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。

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

3,606 subscribers

14,683 次观看 • 1 年前 •via X (Twitter)

财经科学技术

Anya Rossi• Live Now

Private livecam show

11 条评论

cedric 的头像

cedric1 年前

designed for finance teams, analysts, and operations professionals who want quick, accurate insights without the extra hassle. I'm still building and would release an mvp for this soon, what features will make this more useful, let me know what you think?

PDF GPT 的头像

PDF GPT1 年前

This is my favorite AI tool for reviewing reports. Just upload a report, ask for a summary, and get one in seconds. It's like ChatGPT, but built for documents. Try it for free.

‏ً 的头像

‏ً1 年前

@v0 @aisdk 🙌🏿

em. 的头像

em.1 年前

@v0 @aisdk Cee bezos

cedric 的头像

cedric1 年前

@v0 @aisdk 🤣🤣

♱ 的头像

♱1 年前

@v0 @aisdk hard

mike 的头像

mike1 年前

@v0 @aisdk you dey cook waaa

cedric 的头像

cedric1 年前

@v0 @aisdk still cooking

s^muel 的头像

s^muel1 年前

@v0 @aisdk congrats bro!

Eugene🎍 的头像

Eugene🎍1 年前

@v0 @aisdk 🔥🔥🔥

Nana 的头像

Nana1 年前

@v0 @aisdk I don’t understand but it sounds impressive 🤭

相关视频

I built an AI Data Analysis Agent. Just upload CSV/Excel files and analyze data through natural language. It uses DuckDB for lightning-fast processing. 100% Opensource Code with step-by-step tutorial.

I built an AI Data Analysis Agent. Just upload CSV/Excel files and analyze data through natural language. It uses DuckDB for lightning-fast processing. 100% Opensource Code with step-by-step tutorial.

Shubham Saboo

88,517 次观看 • 1 年前

This guy built an entire AI data science team in Python. Then open-sourced (100% free). It automates data science workflows with AI, including data loading, cleaning, exploratory analysis, and feature engineering. And it tracks each step in a 100% reproducible pipeline. 00:00 Project Overview 01:32 Diving into the AI Data Science Workflow and Data Loading 02:10 Data Wrangling and Cleaning 03:33 Data Visualization Insights & Plotting 04:08 Feature Engineering 05:00 Live 1-Hour Workshop 05:44 AI Data Science Team Python Library 🔗 AI Data Science Team On GitHub (Give it a Star) 🔗 Join My Next Live 1-Hour Agentic AI Workshop (Free):

This guy built an entire AI data science team in Python. Then open-sourced (100% free). It automates data science workflows with AI, including data loading, cleaning, exploratory analysis, and feature engineering. And it tracks each step in a 100% reproducible pipeline. 00:00 Project Overview 01:32 Diving into the AI Data Science Workflow and Data Loading 02:10 Data Wrangling and Cleaning 03:33 Data Visualization Insights & Plotting 04:08 Feature Engineering 05:00 Live 1-Hour Workshop 05:44 AI Data Science Team Python Library 🔗 AI Data Science Team On GitHub (Give it a Star) 🔗 Join My Next Live 1-Hour Agentic AI Workshop (Free):

Matt Dancho (Business Science)

85,279 次观看 • 6 个月前

Most businesses heavily rely on Excel to store and work with their data. They can now run AI agents on SwarmNode that can analyze, spot patterns and deliver insights on CSV files.

Most businesses heavily rely on Excel to store and work with their data. They can now run AI agents on SwarmNode that can analyze, spot patterns and deliver insights on CSV files.

SwarmNode.ai

15,038 次观看 • 1 年前

v0 lets you build AI apps that just work. Hear from our product manager Ary about how to use Vercel's AI SDK and AI gateway to easily spin up AI apps with v0. Start building today with $5 in free credits for AI requests each month.

v0 lets you build AI apps that just work. Hear from our product manager Ary about how to use Vercel's AI SDK and AI gateway to easily spin up AI apps with v0. Start building today with $5 in free credits for AI requests each month.

v0

15,128 次观看 • 8 个月前

Introducing DeepSeek based AI Agents! We built an Executive AI Assistant that can, - Listen to incoming emails - Fetch all your unread emails and summarize the important ones - Draft and Send reply emails Connect with Gmail, Calendar, Slack and more with Composio Frontend designed with Vercel v0 Use Deepseek and other LLMs with Groq Inc with AI SDK code:

Introducing DeepSeek based AI Agents! We built an Executive AI Assistant that can, - Listen to incoming emails - Fetch all your unread emails and summarize the important ones - Draft and Send reply emails Connect with Gmail, Calendar, Slack and more with Composio Frontend designed with Vercel v0 Use Deepseek and other LLMs with Groq Inc with AI SDK code:

Karan Vaidya

79,976 次观看 • 1 年前

Data teams spend weeks on simple requests. (This AI answers them in minutes.) Most data analysis is repetitive manual tasks. Data teams spend more time on setup than actual analysis. The workflow usually looks like this: → Run some exploratory data analysis in a local Jupyter notebook or environment → Pull data from multiple disconnected sources → Write code from scratch for every analysis → Export static charts that stakeholders can't explore (or wrestle with legacy BI to create a dashboard) → Manually send updates via email or Slack when data changes → Start over for each new request Most teams accept this as "how data analysis works." While business decisions wait for insights. That's where Fabi changes the entire approach. It's a powerful, AI-native platform built for teams that want to boost productivity and supercharge their data workflows. Instead of working on separate tools and manual processes, you collaborate on analysis that automatically delivers insights where teams work. Here's what makes Fabi different: AI-Native Analysis Environment ↳ SQL and Python work together with AI assistance that handles coding and debugging automatically. Smart Automation Workflows ↳ Automatically send AI-powered reports and summaries right where business works in Slack, email, and spreadsheets. Universal Data Integration ↳ Analyze data from files, Google Sheets, Airtable, plus your data warehouse and databases in one place. Collaborative Data Apps ↳ Create interactive dashboards that stakeholders can explore and ask follow-up questions directly. What you can do with Fabi that legacy BI can't: ➟ Send AI-generated insights directly to Slack channels ➟ Automatically email data summaries to stakeholders ➟ Analyze uploaded files without complex ETL processes ➟ Collaborate on analysis like Google Docs for data ➟ Build workflows that push insights to spreadsheets Perfect for teams that want to move beyond the constraints of legacy and increase their impact. Teams using Fabi see immediate results: ✓ Insights delivered in minutes instead of days ✓ Reduced context switching between tools ✓ Stakeholders explore data independently ✓ Workflows automated to save hours of manual work From analysis to automated delivery - all in one AI-native environment. 📌 Try Fabi today: 👉 Follow Fabi.ai and marc for Fabi updates. 🔄 Repost to help other teams streamline data analysis #DataAnalysis #ModernBI #DataOps #InteractiveDashboards #FabiPartnership #SponsoredByFabi

Data teams spend weeks on simple requests. (This AI answers them in minutes.) Most data analysis is repetitive manual tasks. Data teams spend more time on setup than actual analysis. The workflow usually looks like this: → Run some exploratory data analysis in a local Jupyter notebook or environment → Pull data from multiple disconnected sources → Write code from scratch for every analysis → Export static charts that stakeholders can't explore (or wrestle with legacy BI to create a dashboard) → Manually send updates via email or Slack when data changes → Start over for each new request Most teams accept this as "how data analysis works." While business decisions wait for insights. That's where Fabi changes the entire approach. It's a powerful, AI-native platform built for teams that want to boost productivity and supercharge their data workflows. Instead of working on separate tools and manual processes, you collaborate on analysis that automatically delivers insights where teams work. Here's what makes Fabi different: AI-Native Analysis Environment ↳ SQL and Python work together with AI assistance that handles coding and debugging automatically. Smart Automation Workflows ↳ Automatically send AI-powered reports and summaries right where business works in Slack, email, and spreadsheets. Universal Data Integration ↳ Analyze data from files, Google Sheets, Airtable, plus your data warehouse and databases in one place. Collaborative Data Apps ↳ Create interactive dashboards that stakeholders can explore and ask follow-up questions directly. What you can do with Fabi that legacy BI can't: ➟ Send AI-generated insights directly to Slack channels ➟ Automatically email data summaries to stakeholders ➟ Analyze uploaded files without complex ETL processes ➟ Collaborate on analysis like Google Docs for data ➟ Build workflows that push insights to spreadsheets Perfect for teams that want to move beyond the constraints of legacy and increase their impact. Teams using Fabi see immediate results: ✓ Insights delivered in minutes instead of days ✓ Reduced context switching between tools ✓ Stakeholders explore data independently ✓ Workflows automated to save hours of manual work From analysis to automated delivery - all in one AI-native environment. 📌 Try Fabi today: 👉 Follow Fabi.ai and marc for Fabi updates. 🔄 Repost to help other teams streamline data analysis #DataAnalysis #ModernBI #DataOps #InteractiveDashboards #FabiPartnership #SponsoredByFabi

Andrew Bolis

36,504 次观看 • 9 个月前

We built Open Gamma An Open Source Presentation Generator with Composio Tool Router and AI SDK - Generate Presentations in Google Slides instantly - Can research on the topic with web search - Can connect and fetch data from multiple data sources like Hubspot, Slack, Notion and more Completely free and open source link to the code:

We built Open Gamma An Open Source Presentation Generator with Composio Tool Router and AI SDK - Generate Presentations in Google Slides instantly - Can research on the topic with web search - Can connect and fetch data from multiple data sources like Hubspot, Slack, Notion and more Completely free and open source link to the code:

Karan Vaidya

40,455 次观看 • 6 个月前

I love the concept of having AI interact with the web real-time That’s why I built You can search the web with English prompting or interact with the browser in-app Built with Vercel AI SDK, Next.js Route Handlers, and browserbase 🅱️ API Check it out! It’s open-source :)

I love the concept of having AI interact with the web real-time That’s why I built You can search the web with English prompting or interact with the browser in-app Built with Vercel AI SDK, Next.js Route Handlers, and browserbase 🅱️ API Check it out! It’s open-source :)

AP

33,224 次观看 • 1 年前

Introducing our second Anthropic Quickstart! Meet your AI Financial Analyst - generating beautiful charts instantly from your data. 📊 You can upload documents or images for analysis. Built with Claude, NextJS, and ShadcnUI. Full repo available on GitHub. ⚡️

Introducing our second Anthropic Quickstart! Meet your AI Financial Analyst - generating beautiful charts instantly from your data. 📊 You can upload documents or images for analysis. Built with Claude, NextJS, and ShadcnUI. Full repo available on GitHub. ⚡️

Pietro Schirano

124,125 次观看 • 1 年前

i made a tutorial for my 3D data visualization project it's how I imagine Tony Stark and Jarvis doing data analysis :] built with Three.js, mediapipe AI vision, and web speech API thread below with the code, tutorial, and live demo ↴

i made a tutorial for my 3D data visualization project it's how I imagine Tony Stark and Jarvis doing data analysis :] built with Three.js, mediapipe AI vision, and web speech API thread below with the code, tutorial, and live demo ↴

AA

84,065 次观看 • 11 个月前

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.

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

30,463 次观看 • 1 年前

Introducing - your AI powered Data Scientist - connect a data source (Google Sheets, Excel, Postgres Database) - analyze and transform data - generate insights - create visualizations follow for more updates

Introducing - your AI powered Data Scientist - connect a data source (Google Sheets, Excel, Postgres Database) - analyze and transform data - generate insights - create visualizations follow for more updates

rahul

324,422 次观看 • 2 年前

🤖 OpenZeppelin Wizard now has an AI Assistant built-in. -Uses GPT-4 Turbo and function calling to make updates automatically. -All updates to Wizard generate tested code. -Ask it questions and it will reply with answers. -Built completely open source, including the prompts.

🤖 OpenZeppelin Wizard now has an AI Assistant built-in. -Uses GPT-4 Turbo and function calling to make updates automatically. -All updates to Wizard generate tested code. -Ask it questions and it will reply with answers. -Built completely open source, including the prompts.

OpenZeppelin

11,752 次观看 • 2 年前

I released the first alpha of Datasette Agent - a conversational AI assistant for Datasette that can answer questions about data in SQLite databases, and can be extended with plugins to add extra tools and features Here's a demo

I released the first alpha of Datasette Agent - a conversational AI assistant for Datasette that can answer questions about data in SQLite databases, and can be extended with plugins to add extra tools and features Here's a demo

Simon Willison

22,054 次观看 • 1 个月前

Spreadsheets haven't changed much in decades. Until now. Here is an AI Agent that can take your data and run a complete data analysis on it. It can generate charts, summaries, and reports. You only need to know what questions to ask. Check out this video. I created it.

Spreadsheets haven't changed much in decades. Until now. Here is an AI Agent that can take your data and run a complete data analysis on it. It can generate charts, summaries, and reports. You only need to know what questions to ask. Check out this video. I created it.

Santiago

57,300 次观看 • 1 年前

I built an AI startup insight tool Agent using Firecrawl FIRE-1 and GPT-4o. FIRE-1 AI Agent navigates websites, clicks buttons, and extracts company data autonomously without writing a single line of custom code. 100% Opensource Code with step-by-step tutorial.

I built an AI startup insight tool Agent using Firecrawl FIRE-1 and GPT-4o. FIRE-1 AI Agent navigates websites, clicks buttons, and extracts company data autonomously without writing a single line of custom code. 100% Opensource Code with step-by-step tutorial.

Shubham Saboo

23,184 次观看 • 1 年前

Today we're launching Amplitude MCP MCP was made for data analytics. Ask a vague question and have an AI model iteratively query Amplitude to find you insights. Amplitude MCP exposes all of Amplitude's functionality so an agent can interact with it directly. It allows you to use Amplitude without having to know anything about Amplitude's user interface or your data taxonomy. It uses the intelligence of off the shelf AI agents, including Claude, Gemini, and Cursor, to query Amplitude data you already have to find answers. It leverages all of your existing Amplitude data and how it's used to navigate through large complex behavioral datasets. You can have conversations about your product data directly inside any AI model with MCP. It can create Amplitude dashboards and notebooks with what it finds and even make recommendations. This is the best MCP use case I've ever seen. Watching an AI agent think, reason, query Amplitude, and then repeat that process iteratively is magical. See it in action:

Today we're launching Amplitude MCP MCP was made for data analytics. Ask a vague question and have an AI model iteratively query Amplitude to find you insights. Amplitude MCP exposes all of Amplitude's functionality so an agent can interact with it directly. It allows you to use Amplitude without having to know anything about Amplitude's user interface or your data taxonomy. It uses the intelligence of off the shelf AI agents, including Claude, Gemini, and Cursor, to query Amplitude data you already have to find answers. It leverages all of your existing Amplitude data and how it's used to navigate through large complex behavioral datasets. You can have conversations about your product data directly inside any AI model with MCP. It can create Amplitude dashboards and notebooks with what it finds and even make recommendations. This is the best MCP use case I've ever seen. Watching an AI agent think, reason, query Amplitude, and then repeat that process iteratively is magical. See it in action:

Spenser Skates

32,306 次观看 • 8 个月前

I built a Deep Research AI Agent team using OpenAI Agents SDK and Firecrawl. It combines multiple AI agents that can autonomously search the web, extract content, and generate detailed reports with enhanced analysis. 100% Opensource Code with step-by-step tutorials.

I built a Deep Research AI Agent team using OpenAI Agents SDK and Firecrawl. It combines multiple AI agents that can autonomously search the web, extract content, and generate detailed reports with enhanced analysis. 100% Opensource Code with step-by-step tutorials.

Shubham Saboo

50,831 次观看 • 1 年前

Check this!! Microsoft open-sourced a no-code data analysis tool. It's called Data Formulator and it provides AI-powered data analysis and an drag-and-drop UI for viz tasks. It also works beyond the initial dataset by creating relevant fields and the corresponding viz.

Check this!! Microsoft open-sourced a no-code data analysis tool. It's called Data Formulator and it provides AI-powered data analysis and an drag-and-drop UI for viz tasks. It also works beyond the initial dataset by creating relevant fields and the corresponding viz.

Avi Chawla

260,087 次观看 • 1 年前

With you can create an unlimited number of databases using AI! You can create dummy data or import data from CSV files. You can now deploy the database you built on to Supabase with a click of a button!

With you can create an unlimited number of databases using AI! You can create dummy data or import data from CSV files. You can now deploy the database you built on to Supabase with a click of a button!

Tyler Shukert

94,941 次观看 • 1 年前