Charly Wargnier Profile picture
Dec 6, 2022 13 tweets 7 min read Read on X
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 Image
Let's ask #ChatGPT to give us the main language, latitude, longitude, and country code for each of these countries Image
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 Image
Uber cool! 😎

Let's ask ChatGPT to wrap these results in a table Image
Magic! ✨

Now we'll add an index to that table Image
Boom! 💥

No #Python, PowerBI or code was needed!

Now let's try something a bit harder by asking #ChatGPT to add crime rates and COVID death tolls for the year 2020 Image
No sweat!

... and it's not even limited to tabular data!

Let's ask #ChatGPT to convert our table to a JSON file Image
Pretty impressive, right?

Now say you want to store that data in a database (e.g. the excellent @detahq), but you're not sure how to do it.

You bet! We can ask #ChatGPT how to do that Image
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! 👏 Image
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

.
If you found this helpful, two requests:

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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More from @DataChaz

Sep 16
A must-bookmark for vibe-coders.

@YCombinator’s guide to making the most of vibe coding: Image
Image
Based on @benln’s excellent video here:
PAGE 01

Planning process

— Create a comprehensive plan: Start by working with the AI to write a detailed implementation plan in a markdown file
— Review and refine: Delete unnecessary items, mark features as won’t do if too complex
— Maintain scope control: Keep a separate section for ideas for later to stay focused
— Implement incrementally: Work section by section rather than attempting to build everything at once
— Track progress: Have the AI mark sections as complete after successful implementation
— Commit regularly: Ensure each working section is committed to Git before moving to the next

Version control strategies

— Use Git religiously: Don’t rely solely on the AI tools’ revert functionality
— Start clean: Begin each new feature with a clean Git slate
— Reset when stuck: Use git reset –hard HEAD if the AI goes on a vision quest
— Avoid cumulative problems: Multiple failed attempts create layers and layers of bad code
— Clean implementation: When you finally find a solution, reset and implement it cleanly

Testing framework

— Prioritize high-level tests: Focus on end-to-end integration tests over unit tests
— Simulate user behavior: Test features by simulating someone clicking through the site/app
— Catch regressions: LLMs often make unnecessary changes to unrelated logic
— Test before proceeding: Ensure tests pass before moving to the next feature
— Use tests as guardrails: Some founders recommend starting with test cases to provide clear boundaries

Effective bug fixing

— Leverage error messages: Simply copy-pasting error messages is often enough for the AI
— Analyze before coding: Ask the AI to consider multiple possible causes
— Reset after failures: Start with a clean slate after each unsuccessful fix attempt
— Implement logging: Add strategic logging to better understand what’s happening
— Switch models: Try different AI models when one gets stuck
— Clean implementation: Once you identify the fix, reset and implement it on a clean codebaseImage
Read 5 tweets
Sep 9
AI guides are flooding the scene, but these top 9 picks from OpenAI, Google, and Anthropic are the ones you can't miss 🧵 ↓ Image
1/ 601 GenAI Use Cases – by @Google

The enterprise AI playbook keeps growing!

There are over 600 use cases inside this gigantic guide from Google! 🔥

cloud.google.com/transform/101-…

cloud.google.com/transform/101-…
2/ Agents Companion – by @Kaggle

Here's a great playbook filled with tools and reference material for agent builders.

kaggle.com/whitepaper-age…Image
Read 11 tweets
Sep 7
Wild.

Nate just hooked up @n8n_io with GPT-5 and Nano-Banana 🍌 to create a Photoshop AI agent! 🤯

It:
↳ finds and pulls media files
↳ stitches multiple images together
↳ edits photos on the fly
↳ costs just $0.04 per render

17 min tutorial and Free template in 🧵 ↓
Check out the full video here:
youtu.be/7UNsK9LoORo?si…

youtu.be/7UNsK9LoORo?si…
Join Nate's AIS group (it's free) to grab this n8n template absolutely free.

Plus, you'll also get tons of other awesome n8n automation scripts!

skool.com/ai-automation-…Image
Read 5 tweets
Aug 28
This is wild.

A real-time webcam demo using SmolVLM from @huggingface and llama.cpp! 🤯

Running fully local on a MacBook M3.
more about Llama.cpp here:
github.com/ggml-org/llama…Image
Big kudos to @ngxson for this demo, it’s just brilliant!

try it yourself:
github.com/ngxson/smolvlm…Image
Read 5 tweets
Aug 20
This is huge.

DeepMind just released URL Context.

You can extract content from any webpage, PDF, or image just by pasting a URL.

It pulls live data from up to 20 links per request! 🤯

No setup needed, just pass the links in your prompt.

4 killer use cases + code below 🧵↓ Image
2/

Here are a few powerful use cases:

– Compare reports, PDFs, or articles
– Extract data (names, prices, highlights…)
– Analyze codebases, GitHub repos, or docs
– Summarize multiple sources in one go
3/

You only pay for the tokens used, there's no added cost.

Full code samples (Python, JS and REST) are here:
ai.google.dev/gemini-api/doc…Image
Read 4 tweets
Jul 15
This ChatGPT prompt is like hiring a $500/hr consultant Image
PROMPT:

You are Lyra, a master-level AI prompt optimization specialist. Your mission: transform any user input into precision-crafted prompts that unlock AI's full potential across all platforms.

## THE 4-D METHODOLOGY

### 1. DECONSTRUCT
- Extract core intent, key entities, and context
- Identify output requirements and constraints
- Map what's provided vs. what's missing

### 2. DIAGNOSE
- Audit for clarity gaps and ambiguity
- Check specificity and completeness
- Assess structure and complexity needs

### 3. DEVELOP
- Select optimal techniques based on request type:
- **Creative** → Multi-perspective + tone emphasis
- **Technical** → Constraint-based + precision focus
- **Educational** → Few-shot examples + clear structure
- **Complex** → Chain-of-thought + systematic frameworks
- Assign appropriate AI role/expertise
- Enhance context and implement logical structure

### 4. DELIVER
- Construct optimized prompt
- Format based on complexity
- Provide implementation guidance

## OPTIMIZATION TECHNIQUES

**Foundation:** Role assignment, context layering, output specs, task decomposition

**Advanced:** Chain-of-thought, few-shot learning, multi-perspective analysis, constraint optimization

**Platform Notes:**
- **ChatGPT/GPT-4:** Structured sections, conversation starters
- **Claude:** Longer context, reasoning frameworks
- **Gemini:** Creative tasks, comparative analysis
- **Others:** Apply universal best practices

## OPERATING MODES

**DETAIL MODE:**
- Gather context with smart defaults
- Ask 2-3 targeted clarifying questions
- Provide comprehensive optimization

**BASIC MODE:**
- Quick fix primary issues
- Apply core techniques only
- Deliver ready-to-use prompt

## RESPONSE FORMATS

**Simple Requests:**
```
**Your Optimized Prompt:**
[Improved prompt]

**What Changed:** [Key improvements]
```

**Complex Requests:**
```
**Your Optimized Prompt:**
[Improved prompt]

**Key Improvements:**
• [Primary changes and benefits]

**Techniques Applied:** [Brief mention]

**Pro Tip:** [Usage guidance]
```

## WELCOME MESSAGE (REQUIRED)

When activated, display EXACTLY:

"Hello! I'm Lyra, your AI prompt optimizer. I transform vague requests into precise, effective prompts that deliver better results.

**What I need to know:**
- **Target AI:** ChatGPT, Claude, Gemini, or Other
- **Prompt Style:** DETAIL (I'll ask clarifying questions first) or BASIC (quick optimization)

**Examples:**
- "DETAIL using ChatGPT — Write me a marketing email"
- "BASIC using Claude — Help with my resume"

Just share your rough prompt and I'll handle the optimization!"

## PROCESSING FLOW

1. Auto-detect complexity:
- Simple tasks → BASIC mode
- Complex/professional → DETAIL mode
2. Inform user with override option
3. Execute chosen mode protocol
4. Deliver optimized prompt

**Memory Note:** Do not save any information from optimization sessions to memory.
Read 5 tweets

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