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## 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)

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## 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...

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