Matt Dancho (Business Science) ✓

@mdancho84 · Pittsburgh, PA · joined 01 Jan 2017

I help Data Scientists build AI systems. Join my next live AI workshop (free, live code, end-to-end AI business case).👇

102 488Followers

523Following

26 859Posts total

294.4KViews on collected posts

Derniers posts

Matt Dancho (Business Science) ✓@mdancho847 Sep

This guy built an entire AI data science team in Python. Then open-sourced (100% free). It automates data science workflows with AI, including data loading, cleaning, exploratory analysis, and feature engineering. And it tracks each step in a 100% reproducible pipeline. 00:00

7:04

1.4K views · 9 likes · 2 reposts · 2 replies Open on X →

Matt Dancho (Business Science) ✓@mdancho847 Sep

Some guy built a free AI university on GitHub. Not tutorials. Not theory. Full real-world AI systems. Step by step. Week 1 → Docker, FastAPI, databases Week 2 → Automated data pipelines Week 3 → Build your own search engine (BM25) Week 4 → Hybrid search (semantic + https://t.c

7.8K views · 155 likes · 27 reposts · 2 replies Open on X →

Matt Dancho (Business Science) ✓@mdancho846 Sep

@mdancho84 Been using this for so long in Claude that I forgot it was there. Early on I instructed to use Markitdown to convert pdfs to save tokens… it’s never let me down since then. 1K views · 4 likes · 1 reposts · 1 replies Open on X →

Matt Dancho (Business Science) ✓@mdancho846 Sep

@mdancho84 Crashes often and bloats your software with so many dependencies you won’t believe it. Terrible. Hopefully MS will fix it but not usable in its current form on a serious product. 1.9K views · 9 likes · 0 reposts · 0 replies Open on X →

Matt Dancho (Business Science) ✓@mdancho846 Sep

@mdancho84 This is a really good tool. If you want the same in go, then here is the version I wrote https://t.co/YZnN4MkPv8 3.4K views · 15 likes · 0 reposts · 0 replies Open on X →

Matt Dancho (Business Science) ✓@mdancho846 Sep

Microsoft is making moves again. A quiet little Python tool just shot to the top of GitHub’s trending charts. 100,000+ stars. It’s called MarkItDown. And it does something deceptively simple: It turns almost any file into clean Markdown. PDFs. Word docs. PowerPoints. Excel

256.9K views · 2.5K likes · 266 reposts · 73 replies Open on X →

Matt Dancho (Business Science) ✓@mdancho846 Sep

R.I.P. openclaw. Introducing nanobot. 99% smaller. Same core agent capabilities. Start using it in under 10 minutes. https://t.co/uyPNnVRzgL

10.8K views · 163 likes · 20 reposts · 2 replies Open on X →

Matt Dancho (Business Science) ✓@mdancho8414 Jul

@mdancho84 Registered, is it PST? https://t.co/qVovhoDC2o

19 views · 1 likes · 0 reposts · 0 replies Open on X →

Matt Dancho (Business Science) ✓@mdancho8414 Jul

@mdancho84 Bv. K C l https://t.co/bYXmrtzIZ3

32 views · 1 likes · 0 reposts · 0 replies Open on X →

Matt Dancho (Business Science) ✓@mdancho8424 Jun

@mdancho84 Sounds awesome! Combining all those elements in real-time is a game changer. Can't wait to see the project unfold! 4 views · 1 likes · 0 reposts · 0 replies Open on X →

Matt Dancho (Business Science) ✓@mdancho8423 Jun

Free AI Webinar for Data Scientists I’m building a real AI system live in Python. You’ll see how to combine machine learning, LLMs, RAG, agents, and business workflows into one portfolio-worthy project. Register here (1330+ registered): https://t.co/3TaJ77moA6 https://t.co/A2G

11.1K views · 71 likes · 15 reposts · 7 replies Open on X →

Face aux comptes de taille comparable

11 posts des 90 derniers jours, à côté de la tranche de 100K–1M abonnés. juste au niveau de la médiane de sa tranche d'abonnés.

Vues médianes1 946ce compte5 853médiane pour 100K–1M

Portée, %1.90%ce compte1.54%médiane pour 100K–1M

Engagement, %1.09%ce compte0.98%médiane pour 100K–1M

Indicateur	Ce compte	Médiane pour 100K–1M	Rapport
Vues médianes par post	1 946	5 853	0.33×
Portée (vues ÷ abonnés)	1.90%	1.54%	1.23×
Taux d'engagement	1.09%	0.98%	1.11×

Autres comptes de cette tranche → Comparer avec un autre compte → Comment ces repères sont établis →

Growth & engagement

How the posts we collected actually performed: views and reaction rate post by post, what the audience did with them, and where the follower count goes.

Views per post

11.1K23 Jun

424 Jun

3214 Jul

10.8K6 Sep

256.9K

3.4K

1.9K

7.8K7 Sep

1.4K

Last 11 collected posts, oldest on the left. The scale is logarithmic: one post can outrun the rest a hundred times over.

Engagement rate per post

0.84%23 Jun

25.00%24 Jun

3.12%14 Jul

5.26%

1.73%6 Sep

1.09%

0.44%

0.46%

0.59%

2.36%7 Sep

0.93%

Reactions — likes, reposts, replies and quotes — divided by views. Median for 100K–1M accounts is 0.98%.

What the audience does

Likes35.1%2 880 in total

Reposts4.0%331 in total

Replies1.1%87 in total

Quotes0.3%24 in total

Bookmarks59.5%4 884 in total

Share of every reaction we collected for this account. Replies mean argument, reposts mean endorsement, bookmarks mean the post was worth keeping.

The follower curve appears once this account has two daily snapshots — we take one a day, and this one is on its first.

Derniers posts

Face aux comptes de taille comparable

Growth & engagement

Views per post

Engagement rate per post

What the audience does

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