Carlos Alonso

La vida es dura 😎

[🇲🇽 México Mágico ✨ @MexicoMagico]

🤣🤣🤣🤣🤣

[el jeringas @ElJeringasLoko]

JAJAJAJA Los libros lo predijeron, es arte.

[PHD Comics @PHDcomics]

World Cup vs. the Ph.D.

[Carlos Alonso @Carlos_Hdz_Ch11]

🤣🤣🤣🫣

[A L e 𝕏 @l0rdAlexX]

Mi compa y yo después de poner un partido del mundial en una página ilegal en un bar.

[Carlos Alonso @Carlos_Hdz_Ch11]

🤣🤣🤣

[el jeringas @ElJeringasLoko]

Lo mínimo que espero hoy. #SomosMéxico

[World Cup 2026 @WorldCupMedia]

WORLD CUP TODAY! 🏆 #FIFA

[¿Por qué es Tendencia? @porktendencia]

"Sagrada Familia":
Porque el Papa León XIV ha inaugurado la torre de Jesucristo de la Sagrada Familia en Barcelona

[Juez Central @Juezcentral]

No ha empezado el Mundial y Noruega ya tiene las 3 mejores fotos de equipo.

La primera antes de viajar, disfrazados de vikingos.

La segunda, su foto oficial, con la camiseta de Noruega, todos perfectamente alineados.

La tercera, ahora, todos los jugadores con las camisetas de su primer club.

Qué grandes los vikingos.

[Martinalgui @MartinoliCuri]

“No se siente ambiente mundialista”

Las doñitas en México:

[TNT Sports México @tntsportsmex]

¡Te queremos mucho, @MajolaB! 🫶

Hoy vimos historia en la #BenditaChampions. 🏆🔝

📲 @StreamMaxLA: https://t.co/JQG7BTvG44

#UCLFinal | #TNTSportsEnHBOMax

[Anime Posts @AnimeDailyPost]

This moment was so satisfying

[Mathematica @mathemetica]

Imagine you’re trying to climb the highest peak of a mountain, but you’re forced to stay on a narrow winding trail defined by g(x) = 0. You can’t just head straight up the steepest slope by setting ∇f = 0, the mountain won’t let you.

So we invent a clever trick: the Lagrangian

ℒ(x, λ) = f(x) − ∑ λ_i g_i(x)

By solving ∇ℒ = 0, we find the exact points where the level curves of f touch the constraint boundary tangentially: the sweet spots where the gradients align (∇f = ∑ λ_i ∇g_i). These are your local maxima, minima, or saddles under the constraint.

[SnoopyAndWoodie @Anklebiters0904]

🐾🎶

[Olivier Blanchard @ojblanchard1]

RIP Ned Phelps

Ned Phelps followed his own intellectual journey.

When Keynesians relied on a long-run tradeoff between unemployment and inflation, he showed why this was a weak reed to stand on. Thus was born the natural rate hypothesis (although the coining of the word goes to Friedman, a year after Ned’s paper).

When, later, New Keynesians were focusing on nominal rigidities, he built models of fluctuations where nominal rigidities played no role. When New Classicals were exploring the cyclical implications of competitive markets, he focused on the role of distortions in goods and labor markets, be it efficiency wages, or variable markups.

It would be fair to say that, today, the frontier macro models embody the natural rate hypothesis, and many of the distortions Ned focus on---and, what he did not like, i.e. nominal rigidities.

His style was highly idiosyncratic. He was often a poor expositor of his fundamental insights. He did not listen much to others, pursuing his agenda with focus and passion. But, to use an overused but appropriate expression, he was certainly one of the giants in the field. We often met and sometimes fought, be it on hysteresis or nominal rigidities, but I had infinite respect for him.

[Infinite Logiz @Infinite_Logiz]

Visual Proof of (a+b)² = a² + b² + 2ab
#math #mathematics #mathskills

[Real Madrid C.F. @realmadrid]

LA NOVENA.

[Math Files @Math_files]

John Nash’s Recommendation Letter for Princeton University

[Mathematica @mathemetica]

Shannon Entropy: Measuring Uncertainty in Information

H(X) = - ∑ P(xᵢ) log P(xᵢ)
This is the legendary formula by Claude Elwood Shannon (1916–2001); the father of Information Theory.
Entropy quantifies how much uncertainty (or average information) is contained in the outcome of a random variable X. The more unpredictable the outcomes, the higher the entropy.

From data compression and cryptography to AI and communications; this concept powers the digital world.