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CrowdStrike President Michael Sentonas precisely describes the new cybersecurity paradigm we've found ourselves in: "The best thing that's happened to [cyber-adversaries] is the advancement in the open-weight models." "They can get a Chinese model, or effectively a model from anywhere, and run it inside their framework." "They can build...

19,903 views • 20 days ago •via X (Twitter)

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Rich Roll on why waiting to "feel like it" is a trap: "You can't think your way into the mood that you seek or the state of mind that you aspire to inhabit. Action is the only thing that can trigger that change." Rich uses running as the perfect illustration of this principle. Imagine you wake up in the morning and you're supposed to do a run because you're training for a race. You don't feel like it. So what do most of us do? "We all resort to that state where we think, 'Well, I don't want to do it right now. I'll just wait until I feel like doing it and then I'll do it then.'" But here's the problem with that logic: "If you're waiting until you feel like doing something, chances are you're probably never going to get to it." The mood you're hoping will arrive on its own? It's not coming. Not without action first. "To take the action despite how you feel about it is the thing that catalyzes the state change." You don't run because you feel motivated. You feel motivated because you ran. He points to what every runner knows from experience: "When they finish the run, they're always glad that they did it. They don't generally regret it. And then they feel better." Notice the sequence. The good feeling comes after the action, not before it. The state change is the reward for showing up, not the prerequisite. And this isn't just about running. As Rich puts it: "That example is applicable to all areas of life." The workout you're avoiding. The conversation you're delaying. The project you're putting off until you're "in the right headspace." You're waiting for a feeling that only exists on the other side of doing the thing.

Kevin Tanaka

10,256 views • 4 months ago

David Friedberg: Frontier Models are Training on Your Novel Insights as “De-Identified Data” @jason: “Should they trust any of these LLMs with their proprietary knowledge for fear of having it cribbed into a core LLM?” david friedberg: “I have had experiences where we've asked some fairly novel scientific questions, and (the AI model) identifies it as a novel insight. It's like, ‘Oh, never thought about that, interesting, blah, blah, blah.’ And then using a different account, asking the next version (of the model) later, I've now experienced this. It's like, ‘Oh, well, you could do this,’ and it actually just describes this exact thing that we had in our chat in the previous version. Now, these are a handful of anecdotal experiences, but I know the domain that we work in, and the niche of it, and the ideation of this stuff, and the novelty of this stuff, and the lack of papers being published, and so on. So I know that there isn't some new corpus of information out there that's training the new model. So all I can say at that point is that my conversation or our analyses have been used for training.” David Sacks: “Okay, this does raise a really good question. What does it mean that the model is allowed to train on unidentifiable data?” Friedberg: “Well, that's my point. So it doesn't use any of my personal information, but it can use an insight derived from our chat, which it can then say is some training data that is unrelated. But the truth is, it's actually a piece of IP that's our organization’s IP, and our engagement back and forth. We don't have any NDA or confidentiality provisions or protections with them being a service provider back to us. This is why I care a lot about open source because I don't want them having my chat logs because they can use it for training to create an IP advantage that is now diffused to the rest of the market.”

The All-In Podcast

54,050 views • 9 days ago

If I had to start from zero tomorrow and bring in new customers fast, here's exactly what I'd build. Top to bottom. I'd start with the ad. Open ChatGPT or Claude, paste in what you sell and who you sell it to, ask it to write you ten different hooks for a Meta ad. Pick the best three. Then jump into or one of the AI video tools and have it generate the visual. Thirty minutes, you've got a real ad ready to run. You run that ad to one landing page. Not your homepage. One page, one offer, one call to action. Headline that stops the scroll, a few lines of value, an email or phone field, a button. That's it. Build it in HighLevel in about twenty minutes, drag, drop, you're done. The second someone enters their email, they drop into your CRM, automatically tagged with where they came from. Now the part most people skip, the follow-up. The moment that form is filled, a sequence kicks off automatically. Email and SMS, spaced over five to seven days. First message thanks them and delivers what you promised. Then a story. Then social proof. Then an objection you know they have. Then the offer. They don't have to think about you. You're showing up in their inbox and their texts at exactly the right time. The last message has a calendar link or a buy button right inside it. They tap, they book, or they buy. Done. The whole flow runs on its own from the ad all the way to the sale. You build it once. It works while you're sleeping. AI does the creative work. The system runs the customer journey. You spend your time on strategy. That's the play.

Neil Patel

39,323 views • 2 months ago

Culture is genetic because behavior is genetic. This beaver never saw a dam in its life. No beavers or anything else ever taught it to build a dam. It wants to build a dam because it is a beaver. Many beavers together build a big dam. That is beaver culture. Humans are not different. Nothing is different. This is what life is. This is how life works. Your body is your mind. A caterpillar wants to build a chrysalis. A bee wants to build a hive. A lion wants to build a pride. You are not special. You are not above your nature. you are INSIDE of it. The thoughts that we think are genetic thoughts. The crimes we commit are genetic crimes. The art we create is genetic art. Just like this beaver, you can give the animal different sticks and it will build a different dam, but it will always build a dam. And you can give humans different "education," but the human will always use it to do what its genes tell it to do. This is the first big answer that you need. This is the biggest piece of the puzzle. This is how to understand people 90% of the way. You just... notice what they do, and get out of the way, and watch them do it. And if they need sticks, you give them sticks. And if you don't like what they do, you have to get away from them. You cannot train dam-building into them or out of them any more than you can with a beaver. A beaver wants to build a dam because it is a beaver. Whatever you see people build, that's what they wanted to build from the sticks they got in the river they were in. Stop pretending you can change it.

hoe_math = PsychoMath

1,191,649 views • 1 year ago

David Sacks Predicts the Regulatory Capture Playbook to Ban Open Source AI, Step by Step: David Sacks: “I got bad news for you, Chamath, an open source ban is coming. They're not going to call it that. They're going to say that we simply have to apply the same standards to open models that we apply to closed ones. Here's how they do it step by step, let me explain how regulatory capture actually works. So first of all, you have to get this regulatory apparatus. Dario wants an FDA for AI, but he doesn't have enough political support for that, so instead they do this Trojan horse of a FINRA for AI. They call it self-regulating, it's not really, but anyway, that gets them off the ground. Now they've created the standard-setting organization. Now they've got pre-release model testing. Then the pressure grows to codify that in law, so that happens next. And then what they do is they say, ‘Look, all these standards need to apply equally to all models.’ But here's the problem with that. Open models and closed models are technologically different. Once you release an open model into the world, you can't roll it back and you can't monitor exactly how people are using it because they run it on their own hardware. Dario says this is what makes open models dangerous. So what they're going to do is they're going to have the standard-setting body say, ‘Well, we have to set the standards for AI safety.’ By the way, Dario and OpenAI, they're going to fund the whole thing. They're going to contribute all the compute. They're going to be behind it. They're going to be the ones coordinating with the government officials because frankly, people in government have no idea how to monitor and control and set standards for AI safety. Technologically, this is way beyond them. So they're going to go to these companies and say, ‘Tell us how to do it.’ And so what will happen is the standards will get set, and then it'll be a very simple matter of fairness to say that the standards need to apply to open as well as closed models. The open models cannot comply in the same way, and gradually they will be shut out of the market.”

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298,808 views • 28 days ago