quartz/content/blog/A Simple Honcho Primer.md

title	date	tags
A Simple Honcho Primer	04.16.24	blog honcho

!

[!NOTE] Welcome to our quick, "explain it like I'm 5" guide to Honcho! We'll keep it simple, covering A Simple Honcho Primer#^ef795f, A Simple Honcho Primer#^x125da, A Simple Honcho Primer#^cd2d3c, and A Simple Honcho Primer#^ca46d7. But throughout, we'll link to places you can dive deeper.

What Is Honcho?

^ef795f

Honcho is a personalization platform for large language model (LLM) applications built by Plastic Labs.

It's software infrastructure that lets AI apps to "get to know" their users, resulting in delightful experiences and optimized time to value.

We'll have direct consumer experiences in the future, but today, the product is for application developers. It allows them to Introducing Honcho's Dialectic API#^a14c2f and Introducing Honcho's Dialectic API#^x7f7f8.

Right now, Honcho is in private beta, that means integrating our hosted version requires permission and onboarding¹. You can sign-up here.

In its current form, Honcho has three core components:

1. Announcing Honcho's Private Beta#^x15f37 - managing each user's data
2. Announcing Honcho's Private Beta#^x53717 - processing user data with our proprietary AI models
3. Announcing Honcho's Private Beta#^ee4516 - surfacing user data to personalize user experience (UX)

If you've heard of Retrieval Augmented Generation (RAG), this might sound familiar. But Honcho is doing much more than simple RAG.

Behind the scenes, Honcho learns about users as people--User State is State of the Art. It seeks to understand their beliefs, hopes, dreams, history, interests, and preferences.

It then acts as Introducing Honcho's Dialectic API, allowing apps ask for any personal context they need to improve UX and giving them access to a social cognition layer.

Why We Built Honcho

^x125da

Plastic Labs was founded as an edtech company. The original mission was to build an AI tutor that Open Sourcing Tutor-GPT#^x527dc 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 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 their AI experiences to be personalized and app developers shouldn't be redundantly solving that problem.

But it's not intuitive for a few reasons:

• AI app builders are Machine learning is fixated on task performance just getting general tasks to work
• LLMs' LLMs excel at theory of mind because they read is still under-appreciated
• 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.

Think about personalization here as more like the experience of close human companionship or white glove services than the attention hacking mechanisms of TikTok². There's Announcing Honcho's Private Beta#^xb6ef1 for more positive-sum use of user data and for aligning AI applications more closely with user needs and preferences.

How to Use Honcho

^cd2d3c

Honcho is first and foremost a storage framework. Think of it like an open source version of the OpenAI Assistants API. User sessions store both user and AI generated messages as well as any intermediate inferences you might want to store as metamessages.

user_input = "Here's a message!"
ai_response = "I'm a helpful AI assistant!"

session.create_message(is_user=True, content=user_input)
session.create_message(is_user=False, content=ai_response)

But what about vectorDBs? Don't worry, Honcho has you covered there too. You can embed data and store them as documents in per-user vector DBs called collections.

collection.create_document(content="The user is interested in AI")

Using Honcho as a storage mechanism allows you to retrieve rich insights via the user profiles it's building and managing on the backend. Your application's LLM can access them via the Introducing Honcho's Dialectic API API.

It's simple: just query in natural language using the session.chat() method.

session.chat("What are the user's interests?")

There are a Introducing Honcho's Dialectic API#How It Works to use this, and the current product only scratches the surface³.

What's Next for Honcho?

^ca46d7

Beyond improving our internal AI models so they can get to know users as richly as possible, we see three natural extensions in Announcing Honcho's Private Beta#^eb15f3:

1. Announcing Honcho's Private Beta#^x2dd3b - developer tools to understand & assess the impact of personalization + machine learning tools to build personalized datasets
2. Announcing Honcho's Private Beta#^a84f44 - chat with your Honcho to direct how it manages & shares data + authenticate with Honcho to sign-in to AI apps
3. Announcing Honcho's Private Beta#^ebf071 - a network of apps contributing to & sharing Honcho data, user-owned & stored in confidential environments

And in just a few weeks, we'll be launching a demo platform where anyone can interact with (& eventually build) Honcho powered apps.

Join the Beta

Sign-up for the private beta and start building personalized experiences.

Join Discord, introduce yourself, and tell us what you're working on.

Visit our open-source repo and get your hands dirty.

🫡

---

1. There's also an open source repo for Honcho, so you can self-host a basic version--join our Discord for support. ↩︎

2. If you want to go deeper on the philosophical or machine learning side, take some time to explore the rest of the blog. ↩︎

3. To get further into the technical weeds, head over to our docs. ↩︎