Pedro Domingos ✓

@pmddomingos · Seattle, WA · joined 07 Jul 2015

Professor of computer science at UW and author of '2040' and 'The Master Algorithm'. Into machine learning, AI, and anything that makes me curious.

133 713Followers

178Following

26 718Posts total

983.7KViews on collected posts

Últimas publicaciones

Pedro Domingos ✓@pmddomingos10 Sep

@pmddomingos Thanks for letting know, I’ve been trying to create an infinite vortex in my teacup for an hour now 1.1K views · 16 likes · 0 reposts · 0 replies Open on X →

Pedro Domingos ✓@pmddomingos10 Sep

@pmddomingos And here I was worried about my water faucet exploding 2.1K views · 43 likes · 0 reposts · 1 replies Open on X →

Pedro Domingos ✓@pmddomingos10 Sep

@pmddomingos And because of the sound speed. Fluids with higher velocity stop being incompressible 576 views · 9 likes · 0 reposts · 0 replies Open on X →

Pedro Domingos ✓@pmddomingos10 Sep

BTW, in the real world fluids can't accelerate to infinite speeds because of special relativity. 46K views · 594 likes · 28 reposts · 56 replies Open on X →

Pedro Domingos ✓@pmddomingos9 Sep

@pmddomingos I think it's simpler than that: acceleration is linear in force, but it doesn't mean that the resulting motion can't be nonlinear. 4K views · 11 likes · 0 reposts · 0 replies Open on X →

Pedro Domingos ✓@pmddomingos9 Sep

A: Because in Navier-Stokes the velocity is not the velocity of a particle, but the velocity at a point. The particle at a point keeps changing, and the faster the motion, the faster it changes, making the acceleration quadratic. Without this there'd be no turbulence. (2/2) 12.9K views · 179 likes · 4 reposts · 7 replies Open on X →

Pedro Domingos ✓@pmddomingos9 Sep

Q: Navier-Stokes is just Newton's laws for fluids, and Newton's laws are linear, so why is Navier-Stokes nonlinear? (1/2) 74K views · 328 likes · 26 reposts · 19 replies Open on X →

Pedro Domingos ✓@pmddomingos9 Sep

AlphaGo did not beat Lee Sedol. The humans who built AlphaGo beat Lee Sedol. 4.2K views · 48 likes · 5 reposts · 11 replies Open on X →

Pedro Domingos ✓@pmddomingos9 Sep

Resist the delusional/dishonest attempt by OpenAI and Anthropic to redefine AGI as what they've already done. 6.7K views · 196 likes · 26 reposts · 20 replies Open on X →

Pedro Domingos ✓@pmddomingos9 Sep

And meanwhile LLMs still can't add two numbers of arbitrary length. 10.2K views · 132 likes · 8 reposts · 27 replies Open on X →

Pedro Domingos ✓@pmddomingos22 Oct 2025

Slides from the @ECMLPKDD 2025 opening keynote: https://t.co/hQaSaxJeZP 9.1K views · 20 likes · 3 reposts · 4 replies Open on X →

Pedro Domingos ✓@pmddomingos15 Oct 2025

@pmddomingos @pmddomingos where is the compiler/interpreter, system libs, an examples repo? 10.1K views · 26 likes · 0 reposts · 1 replies Open on X →

Pedro Domingos ✓@pmddomingos15 Oct 2025

I've found the path to AGI: https://t.co/NRvZViBVUc 802.9K views · 2.1K likes · 258 reposts · 205 replies Open on X →

Frente a cuentas del mismo tamaño

10 publicaciones de los últimos 90 días, junto al rango de 100K–1M seguidores. llega mucho más allá de su propia audiencia, y esa audiencia responde.

Visualizaciones medianas5 433esta cuenta5 898mediana de 100K–1M

Alcance, %4.06%esta cuenta1.59%mediana de 100K–1M

Interacción, %1.53%esta cuenta1.04%mediana de 100K–1M

Métrica	Esta cuenta	Mediana de 100K–1M	Proporción
Visualizaciones medianas por publicación	5 433	5 898	0.92×
Alcance (visualizaciones ÷ seguidores)	4.06%	1.59%	2.56×
Tasa de interacción	1.53%	1.04%	1.47×

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

802.9K15 Oct

10.1K

9.1K22 Oct

10.2K9 Sep

6.7K

4.2K

74K

12.9K

46K10 Sep

576

2.1K

1.1K

Last 13 collected posts, oldest on the left. The scale is logarithmic: one post can outrun the rest a hundred times over.

Engagement rate per post

0.32%15 Oct

0.27%

0.30%22 Oct

1.68%9 Sep

3.70%

1.62%

0.51%

1.48%

0.28%

1.47%10 Sep

1.56%

2.08%

1.49%

Reactions — likes, reposts, replies and quotes — divided by views. Median for 100K–1M accounts is 1.04%.

What the audience does

Likes51.7%3 660 in total

Reposts5.1%358 in total

Replies5.0%351 in total

Quotes1.2%87 in total

Bookmarks37.1%2 630 in total

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

Followers by day

9 Sep

Daily snapshots since 09 Sep 2026; the dashed line is the starting count.

Últimas publicaciones

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Growth & engagement

Views per post

Engagement rate per post

What the audience does

Followers by day

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