Update README.md

README.md

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 ## Features
 - __Tiny__: It's in the name. It's literally just an axum server. Extremely easy to customize, around 600 lines of code.
-- __Fast__: Tinyvector will have comparable speed to advanced vector databases when it comes to speed on small to medium datasets.
+- __Fast__: Tinyvector should have comparable speed to advanced vector databases when it comes on small to medium datasets, and slightly better accuracy.
 - __Vertically Scales__: Tinyvector stores all indexes in memory for fast querying. Very easy to scale up to 100 million+ vector dimensions without issue.
 - __Open Source__: MIT Licensed, free forever.
 
 ### Soon
 - __Integrated Models__: Soon you won't have to bring your own vectors, just generate them on the server automaticaly. Will support SBert, Hugging Face models, OpenAI, Cohere, etc.
 - __Python/JS Client__: We'll add a comprehensive Python and Javascript package for easy integration with tinyvector in the next two weeks.
 
-## We're better than ...
+## Why use tinyvector?
 
-In most cases, most vector databases are overkill for something simple like:
-1. Using embeddings to chat with your documents. Most document search is nowhere close to what you'd need to justify accelerating search speed with [HNSW](https://github.com/nmslib/hnswlib) or [FAISS](https://github.com/facebookresearch/faiss).
-2. Doing search for your website or store. Unless you're selling 1,000,000 items, you don't need Pinecone.
-3. Performing complex search queries on a very large database. Even if you have 2 million embeddings, this might still be the better option due to vector databases struggling with complex filtering. Tinyvector doesn't support metadata/filtering just yet, but it's very easy for you to add that yourself.
+Most vector databases are overkill for simple setups. For example:
+- Using embeddings to chat with your documents. Most document search is nowhere close to what you'd need to justify accelerating search speed with [HNSW](https://github.com/nmslib/hnswlib) or [FAISS](https://github.com/facebookresearch/faiss).
+- Doing search for your website or store. Unless you're selling 1,000,000 items, you don't need Pinecone.
 
 ## Embeddings?
 
-What are embeddings?
-
-> As simple as possible: Embeddings are a way to compare similar things, in the same way humans compare similar things, by converting text into a small list of numbers. Similar pieces of text will have similar numbers, different ones have very different numbers.
+Embeddings are a way to compare similar things, in the same way humans compare similar things, by converting text into a small list of numbers. Similar pieces of text will have similar numbers, different ones have very different numbers.
 
 Read OpenAI's [explanation](https://platform.openai.com/docs/guides/embeddings/what-are-embeddings).
 
 ## Acknowledgements
 
-- Will Depue's [tinyvector](https://twitter.com/willdepue/status/1675796236304252928) (python+sqlite+numpy) inspired this project (and the name). Will also contributed plenty of ideas to optimize performance.
+- Will Depue's [tinyvector](https://twitter.com/willdepue/status/1675796236304252928) (python+sqlite+numpy) inspired me to build a vector database from scratch (and borrow the name). Will also contributed plenty of ideas to optimize performance.
 
 ## License