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Introducing Codebase Knowledge Graphs in Cursor 🤩 In this video, I’ll walk you through how we went from using a knowledge graph of a repository on the CodeGPT platform to leveraging it directly within the Cursor editor This tool is specifically designed to navigate massive codebases, identify nodes and... show more

Daniel San

32,621 subscribers

230,185 просмотров • 1 год назад •via X (Twitter)

Образование Наука и технологии

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Комментарии: 3

Фото профиля Daniel San

Daniel San1 год назад

You can sign up for a free account at and start uploading your repository knowledge graphs right away. @codegptAI 🚀

Фото профиля Lab4crypto

Lab4crypto1 год назад

🚀 Don't gamble with your portfolio! Use our advanced hybrid quant risk tool using on/off-chain data daily and make informed decisions. 📈 Acess to 1000+ charts for your crypto journey. 📚Receive free weekly quant analysis. 📊+21 projects supported. 🏗️ Beginners and experts.

Фото профиля Mehmet Ali Peker.eth 🥷

Mehmet Ali Peker.eth 🥷1 год назад

It looks amazing! I'm using o1 pro mode a lot in the Pro plan. It works very well for small services with a repo/folder to prompt tool. However, for larger ones, I've found myself manually copying the code from files to the o1 pro mode or selecting related files. This extension could be very useful for such a prompting use case! Are you considering adding a "graph/context to prompt" option? Here's some examples related to this workflow: - - -

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We’re kicking off 2025 with everything we've got! 🤩 Introducing CodeGPT's Knowledge Graphs, navigating through the entire Anthropic SDK repository. In this example, we loaded Anthropic Python SDK repository and successfully provided the LLM with all the knowledge it needs to fully understand the codebase. You can leverage these knowledge graphs using the CodeGPT extension in VSCode by simply calling the Codebase command 👏 Stay tuned for more tutorials on how to use this powerful tool! Happy 2025! 🎉

We’re kicking off 2025 with everything we've got! 🤩 Introducing CodeGPT's Knowledge Graphs, navigating through the entire Anthropic SDK repository. In this example, we loaded Anthropic Python SDK repository and successfully provided the LLM with all the knowledge it needs to fully understand the codebase. You can leverage these knowledge graphs using the CodeGPT extension in VSCode by simply calling the Codebase command 👏 Stay tuned for more tutorials on how to use this powerful tool! Happy 2025! 🎉

Daniel San

35,680 просмотров • 1 год назад

You can now use Knowledge Graphs with Claude 3.5 Sonnet in Cursor! 🤩 CodeGPT’s Knowledge Graphs (KG) let you explore your entire codebase. In this case, we're navigating through Anthropic Python SDK. We asked how to send messages in batches and the tool identified the nodes and relationships in all relevant functions and classes. If you're curious about how LLMs will start handling large code repositories, you'll want to start programming CodeGPT’s KGs... I bet Andrej Karpathy will find this fascinating! I hope you get to try them out, because for me, reading documentation feels so 2024... now I just turn repos into KGs and get to work! 🧑🏽‍💻

You can now use Knowledge Graphs with Claude 3.5 Sonnet in Cursor! 🤩 CodeGPT’s Knowledge Graphs (KG) let you explore your entire codebase. In this case, we're navigating through Anthropic Python SDK. We asked how to send messages in batches and the tool identified the nodes and relationships in all relevant functions and classes. If you're curious about how LLMs will start handling large code repositories, you'll want to start programming CodeGPT’s KGs... I bet Andrej Karpathy will find this fascinating! I hope you get to try them out, because for me, reading documentation feels so 2024... now I just turn repos into KGs and get to work! 🧑🏽‍💻

Daniel San

42,005 просмотров • 1 год назад

The OpenAI Codex repo turned into a Knowledge Graph! 🤩 If you want to start working on massive codebases and serious projects, you need to use CodeGPT’s Knowledge Graphs. Here’s a step-by-step 🧵 guide to create your Codex graph and connect it to VSCode 👇

The OpenAI Codex repo turned into a Knowledge Graph! 🤩 If you want to start working on massive codebases and serious projects, you need to use CodeGPT’s Knowledge Graphs. Here’s a step-by-step 🧵 guide to create your Codex graph and connect it to VSCode 👇

Daniel San

179,466 просмотров • 1 год назад

I added a Knowledge Graph to Cursor using MCP. You gotta see this working! Knowledge graphs are a game-changer for AI Agents, and this is one example of how you can take advantage of them. How this works: 1. Cursor connects to Graphiti's MCP Server. Graphiti is a very popular open-source Knowledge Graph library for AI agents. 2. Graphiti connects to Neo4j running locally. Now, every time I interact with Cursor, the information is synthesized and stored in the knowledge graph. In short, Cursor now "remembers" everything about our project. Huge! Here is the video I recorded.

I added a Knowledge Graph to Cursor using MCP. You gotta see this working! Knowledge graphs are a game-changer for AI Agents, and this is one example of how you can take advantage of them. How this works: 1. Cursor connects to Graphiti's MCP Server. Graphiti is a very popular open-source Knowledge Graph library for AI agents. 2. Graphiti connects to Neo4j running locally. Now, every time I interact with Cursor, the information is synthesized and stored in the knowledge graph. In short, Cursor now "remembers" everything about our project. Huge! Here is the video I recorded.

Santiago

155,401 просмотров • 1 год назад

Our new short course, Knowledge Graphs for RAG, is now available! Knowledge graphs are a data structure that is great at capturing complex relationships between data of multiple types. By enabling more sophisticated retrieval of text than similarity search alone, knowledge graphs can improve the context you pass to the LLM and the performance of your RAG applications. In this course, taught by Andreas Kollegger of Neo4j, you’ll - Explore how knowledge graphs work by building a graph of public financial documents from scratch - Learn to write queries that retrieve text and data from the graph and use it to enhance the context you pass to an LLM chatbot - Combine a knowledge graph with a question-answer chain to build better RAG-powered chat systems Sign up here!

Our new short course, Knowledge Graphs for RAG, is now available! Knowledge graphs are a data structure that is great at capturing complex relationships between data of multiple types. By enabling more sophisticated retrieval of text than similarity search alone, knowledge graphs can improve the context you pass to the LLM and the performance of your RAG applications. In this course, taught by Andreas Kollegger of Neo4j, you’ll - Explore how knowledge graphs work by building a graph of public financial documents from scratch - Learn to write queries that retrieve text and data from the graph and use it to enhance the context you pass to an LLM chatbot - Combine a knowledge graph with a question-answer chain to build better RAG-powered chat systems Sign up here!

Andrew Ng

244,225 просмотров • 2 лет назад

Build better RAG by letting a team of agents extract and connect your reference materials into a knowledge graph. Our new short course, “Agentic Knowledge Graph Construction,” taught by Neo4j Innovation Lead Andreas Kollegger, shows you how. Knowledge graphs are an important way to store information accurately but they are a lot of work to build manually. In this course you’ll learn how to build a team of agents that turn data– in this case product reviews and invoices from suppliers–into structured graphs of entities and relationships for RAG. Learn how agents can automatically handle the time-consuming work of building graphs — extracting entities and relationships (e.g., Product "contains" Assembly, Part "supplied_by" Supplier, Customer review "mentions" Product), deduplicating them, fact-checking them, and committing them to a graph database — so your retrieval system can find right information to generate accurate output. For example, you can use agents to help trace customer complaints directly to specific suppliers, manufacturing processes, and product hierarchies, thus turning fragmented information into queryable business intelligence. Skills you’ll gain: - Build, store, and access knowledge graphs using the Neo4j graph database - Build multi-agent systems using Google’s Agent Development Kit (ADK) - Set up a loop of agentic workflows to propose and refine a graph schema through fact-checking - Connect agent-generated graphs of unstructured and structured data into a unified knowledge graph This course gets into the practicum of why knowledge graphs give more accurate information retrieval than vector search alone, especially for high-stakes applications where precision matters more than fuzzy similarity matching. Sign up here:

Build better RAG by letting a team of agents extract and connect your reference materials into a knowledge graph. Our new short course, “Agentic Knowledge Graph Construction,” taught by Neo4j Innovation Lead Andreas Kollegger, shows you how. Knowledge graphs are an important way to store information accurately but they are a lot of work to build manually. In this course you’ll learn how to build a team of agents that turn data– in this case product reviews and invoices from suppliers–into structured graphs of entities and relationships for RAG. Learn how agents can automatically handle the time-consuming work of building graphs — extracting entities and relationships (e.g., Product "contains" Assembly, Part "supplied_by" Supplier, Customer review "mentions" Product), deduplicating them, fact-checking them, and committing them to a graph database — so your retrieval system can find right information to generate accurate output. For example, you can use agents to help trace customer complaints directly to specific suppliers, manufacturing processes, and product hierarchies, thus turning fragmented information into queryable business intelligence. Skills you’ll gain: - Build, store, and access knowledge graphs using the Neo4j graph database - Build multi-agent systems using Google’s Agent Development Kit (ADK) - Set up a loop of agentic workflows to propose and refine a graph schema through fact-checking - Connect agent-generated graphs of unstructured and structured data into a unified knowledge graph This course gets into the practicum of why knowledge graphs give more accurate information retrieval than vector search alone, especially for high-stakes applications where precision matters more than fuzzy similarity matching. Sign up here:

Andrew Ng

167,710 просмотров • 9 месяцев назад

🔥 🎥 ➡️ o1 FULL is available in Cursor NOW!!! watch to see how you can use this workflow in Cursor: >>> o1 is the ARCHITECT: brainstorm with o1 and it creates a step-by-step plan for implementation and then... >>> >>> Sonnet 3.5 is the ENGINEER: takes the o1 generated plan and implements it FAST shame that there's no access to o1 BEAST mode via API yet, but it's very useful to have o1 in Cursor and be able to connect it to your full codebase PSA: the o1 Cursor requests are charged on a pay as you go basis, so don't go too crazy and lose your house

🔥 🎥 ➡️ o1 FULL is available in Cursor NOW!!! watch to see how you can use this workflow in Cursor: >>> o1 is the ARCHITECT: brainstorm with o1 and it creates a step-by-step plan for implementation and then... >>> >>> Sonnet 3.5 is the ENGINEER: takes the o1 generated plan and implements it FAST shame that there's no access to o1 BEAST mode via API yet, but it's very useful to have o1 in Cursor and be able to connect it to your full codebase PSA: the o1 Cursor requests are charged on a pay as you go basis, so don't go too crazy and lose your house

Dan Mac

38,294 просмотров • 1 год назад

Knowledge graphs for representing information are unbeatable. After this, you will never build a RAG system without knowledge graphs. It will take you five lines of code to build a knowledge graph with your data. I recorded a video to show you how you can do this. I used Cognee, an open-source library that outperforms any basic vector search approach in terms of retrieval relevance. They are collaborating with me on this post. Cognee is: • Easy to use • Reduces hallucinations • Open-source Here is a link to the repository: They also offer a comprehensive platform and UI with Python notebooks you can utilize to manage your data. Here is the link:

Knowledge graphs for representing information are unbeatable. After this, you will never build a RAG system without knowledge graphs. It will take you five lines of code to build a knowledge graph with your data. I recorded a video to show you how you can do this. I used Cognee, an open-source library that outperforms any basic vector search approach in terms of retrieval relevance. They are collaborating with me on this post. Cognee is: • Easy to use • Reduces hallucinations • Open-source Here is a link to the repository: They also offer a comprehensive platform and UI with Python notebooks you can utilize to manage your data. Here is the link:

Santiago

125,928 просмотров • 8 месяцев назад

Introducing DeepGraph - now live for any public GitHub repo... for FREE! Navigate codebases with complete understanding of dependencies, nodes, and relationships. Turn any repository into an interactive knowledge graph you can chat with.

Introducing DeepGraph - now live for any public GitHub repo... for FREE! Navigate codebases with complete understanding of dependencies, nodes, and relationships. Turn any repository into an interactive knowledge graph you can chat with.

Daniel San

43,807 просмотров • 11 месяцев назад

Say hello to smarter Rust coding with CodeGPT, powered by Gaia 🌱 This tool is built on Gaia’s fine-tuned Rust knowledge model, making Rust code generation and execution seamless and efficient. Integrate CodeGPT into your development setup on the Gaia Network. 🔗 Discover more:

Say hello to smarter Rust coding with CodeGPT, powered by Gaia 🌱 This tool is built on Gaia’s fine-tuned Rust knowledge model, making Rust code generation and execution seamless and efficient. Integrate CodeGPT into your development setup on the Gaia Network. 🔗 Discover more:

Gaia 🌱

28,117 просмотров • 1 год назад

How to create your Based Agent with 0 lines of code Recorded a quick demo video to show how easy it is and walk you through the entire process step-by-step All the links are in the comment below

How to create your Based Agent with 0 lines of code Recorded a quick demo video to show how easy it is and walk you through the entire process step-by-step All the links are in the comment below

lincoln.base.eth

148,805 просмотров • 1 год назад

Introducing Auto Documentation powered by Codebase Knowledge Graphs With CodeGPT, we traverse your entire repo to build a Knowledge Graph that understands how your software works. Capturing relationships between classes, modules, and dependencies. This allows us to generate robust, accurate documentation that misses no detail. We’re helping top companies turn months of documentation work into minutes. ✨ Always in sync with your selected branch. ✨ Updates automatically with every commit. ✨ Generates multiple types of documentation, from technical overviews to function-level docs Currently in preview for selected companies. 👉 Want early access? Apply here:

Introducing Auto Documentation powered by Codebase Knowledge Graphs With CodeGPT, we traverse your entire repo to build a Knowledge Graph that understands how your software works. Capturing relationships between classes, modules, and dependencies. This allows us to generate robust, accurate documentation that misses no detail. We’re helping top companies turn months of documentation work into minutes. ✨ Always in sync with your selected branch. ✨ Updates automatically with every commit. ✨ Generates multiple types of documentation, from technical overviews to function-level docs Currently in preview for selected companies. 👉 Want early access? Apply here:

Daniel San

53,329 просмотров • 1 год назад

CLAUDE + CURSOR + REPLIT = MAGIC (FULL VIDEO TUTORIAL) You can now write create complex web apps with just english (No copy - paste) using the BEST AI Code generation tool on the market - Cursor. The only problem with cursor for a coding beginner like me, is that its not that easy to deploy web apps. Well, Replit ⠕ makes deploying web apps and creating templates incredibly easy. In this video goes over how to sync Cursor and Replit. And then using our new template we made an AI version of Trello by Atlassian in like 6 or 7 cursor composer prompts. --------------------- In this video: 00:00 - Intro 04:08 - Syncing Cursor and Replit 07:40 - setting up Firebase 10:27 - Using Cursor and Replit to Create Trello Front End 19:41 - Setting up Backend API endpoint (we will make dedicated video on this soon w/template) 21:09 - Adding AI features to Trello!

CLAUDE + CURSOR + REPLIT = MAGIC (FULL VIDEO TUTORIAL) You can now write create complex web apps with just english (No copy - paste) using the BEST AI Code generation tool on the market - Cursor. The only problem with cursor for a coding beginner like me, is that its not that easy to deploy web apps. Well, Replit ⠕ makes deploying web apps and creating templates incredibly easy. In this video goes over how to sync Cursor and Replit. And then using our new template we made an AI version of Trello by Atlassian in like 6 or 7 cursor composer prompts. --------------------- In this video: 00:00 - Intro 04:08 - Syncing Cursor and Replit 07:40 - setting up Firebase 10:27 - Using Cursor and Replit to Create Trello Front End 19:41 - Setting up Backend API endpoint (we will make dedicated video on this soon w/template) 21:09 - Adding AI features to Trello!

Riley Brown

309,045 просмотров • 1 год назад

Introducing Deep Graph MCP for Claude Code 🤩 Claude Code by Anthropic is excellent and arguably one of the best tools for working with code today but... It has one major limitation, its native code search capabilities don't scale well to large repositories. By adding Deep Graph MCP, Claude's ability to explore large codebases improves dramatically. In this thread 🧵 I’ll show you how easy it is to connect this MCP to Claude Code.

Introducing Deep Graph MCP for Claude Code 🤩 Claude Code by Anthropic is excellent and arguably one of the best tools for working with code today but... It has one major limitation, its native code search capabilities don't scale well to large repositories. By adding Deep Graph MCP, Claude's ability to explore large codebases improves dramatically. In this thread 🧵 I’ll show you how easy it is to connect this MCP to Claude Code.

Daniel San

31,352 просмотров • 11 месяцев назад

Introducing Grok in VSCode as a Code Assistant 🤩 You can seamlessly integrate Grok through xAI API directly within your code editor, with CodeGPT I’ve been testing it out, and the code generation capabilities are truly impressive!

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Daniel San

20,291 просмотров • 1 год назад

Exploring Andrej Karpathy Nanochat through a Knowledge Graph Love how this project combines simplicity with power. Want to navigate and understand the entire repo? Check it out here: Plus, you can connect it via MCP to any AI coding assistant and work directly on the repository. Powered by Deep Graph MCP AI at Meta and Groq Inc

Exploring Andrej Karpathy Nanochat through a Knowledge Graph Love how this project combines simplicity with power. Want to navigate and understand the entire repo? Check it out here: Plus, you can connect it via MCP to any AI coding assistant and work directly on the repository. Powered by Deep Graph MCP AI at Meta and Groq Inc

Daniel San

126,557 просмотров • 7 месяцев назад

new tutorial: how to get the most out of Cursor, the AI-powered code editor i go over the 4 core features (autocomplete, chat, inline editing, and composer) and how to integrate each of them into your workflow

new tutorial: how to get the most out of Cursor, the AI-powered code editor i go over the 4 core features (autocomplete, chat, inline editing, and composer) and how to integrate each of them into your workflow

zack

133,879 просмотров • 1 год назад

Claude AI Agents using Model Context Protocol (MCP) 📚 Step-by-step Hands-on Tutorial (Create ANYTHING) 🔍 How to Integrate Web Search Brave 💻 How to integrate with GitHub 🎫 It automatically Creates Issues 🔄 It automatically Creates Pull Requests 📝 It automatically summaries search results ⚡ How to Configure? Anthropic Alex Albert

Claude AI Agents using Model Context Protocol (MCP) 📚 Step-by-step Hands-on Tutorial (Create ANYTHING) 🔍 How to Integrate Web Search Brave 💻 How to integrate with GitHub 🎫 It automatically Creates Issues 🔄 It automatically Creates Pull Requests 📝 It automatically summaries search results ⚡ How to Configure? Anthropic Alex Albert

Mervin Praison

63,658 просмотров • 1 год назад

curl This is our Meta-Prompt. It allows LLMs to understand our Reader, Embeddings, Reranker, and Classifier APIs for improved codegen. Using the meta-prompt is straightforward. Just copy the prompt into your preferred LLM interface like ChatGPT, Claude, or whatever works for you, add your instructions, and you're set. In this example, we copied the entire prompt into Anthropic Claude and asked it to grab every sentence from Hacker News front page and visualize them using UMAP with matplotlib. This task is nontrivial as it combines multiple APIs from our Search Foundation, like Reader and Embedding where Claude may not have knowledge of. So if you asked Claude directly, it probably wouldn't give an optimal answer. But with the meta-prompt, Claude now has good knowledge about our APIs and can generate much better code! We can copy paste the code directly to Google Colab and with minimum modification, the code just works!

curl This is our Meta-Prompt. It allows LLMs to understand our Reader, Embeddings, Reranker, and Classifier APIs for improved codegen. Using the meta-prompt is straightforward. Just copy the prompt into your preferred LLM interface like ChatGPT, Claude, or whatever works for you, add your instructions, and you're set. In this example, we copied the entire prompt into Anthropic Claude and asked it to grab every sentence from Hacker News front page and visualize them using UMAP with matplotlib. This task is nontrivial as it combines multiple APIs from our Search Foundation, like Reader and Embedding where Claude may not have knowledge of. So if you asked Claude directly, it probably wouldn't give an optimal answer. But with the meta-prompt, Claude now has good knowledge about our APIs and can generate much better code! We can copy paste the code directly to Google Colab and with minimum modification, the code just works!

Jina AI

209,001 просмотров • 1 год назад