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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

Últimas publicaciones

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 →
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 →
@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 →
@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 →
@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 →
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 →
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 →
@mdancho84 Registered, is it PST? https://t.co/qVovhoDC2o
19 views · 1 likes · 0 reposts · 0 replies Open on X →
@mdancho84 Bv. K C l https://t.co/bYXmrtzIZ3
32 views · 1 likes · 0 reposts · 0 replies Open on X →
@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 →
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 →

Frente a cuentas del mismo tamaño

11 publicaciones de los últimos 90 días, junto al rango de 100K–1M seguidores. justo en la mediana de su rango de seguidores.

Visualizaciones medianas1 946esta cuenta5 853mediana de 100K–1M
Alcance, %1.90%esta cuenta1.54%mediana de 100K–1M
Interacción, %1.09%esta cuenta0.98%mediana de 100K–1M
MétricaEsta cuentaMediana de 100K–1MProporción
Visualizaciones medianas por publicación1 9465 8530.33×
Alcance (visualizaciones ÷ seguidores)1.90%1.54%1.23×
Tasa de interacción1.09%0.98%1.11×

Otras cuentas de este rango →   Comparar con otra cuenta →   Cómo se construyen estas referencias →

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
19
10.8K6 Sep
256.9K
3.4K
1.9K
1K
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.

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