One step better every day.

Joined June 2022
To me, this Apple Duo is just a flex. I honestly don't see anything particularly special about it.
Marques Brownlee
@MKBHD22h
The current state of apps on iPhone Duo: youtu.be/ESABTN4VtrA
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Jensen Huang co-founded NVIDIA in 1993, when the future of AI was far from clear. He spent decades building technology that many people couldn't yet see the need for. Today, NVIDIA is helping power the AI revolution. Sometimes, success means believing in a future nobody else can see yet, and having the courage to keep building until the world chatches up.
NVIDIA
@nvidiaOct 21
AI builders need computing capacity they can put to work sooner. We introduced DSX POD at NVIDIA GTC Berlin: AI factories integrated and tested before delivery, enabling deployment at prepared sites in as little as 14 days.
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Something has been on my mind lately. SpaceX keeps launching rockets and putting satellites into orbit, but who is making sure everything being sent into space won't create problems for us here on Earth years from now? Are there strict regulations monitoring this, and who holds these companies accountable if something goes wrong. Space exploration is exciting, but we shouldn't wait for a problem to happen before we start asking these questions. Wake up, wake up ! Stop dreaming!
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Other car companies watching Elon Musk casually give them another problem to solve 😔
Elon Musk
@elonmuskOct 16
Turns out the future does have flying cars
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AI enthusiasts are fighting over which model is 2% better while robotics is out here waiting for people who can make machines do actual work. but sure, let's build another chatbot and call it a startup.
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Hot take: You probably shouldn't use the same AI agent for work and personal life. Use one that helps you build, code, and get work done.@bot Use another that understands your personal life and stays with you across devices.@Muse One is your coworker. The other is your personal assistant
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If something like this can happen inside an AI company, it raises serious questions about the security of the data we entrust to these platforms. This is why people need to be more careful about what they share with AI tools. Your passwords, API keys, .env files, private documents, and confidential business information should never be shared carelessly. AI models can be incredibly useful, but using them doesn't mean you should abandon basic security practices. Your data may be processed, stored, or handled according to the platform's policies, and you may not always know who can access it or under what circumstances. Never confuse an AI tool being helpful with your data being completely safe. Use AI to improve your productivity, but understand the risks, review the privacy policies, and avoid exposing sensitive information unless you understand how it's handled and have a legitimate reason to share it. The goal isn't to stop using AI. It's to use it intelligently without compromising your security.
We discovered a sophisticated recording device that was hidden inside one of our offices. We used our models to understand the device and where it had come from, identifying that a specific employee had planted it a few days earlier. We referred the matter to the FBI, and we’re grateful to the agents and prosecutors who moved quickly to make an arrest. The FBI’s investigation is ongoing and we will continue to support their work. At this time, we have no indications of this individual inappropriately accessing or exfiltrating model weights, source code, or customer data. The employee was terminated. We take protecting our technology, data, and people seriously, and as a matter of national importance. Our security program is designed to protect against a wide range of threat actors, from insider risks to cybercriminals to nation states, and this case is an example of how AI can help address these sophisticated security threats.
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Gemini 4 Argon used less quota and finished faster than Opus 5.5 in this test. With results like this, what do you think about Gemini Argon ?
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A model can top benchmarks and still frustrate developers with usage limits, inconsistent outputs, or expensive pricing. The real test is how well it performs when people use it for actual work. Intelligence matters, but reliability and cost matter too.
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