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In this interview, we sit down with Nox Xe, the creator of Slick Search, to discuss building an independent, privacy-focused search engine designed around user control. Slick Search gives users customizable ranking controls, local personalization, and an alternative approach to how search results are created.

What does Slick Search do? And why is now the time for it to exist?

Slick is a customizable search engine built for people who want more control over how they search the web. Most search engines use a single ranking system that treats every user the same. Slick allows users to adjust their own experience through features like domain preferences, pinned results, blocked websites, custom search recipes, and ranking controls that stay on the user's device.

Now is a good time for Slick to exist because search has become one of the most important parts of the internet, but the ability to control how information is ranked is still mostly controlled by a small number of companies. Slick is focused on giving users more choice and building independent search technology.

What is your traction to date?

Slick Search reached 90 users within its first few days after launch, with early users coming from communities like Reddit and Hacker News. Based on the initial growth rate, this represents roughly 900 monthly users if maintained.

While Slick is still early, users are already actively testing the search engine, exploring customization features, and providing feedback on how the ranking system can improve.

Who does your Slick Search serve?

Slick is built for people who want more control over search. This includes privacy-conscious users, people looking for alternatives to traditional search engines, developers interested in independent search infrastructure, and anyone who wants to customize how results are ranked.

The most interesting part is seeing users understand that search does not have to be a fixed experience. Different people search differently, and Slick is built around that idea.

What technologies were used in the making of Slick Search? And why did you choose ones most essential to your tech stack?

Slick uses a custom search infrastructure built around crawling, indexing, and ranking web content. The stack includes Elasticsearch for search indexing, custom crawling systems, machine learning models for semantic understanding and reranking, and local browser-based customization systems.

The goal was to build a search engine where the core infrastructure is independent while still allowing users to control their own search experience without sending personal preferences to a server.

Slick Search earned an 81 proof of usefulness score (https://proofofusefulness.com/report/slick-search) - how do you feel about that? Needs reassessment or just right?

The score feels fair for where Slick is today. The strongest areas were the technical innovation and real-world usefulness, which matches the main focus of the project. The biggest areas for improvement are user growth and long-term adoption.

Slick is still very early, so the next step is proving that people continue using it over time and that customizable search provides enough value for users to switch from existing search engines.

What excites you about Slick Search's potential usefulness?

Search is one of the most important tools on the internet, but users have very little control over how results are ranked. Slick is interesting because it explores a different approach where users can influence their own search experience.

The goal is not just to create another search engine, but to build search infrastructure that gives people more transparency and control.

Walk us through your most concrete evidence of usefulness.

The strongest evidence so far is the response from people who discovered Slick organically. Within the first few days after launch, thousands of people viewed discussions about the project, and users actively tested the search engine and its customization features.

The most important signal is not just people visiting Slick, but people understanding the idea behind it and wanting more control over search.

How do you measure genuine user adoption versus "tourists" who sign up but never return?

Right now, Slick is still collecting early usage data. The focus is moving beyond initial curiosity and seeing which features bring users back.

Long-term, retention will come from making Slick useful enough that users prefer their customized search experience over a generic search engine.

If we re-score your project in 12 months, which criterion will show the biggest improvement, and what are you doing right now to make that happen?

Audience reach will likely show the biggest improvement. The technology is already built, so the main challenge is getting more people to discover Slick and understand why customizable search is valuable.

Right now, the focus is improving search quality, expanding the index, adding features users request, and continuing to share Slick with communities interested in privacy and technology.

How Did You Hear About HackerNoon?

I discovered HackerNoon's Proof of Usefulness program while looking for ways to showcase independent technology projects. It has been interesting because the evaluation focuses less on hype and more on whether a product provides real value.

The report also gave useful feedback on where Slick is strong and where more work is needed.

Your early launch on Reddit attracted over 35,000 combined views and sparked discussions on Hacker News. How do you plan to leverage this initial burst of community interest into sustainable daily active usage?

The goal is to continue building features that make people want to return. Search is a daily habit, so long-term usage depends on making Slick genuinely useful rather than relying only on launch attention.

This means improving result quality, expanding coverage, listening to feedback, and continuing to build features that differentiate Slick from existing search engines.

Scaling an independent index is notoriously resource-intensive. As you grow from your initial 91 users to your projected 900 and beyond, how will Slick Search scale its infrastructure while keeping ranking rules processed locally on-device?

Slick is designed with scalability in mind. The search infrastructure can continue expanding through better crawling systems, optimized indexing, and improved ranking pipelines.

User customization is handled locally whenever possible, which allows personalization without needing to store individual user profiles or search preferences.

Allowing users to completely customize search recipes and block sites gives unprecedented control. What is the most surprising or unique way you've seen an early user configure their Slick Search results so far?

One of the most interesting things is seeing users immediately think about search differently. Instead of accepting whatever ranking system they are given, people naturally want to adjust sources, remove websites they dislike, and create a search experience that fits their own preferences.

That feedback reinforces the idea that search does not need to be one-size-fits-all.

Meet our sponsors

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