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.
This commit is contained in:
David Snelling 2026-02-23 15:16:08 -08:00
parent c376fb9b61
commit 3f16e1755b

View file

@ -5,7 +5,7 @@ public: true
category: getting-started category: getting-started
template: guide template: guide
order: 0 order: 0
description: Plain-language explanation of what Brainy does and how Cortex accelerates it. No jargon, no code — just clear analogies. 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.
next: next:
- getting-started/installation - getting-started/installation
- getting-started/quick-start - getting-started/quick-start
@ -86,3 +86,92 @@ Plain language:
- **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. - **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.
If Brainy is what makes knowledge fast, Cortex is what makes Brainy feel instant. If Brainy is what makes knowledge fast, Cortex is what makes Brainy feel instant.
---
## What Does Brainy Replace?
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.
### Before and After
**Before Brainy:**
```
┌──────────┐ ┌──────────┐ ┌──────────┐
│ Pinecone │ │ Neo4j │ │ MongoDB │
│ vectors │ │ graph │ │ docs │
└──────────┘ └──────────┘ └──────────┘
┌──────────┐ ┌──────────┐ ┌──────────┐
│ Algolia │ │ Redis │ │ pgvector │
│ search │ │ cache │ │ SQL+vecs │
└──────────┘ └──────────┘ └──────────┘
...plus glue code, sync jobs, ETL pipelines, and 3am incidents.
```
**After Brainy:**
```
┌──────────────────────────────────────────┐
│ Brainy │
│ search · graph · filter · files · … │
└──────────────────────────────────────────┘
```
### What Each Tool Is Missing
| Tool | Search | Graph | Filter | VFS | Branch | Import |
|---|:---:|:---:|:---:|:---:|:---:|:---:|
| **Brainy** | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |
| *— Vector databases —* | | | | | | |
| Pinecone | ✅ | ❌ | ✅ | ❌ | ❌ | ❌ |
| Weaviate | ✅ | ❌ | ✅ | ❌ | ❌ | ❌ |
| Qdrant | ✅ | ❌ | ✅ | ❌ | ❌ | ❌ |
| Chroma | ✅ | ❌ | ✅ | ❌ | ❌ | ❌ |
| *— Graph databases —* | | | | | | |
| Neo4j | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
| *— Document stores —* | | | | | | |
| MongoDB | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ |
| Firestore | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ |
| DynamoDB | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ |
| *— Relational + vector —* | | | | | | |
| PostgreSQL + pgvector | ✅ | ❌ | ✅ | ❌ | ❌ | ❌ |
| MySQL | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ |
| SQLite | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ |
| *— Search engines —* | | | | | | |
| Elasticsearch | ✅ | ❌ | ✅ | ❌ | ❌ | ❌ |
| Algolia | ✅ | ❌ | ✅ | ❌ | ❌ | ❌ |
| *— Cache —* | | | | | | |
| Redis | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
Brainy is the only row with every box checked. And it runs all of them in a single query — no stitching services together.
### One library, any scale
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.
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.
---
## What Can You Build?
### Common applications
- **AI agents with persistent memory** — Give any AI an always-on, self-organizing knowledge graph that persists between sessions and across agents.
- **Searchable knowledge bases** — Build institutional memory that links documents automatically and surfaces answers across the full web of related information.
- **Semantic document search** — Index PDFs, code, or media and find them by meaning, not just keywords.
- **Relationship-aware recommendations** — Power product catalogs or content platforms where every recommendation understands what connects to what.
- **Safe experiments** — Let teams branch the knowledge base, experiment independently, and merge when ready — just like branching code.
- **Unified business platforms** — Combine booking, CRM, inventory, and analytics in one queryable knowledge graph with no sync pipeline.
### Built with Brainy
Real products built on Brainy — live in production at Soulcraft:
- **Workshop** — AI-powered IDE and creation studio where imagination meets creation.
- **Venue** — Unified physical + digital business operations, from food truck to franchise.
- **Memory** — Infinite context for AI agents that never forgets.
- **Collective** — Multi-agent coordination with shared knowledge and automatic task routing.
- **Heart** — Emotional intelligence and empathy layer for AI communication.