Update images in README, spacing edits, add link (#225)

.gitignore

@@ -21,3 +21,4 @@ sanitizer
 .vscode/
 .devcontainer/
 .cache
+.cosine

README.md

@@ -14,11 +14,13 @@ Lantern builds and uses [usearch](https://github.com/unum-cloud/usearch), a sing
 ## 🔧 Quick Install
 
 If you don’t have PostgreSQL already, use Lantern with [Docker](https://hub.docker.com/r/lanterndata/lantern) to get started quickly:
+
 ```bash
 docker run -p 5432:5432 -e 'POSTGRES_PASSWORD=postgres' lanterndata/lantern:latest-pg15
 ```
 
 To install Lantern from source on top of PostgreSQL:
+
 ```
 git clone --recursive https://github.com/lanterndata/lantern.git
 cd lantern
@@ -29,6 +31,7 @@ make install
 ```
 
 To install Lantern using `homebrew`:
+
 ```
 brew tap lanterndata/lantern
 brew install lantern && lantern_install
@@ -38,7 +41,7 @@ You can also install Lantern on top of PostgreSQL from our [precompiled binaries
 
 Alternatively, you can use Lantern in one click using [Replit](https://replit.com/@lanterndata/lantern-playground#.replit).
 
-## 📖 How to use Lantern 
+## 📖 How to use Lantern
 
 Lantern retains the standard PostgreSQL interface, so it is compatible with all of your favorite tools in the PostgreSQL ecosystem.
 
@@ -62,12 +65,14 @@ CREATE INDEX ON small_world USING hnsw (vector);
 ```
 
 Customize `hnsw` index parameters depending on your vector data, such as the distance function (e.g., `dist_l2sq_ops`), index construction parameters, and index search parameters.
+
 ```sql
 CREATE INDEX ON small_world USING hnsw (vector dist_l2sq_ops)
 WITH (M=2, ef_construction=10, ef=4, dim=3);
 ```
 
 Start querying data
+
 ```sql
 SET enable_seqscan = false;
 SELECT id, l2sq_dist(vector, ARRAY[0,0,0]) AS dist
@@ -89,47 +94,51 @@ There are four defined operator classes that can be employed during index creati
 
 ### Index Construction Parameters
 
-The `M`, `ef`, and `ef_construction` parameters control the performance of the HNSW algorithm for your use case. 
-- In general, lower `M` and `ef_construction` speed up index creation at the cost of recall. 
+The `M`, `ef`, and `ef_construction` parameters control the performance of the HNSW algorithm for your use case.
+
+- In general, lower `M` and `ef_construction` speed up index creation at the cost of recall.
 - Lower `M` and `ef` improve search speed and result in fewer shared buffer hits at the cost of recall. Tuning these parameters will require experimentation for your specific use case.
 
 ### Miscellaneous
+
 - If you have previously cloned Lantern and would like to update run `git pull && git submodule update`
 
-## ⭐️ Features 
+## ⭐️ Features
+
 - Embedding generation for popular use cases (CLIP model, Hugging Face models, custom model)
-- Interoperability with pgvector's data type, so anyone using pgvector can switch to Lantern 
+- Interoperability with pgvector's data type, so anyone using pgvector can switch to Lantern
 - Parallel index creation via an external indexer
 - Ability to generate the index graph outside of the database server
 - Support for creating the index outside of the database and inside another instance allows you to create an index without interrupting database workflows.
-- See all of our helper functions to better enable your workflows 
+- See all of our helper functions to better enable your workflows
 
 ## 🏎️ Performance
 
 Important takeaways:
+
 - There's three key metrics we track. `CREATE INDEX` time, `SELECT` throughput, and `SELECT` latency.
 - We match or outperform pgvector and pg_embedding (Neon) on all of these metrics.
 - We plan to continue to make performance improvements to ensure we are the best performing database.
 
 <p>
-<img alt="Lantern throughput" src="http://docs.lantern.dev/graphs/throughput.png" width="400" style="float: left;" />
-<img alt="Lantern latency" src="http://docs.lantern.dev/graphs/latency.png" width="400" style="float: left;" />
-<img alt="Lantern index creation" src="http://docs.lantern.dev/graphs/create.png" width="400" style="float: left;" />
+<img alt="Lantern throughput" src="https://storage.googleapis.com/lantern-blog/1/throughput.png" width="400" style="float: left;" />
+<img alt="Lantern latency" src="https://storage.googleapis.com/lantern-blog/1/latency.png" width="400" style="float: left;" />
+<img alt="Lantern index creation" src="https://storage.googleapis.com/lantern-blog/1/create.png" width="400" style="float: left;" />
 </p>
 
 ## 🗺️ Roadmap
 
-- Cloud-hosted version of Lantern - [Sign up](https://forms.gle/YwxTzN9138LZEeCw8) for updates
+- Cloud-hosted version of Lantern - Sign up [here](https://lantern.dev)
 - Hardware-accelerated distance metrics, tailored for your CPU, enabling faster queries
 - Templates and guides for building applications for different industries
-- More tools for generating embeddings (support for third party model API’s, more local models) 
+- More tools for generating embeddings (support for third party model API’s, more local models)
 - Support for version control and A/B test embeddings
-- Autotuned index type that will choose appropriate  creation parameters
+- Autotuned index type that will choose appropriate creation parameters
 - Support for 1 byte and 2 byte vector elements, and up to 8000 dimensional vectors ([PR #19](https://github.com/lanterndata/lantern/pull/19))
 - Request a feature at [[email protected]](mailto:[email protected])
 
 ## 📚 Resources
 
 - [GitHub issues](https://github.com/lanterndata/lantern/issues): report bugs or issues with Lantern
-- Need support? Contact [[email protected]](mailto:[email protected]). We are happy to troubleshoot issues and advise on how to use Lantern for your use case 
+- Need support? Contact [[email protected]](mailto:[email protected]). We are happy to troubleshoot issues and advise on how to use Lantern for your use case
 - We welcome community contributions! Feel free to open an issue or a PR. If you contact [[email protected]](mailto:[email protected]), we can find an open issue or project that fits you