Hyperspace More Nodes Is All You Need: Nicolas Schlaepfer

Hello everybody, my name is Nicolas Schlaepfer. I'm an AI engineer. I think I'm at theright place. Today I'm going to be pronouncing a new product we've been working on at Hyperspace. A little bit about us: we are a decentralized AI network.

We have no GPUs. We're building a community who takes the resources from their personal computer and contributes to our decentralized network. We have a product currently out called AIOS. You can download it for Windows and Mac. It uses Llama.cpp and does inference.

So technically, you can download it, get inference from somebody in Belgium, and you can have a chat experience like that. So we're hoping to—oh, actually, sorry. We really believe in diverse models. We think not having just one big closed-source model is the answer for the best AI experience.

Having a mixture of a bunch of great experts will provide the best AI experience. So that gets us to our self-titled product, Hyperspace, which will be built on our network. This is a really interesting product. It's a mix of prompt engineering, visual React flow, Python execution, and RAG-like web browsing.

So for the first thing with this product, we wanted to build a fine-tuned model that outputted agentic planning experiences. So you'd put in a query, you get in a DAG, or you get out a DAG in a JSON format.

And this is a methodical plan from that query.

We also wanted to have a primitive of having an in-house web scraping experience for LLMs. So we're using Puppeteer and Beautiful Soup to scrape websites, convert that HTML into a Markdown, something easier to read for LLMs to digest.

Kind of the product we're going to talk about is we have a node editor and a terminal, and this is going to be a power tool for the power users. This node editor will allow you to change each node in the React flow to fit your needs.

And this is coming from the DAG orchestration model we have. Here's a little video demo of our product. I recorded it yesterday in the hotel, so I'm sorry for the audio. And here we go.

Welcome to a very early look at Hyperspace. Let's begin with a sample query.

Once you submit your query, you're brought into our node editor view, where each node is streamed in from our DAG orchestration model, HyperEngine v3.

We want to emphasize that the user is still in full control. They can edit the title, task description, and expected output. They also have the freedom to add as many nodes as they like.

Let's execute.

Now that our outputs are done, we can talk a little bit about the outputs. Each output is coming from each node, and each node is creating a query based on that task. It's combining the overall goal, the local goal, and whatever has happened in the previous node.

We're using a reasoning model and a summarization model. For reasoning, we're using Qwen 2 instruct, and then for summarization, we're using LLaMA 3 70B. This helps provide a diverse set of synthesized answers.

For the outputs that do have Python, we can go ahead and run them and see them in our terminal.

The terminal will automatically open it up with the output. We want to provide the groundwork for agentic behavior in the future by providing these core primitives.

Over here on ourright, we have our virtual file system that changes our directory. We're trying to build out all the primitives to what an agent would need: memory, Python execution, planning, and code generation.

Thank you so much for watching. We're very excited to get this out in the coming weeks.

Happy to announce that it'll be available later this week via waitlist, so go ahead and pay attention to our Twitter:@HyperspaceAI. Allright, thank you. That's my time.