docs: update docs

README.md

@@ -22,7 +22,6 @@
 ## 💖 Projects using `ort`
 <sub>[Open a PR](https://github.com/pykeio/ort/pulls) to add your project here 🌟</sub>
 
-- **[Twitter](https://twitter.com/)** uses `ort` to serve homepage recommendations to hundreds of millions of users.
 - **[Bloop](https://bloop.ai/)** uses `ort` to power their semantic code search feature.
 - **[edge-transformers](https://github.com/npc-engine/edge-transformers)** uses `ort` for accelerated transformer model inference at the edge.
 - **[Ortex](https://github.com/relaypro-open/ortex)** uses `ort` for safe ONNX Runtime bindings in Elixir.

docs/pages/index.mdx

@@ -23,7 +23,6 @@ import { Callout, Card, Cards, Steps } from 'nextra/components';
 Converting a neural network to a graph representation like ONNX opens the door to more optimizations and broader acceleration hardware support. ONNX Runtime can significantly improve the inference speed/latency of most models and enable acceleration with NVIDIA CUDA & TensorRT, Intel OpenVINO, Qualcomm QNN, Huawei CANN, and [much more](/perf/execution-providers).
 
 `ort` is the Rust gateway to ONNX Runtime, allowing you to infer your ONNX models via an easy-to-use and ergonomic API. Many commercial, open-source, & research projects use `ort` in some pretty serious production scenarios to boost inference performance:
-- [**Twitter**](https://twitter.com/) uses `ort` in [part of their recommendations system](https://github.com/twitter/the-algorithm/tree/main/navi), serving hundreds of millions of requests a day.
 - [**Bloop**](https://bloop.ai/)'s semantic code search feature is powered by `ort`.
 - [**SurrealDB**](https://surrealdb.com/)'s powerful SurrealQL query language supports calling ML models, including ONNX graphs through `ort`.
 - [**Google's Magika**](https://github.com/google/magika) file type detection library is powered by `ort`.