正在加载视频...

视频加载失败

加载此视频时出现问题。这可能是由于临时网络问题，或视频可能不可用。

I recorded a video tutorial on building an AI Code Assistant. Going through the full stack - from data models to the front end. Using Postgres, pg_vector, LangChain, OpenAI, and NextJS.

Gwen (Chen) Shapira

28,260 subscribers

30,463 次观看 • 1 年前 •via X (Twitter)

Anya Rossi• Live Now

Private livecam show

5 条评论

Gwen (Chen) Shapira 的头像

Gwen (Chen) Shapira1 年前

The YouTube version has chapters, so you can skip to the bits that interest you the most:

Bruno Oliveira 的头像

Bruno Oliveira1 年前

Absolutely awesome and I’ll go through this for sure 😄😄🫡 real public service here, building 🫡🔥

Shaun Thomas 的头像

Shaun Thomas1 年前

Nice. How would you change any of this if using pg_vectorize?

Gwen (Chen) Shapira 的头像

Gwen (Chen) Shapira1 年前

I admit that I haven’t looked into it yet. Add to my todo list ✅

Alexander De Ridder 的头像

Alexander De Ridder1 年前

Cool project. Hands-on learning really helps cement those skills.

相关视频

I built an AI data visualization Agent. The AI Agent can automatically generate the code and visualize the data using code interpreter sandbox. 100% Opensource Code with step-by-step tutorial.

I built an AI data visualization Agent. The AI Agent can automatically generate the code and visualize the data using code interpreter sandbox. 100% Opensource Code with step-by-step tutorial.

Shubham Saboo

67,845 次观看 • 1 年前

I built an entire RAG application without writing any code. This is one of the easiest ways to start building AI applications. I used Langflow. It's open-source. It's a visual interface for building and deploying AI applications. I built a couple of workflows to show you how it works, and I'm impressed! Check the video I recorded. I built two workflows: 1. The first one loads data into a vector store database (Astra DB). 2. The second asks questions from that data using OpenAI's models. Langchain does the heavy lifting behind the scenes, but having a visual interface makes this 10 times easier. Here is Langflow's GitHub repository: In the video, I used their pre-release version. The development experience for building AI applications is improving tremendously! What a time to be a builder!

I built an entire RAG application without writing any code. This is one of the easiest ways to start building AI applications. I used Langflow. It's open-source. It's a visual interface for building and deploying AI applications. I built a couple of workflows to show you how it works, and I'm impressed! Check the video I recorded. I built two workflows: 1. The first one loads data into a vector store database (Astra DB). 2. The second asks questions from that data using OpenAI's models. Langchain does the heavy lifting behind the scenes, but having a visual interface makes this 10 times easier. Here is Langflow's GitHub repository: In the video, I used their pre-release version. The development experience for building AI applications is improving tremendously! What a time to be a builder!

Santiago

262,970 次观看 • 2 年前

I recorded a video on how you can build a complete RAG application without writing any code. I used Langflow, an open-source project that sits on top of Langchain. Langflow hides all the complexity and makes the process 10x easier. This is a step-by-step tutorial.

I recorded a video on how you can build a complete RAG application without writing any code. I used Langflow, an open-source project that sits on top of Langchain. Langflow hides all the complexity and makes the process 10x easier. This is a step-by-step tutorial.

Santiago

96,106 次观看 • 2 年前

From AI imagination to GPU acceleration - this new tutorial shows how to build a full Astro Zombie Horde from a single image to final render.🧟‍♂️ Using AI tools on the front end and the The Render Network on the back, the entire pipeline comes together in hours - not days. Watch the full breakdown:

From AI imagination to GPU acceleration - this new tutorial shows how to build a full Astro Zombie Horde from a single image to final render.🧟‍♂️ Using AI tools on the front end and the The Render Network on the back, the entire pipeline comes together in hours - not days. Watch the full breakdown:

The Render Network

11,399 次观看 • 7 个月前

yesterday, I started building an AI assistant for financial data analysis. It can analyze a CSV and provide insights. Next, I’m adding a feature that lets users ask follow-up questions and interact with the data. Built this with nextjs, v0 and AI SDK with Open AI's GPT-4o model

yesterday, I started building an AI assistant for financial data analysis. It can analyze a CSV and provide insights. Next, I’m adding a feature that lets users ask follow-up questions and interact with the data. Built this with nextjs, v0 and AI SDK with Open AI's GPT-4o model

cedric

14,683 次观看 • 1 年前

This "AI Software Engineer" is doing something different to everyone else: Databutton specializes in building full-stack applications using React in the front end and Python in the back end. That's it. I think this gives them a huge advantage: They can fine-tune their models on a very focused tech stack and make them better than anyone else. Instead of having to worry about every programming language and tech stack out there, they specialize and make a much better experience. I think this is smart. In the attached video, I started building a landing page to sell a course. You can see in real time how everything works.

This "AI Software Engineer" is doing something different to everyone else: Databutton specializes in building full-stack applications using React in the front end and Python in the back end. That's it. I think this gives them a huge advantage: They can fine-tune their models on a very focused tech stack and make them better than anyone else. Instead of having to worry about every programming language and tech stack out there, they specialize and make a much better experience. I think this is smart. In the attached video, I started building a landing page to sell a course. You can see in real time how everything works.

Santiago

49,379 次观看 • 1 年前

I recorded a gentle introduction to building RAG applications using open-source models. Prerequisite: You should be comfortable reading Python. I wanted to record a video to introduce as many people as possible to building RAG applications and using Large Language Models. Here, I tried to stay away from complex jargon and break everything down as simple as possible. Some of the topics I cover here: • Fundamental components of a RAG application • How to use Llama 3.1 running locally • How to use Lightning AI Studio's dev platform and GPUs • How to orchestrate everything using Langchain It's a long video with lots of detailed explanations. Here is a link to the video (what you see attached is just a trailer): You'll find a link to all of the code in the video description.

I recorded a gentle introduction to building RAG applications using open-source models. Prerequisite: You should be comfortable reading Python. I wanted to record a video to introduce as many people as possible to building RAG applications and using Large Language Models. Here, I tried to stay away from complex jargon and break everything down as simple as possible. Some of the topics I cover here: • Fundamental components of a RAG application • How to use Llama 3.1 running locally • How to use Lightning AI Studio's dev platform and GPUs • How to orchestrate everything using Langchain It's a long video with lots of detailed explanations. Here is a link to the video (what you see attached is just a trailer): You'll find a link to all of the code in the video description.

Santiago

69,223 次观看 • 1 年前

New to coding? New to Replit? Here's an end-to-end tutorial on building and deploying an app. ZERO code required.

New to coding? New to Replit? Here's an end-to-end tutorial on building and deploying an app. ZERO code required.

matt palmer

47,045 次观看 • 1 年前

I built an MVP of thanks to NextJS and Vercel My stack - Next.js (fullstack) - Neon - Serverless Postgres (cloud postgresql) - Bland (voice agent platform) - GPT-4 (AI) Tools + Libraries - VS Code - Continue running Anthropic Claude Opus - shadcn (react component library) - Screen Studio (demo video recording) Demo of PhoneScreenAI #buildinpublic #indiehackers

I built an MVP of thanks to NextJS and Vercel My stack - Next.js (fullstack) - Neon - Serverless Postgres (cloud postgresql) - Bland (voice agent platform) - GPT-4 (AI) Tools + Libraries - VS Code - Continue running Anthropic Claude Opus - shadcn (react component library) - Screen Studio (demo video recording) Demo of PhoneScreenAI #buildinpublic #indiehackers

Gillinghammer

100,825 次观看 • 2 年前

Code along with me as I show you how to build a full-stack Uber Clone with #ReactNative! 🔥 We tried Expo's latest API route features to integrate a Postgres db (Neon Postgres) with payments using Stripe, and it just works. It's very easy to use and get started without any issues so far. Evan Bacon 🥓 See the tutorial here! ➡️ 🚀

Code along with me as I show you how to build a full-stack Uber Clone with #ReactNative! 🔥 We tried Expo's latest API route features to integrate a Postgres db (Neon Postgres) with payments using Stripe, and it just works. It's very easy to use and get started without any issues so far. Evan Bacon 🥓 See the tutorial here! ➡️ 🚀

Adrian | JavaScript Mastery

14,514 次观看 • 1 年前

The tech stack we are building MystenLabs.sui is going to disrupt existing business models. Yes, it's a force multiplier on the same scale as AI.

The tech stack we are building MystenLabs.sui is going to disrupt existing business models. Yes, it's a force multiplier on the same scale as AI.

evan.sui

57,750 次观看 • 1 年前

I recorded a 54-min tutorial on prompting beautiful landing pages using code references

I recorded a 54-min tutorial on prompting beautiful landing pages using code references

Meng To

82,557 次观看 • 7 个月前

Building 3D worlds in a collaborative way assisted by an AI generating code for you! In this demo, I'm using Microsoft Teams AI and Azure #OpenAI to generate Babylon.js code in a meeting, while using #TeamsToolkit for the developer experience. Is it the future of 3D design?

Building 3D worlds in a collaborative way assisted by an AI generating code for you! In this demo, I'm using Microsoft Teams AI and Azure #OpenAI to generate Babylon.js code in a meeting, while using #TeamsToolkit for the developer experience. Is it the future of 3D design?

David Rousset

15,881 次观看 • 2 年前

Fully local Code Assistant running on NVIDIA GPU! In this tutorial, I'll show you how to run Llama3 using TensorRT and Nvidia's Triton Inference Server to use it as a Code Assistant in VSCode In this thread 🧵, I'll walk you through the integration process, explaining each step simply and clearly 👇 NVIDIA AI NVIDIA AIDev AI at Meta

Fully local Code Assistant running on NVIDIA GPU! In this tutorial, I'll show you how to run Llama3 using TensorRT and Nvidia's Triton Inference Server to use it as a Code Assistant in VSCode In this thread 🧵, I'll walk you through the integration process, explaining each step simply and clearly 👇 NVIDIA AI NVIDIA AIDev AI at Meta

Daniel San

42,152 次观看 • 2 年前

🚀 Excited to release AIBuildAI — an AI agent that automatically builds AI models 🏆 #1 on OpenAI MLE-Bench 💻 GitHub: Building AI models still requires a lot of manual work — designing models, writing code, training them, tuning hyperparameters, and iterating on results. We developed AIBuildAI to automate much of this process. The system runs an agent loop that automatically: • analyzes the task • designs models • writes code to implement them • trains the models • tunes hyperparameters • evaluates model performance • iteratively improves the models 🏆 On OpenAI’s MLE-Bench benchmark, AIBuildAI ranked #1:

🚀 Excited to release AIBuildAI — an AI agent that automatically builds AI models 🏆 #1 on OpenAI MLE-Bench 💻 GitHub: Building AI models still requires a lot of manual work — designing models, writing code, training them, tuning hyperparameters, and iterating on results. We developed AIBuildAI to automate much of this process. The system runs an agent loop that automatically: • analyzes the task • designs models • writes code to implement them • trains the models • tunes hyperparameters • evaluates model performance • iteratively improves the models 🏆 On OpenAI’s MLE-Bench benchmark, AIBuildAI ranked #1:

Pengtao Xie

24,927 次观看 • 3 个月前

Claude Code can now work with 50+ AI models. Zen MCP connects Claude Code with Gemini, OpenAI, Grok, and local models using Ollama for the best analysis on each task. 100% Opensource.

Claude Code can now work with 50+ AI models. Zen MCP connects Claude Code with Gemini, OpenAI, Grok, and local models using Ollama for the best analysis on each task. 100% Opensource.

Shubham Saboo

39,748 次观看 • 8 个月前