Yohei Profile picture
Feb 23, 2023 24 tweets 9 min read Read on X
We had an AMAZING session with @UntappedVC LPs this morning on the "Future of AI in VC".

Here's an abbreviated version as a Twitter thread!

👇 Image
To set the stage, here's our job as a VC.

- Strategy: Thesis, portfolio construction, etc.
- Fundraise: LP comms, fund mgmt
- General Ops: Marketing, legal, HR, etc.
- Investing breaks down into: Source, Decide, Win, Support Image
History of AI in VC

AI in VC isn't new, but it's primarily been in two areas. Image
History of "AI" in Sourcing

It's basically a filter for things like keywords, past jobs, university, or revenue. Image
History of AI in Selecting

Algorithmic scoring has been going on for a while. It works at late stage, where there's lots of data - but even then, it's good for outperforming averages, not being the top 1%.

At pre-seed, not enough data. Image
Future of AI in VC

We're applying AI to every aspect of the business. Image
Our Approach - People focused.

- Why use AI: to meet and learn
- How to use AI: to enable and empower people
- What is the output: AI+human output beats AI or human alone. Image
Built to Learn and Meet

- We "Build-In-Public" to meet people.
- We build tools to support founders at scale.
- The learning from building are passed onto our portfolio companies Image
Here are a few examples of ways we've passed on our learnings to portfolio companies.

They love it! Image
"Augmented Intelligence"

Here's a specific example, Mini Yohei is a tool for portfolio founders in our portal where they get:

- Immediate response from AI (faster than me)
- Reply from me to the AI response (more accurate)

We do this by having me CC'd in the reply to founders. Image
Our AI Capabilities

We'll walk through a few examples... Image
Mini Yohei 2.0

VC Expert who provides answers grounded in VC blog posts.

This is a similar workflow to how I found articles to share with founders, but automated with AI. Image
Chatbot Experiments

We have many chatbot experiments, both public and private.

All chat logs and summaries are available to us as we are using this to build relationships. (Some founders leave their email in the chat for us to reach out) Image
Web/Deck/Email Parsing

Basically the ability to scrape and extract data from any type of content. We use this capability across many of our tooling. Image
Quick Memo

Our investment memo drafting tool is amazing.

Can generate tons of insights as well as similar startups, unicorns, and acquisitions. Also sentiment analysis from ProductHunt and many other features to be added.

Our LPs can use this. Image
Search ProductHunt

We embedded the most recent 22k ProductHunt descriptions so we can find any product we're looking for easily.

We share this with our portfolio companies and LPs. Image
Interaction Summaries

We're experimenting with a tool that auto-generates interaction summaries for every email address, and at the domain level.

A powerful tool for anyone maintaining a large network. Image
OpenAI x Zapier

This was cool. Our unofficial @OpenAI x @zapier integration turned into an official integration.

Thinking about how we've enabled millions of non-coders to leverage building with GPT-3 makes me happy.

Talk about impact. Image
Other Tools

We list a few more here, but this isn't even everything.

You can see most of the stuff I've built at yohei.me (build-in-public log). Image
Fuzzy Compiler

This turns abbreviated code or instructions into clean well formatted code.

This and the Zapier integration are examples of tools we build to help us build faster. Image
Modular Tech Stack

Our tech stack is built modularly with a combination of code and no-code tools.

We designed it so parts can be easily added, replaced or upgraded.

For example, it took us 5 min to add the ProductHunt search tool to our LP portal and Founder portal. Image
Impact on our firm

Tying this back to the beginning of the prezo, this is how our AI tools touch every aspect of our business. Image
And for our secret demo...

Actually, let's keep this redacted 🤫 Image
And there you go! That was...

"The Future of AI in VC" (abbreviated)

Hope this inspires some ideas on how AI can apply to your business.

[#dalle image prompt: minimalist rainbow splatter art unicorn, black background] Image

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More from @yoheinakajima

Apr 19
great to see everyone at AI rabbithole event today!

as promised, here's my deck with abbreviated narrative.

(video was recorded, will share when available) Image
agents agents agents!

40+ agent related github projects today with 500+ stars, 90% of which are less than a year old. Image
where are we going? robots and virtual agents.

biggest difference is that robots are constrained in physical space, and virtual agents can run tasks in parallel and be in multiple "places" Image
Read 30 tweets
Apr 4
Been talking lots about knowledge graphs and LLMs, specifically for autonomous agents, recently.

Organized some thought here if you want to dig in 👇

KG 🤝 LLM

🧵 (a thread)
For those not familiar, knowledge graphs are a type of data representation in the form of nodes (objects) and edges (relationships) and looks like this. Image
This structure allows for unique querying of relationship related data such as finding shortest path between two nodes, or further more complex reasoning. Image
Read 12 tweets
Mar 16
Open-sourcing "MindGraph", a proof of concept starter kit for building and querying against an ever-expanding knowledge graph with natural language!

- multiple input types
- simple interface
- basic search/RAG against knowledge graph
- easily extendable with integrations
- basic database swapping

GitHub in 🧵Image
Quick background is that I paused on BabyAGI work after the BabyFoxAGI mod because I felt I was missing some pieces.

Went deep on knowledge graphs (KGs) and released Instagraph () which lets you build, merge, and edit KGs....instagraph.ai
Oh, and before I keep going, here's the GitHub:

This is one of my bigger projects (multiple files) which I'm not used to, so be kind plz. Hopefully still some interesting and fun patterns in here for you.github.com/yoheinakajima/…
Read 6 tweets
Feb 9
Some Qs to ask when building an autonomous agent, around topic of…

Task planning and mgmt
Task execution and routing
Tool usage and learning
Memory and knowledge
Self improvement
UI/UX (input/output)
Auth and tool auth
Personalization & generalization
Security

Let’s dig into each of these shall we? 👇
Task planning & management

How is your agent generating and tracking tasks? Hand crafted (you are chaining them)? CoT (one at a time)? Task list (BabyAGI style)? Knowledge graph? Are you using past tasklists to help generate new ones?
Task execution & routing

How do you execute tasks? Many specialized agents? Internal vs external? How do you route to them? How do you ensure it was executed correctly? What do you do if it doesn’t?
Read 10 tweets
Nov 26, 2023
Okay @tldraw is pretty addicting



1) draw your UI
2) use text to describe
3) highlight (i.e. draw box around), and
4) click the blue "make real" on top right) makereal.tldraw.com
super quick prototypes that don't actually work, but give you the sense of what it would feel like to play with? count me in!
No but seriously, I could do this all day.

I wonder how many OpenAI calls each one of these takes...
Read 5 tweets
Nov 7, 2023
The Assistants API is awesome, had to build something...

Open-sourcing "GPTvsGPT" 🕵️🕵️
(109 lines of code)

A simple template to have two AI Assistants converse. What's exciting is the ability to extend these with retrieval, data, and custom functions.

GH/Replit in thread 🔽 Image
As you saw above, you set the parameters for two Assistants, then feed that in with the topic, and number of messages you want.

You'll get something like this: Image
Here is the Github:

Here is the Replit:

As usual, this is only meant as a starting point to build something awesome. Lots of directions you can take this!

(img below courtesy of DALLE3 and dingboard) github.com/yoheinakajima/…
replit.com/@YoheiNakajima…
Image
Read 14 tweets

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