From 5232ad281eb5159c1f8493fb1ee342485a42b3e8 Mon Sep 17 00:00:00 2001 From: Bryan Whiting Date: Fri, 15 Dec 2023 20:04:37 -0800 Subject: [PATCH] needle --- lists/{img => ai}/ai.jpeg | Bin lists/{ai.md => ai/index.md} | 8 +++- .../index.md | 38 ++++++++++++++++++ .../photo.jpeg | Bin 0 -> 339667 bytes 4 files changed, 45 insertions(+), 1 deletion(-) rename lists/{img => ai}/ai.jpeg (100%) rename lists/{ai.md => ai/index.md} (73%) create mode 100644 posts/2023-12-15-llms-can-find-a-needle-in-the-haystack/index.md create mode 100644 posts/2023-12-15-llms-can-find-a-needle-in-the-haystack/photo.jpeg diff --git a/lists/img/ai.jpeg b/lists/ai/ai.jpeg similarity index 100% rename from lists/img/ai.jpeg rename to lists/ai/ai.jpeg diff --git a/lists/ai.md b/lists/ai/index.md similarity index 73% rename from lists/ai.md rename to lists/ai/index.md index b2d1450e5..80e4fabdd 100644 --- a/lists/ai.md +++ b/lists/ai/index.md @@ -7,12 +7,18 @@ categories: [tech, futurism, ai] draft: false --- -![](img/ai.jpeg) +![](ai.jpeg) ::: callout-note ## TL;DR: AI is wild. Last updated per last headline. First created 2023-12-14 ::: +# Coffee by Coframe + +Build React UIs super fast. [GitHub](https://t.co/0vBssgp0ue) + +

Announcing Coffee: build and iterate on your UI 10x faster with AI ☕️👇https://t.co/0vBssgp0ue pic.twitter.com/JqwC8WpDzs

— Coframe (@coframe_ai) December 13, 2023
+ # Prompt Engineering Added 2023-12-15 diff --git a/posts/2023-12-15-llms-can-find-a-needle-in-the-haystack/index.md b/posts/2023-12-15-llms-can-find-a-needle-in-the-haystack/index.md new file mode 100644 index 000000000..eabfea357 --- /dev/null +++ b/posts/2023-12-15-llms-can-find-a-needle-in-the-haystack/index.md @@ -0,0 +1,38 @@ +--- +title: LLMs can find a needle in the haystack +description: | + GPT outperforms Claude. +date: 2023-12-15 +categories: [ai] +draft: false +--- + +![](photo.jpeg) + +::: callout-note +## Is RAG necessary when you have incredible memory? +::: + +# Context + +Check out this thread: + +

(1/8) The Needle in the Haystack done by @GregKamradt was an amazing analysis of retrieval performance! Greg has graciously allowed us to build on his work with a repository that is now OSS.@natfriedman We have a much more rigorous test we’ve put out based on this idea.… pic.twitter.com/i5O8zrcwQT

— Aparna Dhinakaran (@aparnadhinak) December 15, 2023
+ +This is a powerful analysis. Sure, Anthropic will find a way to improve or challenge the results. But the point is clear: these technologies can remember hyper specific 7-digit random numbers out of a batch of 126,000 tokens, where a token is roughly 4 characters. GPT is clear winner here, too. + +Also, open source is getting incredibly good. This implies the future is open source. + +

Comparing @OpenAI #GPT4 Turbo to @MistralAI

GPT-4 is pretty good in that region in general. Interesting to see how @MistralAI scales to larger context windows pic.twitter.com/WQo6MmGIHh

— Aparna Dhinakaran (@aparnadhinak) December 15, 2023
+ +# Impact + +RAG can be used to make retrieval more efficient. But if retrieval is already super efficient maybe RAG is only a short term thing. Context lengths of 10m tokens...probably by next year right? + +Start of the year we were at 4K tokens. Now there are 126,000 tokens. 30x improvement. So to do another 30x improvement is 3.76M. So yea, by next year you should be able to just load the entire RAG database into memory. But...gonna be super expensive. + +Point is: would GPT be this effective if it was using RAG over a database? 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