Danny Bravo

Artificial intelligence Product manager

[Thomas Wolf @Thom_Wolf]

We have a huge news to share today!

Today we are unveiling the first truly accessible RL robot - welcome Microduck

A 25 cm tiny open-source biped with 15 actuators and packed with sensors (camera, speaker, LiDAR, NFC, bluetooth, wifi, etc) that you train yourself with reinforcement learning.

It's also playable out of the box with more than half a dozen fun and playful pre-trained policies to have it walk, sit, crouch, roller-skate, pick up objects with its articulated beak, and recover on its own.

And all for less than $400.

See all the details, play with the simulator and order it at: https://t.co/n1Btgs6vKw

(video with sound on 🔊)

[Thariq is on vacation @trq212]

a skill people at Anthropic have been using a lot recently: ELI5

/eli5 <what you want explained>

"explain like I'm someone who knows nothing about this topic, using a HTML artifact with big pictures and few words"

[Danny Bravo @dannybravo]

@andresflorezlpz @ahbilbaoo Información de Hardcore AI en https://t.co/rIJ6pBYUYh

[Andres Bilbao @ahbilbaoo]

Hoy me llegó un DM que resume 2026 en una sola línea:

“No tenemos CTO / talento técnico fundador”.

Y lo mandó un founder que está arrancando una startup AI-first.

O sea: ideas hay. Ambición hay. “AI” por todo lado.

El bloqueo sigue siendo el mismo de siempre.

Talento que pueda construir end-to-end.

Lo chistoso (y medio triste) es que mucha gente cree que “AI-first” baja la barrera técnica.

Mentira.

Ahora no basta con devs que codean

Necesitás devs que:

entiendan el problema (de verdad)

escriban specs claras

diseñen arquitectura sin humo

usen agentes sin volverse “vibe coders”

y shippeen con calidad, seguridad y performance by default

Esos devs serán los nuevos CTOs

y esos CTOs serán los nuevos unicornios

Y ahí es donde pasa algo bonito:

cuando montamos Hardcore AI en 30X con @dannybravo, no solo entrenamos developers.

Creamos un imán.

Porque las compañías no están buscando “más cursos”.

Están buscando gente que pueda ejecutar con criterio.

Y cuando no la encuentran… Nos preguntan a nosotros

Hardcore AI es literalmente eso:

4 semanas para construir un producto e2e con AI (sin perder el alma de ingeniería) y conectar con founders que están contratando / cofundando.

Si eres dev profesional escribile a Danny para que te metás en esta vuelta

[Danny Bravo @dannybravo]

Rechazar $1B de Meta suena loco, pero Furiosa AI sabe que el verdadero muro de la IA hoy es la energía.
Mientras las GPUs queman el 90% moviendo datos, su arquitectura logra 2.5x eficiencia solo optimizando el flujo.
Builders: escalar en producción no es fuerza bruta, es diseño

[elie @eliebakouch]

Training LLMs end to end is hard. Very excited to share our new blog (book?) that cover the full pipeline: pre-training, post-training and infra. 200+ pages of what worked, what didn’t, and how to make it run reliably

https://t.co/iN2JtWhn23

[Shruti @heyshrutimishra]

Anthropic Academy just released free online courses.

No payment required.

Here are 6 courses you don't want to miss in 2025:

[Blaine Brown @blizaine]

Me, every time I get in a pool...

(via @runwayml Aleph)

[World of Statistics @stats_feed]

🇨🇳 China tops the world in artificial intelligence publications, database analysis reveals. The country also leads in patent filings and the number of AI researchers.

The analysis of the proprietary Dimensions database, released in July, finds that the number of AI-related research papers has grown from less than 8500 published in 2000 to more than 57,000 in 2024. In 2000, China-based scholars produced just 671 AI papers, but in 2024 their 23,695 AI-related publications topped the combined output of the United States (6378), the United Kingdom (2747), and the European Union (10,055).

[Gautam Kedia @thegautam]

TL;DR: We built a transformer-based payments foundation model. It works.

For years, Stripe has been using machine learning models trained on discrete features (BIN, zip, payment method, etc.) to improve our products for users. And these feature-by-feature efforts have worked well: +15% conversion, -30% fraud.

But these models have limitations. We have to select (and therefore constrain) the features considered by the model. And each model requires task-specific training: for authorization, for fraud, for disputes, and so on.
Given the learning power of generalized transformer architectures, we wondered whether an LLM-style approach could work here. It wasn’t obvious that it would—payments is like language in some ways (structural patterns similar to syntax and semantics, temporally sequential) and extremely unlike language in others (fewer distinct ‘tokens’, contextual sparsity, fewer organizing principles akin to grammatical rules).

So we built a payments foundation model—a self-supervised network that learns dense, general-purpose vectors for every transaction, much like a language model embeds words. Trained on tens of billions of transactions, it distills each charge’s key signals into a single, versatile embedding.

You can think of the result as a vast distribution of payments in a high-dimensional vector space. The location of each embedding captures rich data, including how different elements relate to each other. Payments that share similarities naturally cluster together: transactions from the same card issuer are positioned closer together, those from the same bank even closer, and those sharing the same email address are nearly identical.

These rich embeddings make it significantly easier to spot nuanced, adversarial patterns of transactions; and to build more accurate classifiers based on both the features of an individual payment and its relationship to other payments in the sequence.

Take card-testing. Over the past couple of years traditional ML approaches (engineering new features, labeling emerging attack patterns, rapidly retraining our models) have reduced card testing for users on Stripe by 80%. But the most sophisticated card testers hide novel attack patterns in the volumes of the largest companies, so they’re hard to spot with these methods.

We built a classifier that ingests sequences of embeddings from the foundation model, and predicts if the traffic slice is under an attack. It leverages transformer architecture to detect subtle patterns across transaction sequences. And it does this all in real time so we can block attacks before they hit businesses.

This approach improved our detection rate for card-testing attacks on large users from 59% to 97% overnight.

This has an instant impact for our large users. But the real power of the foundation model is that these same embeddings can be applied across other tasks, like disputes or authorizations.

Perhaps even more fundamentally, it suggests that payments have semantic meaning. Just like words in a sentence, transactions possess complex sequential dependencies and latent feature interactions that simply can’t be captured by manual feature engineering.

Turns out attention was all payments needed!

[Hashem Al-Ghaili @HashemGhaili]

I did more tests with Google's #Veo3. Imagine if AI characters became aware they were living in a simulation!

[Carlos E. Perez @IntuitMachine]

Do you truly understand the limits of current AI?!

[Ray Dalio @RayDalio]

https://t.co/Xpdl8fYoBg

[Danny Bravo @dannybravo]

¿Pensar? ¡Ay no!, que lo haga la IA https://t.co/u0I7qbBZxq a través de @YouTube

[Danny Bravo @dannybravo]

No te pierdas mi último artículo: Pensar? hay no, que lo haga la IA! https://t.co/mKXoc2JucL a través de @LinkedIn

[Yichao 'Peak' Ji @peakji]

Hi! I'm Peak from Manus AI. Actually, it's not that complicated - the sandbox is directly accessible to each user (see screenshot for method).

Specifically:
* Each session has its own sandbox, completely isolated from other sessions. Users can enter the sandbox directly through Manus's interface.
* The code in the sandbox is only used to receive commands from agents, so it's only lightly obfuscated.
* The tools design isn't a secret - Manus agent's action space design isn't significantly different from common academic approaches. And due to the RAG mechanism, the tools descriptions you get through jailbreaking will vary across different tasks.
* Multi-agent implementation is one of Manus's key features. When messaging with Manus, you only communicate with the executor agent, which itself doesn't know the details of knowledge, planner, or other agents. This really helps to control context length. And that's why prompts obtained through jailbreaking are mostly hallucinations.
* We did use @browser_use's open-source code. In fact, we use many different open-source technologies, which is why I specifically mentioned in the launch video that Manus wouldn't exist without the open-source community. We'll have a series of acknowledgments and collaborations coming up.
* There's no need to rush - our team has always had an open-source tradition, and I personally have been sharing my post-trained models on HuggingFace. We'll be open-sourcing quite a few good things in the near future.

[Danny Bravo @dannybravo]

Reflexiones sobre IA y Educación https://t.co/SRacOUpILJ

[Lewis Tunstall @_lewtun]

Chatting to CNN about why DeepSeek-R1 and the open source community is awesome was not on my bingo card this morning (I’ve never been on TV before 😂)

[Germán Milano @german_milano]

1/8
Everyone's doing great work building agents for bug fixing. But I'm curious—does anyone have insights on which types of issues your agents are good at, and which ones pose the biggest challenges? 🧵

Cc: @__mbel__ @jlamasrios @RodolfoAni72304 @GMilano @mperezjodal

[OpenAI @OpenAI]

Announcing The Stargate Project

The Stargate Project is a new company which intends to invest $500 billion over the next four years building new AI infrastructure for OpenAI in the United States. We will begin deploying $100 billion immediately. This infrastructure will secure American leadership in AI, create hundreds of thousands of American jobs, and generate massive economic benefit for the entire world. This project will not only support the re-industrialization of the United States but also provide a strategic capability to protect the national security of America and its allies.

The initial equity funders in Stargate are SoftBank, OpenAI, Oracle, and MGX. SoftBank and OpenAI are the lead partners for Stargate, with SoftBank having financial responsibility and OpenAI having operational responsibility. Masayoshi Son will be the chairman.

Arm, Microsoft, NVIDIA, Oracle, and OpenAI are the key initial technology partners. The buildout is currently underway, starting in Texas, and we are evaluating potential sites across the country for more campuses as we finalize definitive agreements.

As part of Stargate, Oracle, NVIDIA, and OpenAI will closely collaborate to build and operate this computing system. This builds on a deep collaboration between OpenAI and NVIDIA going back to 2016 and a newer partnership between OpenAI and Oracle.

This also builds on the existing OpenAI partnership with Microsoft. OpenAI will continue to increase its consumption of Azure as OpenAI continues its work with Microsoft with this additional compute to train leading models and deliver great products and services.

All of us look forward to continuing to build and develop AI—and in particular AGI—for the benefit of all of humanity. We believe that this new step is critical on the path, and will enable creative people to figure out how to use AI to elevate humanity.