Merge pull request #61 from plastic-labs/chl_blog

note

content/blog/Honcho; User Context Management for LLM Apps.md

@@ -43,7 +43,7 @@ We're consistently blown away by how many people don't realize large language mo
 
 There are lots of developer tricks to give the illusion of state about the user, mostly injecting conversation history or some personal digital artifact into the context window. Another is running inference on that limited recent user context to derive new insights. This was the game changer for our tutor, and we still can't believe by how under-explored that solution space is (more on this soon 👀).
 
-To date, machine learning has been [[Machine learning is fixated on task performance|far more focused on]] optimizing for general task competition than personalization. This is natural, although many of these tasks are still probably better suited to deterministic code. It's also historically prestiged papers over products--research takes bit to morph into tangible utility. Put these together and you end up with a big blindspot over individual users and what they want.
+To date, machine learning has been [[Machine learning is fixated on task performance|far more focused on]] optimizing for general task competition than personalization. This is natural, although many of these tasks are still probably better suited to deterministic code. It's also historically prestiged papers over products--research takes bit to morph into tangible utility. Put these together and you end up with a big blindspot over individual users and what they want. ^18066b
 
 The real magic of 1:1 instruction isn't subject matter expertise. Bloom and the foundation models it leveraged had plenty of that (despite what clickbait media would have you believe about hallucination in LLMs). Instead, it's personal context. Good teachers and tutors get to know their charges--their history, beliefs, values, aesthetics, knowledge, preferences, hopes, fears, interests, etc. They compress all that and generate customized instruction, emergent effects of which are the relationships and culture necessary for positive feedback loops.
 
@@ -88,6 +88,8 @@ This is the kind of future we can build when we put users at the center of our a
 
 ## Introducing Honcho
 
+^a9d0f8
+
 So today we're releasing the first iteration of [[Honcho name lore|Honcho]], our project to re-define LLM application development through user context management. At this nascent stage, you can think of it as an open-source version of the OpenAI Assistants API. ^8c982b
 
 Honcho is a REST API that defines a storage schema to seamlessly manage your application's data on a per-user basis. It ships with a Python SDK which [you can read more about how to use here](https://github.com/plastic-labs/honcho/blob/main/README.md).

content/notes/Humans like personalization.md

@@ -0,0 +1,30 @@
+To us: it's obvious. But we get asked this a lot:
+
+> Why do I need to personalize my AI application?
+
+Fair question; not everyone has gone down this conceptual rabbithole to the extent we have at [Plastic](https://plasticlabs.ai) and with [Honcho](https://honcho.dev).
+
+Short answer: people like it.
+
+In the tech bubble, it can be easy to forget about what *most* humans like. Isn't building stuff people love our job though?
+
+In web2, it's taken for granted. Recommender algorithms make UX really sticky, which retains users sufficiently long to monetize them. To make products people love and scale them, they had to consider whether *billions*--in aggregate--tend to prefer personalized products/experiences or not.
+
+In physical reality too, most of us prefer white glove professional services, bespoke products, and friends and family who know us *deeply*. We place a premium in terms of time and economic value on those goods and experiences.
+
+The more we're missing that, the more we're typically in a principal-agent problem, which creates overhead, interest misalignment, dissatisfaction, mistrust, and information asymmetry:
+
+<iframe src="https://player.vimeo.com/video/868985592?h=deff771ffe&color=F6F5F2&title=0&byline=0&portrait=0" width="640" height="360" frameborder="0" allow="autoplay; fullscreen; picture-in-picture" allowfullscreen></iframe>
+
+But, right now, most AI applications are just toys and demos:
+
+![[Honcho; User Context Management for LLM Apps#^18066b]]
+
+It's also why everyone is obsessed with evals and benchmarks that have scant practical utility in terms of improving UX for the end user. If we had more examples of good products, ones people loved, killer apps, no one would care about leaderboards anymore.
+
+> OK, but what about services that are purely transactional? Why would a user want that to be personalized? Why complicate it? Just give me the answer, complete the task, etc...
+
+Two answers:
+
+1. Every interaction has context. Like it or not, people have preferences and the more an app/agent can align with those, the more it can enhance time to value for the user. It can be sticker, more delightful, "just work," and entail less overhead. (We're building more than calculators here, though this applies even to those!)
+2. If an app doesn't do this, it'll get out-competed by one that does...or by the ever improving set of generally capable foundation models.