orgin story + edits

content/blog/A Simple Honcho Primer.md

@@ -5,7 +5,7 @@ tags:
   - blog
   - honcho
 ---
-![[Honcho_Final-23.png]]
+![[bot reading primer.png]]
 
 Welcome to our quick, ELI5[^1] guide to [Honcho](https://honcho.dev).
 
@@ -39,16 +39,20 @@ It then acts as [[Introducing Honcho's Dialectic API|an oracle to each user]], a
 ## Why We Built Honcho
 ^x125da
 
-We believe Honcho will be a key part of the AI application development stack.
+Plastic Labs was founded as an edtech company. The original mission was to build an AI tutor that [[Open Sourcing Tutor-GPT#^x527dc|could reason like]] the best human instructors. We quickly found the key limitation was data not on the subject matter, but on the student. To overcome it, he tutor needed [[Theory of Mind Is All You Need|a way to]] get to know *each* of its students deeply.
+
+Honcho was born by running up against this challenge, building technology to solve it, and realizing all AI applications are going to need the same solutions. The promise of *generative* AI isn't one-size-fits-all products, but bespoke experiences in each moment for each user. The same limitation emerges--how well do you know your user?
+
+So we believe Honcho will be a critical, table-stakes part of the AI app development stack.
 
 Why? Because [[Humans like personalization|users will want]] their AI experiences to be personalized and app developers shouldn't be redundantly solving that problem.
 
-But this might not be intuitive for a few reasons:
+But it's not intuitive to many for a few reasons:
 
 - AI app builders are [[Machine learning is fixated on task performance|still focused on]] just getting general tasks to work
 - LLMs' [[LLMs excel at theory of mind because they read|potential to personalize]] is still under-appreciated  
-- Historic examples of personalized apps just leverage our activity and engagement data
-- Those examples tend target only base user needs and have poor privacy records
+- Historic examples of personalized apps usually just leverage our activity & engagement data
+- Those examples tend target only base user desire, lead to addictive behavior, & have poor privacy records
 
 Still, when interacting with an AI app, there's a sense that it *should* be getting to know us. In fact, we're often surprised when we realize it's not learning about us over time. And probably annoyed at having to start over.
 

content/blog/Open Sourcing Tutor-GPT.md

@@ -37,6 +37,7 @@ It's clear generative AI stands a good chance of democratizing this kind of acce
 So how do we create successful learning agents that students will eagerly use without coercion? We think this ability lies latent in foundation models, but the key is eliciting it.
 
 ## Eliciting Pedagogical Reasoning
+^x527dc
 
 The machine learning community has long sought to uncover the full range of tasks that large language models can be prompted to accomplish on general pre-training alone (the capability overhang). We believe we have discovered one such task: pedagogical reasoning. ^05bfd8