Among all the cool things #ChatGPT can do, it is super capable of handling and manipulating data in bulk, making numerous data wrangling, scraping, and lookup tasks obsolete.
Let me show you a few cool tricks, no coding skills are required!
(A thread) 👇🧵
Let's start easy by heading to chat.openai.com/chat and pasting a list of 60 countries in the text field
Let's ask #ChatGPT to give us the main language, latitude, longitude, and country code for each of these countries
That was easy enough, right?
Now let's add more data to our output by asking #ChatGPT to provide the population of each of these countries
Uber cool! 😎
Let's ask ChatGPT to wrap these results in a table
Let's conclude this thread by asking #ChatGPT to create a @streamlit app with a CSV uploader and filter boxes to filter `longitude`, `latitude`, and `country code`.
Not only does #ChatGPT displays the code, but it also provides clear explanations for each step! 👏
This is just a quick overview of what you can do with #ChatGPT.
I'm only scratching the surface here.
For more cool things you can do with it, check out my other thread
1. Follow me @DataChaz to read more content like this. 2. Share it with an RT, so others can read it too! 🙌
Note that while #AI is capable of handling tasks such as sourcing and sorting, as well as some aspects of app development, it is not yet advanced enough to replace the need for human verification.
Even with its impressive capabilities, AI still requires human oversight.
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MIT and Oxford released their $2,500 agentic AI curriculum on GitHub at no cost.
15,000 people already paid for it.
Now it's on GitHub!
It covers patterns, orchestration, memory, coordination, and deployment.
A strong roadmap to production ready systems.
Repo in 🧵 ↓
10 chapters:
Part 1. What agents are and how they differ from plain generative AI.
Part 2. The four agent types and when to use each.
Part 3. How tools work and how to build them.
Part 4. RAG vs agentic RAG and key patterns.
Part 5. What MCP is and why it matters.
Part 6. How agents plan with reasoning models.
Part 7. Memory systems and architecture choices.
Part 8. Multi agent coordination and scaling.
Part 9. Real world production case studies.
Part 10. Industry trends and what is coming next.
This prompt makes ChatGPT (or any LLM) sound way more natural.
Copy–paste it below ↓
Prompt:
"Act like a professional content writer and communication strategist. Your task is to write with a natural, human-like tone that avoids the usual pitfalls of AI-generated content.
The goal is to produce clear, simple, and authentic writing that resonates with real people. Your responses should feel like they were written by a thoughtful and concise human writer.
You are writing the following:
[INSERT YOUR TOPIC OR REQUEST HERE]
Follow these detailed step-by-step guidelines:
Step 1: Use plain and simple language. Avoid long or complex sentences. Opt for short, clear statements.
- Example: Instead of "We should leverage this opportunity," write "Let's use this chance."
Step 2: Avoid AI giveaway phrases and generic clichés such as "let's dive in," "game-changing," or "unleash potential." Replace them with straightforward language.
- Example: Replace "Let's dive into this amazing tool" with "Here’s how it works."
Step 3: Be direct and concise. Eliminate filler words and unnecessary phrases. Focus on getting to the point.
- Example: Say "We should meet tomorrow," instead of "I think it would be best if we could possibly try to meet."
Step 4: Maintain a natural tone. Write like you speak. It’s okay to start sentences with “and” or “but.” Make it feel conversational, not robotic.
- Example: “And that’s why it matters.”
Step 5: Avoid marketing buzzwords, hype, and overpromises. Use neutral, honest descriptions.
- Avoid: "This revolutionary app will change your life."
- Use instead: "This app can help you stay organized."
Step 6: Keep it real. Be honest. Don’t try to fake friendliness or exaggerate.
- Example: “I don’t think that’s the best idea.”
Step 7: Simplify grammar. Don’t worry about perfect grammar if it disrupts natural flow. Casual expressions are okay.
- Example: “i guess we can try that.”
Step 8: Remove fluff. Avoid using unnecessary adjectives or adverbs. Stick to the facts or your core message.
- Example: Say “We finished the task,” not “We quickly and efficiently completed the important task.”
Step 9: Focus on clarity. Your message should be easy to read and understand without ambiguity.
- Example: “Please send the file by Monday.”
Follow this structure rigorously. Your final writing should feel honest, grounded, and like it was written by a clear-thinking, real person.
Take a deep breath and work on this step-by-step."
Crafted by the great @rubenhssd (as seen on LinkedIn)
Google's MCP Toolbox for Databases is now open source 🔥
A single backend for connections, security, and schema-aware SQL tools, built for AI agents.
Compatible with Python, JS, Go, LangChain, and more.
Repo in 🧵 ↓
1/
Features include:
→ Declarative tool definitions (<10 LOC)
→ Secure by default with integrated authentication
→ Auth, pooling & fast queries out of the box
→ Native observability (OpenTelemetry)
→ Postgres, MySQL, Cloud SQL & more supported
Build & manage data pipelines with AI, using plain language!
→ Ingests, transforms & validates data from Cloud Storage
→ Generates SQLx, enforces quality, and updates pipelines
→ Scales across use cases via CLI, UI, or API
Postman's AI Agent Builder lets you turn any API (from over 100,000!) into an MCP server in seconds, no code required 🤯
Your custom MCP server, ready to use in Cursor, Windsurf, Claude Desktop, Docker, plus a lot more! 🧵↓
1/
First, start here →
You’ve got literally 100,000+ APIs to check out.
1. mix and match any endpoints you want 2. download your custom zip file 3. that’s it! postman.com/explore/mcp-ge…
Mind = blown.
That zip file has EVERYTHING:
↳ a readme with setup instructions
↳ your selected endpoints
↳ all the files to run your MCP server locally, on Cursor, Windsurf… even Docker!
You also get an .env file with your prefilled variables → just add your API keys! 🔥