TruthArchive.ai - Tweets Saved By @r0ck3t23

Posted - March 17, 2026 at 12:42 AM

Saved - March 17, 2026 at 5:45 PM

reSee.it AI Summary

I recognize the truth Bezos voices: we’re not mature enough for what we’re building. The danger is exponential speed outpacing our instincts. I’ll favor building rigid systems to protect execution from humanity’s worst impulses, even as benefits arrive before AGI. We don’t need full autonomy to reshape the economy; we should deploy compute now to solve bottlenecks. The next decade is a sprint, and those who pace themselves will be erased by velocity.

@r0ck3t23 - Dustin

Jeff Bezos just delivered the most uncomfortable truth in the AI conversation. We are not mature enough for what we’re building. Bezos: “We as a species are not really sophisticated enough and mature enough to handle these technologies.” This is the real danger of the exponential curve. We’re not gracefully evolving alongside the machine. We’re strapping a primitive, emotional operating system to a multi-dimensional intelligence engine and hoping the wiring holds. The ones who survive the next decade won’t be the ones blindly trusting our species to regulate itself. They’ll be the ones building rigid systems to protect their execution from humanity’s own worst impulses. Bezos: “Before you get to general AI and the possibility of AI having agency, there’s so much benefit that’s going to come from these technologies in the meantime even before there’s general AI, in terms of better medicines and better tools to develop more technologies.” The mainstream is entirely obsessed with the arrival of AGI. The winners are harvesting the multi-trillion-dollar opportunity sitting right in front of them. You don’t need a fully autonomous general intelligence to rewrite the global economy. We’ve already entered the recursive phase where the algorithm is actively building the next generation of tools. You don’t have to wait for the machine to wake up and take agency. Deploy the current compute engine against biological and physical bottlenecks today and the advantage compounds exponentially before AGI ever arrives. Bezos: “I think it’s an incredible moment to be alive and to witness the transformations that are going to happen. Over the next 10 years and 20 years, I think we’re going to see really remarkable advances.” We’re standing at the opening seconds of a hyper-compressed industrial revolution. The organizations that pace themselves for gradual evolution will be erased by the sheer velocity of what’s actually happening. The ones that treat the next decade as a relentless sprint to embed compute into every layer of their infrastructure won’t just survive the transformation. They’ll be the ones who caused it.

@r0ck3t23 — Dustin

Posted - February 17, 2026 at 5:30 AM

Saved - February 17, 2026 at 5:33 PM

reSee.it AI Summary

I hear Musk say AI will wipe out digital, desk jobs first—anything done on a computer—while trades like welding, electrical, and plumbing endure because they require physical presence and real-world friction. Analysts, accountants, and programmers, who produce files, are displaced early since AI handles digital work. Society’s tilt to offices backfired: the devalued trades prove most automation-resistant, while valued, desk-bound roles erase quickest.

@r0ck3t23 - Dustin

Elon Musk just identified which jobs go first, and it destroys every assumption about who’s safe. Musk: “AI is going to take over those jobs like lightning. Anything that is digital, which is like just someone at a computer doing something.” Not factory workers. Office workers. The people who spent decades assuming education and desk jobs meant security are actually first. Musk: “Anything that’s physically moving atoms… those jobs will exist for a much longer time.” Output is a file? Vulnerable. Output is physical? Protected. That’s the entire framework. Musk: “AI is really still digital.” AI doesn’t need a body. Doesn’t need an office. Just needs access to the same software you use. Executes faster. Never tires. Costs nothing to scale. But it can’t weld. Can’t wire a building. Can’t fix pipes or work soil. Musk: “Literally welding, electrical work, plumbing. Those jobs will exist for a much longer time.” Trades aren’t the vulnerable jobs. They’re the durable ones. Physical presence, real-world adaptation, manual dexterity provide protection no digital credential offers. Analyst, accountant, paralegal, programmer, anyone producing files and documents, automates first because digital work is exactly what AI does natively. Person moving atoms has natural defense. Physics, unpredictable environments, material resistance create friction AI can’t scale past. Person moving bits has nothing. No friction. No physical barrier. Just software AI already operates better than most humans. The assumption that desk work and degrees represent safety just inverted completely. College graduate producing documents faces faster displacement than the electrician producing installations. Society spent generations telling people trades were beneath them. Pushed everyone toward offices and screens. Turns out the people who didn’t listen built the most automation-resistant careers. Most ironic outcome of the AI revolution. The work society treated as inferior turned out to be the work society couldn’t replace. And the work society valued most turned out to be the easiest to eliminate.

Video Transcript AI Summary

The speaker argues that AI remains fundamentally tied to digital activity, contrasting it with physical, hand-based work. The core claim is that AI can boost the productivity of people who perform tangible, hands-on tasks, particularly those who build or repair things with their hands. Examples cited include welding, electrical work, plumbing, and other activities that involve moving atoms physically. The speaker also references relatable daily tasks such as cooking food and farming to illustrate the category of physical labor. The underlying point is that jobs rooted in physical manipulation and manual labor are expected to persist for a much longer period. In contrast, the speaker asserts that any work that is digital—defined as activities done at a computer or involving digital, screen-based tasks—will be rapidly taken over by AI. The statement emphasizes speed and inevitability, describing AI’s impact on digital labor as occurring “like Lightning.” This distinction highlights a predicted bifurcation in job longevity based on the nature of the work: enduring physical trades versus soon-to-be-replaced digital tasks. Overall, the speaker presents a dichotomy: AI enhances productivity for hands-on, physical work that involves tangible, atom-level manipulation, suggesting those roles will endure longer, while it rapidly supplants digital, computer-based work. The emphasis is on the differential timeline and scope of AI’s impact across these two broad categories of labor. The language uses concrete examples to anchor the argument in everyday occupations (welding, electrical work, plumbing, cooking, farming) and contrasts them with “anything that’s digital” done at a computer, forecasting a near-term replacement for such digital tasks.

AI Similar Videos

Full Transcript

Speaker 0: AI, will it is is really still digital. Ultimately, AI can improve the productivity of of humans who'd who, build things with their hands or do things with their hands like plum you know, literally welding, electrical work, plumbing, anything that's that's physically moving atoms. Like like cooking food or, you know, farming or or and, like like, anything that's that's physical, those jobs will exist for a much longer time. But anything that is digital, which is like just someone at a computer doing something, AI is gonna take over those jobs like Lightning.

@r0ck3t23 — Dustin

Posted - February 14, 2026 at 10:26 PM

Saved - February 15, 2026 at 8:16 AM

reSee.it AI Summary

I hear Schmidt warn that the U.S. chases AGI while China deploys day-to-day AI now—open, free models, and hardware everywhere. If the world adopts Chinese stacks, a floor of embedded AI could outpace U.S. breakthroughs. Deployment speed, adoption, and standards matter more than pure capability. The war isn’t in labs but in factories, phones, and supply chains—China wins the adoption race as America pursues the ceiling.

@r0ck3t23 - Dustin

Eric Schmidt just identified how America loses the AI war despite building better technology, and most people haven’t noticed it’s already happening. Schmidt: “The U.S. is chasing AGI.” America is fixated on one prize. Artificial General Intelligence. The god model. The moonshot that changes everything. Pouring resources into the ultimate breakthrough. China isn’t playing that game at all. Schmidt: “China is shipping day-to-day AI apps, and robotics.” Not waiting for superintelligence. Deploying current AI everywhere right now. Factory floors. Consumer devices. Supply chains. Physical robots at industrial scale. Today, not eventually. America might win the race to AGI and still lose the world. Schmidt: “If Chinese open-source models get good enough…” The strategic blindness is structural. US models are closed, proprietary, expensive. Chinese models like DeepSeek are open and free. Where does the developing world build its digital future? On technology it can access and afford. Which means Chinese. Schmidt: “Much of the world could end up building on them.” America competes for smartest AI. China competes for most embedded AI. And embedded wins. Technical superiority is worthless when the global standard already runs on your competitor’s freely available stack. Schmidt: “We better also be competing with the Chinese in day-to-day stuff.” The US places all chips on the ten-year AGI bet while handing China the entire commercial present. China deploys relentlessly. Robots, apps, infrastructure, all shipping now while America perfects research. Optimize exclusively for breakthrough and you surrender the industrial base. That base determines who controls what actually matters. Superintelligence is strategically meaningless if China owns the hardware running it, the software layer beneath it, and the deployed systems the world depends on. You can build the most advanced AI in existence. If nobody uses it because they’re locked into competitor ecosystems established years earlier while you focused on moonshots, you didn’t win. You built expensive irrelevance. This isn’t about capability. It’s deployment speed, adoption capture, and which technology becomes the foundation everything else builds on. America chases the ceiling. China is becoming the floor. And in technology, the floor matters more. Standards don’t win by being superior. They win by being everywhere first. And once established, switching costs make replacement nearly impossible regardless of technical advantages. We might invent AGI. China might own every system it runs on, every device it connects to, every market it operates in. At that point, creating the most intelligent AI while controlling none of the infrastructure it needs to function isn’t triumph. It’s building the world’s most advanced engine with no vehicle to put it in while your competitor already sold cars to everyone. The war isn’t won in labs. It’s won in factories, phones, and supply chains. And while we perfect the breakthrough, they’re winning the adoption race that actually determines who shapes the future.

Video Transcript AI Summary

Speaker believes that China and the United States are competing at more than a peer level in AI. They argue China isn’t pursuing crazy AGI strategies, partly due to hardware limitations and partly because the depth of their capital markets doesn’t exist; they can’t raise funds to build massive data centers. As a result, China is very focused on taking AI and applying it to everything, and the concern is that while the US pursues AGI, everyone will be affected and we should also compete with the Chinese in day-to-day applications—consumer apps, robots, etc. The speaker notes the Shanghai robotics scene as evidence: Chinese robotics companies are attempting to replicate the success seen with electric vehicles, with incredible work ethic and solid funding, but without the same valuations seen in America. While they can’t raise capital at the same scale, they can win in these applied areas. A major geopolitical point is emphasized: the mismatch in openness between the two countries. The speaker’s background is in open source, defined as open code and weights and open training data. China is competing with open weights and open training data, whereas the US is largely focused on closed weights and closed data. This dynamic means a large portion of the world, akin to the Belt and Road Initiative, is likely to use Chinese models rather than American ones. The speaker expresses a preference for the West and democracies, arguing they should support the proliferation of large language models learned with Western values. They underline that the path China is taking—open weights and data—poses a significant strategic and competitive challenge, especially given the global tilt toward Chinese models if openness remains constrained in the US.

AI Similar Videos

Full Transcript

Speaker 0: I have thought that China and The United States were competing at the peer level in AI. They're really doing something more different than I thought. They're not pursuing crazy AGI strategies, partly because the hardware limitations that you put in place, but partly because the depth of their capital markets don't exist. They can't raise based on a wing and a prayer a $100,000,000 or a member equivalent to to build the data centers. They just can't do it. And so the result is they're very focused on taking AI and applying it to everything. And so the concern I have is that while we're pursuing AGI, which is incredibly interesting and we should talk about it, all of us will be affected by this. We better also be competing with the Chinese in day to day stuff. Right. Consumer apps, this is something you understand very well, Shmaz. Consumer apps, robots, and so forth and so on. I saw all the the Shanghai robotics companies, and these guys are attempting to do in robots what they've successfully done with electric vehicles. Their work ethic is incredible. They're well funded. It's not the crazy valuations that we have in America. Right. They can't raise the capital, capital, but they can win across that. The other thing the Chinese are doing, and I wanna emphasize this as a major geopolitical issue, is that my own background is open source. In the audience, you all know open source means open code, and weights means open training data. China is competing with open weights and open training data, and The US is largely and majority focused on closed weights, closed data. That means that the majority of the world, think of it as the Belt and Road Initiative, are gonna use Chinese models and not American models. Now I happen to think the West and democracies are correct, and I'd much rather have the proliferation of large language models and that learning be done based on Western values.

@r0ck3t23 — Dustin

Posted - February 12, 2026 at 6:24 PM

Saved - February 13, 2026 at 11:57 AM

reSee.it AI Summary

I hear Huang say the AI race was decided before it began: China now owns half of the world’s AI researchers—50%. It’s not a spike, but a fundamental talent pipeline cornering. Indigenous innovation, not imitation, drives a generational infrastructure that multiplies advantages. The West talks sanctions; the input gap is the real game. When you control the minds shaping what’s possible, you’re not competing—you’re setting the agenda.

@r0ck3t23 - Dustin

Jensen Huang just said what no Western politician will admit: the AI war was decided before it was declared. China controls 50% of the world’s AI researchers. Not the developing world. Not Asia-Pacific. China. Half of every mind advancing artificial intelligence belongs to one nation. Huang: “We aren’t just looking at a spike in production. We are looking at a fundamental cornering of the talent pipeline.” The West celebrates chip sanctions while missing what already happened. Patent dominance isn’t the story. It’s the scoreboard for a game that ended when China built the world’s most aggressive STEM pipeline and nobody noticed until the results became undeniable. This isn’t imitation. It’s indigenous innovation at a scale that makes Western output look like a rounding error. Huang: “vibrant, rich, and incredibly innovative.” Not flattery. Assessment from someone who understands what those researchers represent. Everyone’s debating output. Output is history. Input is destiny. And the input differential isn’t a gap. It’s a chasm. Huang: “50% of the world’s AI researchers are now Chinese.” The narrative is semiconductors and export controls. Tactics. The strategy was talent acquisition, and that war concluded while we argued about TikTok. When you own half the minds defining what’s possible, you’re not competing. You’re setting the agenda while others react to an innovation cycle you control. China didn’t win the year or the decade. They won the generational infrastructure that compounds. Every elite researcher today teaches ten tomorrow. Advantages don’t plateau in knowledge work. They multiply exponentially. The West restricts silicon. China produces the intelligence that architects around restrictions faster than they can be implemented. And half that intelligence already operates under a different flag. This isn’t a competition anymore. It’s a lag time before reality catches up to what the math already proved.

Video Transcript AI Summary

Speaker 0 presents a series of strong statements about China's position in artificial intelligence. He states that 50% of the world's AI researchers are Chinese and that 70% of last year's AI patents are published by China. He describes the AI ecosystem in China as vibrant, rich, and incredibly innovative. He also asserts that nine out of the 10 top science and technology schools in the world are now in China, and claims that China leads in science and technology in many different fields. The speaker notes that this situation has completely flipped in the last half a decade, with China moving from previously leading in most areas to now leading most of them. He highlights that China has a large population of highly qualified students who work incredibly hard. He concludes by characterizing China as a country with enormous might.

AI Similar Videos

Full Transcript

Speaker 0: 50% of the world's AI researchers are Chinese. 70% of last year's AI patents are published by China. The ecosystem of AI in China is vibrant, rich, incredibly innovative. I think it's like nine out of the 10 top science and technology schools in the world are now in China. They lead in science and technology in many different fields. This has completely flipped in the last half to a decade. We used to lead most of them. Now they lead most of them. They have a large population of highly qualified students. They work incredibly hard. This is a country with enormous might.

@r0ck3t23 — Dustin

Posted - February 12, 2026 at 2:28 AM

Saved - February 12, 2026 at 10:50 AM

reSee.it AI Summary

I’m convinced the AI race is far from over; we’re only three years into a 30-year shift. You can argue big model incumbents win, open source destroys margins, or China dominates on cost. Or something nobody’s tracking could topple all three. Might open source eat it, or China match frontier performance for less. If costs go toward zero, the usual business models vanish. The ground is moving, rules aren’t written, and certainty is delusional.

@r0ck3t23 - Dustin

Most people think the AI race is over. It hasn’t even started. Marc Andreessen: “We’re only 3 years into probably a 30-year shift.” You can build a bulletproof case that the big model companies will own the future. You can build an equally convincing one that open source destroys their margins completely. Or that China replicates everything at a tenth of the cost and wins through pure economics. Or something no one is tracking yet obliterates all three. Andreessen: “The whole thing could get eaten by open source. Or by China.” While we obsess over valuations, the ground is moving. China’s Kimi just matched frontier performance at a fraction of Western pricing. If global competition and open models drive intelligence costs toward zero, the business models everyone’s betting on don’t shrink. They vanish. We’re demanding certainty in a market that hasn’t even defined what it’s competing for. Andreessen: “I actually think we don’t know yet.” Maybe a handful of companies control the infrastructure and tax every transaction. Maybe open source commoditizes intelligence and no one captures value. Maybe China matches capability so cheaply that innovation stops mattering and cost becomes everything. Three years into thirty. The players aren’t decided. The rules aren’t written. The finish line hasn’t been drawn. Pretending you know the outcome isn’t strategic. It’s delusional.

Video Transcript AI Summary

Speaker 0 discusses competing narratives about AI model companies, noting that some see them owning everything while others believe open source, China, or a combination of both will dominate. He highlights Kimi, which released a competitive model to the latest Claude at roughly 95% capability for a fraction of the price, illustrating the open-source/china-driven competition. He observes a notable rotation in the market: Nvidia’s sustained success over the past five years has made chips the center of action, and the stock market shows a shift from software to hardware. He asks whether chips will capture all the value and whether software will become open source, suggesting the possibility that even if chips accrue value, they might become commoditized like past tech cycles. He cautions that historically, whenever people proclaimed chips to be where the value is, they often commoditize. This leads to bigger questions about the app layer: will there be specialized apps that harness AI in areas such as medicine, where apps could be tailored and customized, or in legal and various business domains? Or will the models themselves perform all these functions without specialized applications? The speaker emphasizes the novelty of the current moment: AI is a long-standing topic (an 80-year thread), but the mode of operation now—where this set of questions is being resolved—is only partway through. He suggests we are probably in a three-year stage within a likely thirty-year shift and concedes that we do not yet know how these dynamics will unfold.

AI Similar Videos

Full Transcript

Speaker 0: You can paint this picture that says that the AI model companies are going to basically own everything. By the way, you look at their businesses and they're doing fantastically well. You can also look at it and say, oh no, that whole thing is going be eaten by open source. And by by the way, or by China or by a combination of open source in China, which China is doing great. This company, Kimi, just dropped a very competitive model to the latest Claude at like 95% the capability at like a fraction of the price. And so there's like a very big open question there. We happen to be at this moment what everybody believes. And if you look at Nvidia's deserved success over the last five years, the reasonable conclusion is like chips all of a sudden, chips is where the action are. If you look at the stock market, there's like a rotation from software into hardware. And look, it's possible that chips are impossible all the value accrues to the chips the energy and then the software is all open source. Having said that, every other time in history where we said the chips are where the value are, they commoditize, right? And so there's big questions there. And then there's even more questions, I would say, at the app layer, right, which is are you going to have apps that are going to sort of harness AI, for example, in spaces like medicine, where they're going to be particularly like tailored and customized or legal apps or business apps of all kinds? Or are the models just going to do all that? And that's another area. And so I quite honestly, like this is so new. Like, approach of I mean, AI is an 80 year old topic, but AI working in a way where this is the question, I think we're only three years into, you know, probably a thirty year shift. And I actually think we don't know yet.

@r0ck3t23 — Dustin

Posted - February 10, 2026 at 10:16 PM

Saved - February 12, 2026 at 1:12 AM

reSee.it AI Summary

I hear Jensen Huang say technical intelligence is a commodity. Machines solve easy problems; the edge now goes to those who see around corners. True smartness blends experience, context, empathy, and instinct—synthesis AI can’t train. The real value isn’t writing code, but anticipating what needs to exist before anyone asks. Calculation is commodity; synthesis is where power lives.

@r0ck3t23 - Dustin

Software engineering used to be the pinnacle of intelligence. Now it’s the first job AI is replacing. Jensen Huang: “Technical intelligence is becoming a commodity.” The hard technical problems everyone worried about? Those turned out to be the easy ones. Machines solve them faster, cheaper, and without error. So what’s left for humans? Huang: “People who can see around corners are truly, truly smart.” The new intelligence isn’t solving the problem in front of you. It’s sensing the problem before it exists. Connecting patterns that don’t look related. Anticipating what no one has thought to ask for yet. That’s not logic. That’s intuition. A synthesis of experience, context, empathy, and instinct you can’t train into a model. Huang: “My personal definition of smart is someone who sits at the intersection of technical astuteness and human empathy.” Technical skill is table stakes now. The real edge belongs to people who read between the lines, navigate ambiguity, and synthesize across domains AI can’t bridge. Calculation is commodity work. Synthesis is where the power lives. The valuable people aren’t writing the code anymore. They’re seeing what needs to exist before anyone knows to ask for it.

Video Transcript AI Summary

Speaker 0 argues that the common definition of smart—being intelligent, solving problems, technically capable—has become a commodity, and that artificial intelligence is proving able to handle that aspect most readily. They note that many people previously believed software programming was the ultimate smart profession, but begin by asking what AI is ultimately solving first: software programming. They offer a personal definition of smart as residing at the intersection of technical astuteness and human empathy, with the ability to infer the unspoken, the around-the-corners, and the unknowables. In their view, people who are able to see around corners are truly smart, and their value is incredible because they can preempt problems before they show up simply by sensing the vibe. This vibe, they claim, arises from a blend of data, analysis, first principles, life experience, wisdom, and the ability to sense other people. In summary, the speaker asserts that true smartness combines technical skill with deep social and experiential insight, enabling proactive problem anticipation through a nuanced, perceptive awareness of people and situations.

AI Similar Videos

Full Transcript

Speaker 0: The definition of smart is somebody who's intelligent, solve problems, technical. But I find that that's a commodity, and we're not we're about to prove that artificial intelligence is able to handle that part easiest. Everybody thought software programming is the ultimate smart profession. Look. What is the first thing that AI is solving? Software programming. My personal definition of smart is someone who sits at that intersection of being technically astute, but human empathy and having the ability to infer the unspoken, the around the corners, and the unknowables. You know, people who are able to see around corners are truly, truly smart, and that their value is incredible. To be able to preempt problems before they show up just because you feel the vibe. And the vibe came from a combination of data, analysis, first principle, life experience, wisdom, sensing other people. That vibe, that I think, that's smart.

@r0ck3t23 — Dustin

Posted - February 11, 2026 at 5:01 AM

Saved - February 11, 2026 at 1:36 PM

reSee.it AI Summary

I believe the future’s most powerful language is English. We’ll describe what we want, not code it; if it’s off, it fixes itself. The barrier to control drops to zero as we shift from syntax to intent. Prompt engineering becomes clear communication with a new audience. If I can articulate needs and refine through conversation, I’m a developer; the coder becomes obsolete, the orchestrator prevails. Clarity is the essential skill.

@r0ck3t23 - Dustin

The most powerful programming language of the future isn’t C++ or Python. It’s English. Jensen Huang: “Why program in Python? So weird.” You won’t write code anymore. You’ll describe what you want. If the result isn’t right, you won’t debug. You’ll just tell it to fix itself. The barrier to controlling computers is hitting zero. We’re shifting from syntax to intent. You don’t need to know how to write a script to modify a system. You need to know how to explain what should happen. Huang: “English is the best programming language of the future.” Prompt engineering is just clear communication with a new audience. How you talk to people and how you talk to machines is becoming the same competency. If you can articulate what you need clearly, you’re a developer. If you can refine through conversation, you can ship products. The coder is obsolete. The orchestrator is everything. The skill isn’t syntax anymore. It’s clarity. Knowing what to build, how to ask for it, and how to direct until it’s exactly right.

Video Transcript AI Summary

The speaker envisions a future where programming is largely mediated through natural communication with a computer. In this vision, you will tell the computer what you want in plain language, and the computer will respond with concrete outputs such as a build plan that includes all suppliers and a bill of materials aligned with a given forecast. The speaker emphasizes that the initial interaction is in plain English, and the computer can generate a comprehensive plan based on the stated requirements. If the output doesn’t meet the user’s preferences, the user can create a Python program to modify that build plan. A key example given is asking the computer to come up with a build plan with all the suppliers and the bill of materials for a forecast, and then relying on the computer to produce the necessary components in a cohesive plan. The speaker illustrates a workflow where the user can iterate by writing a Python program that adjusts the generated plan, thereby enabling customization and refinement of the suggestions produced by the initial natural-language prompt. The speaker then reiterates the concept of speaking with the computer in English as the first step, and implies that the second step involves using Python or programmable modifications to tailor the result. This underscores a shift in how programming is approached: the user first communicates in English to prompt the computer, and then leverages programming to fine-tune or alter the plan as needed. The underlying message is that the interaction with computers is evolving toward more intuitive human-computer dialogue, where the machine can interpret a plain-English prompt and produce structured, actionable outputs, with a programmable mechanism to adjust those outputs. Central to this discussion is the idea of prompt engineering—the practice of how you prompt the computer and how you interact with people and machines to achieve the desired outcome. The speaker highlights that prompting the computer and refining instructions is an art, describing prompt engineering as an artistry involved in making a computer do what you want it to do. The emphasis is on crafting prompts that elicit precise, useful results and on the skilled, creative process of fine-tuning instructions to achieve the best possible alignment between user intent and machine output.

AI Similar Videos

Full Transcript

Speaker 0: Why program in Python? So weird. In the future, you'll tell the computer what you want. And the computer will will you you say, hi. I would like you to come up with a a build plan with all of the suppliers and bill of material for forecast that we have for you. And based on all the necessary components necessary, you're coming up with a build plan. And then if you don't like that, you just write me a Python program that I can modify of that build plan. And so remember, the first time I talk to the computer, I'm just speaking in plain English. The second time so English, by the way, human, it's the best programming language of the future. How you talk to a computer, how do you prompt it, how do you prompt it, it's called prompt engineering, how you interact with people, how do you interact with computers, how do you make a computer do what you want it to do, how do you fine tune, the instructions with that computer? That's called prompt engineering. There's an there's an artistry to that.