Fernando Bracher Beilke’s Post

𝗘𝘃𝗲𝗿𝘆𝗼𝗻𝗲 𝗶𝘀 𝗼𝘃𝗲𝗿𝘁𝗵𝗶𝗻𝗸𝗶𝗻𝗴 𝗵𝗼𝘄 𝘁𝗵𝗲𝘆 𝘁𝗮𝗹𝗸 𝘁𝗼 𝗔𝗜. And I mean everyone. I watch smart people freeze up like they are about to submit a thesis paper every time they open ChatGPT. They sit there trying to craft the perfect prompt. Over-explaining. Over-structuring. Over formatting. Meanwhile, the people actually winning with AI are just… talking. Clear. Direct. Specific. AI is not a professor grading you. It is a system waiting for instructions. And most people are making it way more complicated than it needs to be. This weekend, I am breaking this down in a way that is going to simplify everything. Because once you understand how to communicate with AI properly, you stop feeling intimidated by it. You start using it. And there is a big difference between knowing AI exists and knowing how to command it. More on that tomorrow. Build different. DM “SYSTEM” and I’ll show you how I structure instructions so AI actually executes.

Most people think LLMs are the peak of AI. Yann LeCun just bet $1 billion that they aren’t. For years, we’ve been obsessed with "predictive text." We’ve built machines that are world-class at talking but fail at the most basic physical tasks. Why? Because you can’t learn how the world works just by reading about it. LeCun’s new venture is pivoting away from the "Large Language Model" obsession toward "World Models." The goal isn't to chat. It's to understand gravity, dimensions, and cause-and-effect in the physical realm. Here is the hard truth: The bottleneck for AGI isn't more data. It's the type of data. We have trillions of words. We have almost zero data on the "common sense" of physical interaction that a 2-year-old child masters effortlessly. If this succeeds, the jump from ChatGPT to a physical AI agent will make the last two years look like a warm-up. We are moving from AI that thinks in symbols to AI that thinks in space. Read the full breakdown here: https://lnkd.in/gYfE3np4 Is the future of AI in the screen or in the streets?

Most people think the value of AI is what the model generates. The emails. The strategies. The plans. The ideas. But.. Where does all that intelligence actually go? Lost in a ChatGPT window? Buried in a Copilot thread? Forgotten as soon as the tab closes? We’ve all done it - generated something brilliant… then had absolutely no way to find it again. No context. No memory. No continuity. It’s like asking an employee to help you - …but never checking whether they understood the task, never making sure they can build on what worked, and never capturing what didn’t work. The model helps in the moment. But it learns nothing. And neither do you. That’s the gap. Not intelligence generation… intelligence retention. Because if you can’t store it, connect it, or build on it - even the smartest output becomes a one‑off. AI Twin taught me something big: Memory is the real multiplier. One place where ideas, decisions, insights, tasks, briefs, failures, and experiments actually live - so they can compound. The next stage of AI isn’t “better models.” It’s better continuity. Better memory. Better context. Better ability to say: “Here’s everything we’ve learned so far - this worked, this didn't.” Because intelligence isn’t the output It’s the part you can return to. The part that grows. The part you can trust. How much of your AI-generated work is actually saved somewhere you can use again?

https://lnkd.in/e5UdRY2a There are loads of articles like this one. Breathless examples of AI Super Users, ecstatic at their productivity uplift, using AI + OpenClaw, if technically savvy, to send 100s of emails, thrawl the web 24/7 for yet more 'stuff', obsessively trying to gain 'control' over their universe. "Busy, busy, busy, more, more, more". I shudder when I read this stuff; it makes me crave a month in a Buddhist enclave, high on a mountain with no WiFi where even Shackleton couldn't dig himself out in a hurry. Humans, supercharged by AI, off doing god knows what, with barely a thought to security or what can go wrong. Or simply taking stock and asking "Why"? Why this frenzied reach for AI? And the absence of the one faculty we humans need to do well- actually thinking. In my career, I have always spent countless hours solving difficult, often entrenched problems, with other thinking humans, across silos, across disciplines, shared thinking, shared experience, shared life lessons. There's no substitute for it & AI can't do it- incredibly poor at reading across, applying expert knowledge and common sense learnt from one domain to another. It's a single track, one trick pony. So before you feel grossly inadequate without an OpenClaw army of AI agents to command, think, think about what AI is not, what kind of world obsessive usage will create? And are we going to blindly drink the (trillion dollar) coolaid? #ArtificialIntelligence #FutureOfWork

ChatGPT made everyone think AI is easy. But here's what nobody tells you: → Prompt engineering is 10% of the work → Data pipelines are 50% → Edge cases are 30% → The last 10% takes 90% of the time The real skill isn't using AI. It's knowing WHEN to use AI and when a simple if-else wins. The best engineers I know don't use AI for everything. They use it surgically. That's the Viborithm way: Build smart. Ship fast. No bloat. #AI #SoftwareEngineering #Viborithm #TechReality #BuildSmart #ShipFast

ALMIGHTY AI The world didn’t just change gradually. It shifted with intention. Most of us didn’t notice at first. We were busy enjoying the convenience. Conformity isn’t forced anymore. It’s offered politely through speed, efficiency, and comfort. Saying no doesn’t feel rebellious. It just feels tiring. AI has definitely made life easier for me. Tasks that used to take hours now take minutes. I get more done. I look more productive. But lately, a quieter question has been sitting in the background: Am I actually thinking better, or simply moving faster? This evening, my son looked up from his homework and asked why he needed to solve his maths problem when ChatGPT could do it instantly. I paused, wondering what I just heard. When I was younger, solving those problems meant thinking through them step by step. There was a glowing pride in getting to the right answer on your own. Now the shortcut is always within reach. If we outsource our thinking, streamline our choices, and let algorithms shape what we read, buy, and believe, what parts of our judgement remain truly ours? Keeping up with technology once felt like progress. Now it sometimes feels like the minimum requirement. So here’s the question I keep coming back to: Will AI give us some room to grow and think, or are we slowly placing the crown on its head ourselves and calling it evolution? I don’t have a final answer. But it feels like a question worth asking out loud.

Today I caught myself doing something that didn’t feel very human (at least from my perspective). I asked ChatGPT for advice on how to deal with a personal problem… and I trusted the answer almost instantly. No verification. No second thought. JUST TRUST. It made me realize something: AI adoption is moving faster than our habits of questioning it. And the irony? I work in AI governance!! I spend my days thinking about risks, limitations, and the importance of oversight in AI systems. I know very well that these systems have flaws. Yet in that moment, I behaved like many users do: I simply trusted the output. Maybe this is one of the biggest challenges with AI today. Not just building reliable systems, but making sure our human reflexes keep up with the technology we’re adopting so quickly. Because governance frameworks, policies, and controls matter. But so does something simpler: our ability to stay critical, even when the answer sounds convincing. Curious to hear your thoughts: Have you ever caught yourself trusting AI a bit too quickly?

I watched the movie "The Imitation Game" recently. It sent me down a rabbit hole. Most people think artificial intelligence started with ChatGPT. It didn't! It started in 1950, with a British mathematician, a dangerous question, and an idea so far ahead of its time that the world took 70 years to catch up. The question was simple: 𝗖𝗮𝗻 𝗺𝗮𝗰𝗵𝗶𝗻𝗲𝘀 𝘁𝗵𝗶𝗻𝗸? At the time, computers filled entire rooms and could barely do arithmetic. Today, AI systems write code, pass the bar exam, and hold conversations indistinguishable from a human being. One man saw all of this coming. His name was Alan Turing. And almost nobody talks about what he actually built or what happened to him. After going deep into his story, I realized the real history of artificial intelligence is far more fascinating than anything the headlines tell you. So I'm writing a 12-part series: The Intelligence Revolution. From the earliest ideas about thinking machine, to the modern labs racing to build artificial general intelligence today. Article 1 is coming very soon. If you've ever wondered where AI actually came from, not the hype, not the marketing, but the real origin story. Then this series is for you. Follow along. You won't want to miss where this story goes #ArtificialIntelligence #AIHistory #AlanTuring #MachineLearning #FutureOfTechnology #TechLeadership #TheIntelligenceRevolution

https://lnkd.in/erbBGjGV Testing AI's critical thinking requires crafting questions with flawed logic, disguised in credible terminology. This approach reveals how readily models accept nonsensical premises. Questions span software, finance, legal, medical, and physics, employing techniques like cross-domain concept stitching (e.g., product backlog and solvency) and false granularity (e.g., "95% confidence interval on team's morale trajectory"). A statistically undefined concept, yet presented as rigorous. Remarkably, many advanced AIs, including ChatGPT, struggle to identify and reject these logical fallacies. Only a few model families, like Anthropic and Alibaba's Qwen, demonstrate a stronger ability to push back against fabricated precision and flawed premises. This highlights a crucial gap in AI reasoning capabilities. #AI #ArtificialIntelligence #CriticalThinking #LLM #Tech

𝐈 𝐦𝐨𝐯𝐞𝐝 𝟔 𝐦𝐨𝐧𝐭𝐡𝐬 𝐨𝐟 𝐀𝐈 𝐦𝐞𝐦𝐨𝐫𝐲 𝐟𝐫𝐨𝐦 𝐂𝐡𝐚𝐭𝐆𝐏𝐓 𝐭𝐨 𝐂𝐥𝐚𝐮𝐝𝐞 𝐢𝐧 𝟔𝟎 𝐬𝐞𝐜𝐨𝐧𝐝𝐬. Here's why that's a bigger deal than it sounds. Every time you've corrected your AI's tone... Every time you've explained your job title, your preferences, your projects... Every time you've said "stop using Em dashes"... You weren't just chatting. You were building something. After months of daily use, that accumulated context is quietly worth more than the AI itself. Not because ChatGPT was the best tool. But because starting over felt expensive. Well, the good news is... Anthropic just changed that. Claude shipped a memory import feature that lets you pull your stored preferences, context, and personal details from ChatGPT, Gemini, or Copilot in about 60 seconds. It's a smart, competitive move. But more importantly, it's the first real signal that your AI context might actually be portable, and that you own it more than you think. I recorded a quick walkthrough showing exactly how to do it below 👇 The process takes less than a minute. Let me know in the comments, have you built up significant memory/context in any AI tool? Would you move it?