Your Brain, Mapped

Today, Arivu stops being a bookmark manager.

For years, bookmarking tools have asked the same question: where do you want to put this? Folders. Tags. Collections. We organize our knowledge like filing cabinets because that’s what computers understood.

But your brain doesn’t work that way. Ideas don’t live in boxes—they live in webs of meaning, each thought connected to dozens of others through invisible threads of context, curiosity, and coincidence.

The Knowledge Graph

The Semantic Knowledge Graph sees those threads.

When you open it for the first time, you’re looking at your mind mapped. Every bookmark becomes a node. Every connection the AI discovers becomes an edge—not the connections you told it to make, but the ones that were always there, hiding in the semantics of what you saved.

That article about stoic philosophy? It’s connected to the productivity essay you saved last month, because both explore the same ideas about intentionality.

The research paper on neural networks? It shares conceptual DNA with that blog post about learning to draw, because both describe the same fundamental process of pattern recognition.

Click any node and watch your knowledge light up, revealing clusters you never knew existed.

The graph isn’t static. It breathes with your collection, reorganizing itself as you add new bookmarks and as the AI discovers deeper semantic relationships. Save something new about machine learning, and watch it find its tribe—connecting to that article on cognitive biases you saved six months ago because both explore how systems (artificial and human) make decisions under uncertainty.

Zoom out and you see the topology of your interests: dense clusters where you’ve gone deep, sparse regions of passing curiosity, and the surprising bridges between domains you never consciously connected. That bridge between your cooking bookmarks and your management articles? Both are about timing, preparation, and knowing when to step back.

The physics of the visualization matters. Nodes with stronger semantic connections pull closer together. Weakly connected ideas drift to the periphery. The result is a map that rewards exploration—hover over any node and its connections illuminate, revealing pathways through your knowledge you can actually follow.

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Understanding, Not Just Organizing

This isn’t organization. It’s comprehension.

Behind the visualization, a new intelligence layer powers everything. Every bookmark now gets analyzed not just for keywords, but for meaning—concepts extracted, themes identified, connections mapped.

This is how Arivu learns the shape of your thinking, building a model of your interests that gets sharper with every save.

The AI doesn’t just read your bookmarks—it reads between the lines. When you save an article about “remote work culture,” the system extracts not just those surface keywords but the underlying concepts: autonomy, communication patterns, trust in distributed systems, the psychology of presence. These deeper semantics become the vocabulary for discovering connections.

Every bookmark gets a concept fingerprint: a dense vector representation of its core ideas. When new content arrives, it’s compared against your entire collection, not through string matching but through meaning. An article about “gardening patience” might connect to your saved piece on “compound interest” because both encode the concept of delayed gratification.

The result is an understanding layer that mirrors how human memory actually works. You don’t remember articles by their titles—you remember them by what they meant to you, by the problems they addressed, by the emotions they evoked. The intelligence layer captures that same richness.

Intelligent Resurfacing

That model enables something we’ve been building toward since day one: Intelligent Resurfacing.

The best ideas aren’t the ones you saved yesterday. They’re the forgotten gems buried under months of newer saves—the essay that perfectly addresses the problem you’re facing today, the framework that finally makes sense now that you have more context.

The Resurfacing Engine scores every bookmark in your collection against your recent activity and surfaces the ones that matter most, at exactly the right moment.

Your knowledge collection is no longer an archive. It’s alive.

Here’s how the scoring works: every time you save, search, or interact with a bookmark, the engine recalculates relevance across your entire collection. It weighs three factors—semantic similarity to your current focus, recency decay (older bookmarks need stronger connections to surface), and interaction history (bookmarks you’ve engaged with before get a boost when they become relevant again).

The result is surprisingly intuitive. Start researching a new topic and watch old bookmarks bubble up from the depths. The article about negotiation tactics you saved during a job search two years ago? It surfaces when you’re saving content about salary transparency, because the semantic overlap finally became relevant to your current context.

Resurfacing happens in multiple places: the dedicated “Memory Jogger” in your sidebar for serendipitous rediscovery, in search results where older gems rank alongside new saves, and as contextual suggestions when you’re viewing related bookmarks. Your knowledge follows you through the interface, appearing when and where it’s most useful.

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