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

Apr 3
🚨 Karpathy’s new set-up is the ultimate self-improving second brain, and it takes zero manual editing 🤯

It acts as a living AI knowledge base that actually heals itself.

Let me break it down.

Instead of relying on complex RAG, the LLM pulls raw research directly into an @Obsidian Markdown wiki. It completely takes over:

✦ Index creation
✦ System linting
✦ Native Q&A routing

The core process is beautifully simple:

→ You dump raw sources into a folder
→ The LLM auto-compiles an indexed .md wiki
→ You ask complex questions
→ It generates outputs (Marp slides, matplotlib plots) and files them back in

The big-picture implication of this is just wild.

When agents maintain their own memory layer, they don’t need massive, expensive context limits.

They really just need two things:

→ Clean file organization
→ The ability to query their own indexes

Forget stuffing everything into one giant prompt.

This approach is way cheaper, highly scalable... and 100% inspectable!Image
Wow. Insanely fast turnaround from @himanshustwts!

A full breakdown of @karpathy’s self-improving wiki framework,

walking through every stage from ingestion to what comes next 👀 Image
@himanshustwts @karpathy Omar took a v. similar approach with @Obsidian

You can check it out here:

Read 5 tweets
Mar 19
With Voicebox, @ElevenLabs just lost its moat.

→ Powered by Alibaba's Qwen3-TTS for near-perfect cloning
→ Ships with a DAW-like "Stories Editor"
→ No cloud, runs locally on your machine

100% Open Source. 100% Local.

Link to repo in 🧵↓
It features a full-blown "Stories Editor" (DAW stylee!):

→ Drag & drop multi-track timeline 🎚️
→ Complex conversation mixing
→ Precise inline trimming

Perfect for creating podcasts or multi-speaker narratives locally! Image
Massive shoutout to @jamiepine for shipping this in open source!

voicebox.sh

Mac & Windows builds are already available.

Don't forget to give a ⭐ on GitHub to support Jamie!
github.com/jamiepine/voic…
Read 5 tweets
Mar 17
Someone built the ultimate visual LLM Architecture Gallery, packing 38 models from 2024-2026 into a single hub 🤯

It completely breaks down the complexity for you.

Inside:
→ Annotated diagrams
→ Key design choices
→ Actual code implementations

link to the gallery in 🧵↓ Image
Here is the full roster!

- Llama 3 8B
- OLMo 2 7B
- DeepSeek V3
- DeepSeek R1
- Gemma 3 27B
- Mistral Small 3.1 24B
- Llama 4 Maverick
- Qwen3 235B-A22B
- Qwen3 32B
- Qwen3 8B
- Qwen3 4B
- SmolLM3 3B
- Kimi K2
- GLM-4.5 355B
- GPT-OSS 20B
- GPT-OSS 120B
- Grok 2.5 270B
- Qwen3 Next 80B-A3B
- MiniMax M2 230B
- Kimi Linear 48B-A3B
- OLMo 3 7B
- OLMo 3 32B
- DeepSeek V3.2
- Mistral 3 Large
- Nemotron 3 Nano 30B-A3B
- Xiaomi MiMo-V2-Flash 309B
- GLM-4.7 355B
- Arcee AI Trinity Large 400B
- GLM-5 744B
- Nemotron 3 Super 120B-A12B
- Step 3.5 Flash 196B
- Nanbeige 4.1 3B
- MiniMax M2.5 230B
- Tiny Aya 3.35B
- Ling 2.5 1T
- Qwen3.5 397B
- Sarvam 105B
- Sarvam 30B
Access the high-resolution gallery and the blog post here:

sebastianraschka.com/llm-architectu…
sebastianraschka.com/llm-architectu…
Read 4 tweets
Mar 16
THIS is the wildest open-source project I’ve seen this month.

We were all hyped about @karpathy's autoresearch project automating the experiment loop a few weeks ago.
(ICYMI → github.com/karpathy/autor…)

But a bunch of folks just took it ten steps further and automated the entire scientific method end-to-end.

It's called AutoResearchClaw, and it's fully open-source.

You pass it a single CLI command with a raw idea, and it completely takes over 🤯

The 23-stage loop they designed is insane:

✦ First, it handles the literature review.
- It searches arXiv and Semantic Scholar for real papers
- Cross-references them against DataCite and CrossRef.
- No fake papers make it through.

✦ Second, it runs the sandbox.
- It generates the code from scratch.
- If the code breaks, it self-heals.
- You don't have to step in.

✦ Finally, it writes the paper.
- It structures 5,000+ words into Introduction, Related Work, Method, and Experiments.
- Formats the math, generates the comparison charts,
- Then wraps the whole thing in official ICML or ICLR LaTeX templates.

You can set it to pause for human approval, or you can just pass the --auto-approve flag and walk away.

What it spits out at the end:
→ Full academic paper draft
→ Conference-grade .tex files
→ Verified, hallucination-free citations
→ All experiment scripts and sandbox results

This is what autonomous AI agents actually look like in 2026.

Free and open-source. Link to repo in 🧵 ↓Image
Here is the repo:


Don't forget to ⭐ to boost visibility.

Massive kudos to the team for making this open-source 🤗github.com/aiming-lab/Aut…
Built by:
@HuaxiuYaoML @JiaqiLiu835914 @richardxp888 @lillianwei423 @StephenQS0710 @Xinyu2ML @HaoqinT @zhengop @cihangxie @dingmyu

They are actively looking for open-source contributors, so jump in.
Read 4 tweets
Feb 18
Anthropic quietly dropped 9 new Free Claude Skills tutorials 💥

Covering Projects, Excel workflows, browsing in Chrome, file editing, app integrations, task automation, and more!

Zero tech background required, so anyone can learn practical AI free and fast. 🧵↓ Image
1/ First things first, did you know you can create and edit files directly with Claude?

Draft documents, iterate on content, and keep everything in one place.
claude.com/resources/tuto…Image
2/ Claude can also assist you while you browse in Chrome.

Summaries, explanations, and quick insights as you move across pages.
claude.com/resources/tuto…Image
Read 11 tweets
Feb 14
Chinese engineers literally refactored @OpenClaw in Go for insane efficiency 🤯

It runs on a $10 @Raspberry_Pi instead of a $399 Mac mini.

→ Uses <10 MB of memory (99% smaller than OpenClaw)
→ 400× faster startup time
→ Boots in 1 second

Free and open source, repo in 🧵↓ Image
A side-by-side comparison between @OpenClaw, Nanobot and Picoclaw ↓ Image
here's the repo:
github.com/sipeed/picocla…Image
Read 4 tweets

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