Interests

Interests are how you tell Scour what you care about. Add a few topics, and Scour uses semantic matching to find relevant content across thousands of sources.

What Interests Do

Unlike other platforms where clicking one article can flood your feed with a topic, Scour only shows content based on interests you've explicitly added. Your clicks and reactions influence what gets suggested, but only interests you choose to add affect your feed.

This keeps you in control. No more wondering why your feed suddenly changed after clicking on one random article.

Creating Interests

Start by adding 3-5 topics you genuinely want to read about. Interests can be anything:

• Broad: "AI" will surface content about machine learning, LLMs, robotics, and more
• Specific: "Rust async runtime internals" will find exactly that
• Personal: "Home Cooking" or "Photography" are good too. Scour isn't just for tech or work

Here are some examples of what other users are interested in:

Not sure what to add? Browse popular interests to see what others have found valuable. You can copy any interest with one click.

Feel free to add as many interests as you like. Some users have 500+. Scour automatically diversifies your feed so no single topic dominates.

Using Related Words

Each interest has an optional "related words" field. Use it to refine or disambiguate:

• Disambiguation: "Rust" with "programming, cargo, crates" clarifies you mean the programming language rather than oxidation
• Narrowing scope: "Cooking" with "Italian, pasta, Mediterranean" focuses your results

Tips for Good Interests

• Labels must be unique — You can't have two interests with the same label. Use distinct names like "AI Research" and "AI Tools" instead of both being "AI".
• Labels appear in your feed — When a post matches your interest, the label shows up below the title. Keep them concise.
• Acronyms work — Use "LLM" as a label with "large language models, GPT, Claude" in related words.

How Matching Works

Scour uses semantic (meaning-based) matching rather than keyword matching:

• "Machine Learning" will also find articles about "neural networks" and "deep learning"
• "PyTorch" will find articles about PyTorch even if they don't contain that exact word

This flexibility is powerful but can sometimes surface loosely related content. Related words help you guide the matching. Making the precision of matching configurable is on the roadmap. For the full picture of how content gets ranked, see How Ranking Works.

Interest Recommendations

As you use Scour (clicking articles, reacting to posts, subscribing to feeds) the system learns what topics might interest you. You'll see recommendations on your interests page and sometimes while browsing your feed.

Here's what a recommendation looks like:

Accept (+) to add it to your interests, or reject (×) to help Scour learn what you don't want.