Mimic

[Mimic @Mimiccc]

@joraplas @GCorreaCabrera Este también es bueno profe.

[Mimic @Mimiccc]

@joraplas Creo que ya es hora de mirar y movernos hacia una democracia líquida.

[Mimic @Mimiccc]

@joraplas La cantidad de datos que se venden en el mercado negro, los cuales cruzan y procesan para dar con el paradero de miles o millones de mexicanos. La vulnerabilidad más grande siempre es el humano, por miedo o por dinero.

[Mimic @Mimiccc]

@joraplas Efecto cucaracha 🪳

[Mimic @Mimiccc]

@joraplas Estamos en la era de idiotas, mucho yo y poco nosotros.

[Mimic @Mimiccc]

@joraplas Cuando la influencia intelectual está comprada de alguna u otra forma, siempre cae en la incongruencia.

[Mimic @Mimiccc]

@joraplas Van a mostrar 1 de cada 100, no creo que vaya haber mucha transparencia por parte de los gobiernos. Es el problema de ofuscación de los datos y la falta de transparencia.

[Mimic @Mimiccc]

@VicGarSan75 @joraplas De pollito de color?

[Mimic @Mimiccc]

@joraplas Filosofía mamalona

[Stanislav Kozlovski @kozlovski]

VCs, please stop funding this slop. We’re entering clown world with the amount of AI building we’re seeing. 🤡🌎

All I see in the data infra space is companies building infrastructure for AI Agents with the obligatory buzzwords like A2A or MCP.

Who is productionizing these AI Agents?

Two years of marketing hype in, I have literally only seen ONE talk about an AI "Agent" in prod. It's by Uber and it's used for code generation. First off - it's a dev tool and second - it's not even an agent, it's a workflow.

Who honestly thinks the future of software architecture will be hundreds of AI Agents talking to each other, similar to microservices today?

Does anyone ACTUALLY believe this is what the world will be in 5 years?

Can they comment here and share their perspective?

The data is pointing to the opposite.

• We haven't seen any major LLM advancements since GPT-4.
• All frontier model companies are grossly unprofitable with no sustainable path.
• A lot of investments between big tech are circular (recycling revenue)

Even in a winner-take-all model, OpenAI is burning $5 BILLION a year.

Anthropic, the number two, is looking way, way worse regarding its revenue model.

What happens when the funding stops?

Companies chasing investor interest via shiny narratives are missing the forest for the trees - we, the engineers, need practical solutions.

That's what's going to last.

Fortunately, I see two trends growing quietly behind the spotlight (the sloplight?) of AI.

They will most certainly survive a dot-com like crash if that were to happen.

They are:
• Small Data
• The “Just Use Postgres” Renaissance

The common theme between both?

Simplification.

♦️ Small data is the cure for the Big Data hangover that tech collectively went through in the 2010s. It’s the group realization that:

• organizations don’t use that much data
• hardware is getting really, really good

Most applications will never see a terabyte of data, even if they’re successful.

Snowflake and Redshift-published data showed that 99.9% of real-world queries fit on one node. 💡

And the nodes are becoming super beefy. AMD released a 192-core (!) CPU this summer. 2025-era SSDs can do 5.5 million random reads a second and 28 GiB/s sequential reads.

Embedded and simple systems like DuckDB are going viral for a reason.

♦️ Postgres is doing to data infrastructure what the monolith comeback is doing to the microservices architecture.

It’s the group realization that Postgres can handle 80% of companies' needs. And it does it with 20% the effort.

It’s not the extreme view where "when you have a hammer, everything looks like a nail".

👉 It’s the extremely reasonable and practical application of reducing the amount of system sprawl in your organization when that sprawl isn’t warranted.

Instead of MongoDB, Redis, an AI Vector database, Snowflake and Elasticsearch - you can use tsvector, pg_mooncake, pgvector, unlogged tables and jsonb in Postgres.

Life becomes significantly simpler when you don't have to manage 5 different systems. There is a ton of organizational overhead in having to:
• learn their nuances/configs/gotchas,
• learn their UIs and terminology
• learn how to deploy and upgrade them safely
• build operational expertise
• write runbooks for them
• debug them
• test them
• find, hire and retain talent that understand them
• keep up with their ecosystems
• etc.

Paying this organizational overhead for zero significant gain is not worth it.

It's all common sense.

[Jo Kristian Bergum @jobergum]

Billions worth of system prompts are exposed in a single repo. The scale of IP leaks is remarkable in the LLM era.

[Luis Pazos @luispazos1]

Embajada mexicana en España obstaculiza el voto por órdenes superiores, pues consideran que los votantes en + del 90% no van a votar por MORENA. La Ley General de delitos electorales tipifica ese hecho como delito penal, con multas de 3 años de cárcel o más.

[Carlos Torres @CarlosTorresF_]

#Ensenada 🚨

Encapuchados ingresaron a la casilla 2006 Col. Moderna para robarse las boletas electorales.
Al huir provocaron un choque y quedaron ahí tendidos.
Observen esta joya. 😵

Imágenes de @BereniceDiazG

[Elon Musk @elonmusk]

Watching it now

[Mimic @Mimiccc]

@MattWallace888 Black soul

[Wall Street Mav @WallStreetMav]

Are you ready for this? 🤨

#WEF #KlausSchwab

[Stoic Ideology @GeneralxZoom]

@Morpheusresist Tate haters right now

[KemuelNGeorge @KemuelNGeorge]

BREAKING

Wiretaps reveal the women who accused Andrew Tate of heinous crimes.

PLANNED TO FRAME THEM all along.

[Censored Men @CensoredMen]

🔴 VICE Reuse clips to falsify reactions being made by Tate in order to portray him negatively.

They then proceed to unlist the video.

Credit: Wudanswisdom on IG

[animalitos gorditos @Gordito_Perrito]

se comió los deberes de filosofía 😔 https://t.co/aQ5ppuHEsE