So, we have to start quickly, so let me introduce Olivia, who will be talking about building
your LN second brain and Mike is yours.
All right, we're just going to have to go because we've got five minutes flat in order
to do a whirlwind tour of building your second brain.
I'm Olivia, I've been doing AI, I'm machine learning for about 15 years and I'm new here
at Phosphum.
Here's what you already know, AI is everywhere and we need Phosph AI solutions now.
What you might know, you can download open source models today, local open source frameworks,
make them easy to use, and they even mimic the open AI API spec.
You've probably learned that this morning, but now you'll see it in detail.
So, what do we mean when we talk about first brain versus second brain?
Your first brain is great at things like creativity, logic, reasoning, and identifying facts.
Your mileage may vary, but don't use an AI for that.
Your second brain, memory aid, combining concepts, summarizing info into new formats.
Those are the things that we can use our local second brain for.
Our big goal interaction here is, can you break down the big project and to several possible steps
for me and add reminders to my to-do list.
You want it really using the data that belongs to you in your own way.
What does that look like when we break it down into a stack?
We need something private, local, low energy.
We need a clever model and curated data sources.
We need a chat interface that's just like chatGbT that uses your notes.
Process the information the way you need it to do and can act on your behalf.
So, let's break that down.
How are we going to get to our private, low energy, clever model with curated data sources?
We're going to be using Olamma and Granite.
Olamma, you've heard this already today if you've been here for a while,
but it's got an easy to use command line interface, a great model registry.
It's open AI API compatible and useslamma.cpp for model inference.
Granite, I am extremely biased here.
My team created this, but the Granite model is great because it's small.
It's highly performant across all of the benchmarks and on 95% open data.
And it's designed for enterprise tasks.
So, now we can go ahead and pull our model down, run it on our laptops,
and get to a quick response.
Okay, so now we want to add a chat interface.
So, it looks like chatGbT.
Our choices are going to be open web UI or anything at all.
Anything at all in an open web UI, both awesome options in the open source space.
Anything at all in an MIT license, it's got a standalone app and cross-operating system support.
Open web UI, BSD3 clause license, web interface, it's multi-user enterprise hostable.
Both of them can use our Olamma that we've already put together.
It's got a familiar chat interface, much like chatGbT,
the ability to do collections of data.
So, here we are.
Now we can talk in our little open web UI instance that's running on our laptop,
and hey, now we can get the same response in a chat interface.
Okay, great.
Now we wanted to actually use our notes.
So, we can add this in two ways.
Langchain or Lama Index.
Langchain, Lama Index, both awesome ways to add new things to your models.
Langchain is more of a general purpose.
LLem pipeline framework enables rag, but also does things like tool use and all kinds of stuff.
Lama Index is a little bit more tuned towards actually doing rag in particular.
It's got a lot of data indexing specialty, and it's more document-specific.
But cool thing.
Open web UI and anything else both use Langchain under the hood.
So, we're just going to use that for a moment and use their collections in our face.
So, we're going to sit here.
We're going to create an knowledge base in our open web UI interface.
We're going to pull in our Obsidian notes, which are already sitting on our laptop and
Markdown format, and that it'll go ahead and start syncing that directory into our thing.
So, now, when we actually ask our chat, you can see instance questions.
Hey, now we can reference our Obsidian collection, and it's using real actual meeting notes that are on our laptop.
Okay.
Now, we need to process information like we would.
That means we need to add agents.
So, we're looking at auto-gen.
What's an agent, by the way, LLem prompt designed to operate autonomously.
Don't have time to tell you more.
A agent framework, auto-gen 2, abstract complex agent interaction away and allows multi-agent
interaction for easy tool use.
Okay.
Now, we get a whole bunch of agents running in the background, running a big execution plan,
searching the internet to bring more information into our project.
And the last thing we need to do is be able to actually modify our things.
So, we want to add tools tool calling uses JSON generated by the LLem to make a function call.
So, we can do just about anything.
We want it to go ahead and store these tasks back in our task manager.
What it's in sprocket down.
And now, we have something that actually gives us that second brain goal interaction.
That was easy, right?
Fantastic.
This is great.
Thank you.
Like speed dating almost.
There you go.
Yep.