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40+ live apps, open data APIs, MCP servers, and 200+ guides - more than anyone wants to click through. Point your AI here and it reads the whole map and does the work: finds the tool, pulls the data, runs the analysis, and hands you the links.

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Prefer to explore on your own? Go right ahead.

Paste this to Claude Code, Codex, or any AI agent:
Go to tigzig.com and read tigzig.com/llms.txt. It is a practitioner toolkit - 40+ analytics apps, open no-auth data APIs, MCP servers, open-source repos (github.com/amararun), and 200+ build guides. Help me [your task]. Surface the exact links; where there is an API or MCP, call it directly; and if I want to self-host, find the repo and help me deploy it.

Analyze Live Data | AWS-Azure DW | via Custom GPT & LLM Apps

Published: July 27, 2024

Query. Transform. Analyze. Chart. File Ops. Build ML Models.

All in the Natural Language of your choice.

From within Custom GPT (ChatGPT Plus) as well as via externally deployed LLM apps on your intranet or public website.

Background

Earlier this year, I published a video demonstrating how to build a machine learning model with ChatGPT Plus using natural language. That required an offline data upload.

LinkedIn Post here: Build ML Model with ChatGPT

What if we could build ML models and perform analyses by directly connecting to live data warehouses in AWS and Azure?

And not just the final analysis and model building, but also data transformations, modeling dataset creation, table level operations, record insertions, modifications, charts, and cross tabs. Pretty much anything you can do with Python/SQL, but with a simple UI and natural language.

I had to do something similar for a client recently.

This Series

In this series, I'll show you how to do just that. I'll be working with a prototype data warehouse I set up in AWS (RDS-MySQL) and Azure (MySQL), with tables ranging from just a few records to millions (the largest table has 10 Million records).

This is the kick-off video and a light-hearted introduction to connecting and working with AWS and Azure data warehouses via Custom GPT.

Hope you have as much fun watching this video as I had making it.

Edit: Video available at my old blog

Upcoming Episodes

GPT-LLM Capability Demonstration Videos

How-To Guides

With Codes / Schemas / Github Repos

With special focus on how to use GPTs to get all this done quickly and efficiently: