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πŸ”₯ Matt Dancho (Business Science) πŸ”₯ Profile picture
@mdancho84
May 20, 2023 β€’ 7 tweets β€’ 5 min read β€’ Read on X
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As a data scientist, productivity is a 10X super power.

Here's a short list of AI tools to help data scientists with: 🧡

#ai #datascience #career #skills #tools Image
1. Writing code

AI pair programming is a huge benefit.

Tools like #chatgpt & github #copilot can help debug complex code and replace Googling + Stack Overflowing for common scripting.

Key skill: ChatGPT prompting (more on this in my free ChatGPT for Data Scientists) Image
2. Code Quality & Documentation

Great products have great documentation. AI can help produce documentation, comment code, and replace time-consuming manual documentation with automated AI docs.

Key Skill: Using @mintlify to build your docs: mintlify.com Image
3. Presentations

Great data scientists are storytellers. Use persuasion to your advantage.

Key Skill: Generating images with AI using @midjourney_ai . midjourney.com Image
I'm road-testing all of these.

And I've been quietly researching #ChatGPT for Data Scientists (My NUMBER 1 TOOL) for the past 4 months.

I have good news - I'm ready to reveal my chatgpt research!
If you want to understand how ChatGPT can make you a better data scientist (and mistakes to avoid)...

I'll be sharing my research in a Free WORKSHOP: ChatGPT for Data Scientists (Wednesday, June 7th)!
What's Your Next Step?

Join me and 1,000 data scientists as we crush AI in my LIVE ChatGPT for Data Scientists Workshop.

Seats are limited (1,000 max).

πŸ‘‰Register Here: us02web.zoom.us/webinar/regist… Image

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More from @mdancho84

πŸ”₯ Matt Dancho (Business Science) πŸ”₯ Profile picture
Dec 31, 2025
🚨 Google published a 69-page prompt engineering masterclass.

This is what's inside: Image
Table of Contents:

- Prompt Engineering
- LLM Output Configuration
- Prompting Techniques
- Best Practices Image
Important concepts:

1. One-shot versus multi-shot

Google does a great job examining both approaches and demonstrating when to use them and how they work. Image
Read 10 tweets
πŸ”₯ Matt Dancho (Business Science) πŸ”₯ Profile picture
Dec 29, 2025
🚨 BREAKING: Microsoft launches a free Python library that converts ANY document to Markdown

Introducing Markitdown. Let me explain. 🧡 Image
1. Document Parsing Pipelines

MarkItDown is a lightweight Python utility for converting various files to Markdown for use with LLMs and related text analysis pipelines. Image
2. Supported Documents

MarkItDown supports:

- PDF
- PowerPoint
- Word
- Excel
- Images (EXIF metadata and OCR)
- Audio (EXIF metadata and speech transcription)
- HTML
- Text-based formats (CSV, JSON, XML)
- ZIP files (iterates over contents)
- Youtube URLs
- EPubs Image
Read 9 tweets
πŸ”₯ Matt Dancho (Business Science) πŸ”₯ Profile picture
Dec 27, 2025
🚨 BREAKING: IBM launches a free Python library that converts ANY document to data

Introducing Docling. Here's what you need to know: 🧡 Image
1. What is Docling?

Docling is a Python library that simplifies document processing, parsing diverse formats β€” including advanced PDF understanding β€” and providing seamless integrations with the gen AI ecosystem. Image
2. Document Conversion Architecture

For each document format, the document converter knows which format-specific backend to employ for parsing the document and which pipeline to use for orchestrating the execution, along with any relevant options. Image
Read 8 tweets
πŸ”₯ Matt Dancho (Business Science) πŸ”₯ Profile picture
Dec 20, 2025
Google just dropped a masterclass on Agents.

Here's what's covered in the 54 page PDF: Image
Here's what they cover:

1. From models to agents

2. What an AI Agent is

3. Agentic problem-solving loop (5 steps)

4. Taxonomy of agentic systems (levels 0–4)

5. Core architecture decisions

6. Multi-agent patterns (design patterns)
7. Deployment & Agent Ops (GenAIOps)

8. Interoperability: humans, agents, and payments

9. Security, identity, and governance at scale

10. Learning, self-evolution, and Agent Gym
Read 7 tweets
πŸ”₯ Matt Dancho (Business Science) πŸ”₯ Profile picture
Dec 17, 2025
This 277-page PDF unlocks the secrets of Large Language Models.

Here's what's inside: 🧡 Image
Chapter 1 introduces the basics of pre-training.

This is the foundation of large language models, and common pre-training methods and model architectures will be discussed here. Image
Chapter 2 introduces generative models, which are the large language models we commonly refer to today.

After presenting the basic process of building these models, you explore how to scale up model training and handle long texts. Image
Read 10 tweets
πŸ”₯ Matt Dancho (Business Science) πŸ”₯ Profile picture
Dec 16, 2025
Stanford just made fine-tuning irrelevant with a single paper.

It’s called Agentic Context Engineering (ACE) and it proves you can make models smarter without touching a single weight.

Key takeaways (and get the 23 page PDF): Image
Stanford just released a 23 page paper on Agentic Context Enginnering to improve Agents. Key ideas:

1. ACE = Agentic Context Engineering: treat system prompts + agent memory as a living playbook. Image
2. Log agent trajectories; reflect to extract actionable bullets: strategies, tool schemas, failure modes.

3. Merge updates as append-only deltas with periodic semantic de-duplication to keep the playbook clean.
Read 9 tweets

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