Video wird geladen...

Video konnte nicht geladen werden

Zur Startseite

🤯 this genius stores his entire codebase syntax in a graph database and queries it so provide context to an llm

1,991,118 Aufrufe • vor 1 Jahr •via X (Twitter)

11 Kommentare

Profilbild von Dan Mac
Dan Macvor 1 Jahr

one day things like this will be standard

Profilbild von Milk Road
Milk Roadvor 1 Jahr

Wall Street ain't ready for this... Coinbase launched Base ~1 year ago This Layer 2 blockchain has raked in ~$1.2M every week on average Now Wall Street is FOMOing into crypto. Front run them by reading Milk Road. 5 minutes. Every day. For free.

Profilbild von kev
kevvor 1 Jahr

@Austen Hey that’s me! I’m currently working on v2 of this app and will be launching a public beta in the near future. Anybody is free to shoot me a DM with questions or follow me if they want to hear when the beta drops.

Profilbild von Dan Mac
Dan Macvor 1 Jahr

@Austen that’s awesome Kev - such a cool idea following and looking forward to hearing more about the project!

Profilbild von Gobie Nanthakumar
Gobie Nanthakumarvor 1 Jahr

Isn’t that already part of every IDE…like indexing and referencing function definitions?

Profilbild von joey - e/acc 🇿🇦
joey - e/acc 🇿🇦vor 1 Jahr

You can do it for free on mordecai, it’s very very simple to implement, it doesn’t make you a genius, it’s called “RAG” and uses a vector database not a graph database.

Profilbild von Shantanu Goel
Shantanu Goelvor 1 Jahr

Guy said AST. Tweeter picks up on "syntax" and says guy stores "codebase syntax in a graph database". 🤦‍♂️

Profilbild von Johannes Schmidt 🌌👾
Johannes Schmidt 🌌👾vor 1 Jahr

cursor does this, they create embeddings and use them to look up code

Profilbild von Dan Mac
Dan Macvor 1 Jahr

think that’s a little different though embeddings arent legible to a human, whereas a graph is

Profilbild von Saurabh Suri⚡🥷🏼
Saurabh Suri⚡🥷🏼vor 1 Jahr

alright you have my attention @taykv2

Profilbild von Bryce DeFigueiredo
Bryce DeFigueiredovor 1 Jahr

@Austen This project brought to you by Velo nicotine pouches

Ähnliche Videos

New short course: LLMs as Operating Systems: Agent Memory, created with Letta, and taught by its founders Charles Packer and Sarah Wooders. An LLM's input context window has limited space. Using a longer input context also costs more and results in slower processing. So, managing what's stored in this context window is important. In the innovative paper MemGPT: Towards LLMs as Operating Systems, its authors (which include the instructors) proposed using an LLM agent to manage this context window. Their system uses a large persistent memory that stores everything that could be included in the input context, and an agent decides what is actually included. Take the example of building a chatbot that needs to remember what's been said earlier in a conversation (perhaps over many days of interaction with a user). As the conversation's length grows, the memory management agent will move information from the input context to a persistent searchable database; summarize information to keep relevant facts in the input context; and restore relevant conversation elements from further back in time. This allows a chatbot to keep what's currently most relevant in its input context memory to generate the next response. When I read the original MemGPT paper, I thought it was an innovative technique for handling memory for LLMs. The open-source Letta framework, which we'll use in this course, makes MemGPT easy to implement. It adds memory to your LLM agents and gives them transparent long-term memory. In detail, you’ll learn: - How to build an agent that can edit its own limited input context memory, using tools and multi-step reasoning - What is a memory hierarchy (an idea from computer operating systems, which use a cache to speed up memory access), and how these ideas apply to managing the LLM input context (where the input context window is a "cache" storing the most relevant information; and an agent decides what to move in and out of this to/from a larger persistent storage system) - How to implement multi-agent collaboration by letting different agents share blocks of memory This course will give you a sophisticated understanding of memory management for LLMs, which is important for chatbots having long conversations, and for complex agentic workflows. Please sign up here!

Andrew Ng

200,673 Aufrufe • vor 1 Jahr