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content/_index.md

@@ -11,11 +11,19 @@ It’s our mission to realize this future.
 
 ## Blog
 
-[[Extrusion 01.24]]  
 [[Honcho; User Context Management for LLM Apps|Honcho: User Context Management for LLM Apps]]  
 [[blog/Theory-of-Mind Is All You Need]]  
 [[blog/Open-Sourcing Tutor-GPT]]
 
+## Extrusions
+
+[[extrusions/Extrusion 01.24|Extrusion 01.24]]  
+## Notes
+
+[[Honcho name lore]]  
+[[Metacognition in LLMs is inference about inference]]  
+[[The machine learning industry is too focused on general task performance]]  
+
 ## Research
 
 [Violation of Expectation Reduces Theory-of-Mind Prediction Error in Large Language Models](https://arxiv.org/pdf/2310.06983.pdf)

content/extrusions/Extrusion 01.24.md

@@ -6,7 +6,7 @@ No one needs another newsletter, so we'll work to make these worthwhile. Expect
 
 ## 2023 Recap
 
-Last year was wild. We started as an edtech company and ended as anything but. There's a deep dive on some of the conceptual lore in last week's blog post, "[[Honcho; User Context Management for LLM Apps]]":
+Last year was wild. We started as an edtech company and ended as anything but. There's a deep dive on some of the conceptual lore in last week's "[[Honcho; User Context Management for LLM Apps|Honcho: User Context Management for LLM Apps]]":
 
 >[Plastic Labs](https://plasticlabs.ai) was conceived as a research group exploring the intersection of education and emerging technology...with the advent of ChatGPT...we shifted our focus to large language models...we set out to build a non-skeuomorphic, AI-native tutor that put users first...our [[Open-Sourcing Tutor-GPT|experimental tutor]], Bloom, [[Theory-of-Mind Is All You Need|was remarkably effective]]--for thousands of users during the 9 months we hosted it for free...
 

content/notes/The machine learning industry is too focused on general task performance.md

@@ -4,4 +4,4 @@ However, general capability doesn't necessarily translate to completing tasks as
 
 Take summarization. It’s a popular machine learning task at which models have become quite proficient, at least from a benchmark perspective. However, when models summarize for users with a pulse, they fall short. The reason is simple: the models don’t know this individual. The key takeaways for a specific user differ dramatically from the takeaways _any possible_ internet user _would probably_ note.
 
-So a shift in focus toward user-specific task performance would provide a much more dynamic & realistic approach. Catering to individual needs & paving she way for more personalized & effective ML applications.
+So a shift in focus toward user-specific task performance would provide a much more dynamic & realistic approach. Catering to individual needs & paving the way for more personalized & effective ML applications.