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🔥 Matt Dancho (Business Science) 🔥 Profile picture
@mdancho84
Sep 17, 2022 • 10 tweets • 6 min read • Read on X
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Shiny is a powerful tool that data scientists can use for web apps & production.

But most data scientists struggle.

Here are 7 resources on shiny that helped me.

#rstats #shiny #excel #python
1. The Shiny website

The 1st place to go to learn shiny.

shiny.rstudio.com
2. Flexdashboard website

Flexdashboard combines Rmarkdown & Shiny to make quick apps.

pkgs.rstudio.com/flexdashboard/
3. Shiny Widgets gallery

See dozens of example reactive widget input / outputs for shiny

shiny.rstudio.com/gallery/widget…
4. shinyWidgets by dreamrs

Advanced & customizable reactive widgets that can really take your shiny apps to the next level

dreamrs.github.io/shinyWidgets/i…
5. HTML Widgets

Interactive visuals for shiny apps

htmlwidgets.org/showcase_leafl…
6. Shiny JS

Makes it easy to add JavaScript actions to your shiny apps.

deanattali.com/shinyjs/
7. Bslib

Upgrade shiny From Bootstrap 3 to 4 or 5 and makes it easy to make custom themes.

rstudio.github.io/bslib/
And if you want all of these 7 R packages plus 93 more in one consolidated #cheatsheet, download my ultimate #R cheat sheet.

business-science.io/r-cheatsheet.h…
One last resource.

If you've been struggling to learn R, I’d like to help.

I put together a free R webinar that consolidates the 10 secrets that helped me in my career.

learn.business-science.io/free-rtrack-ma…

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More from @mdancho84

🔥 Matt Dancho (Business Science) 🔥 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
🔥 Matt Dancho (Business Science) 🔥 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
🔥 Matt Dancho (Business Science) 🔥 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 — including advanced PDF understanding — 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
🔥 Matt Dancho (Business Science) 🔥 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
🔥 Matt Dancho (Business Science) 🔥 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
🔥 Matt Dancho (Business Science) 🔥 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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