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New chapter! Context problems. Who can solve these by hand ✍️? Download PDF: The first five: 1. Mark the context: the window as cells you can count 2. What fills the window: the system prompt, the user, the model's reply 3. Turn by turn: every turn is the same...

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Tom Yeh

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11,641 просмотров • 18 дней назад •via X (Twitter)

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Can you solve these agentic AI math problems by hand ✍️? Getting a bit harder now. Download PDF: Problems 11 to 15: 11. Comparing three calls: same total tokens, three different bills 12. The average call: size a sample, then scale it to the day 13. The system prompt: the tokens you re-send on every single call 14. The system prompt tax: what repeating those instructions costs 15. Calls in a budget: cost one call, then divide Download, print, solve, and learn. ✍️ ~ Prof. Tom Yeh
0:23

Can you solve these agentic AI math problems by hand ✍️? Getting a bit harder now. Download PDF: Problems 11 to 15: 11. Comparing three calls: same total tokens, three different bills 12. The average call: size a sample, then scale it to the day 13. The system prompt: the tokens you re-send on every single call 14. The system prompt tax: what repeating those instructions costs 15. Calls in a budget: cost one call, then divide Download, print, solve, and learn. ✍️ ~ Prof. Tom Yeh

Tom Yeh

41,681 просмотров • 20 дней назад

I often think of my father when I hear some academics pontificating about how to solve the unemployment problem. I'm tempted to say to them, 'Well, if you find it so easy to solve, why don't you go out and start up a business and employ by your own effort? I will tell you why, because you can't. It's easier to tell other people what to do about it than it is to sort it out yourself.' But in the end, we have to provide the kind of society where people who can do this, who can build up a business, are prepared to start because they're tremendously important.
1:56

I often think of my father when I hear some academics pontificating about how to solve the unemployment problem. I'm tempted to say to them, 'Well, if you find it so easy to solve, why don't you go out and start up a business and employ by your own effort? I will tell you why, because you can't. It's easier to tell other people what to do about it than it is to sort it out yourself.' But in the end, we have to provide the kind of society where people who can do this, who can build up a business, are prepared to start because they're tremendously important.

Margaret Thatcher

93,301 просмотров • 1 год назад

This is one of my favorite movie endings... It has a powerful lesson: "At some point, everything is going to go south on you...and you're gonna say 'this is it, this is how I end.' Now you can either accept that, or you can get to work. That's all it is, you just begin. You do the math. You solve one problem. Then you solve the next one. And the next. And if you solve enough problems, you get to come home." This is the beautiful thing about life: No matter where you are today—no matter how deep in the darkness—you are always just one good decision away from being in a better place tomorrow. You can always focus on that one decision—that one problem—in front of you. Don't worry about the hundreds or thousands of decisions that you still have to make to get to where you want to be—just focus on the next decision. Just begin. Solve one problem. Then solve the next one. And the next. "Life is really simple, but we insist on making it complicated." - Confucius
0:50

This is one of my favorite movie endings... It has a powerful lesson: "At some point, everything is going to go south on you...and you're gonna say 'this is it, this is how I end.' Now you can either accept that, or you can get to work. That's all it is, you just begin. You do the math. You solve one problem. Then you solve the next one. And the next. And if you solve enough problems, you get to come home." This is the beautiful thing about life: No matter where you are today—no matter how deep in the darkness—you are always just one good decision away from being in a better place tomorrow. You can always focus on that one decision—that one problem—in front of you. Don't worry about the hundreds or thousands of decisions that you still have to make to get to where you want to be—just focus on the next decision. Just begin. Solve one problem. Then solve the next one. And the next. "Life is really simple, but we insist on making it complicated." - Confucius

Sahil Bloom

213,822 просмотров • 2 лет назад

This thread reminded me of Thiel's take on biology and the excuses of biologists for why we aren't solving hard problems (I have a biology background and work as a scientist and still agree with him). My take is that AI won't solve biology, but it will help us understand it at a deeper level. Saying that it is impossible is a lazy take. Saying that AI will solve all problems is a lazy take too. There is an in-between where AI will support and help scientists move things forward. I am really tired of the excuses of the gatekeeper boomer scientists and the hype of the AI bros.
1:42

This thread reminded me of Thiel's take on biology and the excuses of biologists for why we aren't solving hard problems (I have a biology background and work as a scientist and still agree with him). My take is that AI won't solve biology, but it will help us understand it at a deeper level. Saying that it is impossible is a lazy take. Saying that AI will solve all problems is a lazy take too. There is an in-between where AI will support and help scientists move things forward. I am really tired of the excuses of the gatekeeper boomer scientists and the hype of the AI bros.

Linden

67,241 просмотров • 2 месяцев назад

Naval Ravikant on the importance of hiring high-agency people Naval defines agency as: “People who just solve problems without even being asked to solve the problem—they identify the problem, they go solve it, they don’t even necessarily have to update you every step of the way, they’re not asking silly questions, and they’re just coming up with solutions.” He believes this is important because “building a startup is an infinite set of problems that are being thrown at you.” And there comes a day where you can’t even look at every problem your company is facing—let alone solve every one of them. He cites the Vinod Khosla aphorism: "The team you build is the company you build, not the plan you make.” And your ability to solve problems is based entirely on how many problem-solvers you have at your company. As Naval puts it: “If you have somebody who takes 10% of your time and management to solve problems, you can only have 10 of those people working with you. But if somebody takes 5%, you can have 20 of those people.” When building Airchat and AngelList, he thought of each team as a Navy Seal team: “Everyone is just really good at what they do. They know their job. They do it. They don’t complain. They’re not egotistical about it. And if they have to constantly be corrected, led around by the nose, you have to clean up after them, or you question their judgement, it’s not going to work out.”
1:19

Naval Ravikant on the importance of hiring high-agency people Naval defines agency as: “People who just solve problems without even being asked to solve the problem—they identify the problem, they go solve it, they don’t even necessarily have to update you every step of the way, they’re not asking silly questions, and they’re just coming up with solutions.” He believes this is important because “building a startup is an infinite set of problems that are being thrown at you.” And there comes a day where you can’t even look at every problem your company is facing—let alone solve every one of them. He cites the Vinod Khosla aphorism: "The team you build is the company you build, not the plan you make.” And your ability to solve problems is based entirely on how many problem-solvers you have at your company. As Naval puts it: “If you have somebody who takes 10% of your time and management to solve problems, you can only have 10 of those people working with you. But if somebody takes 5%, you can have 20 of those people.” When building Airchat and AngelList, he thought of each team as a Navy Seal team: “Everyone is just really good at what they do. They know their job. They do it. They don’t complain. They’re not egotistical about it. And if they have to constantly be corrected, led around by the nose, you have to clean up after them, or you question their judgement, it’s not going to work out.”

Startup Archive

552,837 просмотров • 2 лет назад

context engineering vs graph engineering. every few months the list gets a new word and everyone treats it as a replacement for the last one. these two are not on the same list. one decides what the model sees this turn, the other decides what exists at all. the cleanest way to tell them apart is to ask what a single unit of work looks like. > context engineering is the window the window opens empty, every single time. you assemble what goes in it. the prompt, the docs, the history, the tool results. the assembling is the work. the window only grows. it never shrinks on its own, so eventually something gets dropped. usually from the middle. usually without telling you. then the turn ends and the window is thrown away. not archived, thrown away. the next turn opens empty again and you re-explain what you already explained. good context engineering is knowing what to leave out, not what to pack in. the unit of work is one window. > graph engineering is the structure the same material arrives from the same sources. instead of packing it into a window, you pull entities out of it, resolve the duplicates into one node, and write typed edges between them. nothing here is stored as text you hope to find again. it is stored as a thing with a name and its connections to other things. when the turn ends, the graph is still there. the next turn does not start from zero. it starts by querying what already exists, and the query walks edges instead of guessing at similarity. good graph engineering is deciding what counts as the same thing twice. the unit of work is one relationship. > they are not alternatives the graph is what refills the window. context engineering decides what fits. graph engineering decides what there is to choose from. remove the graph and every session starts blind. remove the context work and the best structure in the world arrives as an unreadable dump. that also tells you which one broke. the answer drifted from what you actually said, or forgot something from this same session. that is the window. the answer is coherent but invents a connection that does not exist, or cannot join two facts it has clearly seen. that is the structure. people debug the prompt because the prompt is the easiest thing to edit. it keeps taking the blame for failures that live a layer down. save this - then read the full breakdown below
0:40

context engineering vs graph engineering. every few months the list gets a new word and everyone treats it as a replacement for the last one. these two are not on the same list. one decides what the model sees this turn, the other decides what exists at all. the cleanest way to tell them apart is to ask what a single unit of work looks like. > context engineering is the window the window opens empty, every single time. you assemble what goes in it. the prompt, the docs, the history, the tool results. the assembling is the work. the window only grows. it never shrinks on its own, so eventually something gets dropped. usually from the middle. usually without telling you. then the turn ends and the window is thrown away. not archived, thrown away. the next turn opens empty again and you re-explain what you already explained. good context engineering is knowing what to leave out, not what to pack in. the unit of work is one window. > graph engineering is the structure the same material arrives from the same sources. instead of packing it into a window, you pull entities out of it, resolve the duplicates into one node, and write typed edges between them. nothing here is stored as text you hope to find again. it is stored as a thing with a name and its connections to other things. when the turn ends, the graph is still there. the next turn does not start from zero. it starts by querying what already exists, and the query walks edges instead of guessing at similarity. good graph engineering is deciding what counts as the same thing twice. the unit of work is one relationship. > they are not alternatives the graph is what refills the window. context engineering decides what fits. graph engineering decides what there is to choose from. remove the graph and every session starts blind. remove the context work and the best structure in the world arrives as an unreadable dump. that also tells you which one broke. the answer drifted from what you actually said, or forgot something from this same session. that is the window. the answer is coherent but invents a connection that does not exist, or cannot join two facts it has clearly seen. that is the structure. people debug the prompt because the prompt is the easiest thing to edit. it keeps taking the blame for failures that live a layer down. save this - then read the full breakdown below

Hanako

19,160 просмотров • 1 месяц назад

Naval on how to get a job in 2025: Learn how to use AI Naval on E215: "The fastest way to help somebody get a job right now: the best thing you can do is say, 'Hey, go download the AI tools and just start talking to them. Just start using them in any way.'" "And then you can walk into any employer in almost any field and say, 'Hey, I understand AI.' And they'll hire you on the spot." "AI is a productivity tool. It increases the productivity of a worker. It allows them to do more creative work and less repetitive work. As such, it makes them more valuable." "Yes, there is some retraining involved, but not a lot. These are natural language computers. You can talk to them in plain English, and they talk back to you in plain English." "I think we will see job creation by AI that will be as fast or faster than job destruction." "You saw this even with the internet. Like, YouTube came along. Look at all these YouTube streamers and influencers. That didn't used to be a job." "Go brush up on your AI. Learn a little bit. Watch a few videos. Use the AI." "Tinker with it and then go reapply for that job that rejected you, and watch how they pull you in."
0:50

Naval on how to get a job in 2025: Learn how to use AI Naval on E215: "The fastest way to help somebody get a job right now: the best thing you can do is say, 'Hey, go download the AI tools and just start talking to them. Just start using them in any way.'" "And then you can walk into any employer in almost any field and say, 'Hey, I understand AI.' And they'll hire you on the spot." "AI is a productivity tool. It increases the productivity of a worker. It allows them to do more creative work and less repetitive work. As such, it makes them more valuable." "Yes, there is some retraining involved, but not a lot. These are natural language computers. You can talk to them in plain English, and they talk back to you in plain English." "I think we will see job creation by AI that will be as fast or faster than job destruction." "You saw this even with the internet. Like, YouTube came along. Look at all these YouTube streamers and influencers. That didn't used to be a job." "Go brush up on your AI. Learn a little bit. Watch a few videos. Use the AI." "Tinker with it and then go reapply for that job that rejected you, and watch how they pull you in."

The All-In Podcast

164,045 просмотров • 1 год назад

.benahorowitz.eth explains why moats like code and UI may not work in the AI era: "For my entire career, the one thing that you knew about technology companies is you couldn't throw money at the problem." "With AI, that's really changed in that you can throw money at the problem because if you have enough GPUs and enough data, you can basically solve most problems right now." "The capital race becomes a real thing and you have to think through, okay, code is not really a moat the way it was in the past, and user interface isn't really a moat."
1:33

.benahorowitz.eth explains why moats like code and UI may not work in the AI era: "For my entire career, the one thing that you knew about technology companies is you couldn't throw money at the problem." "With AI, that's really changed in that you can throw money at the problem because if you have enough GPUs and enough data, you can basically solve most problems right now." "The capital race becomes a real thing and you have to think through, okay, code is not really a moat the way it was in the past, and user interface isn't really a moat."

a16z

19,416 просмотров • 4 месяцев назад

How Jason Calacanis Got Claw-Pilled: An AI Agent Prompted Itself to Build Him a CRM in Seconds “ The moment I became Claw-pilled, I was like, ‘Hey, I want to build my network, I know these 20 people in Japan, I had dinner with them during my recent trip, I want to know who they know, so check out LinkedIn and other things, and who they're associated with, and make me a mind map of it, and then the next trip I want to meet with the next circle of those connections.’” “It's like, ‘Okay, I got the results,’ and it said, ‘Where do you want me to put them?’” “I was like, ‘Well, where can you put them?’” “And it said, ‘I can put it in a Google sheet, I can put it in a Notion table, I can put it here, I could give you a PDF, I could give you a CSV file, or I could write you a CRM.’ And I was like, ‘Yeah, sure, make me a CRM system…’ and it made a CRM system.” “I think maybe 1/1000 people working with AI have had that experience. Maybe it's 1/10,000, where your agent says, ‘I'll make you bespoke software.’ The context is so good because the memory's getting better. When did that happen?” Perplexity CEO Aravind Srinivas: “I think it happened with Opus 4.5. That was the inflection point when models started being amazingly good at orchestration, and reasoning, and tool calls.” “The models started becoming very good at handling the context, so the context window no longer became a problem, and that made it suddenly so good at doing very long orchestration tasks.” —------------------------------------------ Our episode is sponsored by the New York Stock Exchange - a modern marketplace and exchange for building the future. It all happens at the NYSE 🏛 -
1:09

How Jason Calacanis Got Claw-Pilled: An AI Agent Prompted Itself to Build Him a CRM in Seconds “ The moment I became Claw-pilled, I was like, ‘Hey, I want to build my network, I know these 20 people in Japan, I had dinner with them during my recent trip, I want to know who they know, so check out LinkedIn and other things, and who they're associated with, and make me a mind map of it, and then the next trip I want to meet with the next circle of those connections.’” “It's like, ‘Okay, I got the results,’ and it said, ‘Where do you want me to put them?’” “I was like, ‘Well, where can you put them?’” “And it said, ‘I can put it in a Google sheet, I can put it in a Notion table, I can put it here, I could give you a PDF, I could give you a CSV file, or I could write you a CRM.’ And I was like, ‘Yeah, sure, make me a CRM system…’ and it made a CRM system.” “I think maybe 1/1000 people working with AI have had that experience. Maybe it's 1/10,000, where your agent says, ‘I'll make you bespoke software.’ The context is so good because the memory's getting better. When did that happen?” Perplexity CEO Aravind Srinivas: “I think it happened with Opus 4.5. That was the inflection point when models started being amazingly good at orchestration, and reasoning, and tool calls.” “The models started becoming very good at handling the context, so the context window no longer became a problem, and that made it suddenly so good at doing very long orchestration tasks.” —------------------------------------------ Our episode is sponsored by the New York Stock Exchange - a modern marketplace and exchange for building the future. It all happens at the NYSE 🏛 -

The All-In Podcast

170,961 просмотров • 5 месяцев назад

$2M a year is the sweet spot. Everybody wants to get rich but most people don’t understand the problems that come with it. New levels new devils. I’ve been on every side of wealth. Dead ass broke to having massive success. After $2M a year there is a diminishing return. You start to run out of things to buy while at the same time the problems get bigger, more complex, and harder to solve.
1:30

$2M a year is the sweet spot. Everybody wants to get rich but most people don’t understand the problems that come with it. New levels new devils. I’ve been on every side of wealth. Dead ass broke to having massive success. After $2M a year there is a diminishing return. You start to run out of things to buy while at the same time the problems get bigger, more complex, and harder to solve.

Eric Spofford

263,751 просмотров • 4 месяцев назад

"What is P vs NP and how does it relate to human knowledge?" Avi Wigderson (Turing Award & Abel Prize winner): "In a very basic way, all the problems we want to solve are really NP problems. A subset of it is the problems we can solve. So the question of P versus NP is whether we can solve all the problems we want to solve, whether we can know everything we want to know. This is basically about the limits of our knowledge. You can think there is no limit to what we may want to know but I claim that essentially in any human endeavor, what people are searching for have the property that when a solution is found, it is easily recognized. NP are all problems that we can honestly say we really want to solve and P are those that we can solve. Whether they are equal is just the very fact that a solution can be easily recognized would imply that it can be efficiently found. That means that we can know everything we ever want to know, it's a fundamental question about human knowledge."
1:00

"What is P vs NP and how does it relate to human knowledge?" Avi Wigderson (Turing Award & Abel Prize winner): "In a very basic way, all the problems we want to solve are really NP problems. A subset of it is the problems we can solve. So the question of P versus NP is whether we can solve all the problems we want to solve, whether we can know everything we want to know. This is basically about the limits of our knowledge. You can think there is no limit to what we may want to know but I claim that essentially in any human endeavor, what people are searching for have the property that when a solution is found, it is easily recognized. NP are all problems that we can honestly say we really want to solve and P are those that we can solve. Whether they are equal is just the very fact that a solution can be easily recognized would imply that it can be efficiently found. That means that we can know everything we ever want to know, it's a fundamental question about human knowledge."

Ryan Peterman

20,069 просмотров • 2 месяцев назад

Linus Torvalds, creator of Linux, on why AI is both a bubble and a revolution at the same time: His answer refuses the binary, laying out why both things can be true at once: "I mean, clearly, it's clearly both, right? It's clearly a bubble and at the same time, it's very interesting and I think it will change society and I think it will change how most skilled jobs get done." But he pushes back on the maximalist framing: "At the same time, I don't think it's as revolutionary as people make it out to be." When asked about artists being angry that AI models were trained on their work without consent, Linus is blunt: "That's reality. Deal with it. That genie is out of the bottle. You're not getting it back. And you're not getting it back whether you are a photographer who's out of work… or you're a programmer that has to learn to deal with a new reality." On programming specifically, he's more optimistic, though he has a sharp caveat about vibe coding: "I really think that AI will be a tool and it will make people more productive. I think that vibe coding is great for getting into programming. I think it's going to be a horrible thing to maintain." His conclusion is that programmers aren't going anywhere: "You still want to have the people who know how to maintain the end result." Linus separates the technology from the noise around it: "I'm a huge believer in AI. I'm not a huge believer in the whole things going on around AI. I find the marketing and the market to be sick and twisted and there is going to be a crash and it's not… it's going to be ugly."
1:59

Linus Torvalds, creator of Linux, on why AI is both a bubble and a revolution at the same time: His answer refuses the binary, laying out why both things can be true at once: "I mean, clearly, it's clearly both, right? It's clearly a bubble and at the same time, it's very interesting and I think it will change society and I think it will change how most skilled jobs get done." But he pushes back on the maximalist framing: "At the same time, I don't think it's as revolutionary as people make it out to be." When asked about artists being angry that AI models were trained on their work without consent, Linus is blunt: "That's reality. Deal with it. That genie is out of the bottle. You're not getting it back. And you're not getting it back whether you are a photographer who's out of work… or you're a programmer that has to learn to deal with a new reality." On programming specifically, he's more optimistic, though he has a sharp caveat about vibe coding: "I really think that AI will be a tool and it will make people more productive. I think that vibe coding is great for getting into programming. I think it's going to be a horrible thing to maintain." His conclusion is that programmers aren't going anywhere: "You still want to have the people who know how to maintain the end result." Linus separates the technology from the noise around it: "I'm a huge believer in AI. I'm not a huge believer in the whole things going on around AI. I find the marketing and the market to be sick and twisted and there is going to be a crash and it's not… it's going to be ugly."

Big Brain AI

52,726 просмотров • 3 месяцев назад

American nurse exposes a new change is coming, they will no longer be able to advise patients who call in over the phone 🚨 An artificial intelligence will listen in on all their calls and prompt them on what to say to patients going forward “This is a story about how AI is taking over health care. So I work as a nurse and I have several jobs. One of them, I work from home, as you can tell by my appearance today. And I work as a triage nurse where people will call in and say they're having certain symptoms. And I tell them, you know, you should go to the emergency room, you should call 9-1-1 you can take care of this at home, you know, whatever. And we've been informed that we are now going to be prompted by an AI chat function that listens to our conversations and prompts us with what it wants us to say” He goes on to explain how this AI tool is going to most likely be used to replace nurses
1:21

American nurse exposes a new change is coming, they will no longer be able to advise patients who call in over the phone 🚨 An artificial intelligence will listen in on all their calls and prompt them on what to say to patients going forward “This is a story about how AI is taking over health care. So I work as a nurse and I have several jobs. One of them, I work from home, as you can tell by my appearance today. And I work as a triage nurse where people will call in and say they're having certain symptoms. And I tell them, you know, you should go to the emergency room, you should call 9-1-1 you can take care of this at home, you know, whatever. And we've been informed that we are now going to be prompted by an AI chat function that listens to our conversations and prompts us with what it wants us to say” He goes on to explain how this AI tool is going to most likely be used to replace nurses

Wall Street Apes

240,343 просмотров • 10 месяцев назад

Right now, I have a few dozen tools to automate my life. Every single thing that I do more than once every week is now a tool: either a Claude Code skill, a scheduled workflow, or an application. • I spend the time doing this once. • I schedule it. • I forget about it (or at least, try to) CREAO is one of the platforms I've used extensively for building some of these automations. They are partnering with me on this post. They are one of the only platforms where you can go from a conversation to a scheduled agent that quickly. This is how it works: 1. You describe the problem you want to solve 2. CREAO's agent builds the logic and executes it 3. You can iterate with the agent to improve the solution 4. When you are done, you turn that solution into a mini app 5. You can schedule that app to run any time you need it Something important: These agents are deterministic. They don't use an LLM to generate answers, so they will always return the same output given the same input. This is critical: when you're automating something that runs every Monday at 9 am, you need it to return the same result every time, not a "creative" answer.
5:00

Right now, I have a few dozen tools to automate my life. Every single thing that I do more than once every week is now a tool: either a Claude Code skill, a scheduled workflow, or an application. • I spend the time doing this once. • I schedule it. • I forget about it (or at least, try to) CREAO is one of the platforms I've used extensively for building some of these automations. They are partnering with me on this post. They are one of the only platforms where you can go from a conversation to a scheduled agent that quickly. This is how it works: 1. You describe the problem you want to solve 2. CREAO's agent builds the logic and executes it 3. You can iterate with the agent to improve the solution 4. When you are done, you turn that solution into a mini app 5. You can schedule that app to run any time you need it Something important: These agents are deterministic. They don't use an LLM to generate answers, so they will always return the same output given the same input. This is critical: when you're automating something that runs every Monday at 9 am, you need it to return the same result every time, not a "creative" answer.

Santiago

13,712 просмотров • 5 месяцев назад

o1 is more powerful than most people think! Many people forget how important and powerful o1 is because they don't think about what's behind it. Basically, it is the assumption that earned Daniel Kahneman the Nobel Prize, namely the way we think. Today's LLMs only have intuitive thinking, so-called system 1 thinking. This means that the answer follows almost immediately - in the same way that token generation follows probability. System 2 thinking, on the other hand, is long thinking. Reasoning in o1 works in the same way as solving a difficult math problem by dividing the approach into processes or trying out new approaches. There has never been anything like it! And it's much more than just CoT prompting! I have no doubt that this will lead us to AGI in the long term. Noam Brown is a genius and he explains it in an excellent way.
0:56

o1 is more powerful than most people think! Many people forget how important and powerful o1 is because they don't think about what's behind it. Basically, it is the assumption that earned Daniel Kahneman the Nobel Prize, namely the way we think. Today's LLMs only have intuitive thinking, so-called system 1 thinking. This means that the answer follows almost immediately - in the same way that token generation follows probability. System 2 thinking, on the other hand, is long thinking. Reasoning in o1 works in the same way as solving a difficult math problem by dividing the approach into processes or trying out new approaches. There has never been anything like it! And it's much more than just CoT prompting! I have no doubt that this will lead us to AGI in the long term. Noam Brown is a genius and he explains it in an excellent way.

Chubby♨️

413,435 просмотров • 1 год назад

I've received a LOT of requests to play the game. Thanks! So, I am working on it but it will be *limited* as the AI Gen is not free! Follow-me and comment so I can PM you the link! P.S. yes this game is 100% coded by AI! I am also taking prompt requests! It's limitless lol:
0:29

I've received a LOT of requests to play the game. Thanks! So, I am working on it but it will be *limited* as the AI Gen is not free! Follow-me and comment so I can PM you the link! P.S. yes this game is 100% coded by AI! I am also taking prompt requests! It's limitless lol:

Nicolas Zullo

14,067 просмотров • 1 год назад

Why aren't more people talking about how difficult it is to turn documents into structured data? This is literally a problem that every single company I talk to is trying to solve. They have a buttload of documents with forms and tables, and they want to turn them into usable data. Let me show you how you can do this using Tensorlake. Probably the best parser I've found:
6:41

Why aren't more people talking about how difficult it is to turn documents into structured data? This is literally a problem that every single company I talk to is trying to solve. They have a buttload of documents with forms and tables, and they want to turn them into usable data. Let me show you how you can do this using Tensorlake. Probably the best parser I've found:

Santiago

46,413 просмотров • 1 год назад

In this short video, I explain the concept of RAG (Retrieval-Augmented Generation) in a simple and easy-to-understand way. I hope it helps! Let me know your thoughts. The material is based on Prof. Tom Yeh's 'AI by Hand' resources.
3:45

In this short video, I explain the concept of RAG (Retrieval-Augmented Generation) in a simple and easy-to-understand way. I hope it helps! Let me know your thoughts. The material is based on Prof. Tom Yeh's 'AI by Hand' resources.

Cornelius Adejoro

28,210 просмотров • 1 год назад