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spiceaidocs/docs/index.md

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 The Spice runtime is written in Rust and leverages industry leading technologies like Apache DataFusion, Apache Arrow, Apache Arrow Flight, and DuckDB.
 
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 ## Why Spice?
 
 Spice makes querying data by SQL across one or more data sources simple and fast. Easily co-locate a managed working set of your data with your application or ML, locally accelerated in-memory with Arrow, with SQLite/DuckDB, or with an attached database like PostgreSQL for high-performance, low-latency queries.
 
 ### Before Spice
 
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 ### With Spice
 
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 ### Example Use-Cases
 

spiceaidocs/docs/intelligent-applications/index.md

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 With Spice.ai OSS, federated data and ML models are colocated with applications, creating lightweight, high-performance, AI-copilot sidecars.
 
-<img src="https://user-images.githubusercontent.com/80174/140449760-97974f3c-8a78-4ea2-9ec5-843dc3cff5b6.png" alt="A Spice.ai Intelligent Application" />
+<img width="600" src="https://user-images.githubusercontent.com/80174/140449760-97974f3c-8a78-4ea2-9ec5-843dc3cff5b6.png" alt="A Spice.ai Intelligent Application" />
 
 ## The Intelligent Application Workflow
 
 Dataset definitions and ML Models are packaged as Spicepods and can be published and distributed through the [Spicerack.org](https://spicerack.org) Spicepod registry. Federated datasets are locally materialized, accelerated, and provided to colocated Models. Applications call high-performance, low-latency ML inference APIs for AI generation, insights, recommendations, and forecasts ultimately to make intelligent, AI-driven decisions. Contextual application and environmental data is ingested and replicated back to cloud-scale compute clusters where improved versions of Models are trained and fined-tuned. New versioned Models are automatically deployed to the runtime and are A/B tested and flighted by the application in realtime.
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