Set up your discovery feed

Setup phase · ~3 minutes A generic AI discovery feed surfaces what’s hot. A discovery feed grounded in your project surfaces what’s relevant to the directions your team has actually shipped, ranked against real history. In this tutorial, you’ll wire up that feed using the experiment history from the previous tutorial so the recommendations Remyx delivers are scoped to your work.

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What a discovery feed gives you

A research interest is a saved configuration that tells Remyx what kinds of papers, repos, models, and datasets you want to hear about. The system runs daily and surfaces the top results that match, ranked against the experiments your project has already shipped. In practice, this means:

• A paper about a new depth estimator gets ranked higher if your project has been swapping depth estimators.
• A retrieval rerank technique gets ranked higher if your project’s history shows retrieval-quality experiments.
• Off-direction work (an unrelated benchmark, a refactor framework) gets pushed down or filtered out.

Setting this up takes one minute.

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Create the interest from your project

If you checked Create a research interest from this project during the wizard in the previous tutorial, an interest already exists for this project. Skip to the curation step below. Otherwise:

1. Open Feed in the sidebar and click + New Interest in the page header.
2. Pick the From project tab (it’s the default).
3. Select the project you created in the previous tutorial.
4. Save.

The interest pre-fills with every experiment in the project. A “Preparing recommendations…” overlay runs while Remyx assembles the context, then a refresh kicks off automatically and the first set of recommendations populates as soon as the build completes.

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Curate the experiment list

The pre-filled list is a checklist. The experiments you keep checked are the signal Remyx uses to rank papers. The experiments you uncheck become noise to ignore. Keep:

• Model swaps and architecture changes
• Evaluation method changes (new benchmarks, new metrics)
• Routing or policy changes
• Prompt-engineering experiments
• Data-pipeline-stage upgrades

Uncheck:

• Pure refactors (file moves, type-hint cleanups)
• Dependency bumps
• Security patches
• Infrastructure-only changes (CI, build, packaging)

These don’t reflect direction-setting decisions, so leaving them in the context dilutes the signal. Save the curated list. The refresh re-fires.

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Set the daily count

In the interest’s settings, choose how many recommendations you want delivered each day. You can update this value as needed.

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Optional: let the feed open PRs for you

If this interest is backed by a GitHub repo, you can go a step further than a digest and have Remyx open a reviewable draft PR for the most implementable recommendation on a schedule — the cold-start reading and scaffolding done for you, with you still reviewing and merging. This is covered in Automated discovery PRs.

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Recap

You now have:

• A research interest that knows your project’s direction
• A daily count for how much research Remyx surfaces
• A feedback loop where curating the experiment list sharpens future recommendations

Once the first batch of recommendations lands, Scope an experiment from a recommendation shows how to turn one of them into a structured experiment.

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