docs: redesign ELI5 comparison section and add What Can You Build?
Replace flat 15-row table with a Before/After ASCII diagram plus a capability grid (Search/Graph/Filter/VFS/Branch/Import) with competitors grouped by category. Add new What Can You Build? section with generic use cases and Built with Brainy showcase. Tighten header copy.
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docs/eli5.md
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docs/eli5.md
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@ -5,7 +5,7 @@ public: true
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category: getting-started
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category: getting-started
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template: guide
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template: guide
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order: 0
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order: 0
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description: Plain-language explanation of what Brainy does and how Cortex accelerates it. No jargon, no code — just clear analogies.
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description: Plain-language guide covering what Brainy does, how it compares to other tools, and what you can build with it. No jargon, no code — just clear analogies.
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next:
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next:
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- getting-started/installation
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- getting-started/installation
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- getting-started/quick-start
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- getting-started/quick-start
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@ -86,3 +86,92 @@ Plain language:
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- **Analytics** that aren't even possible in pure JavaScript — real-time anomaly detection, streaming percentile estimates, approximate unique counts — become available because Cortex brings the native capabilities required to run them efficiently.
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- **Analytics** that aren't even possible in pure JavaScript — real-time anomaly detection, streaming percentile estimates, approximate unique counts — become available because Cortex brings the native capabilities required to run them efficiently.
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If Brainy is what makes knowledge fast, Cortex is what makes Brainy feel instant.
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If Brainy is what makes knowledge fast, Cortex is what makes Brainy feel instant.
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---
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## What Does Brainy Replace?
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Most applications that need to store and search knowledge end up stitching together several specialized tools. Brainy replaces all of them with one — a single free, open-source library in place of multiple paid services.
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### Before and After
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**Before Brainy:**
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```
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┌──────────┐ ┌──────────┐ ┌──────────┐
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│ Pinecone │ │ Neo4j │ │ MongoDB │
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│ vectors │ │ graph │ │ docs │
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└──────────┘ └──────────┘ └──────────┘
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┌──────────┐ ┌──────────┐ ┌──────────┐
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│ Algolia │ │ Redis │ │ pgvector │
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│ search │ │ cache │ │ SQL+vecs │
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└──────────┘ └──────────┘ └──────────┘
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...plus glue code, sync jobs, ETL pipelines, and 3am incidents.
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```
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**After Brainy:**
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```
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┌──────────────────────────────────────────┐
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│ Brainy │
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│ search · graph · filter · files · … │
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└──────────────────────────────────────────┘
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```
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### What Each Tool Is Missing
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| Tool | Search | Graph | Filter | VFS | Branch | Import |
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|---|:---:|:---:|:---:|:---:|:---:|:---:|
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| **Brainy** | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |
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| *— Vector databases —* | | | | | | |
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| Pinecone | ✅ | ❌ | ✅ | ❌ | ❌ | ❌ |
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| Weaviate | ✅ | ❌ | ✅ | ❌ | ❌ | ❌ |
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| Qdrant | ✅ | ❌ | ✅ | ❌ | ❌ | ❌ |
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| Chroma | ✅ | ❌ | ✅ | ❌ | ❌ | ❌ |
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| *— Graph databases —* | | | | | | |
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| Neo4j | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
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| *— Document stores —* | | | | | | |
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| MongoDB | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ |
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| Firestore | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ |
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| DynamoDB | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ |
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| *— Relational + vector —* | | | | | | |
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| PostgreSQL + pgvector | ✅ | ❌ | ✅ | ❌ | ❌ | ❌ |
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| MySQL | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ |
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| SQLite | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ |
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| *— Search engines —* | | | | | | |
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| Elasticsearch | ✅ | ❌ | ✅ | ❌ | ❌ | ❌ |
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| Algolia | ✅ | ❌ | ✅ | ❌ | ❌ | ❌ |
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| *— Cache —* | | | | | | |
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| Redis | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
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Brainy is the only row with every box checked. And it runs all of them in a single query — no stitching services together.
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### One library, any scale
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Brainy scales from a single laptop to billions of entities without changing a line of code. Small datasets live in memory. Larger ones spill to disk. At cloud scale, Brainy uses S3-compatible storage and automatically shards across nodes — the same API the whole way. Up to ten billion entities is fully implemented today.
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Add Cortex and you also unlock memory-mapped storage — aggregate state lives directly in the operating system's memory with zero serialization overhead, as fast as the hardware allows.
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---
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## What Can You Build?
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### Common applications
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- **AI agents with persistent memory** — Give any AI an always-on, self-organizing knowledge graph that persists between sessions and across agents.
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- **Searchable knowledge bases** — Build institutional memory that links documents automatically and surfaces answers across the full web of related information.
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- **Semantic document search** — Index PDFs, code, or media and find them by meaning, not just keywords.
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- **Relationship-aware recommendations** — Power product catalogs or content platforms where every recommendation understands what connects to what.
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- **Safe experiments** — Let teams branch the knowledge base, experiment independently, and merge when ready — just like branching code.
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- **Unified business platforms** — Combine booking, CRM, inventory, and analytics in one queryable knowledge graph with no sync pipeline.
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### Built with Brainy
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Real products built on Brainy — live in production at Soulcraft:
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- **Workshop** — AI-powered IDE and creation studio where imagination meets creation.
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- **Venue** — Unified physical + digital business operations, from food truck to franchise.
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- **Memory** — Infinite context for AI agents that never forgets.
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- **Collective** — Multi-agent coordination with shared knowledge and automatic task routing.
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- **Heart** — Emotional intelligence and empathy layer for AI communication.
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