Omar Saadoun

Founder @replikteai | Entrepreneur, runner and father of one!

[Omar Saadoun @osaadoun]

https://t.co/spXdodRgbR

[Omar Saadoun @osaadoun]

Todos tenemos historias así más o menos cercanas. Como siempre no aplica a todos pero pareciera que a la gran mayoría sobre todo en especialidades. Sería bueno que sumen más horas de trato humano durante la facultad y post grados para no olvidar que son profesionales como muchos que nos debemos a nuestros “clientes” a brindar un buen servicio y atención.

[Luke Pierce @lukepierceops]

Starbucks spends $400 million a year on software. Yesterday they announced they're moving off IBM and Microsoft to build their own custom systems in-house.

IBM dropped 3% and Salesforce dropped 4% on the news.

And honestly this is, unequivocally, the biggest signal I've seen since OpenAI and Anthropic launched their consulting arms back in Q1. The largest companies in the world are done paying for software that half fits how they work.

We saw this coming about a year ago. Moved everything we build off Airtable and low-code tools and went fully custom. Already paying off, and it's only going to compound from here.

This is the opportunity right now.

You get all of a company's data into one system. You build out a single operating system for the entire business. You cut out bad, redundant processes. Then you layer AI on top of it, under the correct processes.

That's the core of AI consulting. Helping companies actually operate better.

There are a lot of fly-by-night offerings circulating right now when it comes to Ai Services.

For example, 'second brains'.

Throwing scattered data into a second brain while the processes underneath stay broken does nothing. The companies who will absolutely destroy their competition over the next 5 years are rebuilding how they work from the ground up.

Starbucks is showing you what other companies will be doing over the next several years.

Your job is to position yourself to facilitate that process for as many companies as you can.

[SpaceX @SpaceX]

SpaceX has exercised the option to acquire @cursor_ai in an all-stock transaction with the goal of building the world’s most useful AI models.

For the past few months, SpaceXAI has been jointly training a model with Cursor, which will be released in Cursor and Grok Build soon.

We look forward to working closely with the Cursor team to advance our frontier AI capabilities

[Omar Saadoun @osaadoun]

See you later Fable

[Omar Saadoun @osaadoun]

That was fast, I'm out tokens until the next session.

[Omar Saadoun @osaadoun]

Lets take a deep look into it. As many tokens at the time as possible.

[Claude @claudeai]

Introducing Claude Fable 5: a Mythos-class model that we’ve made safe for general use.

Its capabilities exceed those of any model we’ve ever made generally available.

[Omar Saadoun @osaadoun]

That's why orchestration toward business-reality limits matters. Call them harnesses, call them whatever you like better, but they only come from real experience turned into the right environment for agents and the code & tools they run to do it safely while still being autonomous.

A very important piece of what I have under the hood of @ReplikteAI solves that.

[alon @alonzuman]

just asked codex to pull investors meetings from my calendar and draft an email, it went ahead and emailed *all of them*

[Omar Saadoun @osaadoun]

Solid case for Erlang and Elixir in blockchain but it reads oddly without a single real chain named.

The obvious one is @aeternity

cc: @noyyy

[Erlang Solutions @ErlangSolutions]

Blockchain needs speed, resilience, and fault tolerance at scale. Erlang and Elixir deliver all three and then some. Our deep dive makes the case for BEAM in blockchain. 🔗 https://t.co/MshPfCAR4U

[Omar Saadoun @osaadoun]

Hopefully nothing goes wrong, I can already imagine movie studios registering their next film name.

[Reuters @Reuters]

Live updates: ISS astronauts prepare for possible evacuation due to air leak https://t.co/GZNrSa8wLM

[Omar Saadoun @osaadoun]

@OMOREYY___ you should ask the CEO, I'm not part of the project since more than a year ago.

[Omar Saadoun @osaadoun]

Opus 4.8 is so hungry for tokens, hard to imagine it can be used as part of AI Native Systems.

[Omar Saadoun @osaadoun]

@joaomdmoura seems to be a general practice now

[Jason @mytechceoo]

CEO obsessed with token maxxing

[How To Prompt @HowToPrompt__]

Yann LeCun was right the entire time. And generative AI might be a dead end.

For the last three years, the entire industry has been obsessed with building bigger LLMs. Trillions of parameters. Billions in compute.

The theory was simple: if you make the model big enough, it will eventually understand how the world works.

Yann LeCun said that was stupid.

He argued that generative AI is fundamentally inefficient.

When an AI predicts the next word, or generates the next pixel, it wastes massive amounts of compute on surface-level details.

It memorizes patterns instead of learning the actual physics of reality.

He proposed a different path: JEPA (Joint-Embedding Predictive Architecture).

Instead of forcing the AI to paint the world pixel by pixel, JEPA forces it to predict abstract concepts. It predicts what happens next in a compressed "thought space."

But for years, JEPA had a fatal flaw.

It suffered from "representation collapse."

Because the AI was allowed to simplify reality, it would cheat. It would simplify everything so much that a dog, a car, and a human all looked identical.

It learned nothing.

To fix it, engineers had to use insanely complex hacks, frozen encoders, and massive compute overheads.

Until today.

Researchers just dropped a paper called "LeWorldModel" (LeWM).

They completely solved the collapse problem.

They replaced the complex engineering hacks with a single, elegant mathematical regularizer.

It forces the AI's internal "thoughts" into a perfect Gaussian distribution.

The AI can no longer cheat. It is forced to understand the physical structure of reality to make its predictions.

The results completely rewrite the economics of AI.

LeWM didn't need a massive, centralized supercomputer.

It has just 15 million parameters.

It trains on a single, standard GPU in a few hours.

Yet it plans 48x faster than massive foundation world models. It intrinsically understands physics. It instantly detects impossible events.

We spent billions trying to force massive server farms to memorize the internet.

Now, a tiny model running locally on a single graphics card is actually learning how the real world works.

[Omar Saadoun @osaadoun]

@claudeai You shouldn't be biased like that, we know :)

[Omar Saadoun @osaadoun]

@HeyGen Rough first test of Tina, the Customer Retention Engine.

https://t.co/bTpr2Z3X0W

[Omar Saadoun @osaadoun]

@HeyGen Did you send it? I don't see it.

[Omar Saadoun @osaadoun]

@mauricioclausen @AntelDeTodos Prendele a starlink, ni las tormentas lo afectan!