. Each interest has its own name, context description, and recommendation cadence.

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Creating an Interest

Click + New Interest at the top of the Feed view. You can seed an interest from free text, from a GitHub repo (see below), or from an existing Project. The Project source draws on that project’s experiments, so the ranker scores candidates against the work you’ve actually run.
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.

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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

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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

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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. This is the same extracted history that powers a Project; a repo-driven interest and a project that links the same repo converge on one history. A repo-backed interest also triggers a typed, cited deep-research brief for the repo. Its “future directions” become a forward-looking ranking axis alongside your shipping history, and its “candidate changes” seed recommendations, so the feed can weigh where the work is headed. When you create an interest from a repo, the dialog also offers an auto-PR setting that can provision automated discovery PRs for that repo:
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.

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Recommendation Cards

Each recommendation shows: Cards are sorted by recency by default. Use the Sort control to change ordering.

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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.

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How recommendations are ranked

For interests backed by a shipping history (any repo-sourced interest 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.

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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 for full details.

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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 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.

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CLI & API

You can also access your feed programmatically. By default the digest honors each interest’s per-day limit; the API’s unlimited mode (limit=0) returns every alerted recommendation instead, along with a truncated flag indicating whether anything was held back.

Automated discovery PRs

Let the feed open draft PRs into your repo on a schedule (Outrider)

Search

On-demand semantic search across all resources

CLI: Interests & Papers

Manage interests and recommendations from the terminal