diff --git a/Monthly Notes/Feb 2024 notes.md b/Monthly Notes/Feb 2024 notes.md
index 39a65aee..3dfa3be9 100644
--- a/Monthly Notes/Feb 2024 notes.md	
+++ b/Monthly Notes/Feb 2024 notes.md	
@@ -28,6 +28,7 @@ Frontier models
 		- The first Gemini 1.5 model we’re releasing for early testing is Gemini 1.5 Pro. It’s a mid-size multimodal model, optimized for scaling across a wide-range of tasks, and [performs at a similar level to 1.0 Ultra](https://goo.gle/GeminiV1-5), our largest model to date. It also introduces a breakthrough experimental feature in long-context understanding.
 		- **Gemini 1.5 Pro comes with a standard 128,000 token context window. But starting today, a limited group of developers and enterprise customers can try it with a context window of up to 1 million tokens via [AI Studio](https://aistudio.google.com/) and [Vertex AI](https://cloud.google.com/vertex-ai) in private preview.**
 		- Through a series of machine learning innovations, we’ve increased 1.5 Pro’s context window capacity far beyond the original 32,000 tokens for Gemini 1.0. We can now run up to 1 million tokens in production.
+		- when tested on long code prompts, [HVM author agrees gemini retrieval is better than GPT4](https://old.reddit.com/r/singularity/comments/1atjz9v/ive_put_a_complex_codebase_into_a_single/)
 	-  Sparse MoE multimodal model - Can handle 3 hours of video, 22 hours of audio or 10M tokens with almost perfect recall! - Better than Gemini 1 Pro, better than Ultra for text, worse for audio and vision - Sadly not much info regarding model size, # of experts, architecture explorations, etc
 - https://twitter.com/evowizz/status/1753795479543132248