Juan Carlos Camarena | Entrepreneur

Ideas que escalan, experimentos que enseñan | IA, datos y mentalidad Lean | Formando una comunidad que construya el futuro

[Juan Carlos Camarena | Entrepreneur @juccapx]

La calidad de una plataforma se mide por sus integraciones.

No por la cantidad de funcionalidades que tiene.

Las mejores plataformas no intentan hacerlo todo.

Se conectan con el ecosistema de sus clientes.

[Juan Carlos Camarena | Entrepreneur @juccapx]

Validar es incómodo. Por eso casi nadie lo hace. Y por eso casi todos fallan.

[Juan Carlos Camarena | Entrepreneur @juccapx]

Una visión fundacional para una startup es similar a una hipótesis científica.

Rashmi Sinha
Founder, Slideshare

[Juan Carlos Camarena | Entrepreneur @juccapx]

Busca 3 soluciones
similares a tu idea.

Contacta a 5
usuarios que las usan.

Pregunta:
“¿Qué te frustra de esto?”

Tu ventaja competitiva
puede estar ahí.

#XValidator #EntrepreneurTips

[Juan Carlos Camarena | Entrepreneur @juccapx]

#ideaGenerator
Que te molesta cada día?

Las mejores ideas surgen resolviendo
los problemas personales.

Seguramente si a ti te molesta también a otros.

#Entrepreneur #WorthyDreams #startup

[Juan Carlos Camarena | Entrepreneur @juccapx]

Tu MVP debe hacerte sentir incómodo. Si no es así lo mas probable es que hayas construido demasiado antes de lanzarlo.

[Juan Carlos Camarena | Entrepreneur @juccapx]

Siempre que te descubras en el lado de la mayoría, es hora de detenerse a reflexionar.

Mark Twain

[Josep Ferrer @iamjosepferrer]

The Transformers architecture clearly explained 👇🏻

[Josep Ferrer @iamjosepferrer]

The Transformers architecture clearly explained 👇🏻

[MIT CSAIL @MIT_CSAIL]

All major neural networks, in one chart: https://t.co/F6da67oQc4

v/The Asimov Institute

[Elon Musk @elonmusk]

Impressive

[Andy Jassy @ajassy]

When we started AWS, we looked at the undesirable situation of developers spending 70% of their time on the undifferentiated muck of managing infrastructure (vs only 30% on what differentiated their customer experiences) and aimed to flip that equation on its head. It’s made a pretty big difference in innovation and developer productivity.

Roughly 18 years later, we’re trying to solve another 70-30-like misalignment for developers, where developers are telling us that they’re spending roughly 70% of their time on repetitive and tedious tasks and code.

Today, we’re launching the general availability of Amazon Q, the world’s most capable GenAI-powered assistant for accelerating software development and leveraging companies’ internal data—and that aims to remove much of the muck of repetitive and tedious coding and data tasks for developers and employees at large.

On the software development side, Q doesn’t just generate code, it also tests code, debugs coding conflicts, and transforms code from one form to another (today, developers can save months using Q to move from older versions of Java to newer, more secure and capable ones; in the near future, Q will help developers transform their .net code as well). Q Agents does multi-step planning and reasoning to allow developers to string together multiple requests and have Q implement them.

On the internal data side, most companies have voluminous troves of internally relevant data that resides in wikis, intranet pages, Salesforce, storage repositories like Amazon S3, and a bevy of other data stores and SaaS apps that are hard to access. It makes answering straightforward questions about company policies, products, business results, code, people, and many other topics hard and frustrating. Q makes this much simpler. You can point Q at all of your enterprise data repositories, and it’ll search all this data, summarize logically, analyze trends, and engage in dialog with customers about it.

We also introduced today a powerful new capability called Q Apps, which lets employees describe, in natural language, what apps they want to build on top of this internal data, and Q Apps will quickly generate that app. This is going to make it *so* much easier for internal teams to build useful apps from their own data.
Customers are gravitating to Q, and we already see companies like Brightcove, British Telecom, Datadog, GitLab, GoDaddy, National Australia Bank, NCS, Netsmart, Slalom, Smartsheet, Sun Life, Tata Consultancy Services, Toyota, and Wiz using Q. And, we’ve only been in beta til today.

Very excited about how Q will change what’s possible for our customers, and being a part of helping them innovate more quickly. https://t.co/Bcf7k7huOX

[Elon Musk @elonmusk]

First ever post made just by thinking, using the @Neuralnk Telepathy device!

[OpenAI @OpenAI]

Introducing Sora, our text-to-video model.

Sora can create videos of up to 60 seconds featuring highly detailed scenes, complex camera motion, and multiple characters with vibrant emotions.

https://t.co/YYpOAcrXQ3

Prompt: “Beautiful, snowy Tokyo city is bustling. The camera moves through the bustling city street, following several people enjoying the beautiful snowy weather and shopping at nearby stalls. Gorgeous sakura petals are flying through the wind along with snowflakes.”

[Carlos Santana @DotCSV]

Siempre que desarrollemos a AGI en un "sandbox" controlado y delimitado no habrá problemas de seguridad.

La AGI en el sandbox:

[OpenAI @OpenAI]

ChatGPT can now see, hear, and speak. Rolling out over next two weeks, Plus users will be able to have voice conversations with ChatGPT (iOS & Android) and to include images in conversations (all platforms).
https://t.co/uNZjgbR5Bm

[AI at Meta @AIatMeta]

Meta AI researchers show how current language models differ from the human brain & highlight the role of long-range & hierarchical predictions.

Read the open accessed article in Nature ➡️ https://t.co/Kf3CCRiqug

[Juan Carlos Camarena | Entrepreneur @juccapx]

The ethical implications of LLMs are no joke. How do we strike a balance between AI progress and privacy? Ideas?🤔🔐 #AIethics #LLM #GPT4 #AI