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THIS GUY TURNED 5 PROMPTING TIPS INTO A FREE AI CEO CHALLENGE The useful part is treating every prompt like you are briefing a very fast employee who has zero context. Most people open ChatGPT and type a wish. Pros give it a job. Try this instead: 1. Give... it a role Not “help me with marketing.” Say: “Act as a B2B SaaS growth operator reviewing a landing page.” 2. Give it the real context Who is the customer? What are they buying? What have you already tried? What does success look like? 3. Give it constraints Length, tone, format, audience, banned words, examples to copy, examples to avoid. A vague prompt gets a vague answer. A constrained prompt gets something you can edit. 4. Ask for options before answers “Give me 5 angles, rank them, then explain the tradeoff.” This turns AI from an autocomplete box into a thinking partner. 5. Force it to show assumptions Before it writes, ask: “What are you assuming, what info is missing, and what would change your answer?” That one line saves a lot of fake confidence. Dan Martell’s video works because the promise is simple: 5 prompting habits that make AI feel less random. The reusable move is even simpler: Stop prompting for outputs. Start prompting for decisions. Bad: “Write me a post.” Better: “Here is the source, here is the reader, here is the angle, give me 3 hooks, choose the strongest, then draft in this style.” That is the difference between getting content-shaped noise and getting work you can actually ship. Caveat: prompts do not fix weak taste, bad data, or unclear strategy. But they do expose those problems faster. If your AI answers are generic, your prompt probably has no job, no context, no constraints, and no standard for what “good” means.show more

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25,573 Aufrufe • vor 21 Tagen •via X (Twitter)

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Most people just prompt ChatGPT for 10 tweets. Result is AI slop. Here's what Dan Koe does instead: Step 1: Find 3 posts that went viral Feed them to AI. Ask it to break down: - Structure - Psychology - Why it worked Step 2: Combine the patterns into one guide Step 3: Turn that guide into a two-phase prompt - Phase 1: AI interviews YOU for context - Phase 2: AI writes in YOUR voice He does this for tweets, landing pages, even book paragraphs. Stop prompting like a Neanderthal. Context is king.

Most people just prompt ChatGPT for 10 tweets. Result is AI slop. Here's what Dan Koe does instead: Step 1: Find 3 posts that went viral Feed them to AI. Ask it to break down: - Structure - Psychology - Why it worked Step 2: Combine the patterns into one guide Step 3: Turn that guide into a two-phase prompt - Phase 1: AI interviews YOU for context - Phase 2: AI writes in YOUR voice He does this for tweets, landing pages, even book paragraphs. Stop prompting like a Neanderthal. Context is king.

The Startup Ideas Podcast (SIP) 🧃

28,189 Aufrufe • vor 6 Monaten

Anthropic team: "Most people are prompting Claude wrong" They dropped a 24-minute workshop on how Claude prompts are actually built Free From the people who built the model There are 6 elements in a real Claude prompt Most people use 1 That is why their outputs feel generic Better wording is not enough Claude rewards structure, context, examples, constraints, and feedback I watched it twice Then built a Claude Skill that applies all 6 automatically You write normally The skill structures the prompt before Claude sees it Bookmark and watch this Then save the setup below

Anthropic team: "Most people are prompting Claude wrong" They dropped a 24-minute workshop on how Claude prompts are actually built Free From the people who built the model There are 6 elements in a real Claude prompt Most people use 1 That is why their outputs feel generic Better wording is not enough Claude rewards structure, context, examples, constraints, and feedback I watched it twice Then built a Claude Skill that applies all 6 automatically You write normally The skill structures the prompt before Claude sees it Bookmark and watch this Then save the setup below

rari

15,655 Aufrufe • vor 14 Tagen

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.

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

1,189,683 Aufrufe • vor 10 Monaten

I asked Garry Tan how to use meta prompting to get better at AI: "My partners at YC Jared Friedman and Pete Koomen showed me how to do this. You can take almost anything that you do all the time and just drop it into a context window. And then say, “Here’s a bunch of inputs and outputs." And maybe you also add a bunch of notes. And then you tell it, “Write me a prompt that can act as an agent that takes this input and makes this output over here.” You can do this for almost any type of knowledge work. And you can even introspect. "What are things you notice that I did to convert this from the input to the output?”. And then you can just start using the prompt. Initially, it’s going to suck. Because it’s just not that smart yet. But what’s funny is now, I also use it to Iterate my writing. You can be very direct, "I would never say that", "Don’t say it like this", or "Oh, you used the long word there, use the short word". Just speak to it conversationally. And then when you're happy with the output, you can use that new output to make a new prompt. "Based on this conversation, give me a better initial prompt that incorporates all the things we talked about." And you can do this with literally everything. And in theory, there’s so much it applies to that people do day-to-day. You could use it for tweets. You could use it for editing podcasts. You can use it for pretty much everything. I have a folder of prompts that I use all the time. My YouTube prompt is on v27 or something. I'll go through this process with all the different max models. I'll use GPT 5.2 Pro. I’ll use Grok. I'll use Claude. Then, I’ll take all the outputs from all the models and put them into Claude and say "Here’s my prompt, here’s the output from four LLMs, including yourself. Rate each response and tell me what the pros and cons of each approach are." And I usually say "give it to me in numbered form". And then you can agree with one, disagree with two, tell it three is this or that. And then after that, you say given all of this, synthesize it."

I asked Garry Tan how to use meta prompting to get better at AI: "My partners at YC Jared Friedman and Pete Koomen showed me how to do this. You can take almost anything that you do all the time and just drop it into a context window. And then say, “Here’s a bunch of inputs and outputs." And maybe you also add a bunch of notes. And then you tell it, “Write me a prompt that can act as an agent that takes this input and makes this output over here.” You can do this for almost any type of knowledge work. And you can even introspect. "What are things you notice that I did to convert this from the input to the output?”. And then you can just start using the prompt. Initially, it’s going to suck. Because it’s just not that smart yet. But what’s funny is now, I also use it to Iterate my writing. You can be very direct, "I would never say that", "Don’t say it like this", or "Oh, you used the long word there, use the short word". Just speak to it conversationally. And then when you're happy with the output, you can use that new output to make a new prompt. "Based on this conversation, give me a better initial prompt that incorporates all the things we talked about." And you can do this with literally everything. And in theory, there’s so much it applies to that people do day-to-day. You could use it for tweets. You could use it for editing podcasts. You can use it for pretty much everything. I have a folder of prompts that I use all the time. My YouTube prompt is on v27 or something. I'll go through this process with all the different max models. I'll use GPT 5.2 Pro. I’ll use Grok. I'll use Claude. Then, I’ll take all the outputs from all the models and put them into Claude and say "Here’s my prompt, here’s the output from four LLMs, including yourself. Rate each response and tell me what the pros and cons of each approach are." And I usually say "give it to me in numbered form". And then you can agree with one, disagree with two, tell it three is this or that. And then after that, you say given all of this, synthesize it."

The Peel

51,632 Aufrufe • vor 4 Monaten

THIS GUY SPENT 5 MINUTES SETTING UP A $20 CLAUDE WORKSPACE AND TURNED 30 MINUTES OF DAILY PROMPT CHAOS INTO A SYSTEM Most people open Claude, type 1 messy prompt, rewrite it 10 times, get the same generic answer and call the model “bad.” He opens Claude Cowork, adds the project rules, past context, working files, examples, decisions and a clean place for the model to remember what it is doing. Same Claude. Same $20 tool. Completely different output. The funny part is there is no magic prompt here. No secret template. No “10x Claude” trick. Just the boring layer Karpathy keeps pointing at: context, memory and structure before output. A setup like this can save 30 minutes a day, which is 15 hours a month. If you use Claude for client work at $50 to $100/hour, that is $750 to $1,500/month in time you stop burning on repeated prompts. The model is real. The output is real. The edge is in not making Claude start from zero every single time.

THIS GUY SPENT 5 MINUTES SETTING UP A $20 CLAUDE WORKSPACE AND TURNED 30 MINUTES OF DAILY PROMPT CHAOS INTO A SYSTEM Most people open Claude, type 1 messy prompt, rewrite it 10 times, get the same generic answer and call the model “bad.” He opens Claude Cowork, adds the project rules, past context, working files, examples, decisions and a clean place for the model to remember what it is doing. Same Claude. Same $20 tool. Completely different output. The funny part is there is no magic prompt here. No secret template. No “10x Claude” trick. Just the boring layer Karpathy keeps pointing at: context, memory and structure before output. A setup like this can save 30 minutes a day, which is 15 hours a month. If you use Claude for client work at $50 to $100/hour, that is $750 to $1,500/month in time you stop burning on repeated prompts. The model is real. The output is real. The edge is in not making Claude start from zero every single time.

Gipp 🦅

14,871 Aufrufe • vor 1 Monat

Proactive AI Agents Today’s AI products are reactive. You give the model a prompt, it responds with an answer. These are useful, but I’m excited about products that take this further and shift the paradigm from “ask → answer” to “observe → act." These agents will continuously monitor context in the background across all of your connected tools and data, predict what matters, and take action before being asked to do so – much like a human does. So instead of you prompting the model, the model will start prompting you. Examples here could include agents that remind you about tasks you forgot to complete, resolve customer issues before support tickets are filed, or debug and ship code fixes automatically. This shift represents a new paradigm where AI products behave more like humans and less like tools. We’re already starting to see this dynamic with products like OpenClaw, Poke, and more - and we’ve only scratched the surface of capabilities here. We’re accepting applications for the next cohort of a16z a16z speedrun 🧊 – If you’re building the next generation of AI products, apply online.

Proactive AI Agents Today’s AI products are reactive. You give the model a prompt, it responds with an answer. These are useful, but I’m excited about products that take this further and shift the paradigm from “ask → answer” to “observe → act." These agents will continuously monitor context in the background across all of your connected tools and data, predict what matters, and take action before being asked to do so – much like a human does. So instead of you prompting the model, the model will start prompting you. Examples here could include agents that remind you about tasks you forgot to complete, resolve customer issues before support tickets are filed, or debug and ship code fixes automatically. This shift represents a new paradigm where AI products behave more like humans and less like tools. We’re already starting to see this dynamic with products like OpenClaw, Poke, and more - and we’ve only scratched the surface of capabilities here. We’re accepting applications for the next cohort of a16z a16z speedrun 🧊 – If you’re building the next generation of AI products, apply online.

Kenan Saleh

76,787 Aufrufe • vor 2 Monaten

Introducing az8 studio — the Agentic Studio for AI video creation. AI video is no longer just about typing a prompt and hoping for the best. It is about creating in a workspace where your agent understands the whole project, remembers the context, and helps before you ask. Meet Zumi — your creative agent inside az8. Zumi sees your assets, prompts, scenes, results, and decisions in one visual studio. It can spot what is missing, suggest what to try next, and help turn vanilla ideas into finished videos.

Introducing az8 studio — the Agentic Studio for AI video creation. AI video is no longer just about typing a prompt and hoping for the best. It is about creating in a workspace where your agent understands the whole project, remembers the context, and helps before you ask. Meet Zumi — your creative agent inside az8. Zumi sees your assets, prompts, scenes, results, and decisions in one visual studio. It can spot what is missing, suggest what to try next, and help turn vanilla ideas into finished videos.

AZ8

379,203 Aufrufe • vor 14 Tagen

THIS IS HOW A $10K/MONTH PRODUCT CONTENT PIPELINE GETS BUILT FROM 1 REFERENCE IMAGE AND A JSON PROMPT The video is not about "better prompting." It is a very simple cheat code: find an image with the exact lighting, color grade and camera feel you want, make AI reverse-engineer the style into a detailed JSON, then feed that structure into Nano Banana Pro with your own product. That is the part people keep missing. They write vague prompts like “make it realistic” and blame the model when it produces plastic garbage. The better workflow gives the model a style contract before asking for the final image. One reference image becomes the art director. The JSON becomes the brief. Your product becomes the asset dropped into a visual language that already works. This is how a solo operator can sell 20-30 "premium" product shots a week without owning a camera, renting a studio or touching a lighting rig. At $150-$300 per image, that is a $3K -$9K/week offer if you can bring clients. Not guaranteed. But the math is why this workflow matters. The article is the same business model underneath: AI output gets valuable when you stop asking for random generation and start building reusable instructions that copy proven formats. No photographer, no studio, no lighting setup, no retoucher, no 12-round creative call. Just reference, extraction, structure and execution. The prompt does not make the photo look expensive. The stolen style system does.

THIS IS HOW A $10K/MONTH PRODUCT CONTENT PIPELINE GETS BUILT FROM 1 REFERENCE IMAGE AND A JSON PROMPT The video is not about "better prompting." It is a very simple cheat code: find an image with the exact lighting, color grade and camera feel you want, make AI reverse-engineer the style into a detailed JSON, then feed that structure into Nano Banana Pro with your own product. That is the part people keep missing. They write vague prompts like “make it realistic” and blame the model when it produces plastic garbage. The better workflow gives the model a style contract before asking for the final image. One reference image becomes the art director. The JSON becomes the brief. Your product becomes the asset dropped into a visual language that already works. This is how a solo operator can sell 20-30 "premium" product shots a week without owning a camera, renting a studio or touching a lighting rig. At $150-$300 per image, that is a $3K -$9K/week offer if you can bring clients. Not guaranteed. But the math is why this workflow matters. The article is the same business model underneath: AI output gets valuable when you stop asking for random generation and start building reusable instructions that copy proven formats. No photographer, no studio, no lighting setup, no retoucher, no 12-round creative call. Just reference, extraction, structure and execution. The prompt does not make the photo look expensive. The stolen style system does.

Nekt0

14,833 Aufrufe • vor 1 Monat

Your government works for YOU. And the right to vote is not a bargaining chip. It is not something that a president gets to dangle over your head until you give him what he wants. The people of Michigan are not pawns in this game.

Your government works for YOU. And the right to vote is not a bargaining chip. It is not something that a president gets to dangle over your head until you give him what he wants. The people of Michigan are not pawns in this game.

Mallory McMorrow

13,944 Aufrufe • vor 20 Tagen

But money is not the test. Money is the question. What this moment really exposes is not ambition or hunger. It exposes whether you have a line. Money is powerful. It gives options. It creates leverage. It can change circumstances fast. But it cannot replace character. And it cannot give you a backbone if you never built one. At some point in life, something will be put in front of you that feels like freedom. But what it is really asking is how flexible you are willing to become. If you never decide that answer ahead of time, the offer will decide it for you. History does not remember people for what they accumulated. It remembers them for what they refused to sell. And that is the part nobody prepares you for. Getting paid is easy. Living with your choices is not. ✨🙌🏾💫 © Quaison Moseley

But money is not the test. Money is the question. What this moment really exposes is not ambition or hunger. It exposes whether you have a line. Money is powerful. It gives options. It creates leverage. It can change circumstances fast. But it cannot replace character. And it cannot give you a backbone if you never built one. At some point in life, something will be put in front of you that feels like freedom. But what it is really asking is how flexible you are willing to become. If you never decide that answer ahead of time, the offer will decide it for you. History does not remember people for what they accumulated. It remembers them for what they refused to sell. And that is the part nobody prepares you for. Getting paid is easy. Living with your choices is not. ✨🙌🏾💫 © Quaison Moseley

🧬Maxpein🧬

15,280 Aufrufe • vor 5 Monaten

Built an AI maturity assessment. It's designed to answer... • how AI-native your team really is • where the bottlenecks are • what good looks like • and what to do next We turned the AI transformation model into something practical you can actually use. Takes about 3 minutes. Chat with it and it gives back: • a detailed AI maturity assessment • strengths and gaps across your workflows • practical recommendations • a downloadable report you can share internally The goal wasn’t to make a gimmicky quiz (please give me direct feedback on how to improve). It’s meant to be a practical diagnostic for how modern teams are actually adopting AI at work. Give it shot!

Built an AI maturity assessment. It's designed to answer... • how AI-native your team really is • where the bottlenecks are • what good looks like • and what to do next We turned the AI transformation model into something practical you can actually use. Takes about 3 minutes. Chat with it and it gives back: • a detailed AI maturity assessment • strengths and gaps across your workflows • practical recommendations • a downloadable report you can share internally The goal wasn’t to make a gimmicky quiz (please give me direct feedback on how to improve). It’s meant to be a practical diagnostic for how modern teams are actually adopting AI at work. Give it shot!

Hurley

33,202 Aufrufe • vor 2 Monaten

Building with AI gets easier every day. Here is an open-source library that makes integrating AI into an application extremely easy: Star the repository! This library alone can make React the best front-end framework out there! There are a bunch of cool things I like about CopilotKit. Here are 3 of them: 1. It allows you to take any -powered agent and bring it into your application. (This is a brand-new feature!) 2. You can build an AI-powered chatbot in your application. The chatbot will have access to your context and can act on the application. 3. You can build a RAG workflow to process and answer questions from a real-time knowledge base. I recorded a video to show you how simple it is to make some of this happen. A few lines of code, and you are in business. Here is a link to the sample application: CopilotKit is open-source. You can self-host it. You can use it with any LLM. Thanks to the team for showing me their tool and collaborating with me on this post!

Santiago

108,824 Aufrufe • vor 2 Jahren

New! ✨ Lex 🤝 Kit ✨ We built a new technique to train AI to write in your voice—using your Kit newsletters—that's the closest I've ever gotten AI to sound like me. 👉 > "Damn. This is really solid. Immediately obvious that it is trained on my newsletters." — Nathan Barry > "damn finally just read this and those subject lines and that example newsletter, it feels 90% nailed, super intrigued how we can build this tone of voice into the app more, feels like a total gear shift for any AI suggestions." — fred rivett 🇬🇧📈 (usually a skeptic of the "write a draft for me" approach) The way we did it is cool and (I think?) new! We all know if you go to ChatGPT or Claude and ask it to write for you, it's gonna sound like AI. Maybe you've tried uploading some examples or a style guide and still get disappointing results. Lots of AI writing apps purport to "write in your style," but they typically just generate a short summary of your style using a prompt like "Analyze the style and tone of these writing samples" and then stick it into the prompt. Maaaaybe they'll throw in a few examples. We've tried this and didn't think it was great, so we killed it. But when Nathan Barry pushed us to think about this problem again, we came up with a subtly new technique that had a big impact on the results. (The reason I'm sharing it is because our goal is to build the world's best interface for collaborating on text, and the world's best platform for saving, sharing, and running prompts. Proprietary prompting techniques are not our thing.) Instead of just asking what the "style" is (a very fuzzy question) we ask AI what patterns it can find. Specifically we ask it to look for patterns in structure and tone. Then—and this is crucial—we ask the AI to generate a detailed set of instructions that a new writer could use to consistently reproduce those patterns. We include those instructions and a bunch of examples in a prompt (often quite a large one, it's kinda expensive for us tbh). I think it works so much better than just giving examples or giving a broad overview of "style" because LLMs are trained to pay close attention to instructions and thrive on specificity. Here we ask the LLM to look for very specific patterns and generate equally specific instructions to reproduce those patterns. The other cool thing is unlike a fine-tuned model this is powered by a big-ass prompt that you can inspect and modify to your liking. Does it sound a little too enthusiastic? A bit cheesy? Just edit the prompt. Of course it's not perfect, it's still gonna need careful editing, but to us this feels like an obvious leap. I'd be really curious to hear if it feels the same to you. To start this is only available via our Kit integration but we'll start rolling it out more broadly soon. You can sign up at 👉 Would love your honest feedback!

New! ✨ Lex 🤝 Kit ✨ We built a new technique to train AI to write in your voice—using your Kit newsletters—that's the closest I've ever gotten AI to sound like me. 👉 > "Damn. This is really solid. Immediately obvious that it is trained on my newsletters." — Nathan Barry > "damn finally just read this and those subject lines and that example newsletter, it feels 90% nailed, super intrigued how we can build this tone of voice into the app more, feels like a total gear shift for any AI suggestions." — fred rivett 🇬🇧📈 (usually a skeptic of the "write a draft for me" approach) The way we did it is cool and (I think?) new! We all know if you go to ChatGPT or Claude and ask it to write for you, it's gonna sound like AI. Maybe you've tried uploading some examples or a style guide and still get disappointing results. Lots of AI writing apps purport to "write in your style," but they typically just generate a short summary of your style using a prompt like "Analyze the style and tone of these writing samples" and then stick it into the prompt. Maaaaybe they'll throw in a few examples. We've tried this and didn't think it was great, so we killed it. But when Nathan Barry pushed us to think about this problem again, we came up with a subtly new technique that had a big impact on the results. (The reason I'm sharing it is because our goal is to build the world's best interface for collaborating on text, and the world's best platform for saving, sharing, and running prompts. Proprietary prompting techniques are not our thing.) Instead of just asking what the "style" is (a very fuzzy question) we ask AI what patterns it can find. Specifically we ask it to look for patterns in structure and tone. Then—and this is crucial—we ask the AI to generate a detailed set of instructions that a new writer could use to consistently reproduce those patterns. We include those instructions and a bunch of examples in a prompt (often quite a large one, it's kinda expensive for us tbh). I think it works so much better than just giving examples or giving a broad overview of "style" because LLMs are trained to pay close attention to instructions and thrive on specificity. Here we ask the LLM to look for very specific patterns and generate equally specific instructions to reproduce those patterns. The other cool thing is unlike a fine-tuned model this is powered by a big-ass prompt that you can inspect and modify to your liking. Does it sound a little too enthusiastic? A bit cheesy? Just edit the prompt. Of course it's not perfect, it's still gonna need careful editing, but to us this feels like an obvious leap. I'd be really curious to hear if it feels the same to you. To start this is only available via our Kit integration but we'll start rolling it out more broadly soon. You can sign up at 👉 Would love your honest feedback!

Nathan Baschez

16,279 Aufrufe • vor 1 Jahr

Coinbase CEO Explains “Reverse Prompting” and the Rise of the AI CEO Brian Armstrong: “One of the big pushes we made in the last year was we got our own internal hosted AI model that was connected to all of our data sources, right?” “So it's like every Slack message, every Google doc, Salesforce data, Confluence, you know.” “So now the data is all aggregated and I've started to ask it really… it's not just like prompting it, ‘Hey, can you write this kind of memo for me,’ or something.” “I'm asking these AI agents now, ‘As CEO, what should I be aware of in the company that I might not be aware of?’ And it'll tell me, ‘Did you know that there's actually disagreement on this team about the strategy?’ And I was like, actually, I didn't know that.” “This is like reverse prompting. So instead of telling the AI agent what you want it to do, you ask it what you should be thinking more about.” @jason: “It's a mentor. It's a coach.” Brian: “Yeah. Like, what could make me a better CEO? And it's like, ‘Well, I looked at how you spent your time in the last quarter and here's how you said that you wanted to spend it, but you actually spent 32% of your time on this instead of 20%.’” “I've asked it other questions like, ‘What's the thing that I changed my mind on the most over the last year?’ Things like that.” “It'll prompt you with information you should be thinking about instead of the other way around.” Thanks to our partner for making this happen!: 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 🏛.

Coinbase CEO Explains “Reverse Prompting” and the Rise of the AI CEO Brian Armstrong: “One of the big pushes we made in the last year was we got our own internal hosted AI model that was connected to all of our data sources, right?” “So it's like every Slack message, every Google doc, Salesforce data, Confluence, you know.” “So now the data is all aggregated and I've started to ask it really… it's not just like prompting it, ‘Hey, can you write this kind of memo for me,’ or something.” “I'm asking these AI agents now, ‘As CEO, what should I be aware of in the company that I might not be aware of?’ And it'll tell me, ‘Did you know that there's actually disagreement on this team about the strategy?’ And I was like, actually, I didn't know that.” “This is like reverse prompting. So instead of telling the AI agent what you want it to do, you ask it what you should be thinking more about.” @jason: “It's a mentor. It's a coach.” Brian: “Yeah. Like, what could make me a better CEO? And it's like, ‘Well, I looked at how you spent your time in the last quarter and here's how you said that you wanted to spend it, but you actually spent 32% of your time on this instead of 20%.’” “I've asked it other questions like, ‘What's the thing that I changed my mind on the most over the last year?’ Things like that.” “It'll prompt you with information you should be thinking about instead of the other way around.” Thanks to our partner for making this happen!: 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

80,524 Aufrufe • vor 5 Monaten

this video is the CLEAREST explanation of how claude skills + AI agents work and how to use them most people set up an AI agent and wonder why it keeps disappointing them. the context window is everything context is what the model assembles before it takes any action. think of it like everything the agent needs to read before it does anything. the quality of what goes in determines the quality of what comes out. the models are genuinely really good right now. claude and gpt are exceptional. the variable is almost always the context you give them. 1. agent.md files are mostly unnecessary every single line you put in an agent.md file gets added to every single conversation you have with your agent. a 1000 line file is around 7000 tokens burning on every run. the model already knows to use react. it can read your codebase. save the agent.md for proprietary information specific to your company that the model genuinely cannot know on its own. 2. skills are the actual unlock a skill.md file works differently. what loads into context is only the name and description, around 50 tokens. the full instructions only appear when the agent recognizes it needs that skill. so instead of 7000 tokens on every run you have 50. and the agent stays sharp because the context window stays lean. the closer you get to filling the context window the worse the agent performs, same way you perform worse when someone dumps 10 things on you at once. 3. here is how to actually build a skill the right way most people identify a workflow and immediately try to write the skill. what you want to do instead is run the workflow by hand with the agent first. walk it through every single step. tell it what to check, what good looks like, what bad looks like. correct it in real time. once you have had a full successful run from start to finish, tell the agent to review everything it just did and write the skill itself. it writes a better skill than you will because it has the full context of what actually worked in practice not in theory. 4. recursively building skills is how you go from frustrated to reliable when the skill breaks, and it will break, ask the agent exactly why it failed. it will tell you specifically what went wrong. fix it together in that same conversation. then tell it to update the skill file so that failure mode never happens again. ross mike did this five times with his youtube report generator. it now pulls from eight different data sources and runs flawlessly every single time without him touching it. 5. sub agents are something you earn not something you set up on day one start with one agent. build one workflow. turn it into one skill. once that works add another. ross mike has five sub agents now covering marketing, business, personal and more. it took months to get there and every single one exists because a workflow proved it deserved to exist. the people who set up 15 sub agents on day one and wonder why nothing works skipped all the steps that make the thing actually run. 6. your workflow is the thing the model cannot get anywhere else the model has been trained on everything. it knows more than you about most things. what it does not have is your specific process, your taste, your way of doing things. that is what skills capture. that is what makes your agent actually useful versus a generic one. downloading someone else's skill means downloading their context onto your setup and it will not work the way you want it to because it was never built around how you work. this is the clearest explanation of how agents actually work i have heard. Micky runs this stuff every single day and the results show it. full episode is now live on The Startup Ideas Podcast (SIP) 🧃 where you get your pods people charge for this sorta stuff i give away the sauce for free i just want you to win watch

this video is the CLEAREST explanation of how claude skills + AI agents work and how to use them most people set up an AI agent and wonder why it keeps disappointing them. the context window is everything context is what the model assembles before it takes any action. think of it like everything the agent needs to read before it does anything. the quality of what goes in determines the quality of what comes out. the models are genuinely really good right now. claude and gpt are exceptional. the variable is almost always the context you give them. 1. agent.md files are mostly unnecessary every single line you put in an agent.md file gets added to every single conversation you have with your agent. a 1000 line file is around 7000 tokens burning on every run. the model already knows to use react. it can read your codebase. save the agent.md for proprietary information specific to your company that the model genuinely cannot know on its own. 2. skills are the actual unlock a skill.md file works differently. what loads into context is only the name and description, around 50 tokens. the full instructions only appear when the agent recognizes it needs that skill. so instead of 7000 tokens on every run you have 50. and the agent stays sharp because the context window stays lean. the closer you get to filling the context window the worse the agent performs, same way you perform worse when someone dumps 10 things on you at once. 3. here is how to actually build a skill the right way most people identify a workflow and immediately try to write the skill. what you want to do instead is run the workflow by hand with the agent first. walk it through every single step. tell it what to check, what good looks like, what bad looks like. correct it in real time. once you have had a full successful run from start to finish, tell the agent to review everything it just did and write the skill itself. it writes a better skill than you will because it has the full context of what actually worked in practice not in theory. 4. recursively building skills is how you go from frustrated to reliable when the skill breaks, and it will break, ask the agent exactly why it failed. it will tell you specifically what went wrong. fix it together in that same conversation. then tell it to update the skill file so that failure mode never happens again. ross mike did this five times with his youtube report generator. it now pulls from eight different data sources and runs flawlessly every single time without him touching it. 5. sub agents are something you earn not something you set up on day one start with one agent. build one workflow. turn it into one skill. once that works add another. ross mike has five sub agents now covering marketing, business, personal and more. it took months to get there and every single one exists because a workflow proved it deserved to exist. the people who set up 15 sub agents on day one and wonder why nothing works skipped all the steps that make the thing actually run. 6. your workflow is the thing the model cannot get anywhere else the model has been trained on everything. it knows more than you about most things. what it does not have is your specific process, your taste, your way of doing things. that is what skills capture. that is what makes your agent actually useful versus a generic one. downloading someone else's skill means downloading their context onto your setup and it will not work the way you want it to because it was never built around how you work. this is the clearest explanation of how agents actually work i have heard. Micky runs this stuff every single day and the results show it. full episode is now live on The Startup Ideas Podcast (SIP) 🧃 where you get your pods people charge for this sorta stuff i give away the sauce for free i just want you to win watch

GREG ISENBERG

192,483 Aufrufe • vor 3 Monaten

Seth Godin gave a masterclass on how to build an unforgettable brand in the age of AI: 1. Marketing is not about spend. It is about creating the conditions for other people to eagerly spread your idea. 2. Authenticity is overrated. What customers actually want is consistency. Show up the same way every single time and that is worth more than any Super Bowl ad. 3. Everything your company does is a marketing decision. How you answer the phone. What you charge. How you design things. Marketing is not a department. It is everything. 4. Trust is simple. Make a promise. Keep it. Especially when it is hard. 5. Successful brands are built with your customers talking about you. Not you talking about you. 6. A brand is not a logo. A brand is a promise. Nike has a brand. Hyatt has a logo. One of them you know exactly what to expect. The other you do not. 7. You are measuring the wrong things. Follower counts. Stock price. Open rates. False proxies will take your business in the wrong direction faster than anything else. 8. Social media followers mean nothing. Godin has 400,000 Instagram followers and says if he posts about a new book maybe 12 people buy it. The number is a distraction. 9. Stop trying to be famous. The goal is not to get more famous. The goal is to get less famous and more trusted. 10. Average marketing reaches average people. Average people will not buy your product. You need the people who will talk about you, challenge you, and eagerly pay more for better. 11. When you pick your customers you pick your future. Stop trying to reach everyone. Start trying to deeply serve someone specific. 12. Better beats louder every time. One guy running a wine email list with 130,000 subscribers does $30 million a year in revenue. No ads. No social media hustle. Just consistently better. 13. The real opportunity with AI is not making things cheaper. It is making things better. The businesses that use AI to deepen relationships will win. The ones using it to cut costs will race to the bottom. 14. Your job is not to do your job. Your job is to solve problems for other people and make things better by making better things. Everything else is just noise. 15. When AI becomes the buyer it will always choose the cheapest option. If your entire business strategy is being the cheapest, AI will destroy you. The only protection is being worth it in ways that cannot be easily measured. 16. The next level of marketing is permission at a depth nobody has achieved before. The brand that knows your tools, your projects, your needs, and shows up to help without being asked will be impossible to replace. 17. Most businesses will use AI to spam more people faster. The businesses that win will use AI to serve fewer people better. That gap is the biggest opportunity in marketing right now. 18. You have a squadron of summer interns available for twenty dollars a month. They are not that good but they are very eager. The businesses learning to be good bosses of AI right now will have an enormous advantage over everyone waiting to figure it out later. 19. The question every business should be asking is not how do I get more attention. It is how do I become the kind of business that people would genuinely miss if it disappeared tomorrow. That answer is your entire marketing strategy.

Yasmine Khosrowshahi

126,941 Aufrufe • vor 29 Tagen

Jordan Peterson on why writing is the most powerful skill you can learn: 1. writing and thinking are the same thing. there is no difference between them. when you learn to write clearly you are not learning to express thoughts you already have. you are learning to have the thoughts in the first place. 2. universities never tell students why they are writing. the answer they get is you need the grade. the real answer is you need to learn to think. because thinking is what makes you act effectively in the world and win the battles you choose to fight. 3. if you can think, speak, and write you are absolutely dangerous. people give you money. they give you opportunities. you have influence. peterson says nothing can get in your way. that is what you are at university for. 4. the most articulate person in the room always wins the argument. peterson has watched staggeringly successful people his whole life. the common thread: you do not want to argue with them. not because they are aggressive. because their points are organized and yours probably aren't. 5. someone who cannot write has done almost everything wrong before they start. wrong words, wrong sentences, wrong paragraph order, wrong structure, wrong conclusion. peterson says marking a bad essay is agony because the answer to what did i do wrong is essentially everything.

Jordan Peterson on why writing is the most powerful skill you can learn: 1. writing and thinking are the same thing. there is no difference between them. when you learn to write clearly you are not learning to express thoughts you already have. you are learning to have the thoughts in the first place. 2. universities never tell students why they are writing. the answer they get is you need the grade. the real answer is you need to learn to think. because thinking is what makes you act effectively in the world and win the battles you choose to fight. 3. if you can think, speak, and write you are absolutely dangerous. people give you money. they give you opportunities. you have influence. peterson says nothing can get in your way. that is what you are at university for. 4. the most articulate person in the room always wins the argument. peterson has watched staggeringly successful people his whole life. the common thread: you do not want to argue with them. not because they are aggressive. because their points are organized and yours probably aren't. 5. someone who cannot write has done almost everything wrong before they start. wrong words, wrong sentences, wrong paragraph order, wrong structure, wrong conclusion. peterson says marking a bad essay is agony because the answer to what did i do wrong is essentially everything.

Jaynit

104,595 Aufrufe • vor 1 Monat

.David Deutsch: "What's currently called AI and AGI are not only different from each other, they are very close to being the exact opposites of each other. The reason is that an AI, current AI is like an AI that diagnoses diseases or an AI that plays chess or an AI that controls a huge factory. Those things have objective functions, that is they have a function that they are designed to maximize and that is why they are used in those particular applications. Or in military terms, you could say the objective is to hit the target. You might say the objective is to hit the target unless some thing specified, but it's a specified thing comes up in which case don't hit the target and so on. This is, as I said, almost the opposite of what humans do when humans think. For a start, the AI has to be obedient, that is it has to actually do the things it is programmed to do, whereas a human is fundamentally disobedient, especially when being creative. When a human plays chess, they are performing a completely different kind of computation. They don't do the same things, they don't investigate the same possibilities that the artificial chess playing machine does, because the artificial one is capable of looking at billions and billions of possibilities, whereas the human can only look at hundreds or something. They are doing something completely different. Another difference is that the human can explain, can write a book later, having become world champion, can write a book saying how I did it, as the computer program that beats the world champion can write no such book, because it has no idea how it did it. It was just following a program. I was doing this and that and that and none of that is illuminating. Also, third thing, the chess player can decide I don't want to play chess anymore, from now on I will play Go or from now on I will play tennis. If commanded to play chess, the functionality will deteriorate completely. Those things are different. What we want in an AGI is that it behaves in a way that cannot be specified in advance, because if you specified it, you would already have the answer. The AGI program has to give unexpected answers, answers to questions we didn't even know how to ask."

.David Deutsch: "What's currently called AI and AGI are not only different from each other, they are very close to being the exact opposites of each other. The reason is that an AI, current AI is like an AI that diagnoses diseases or an AI that plays chess or an AI that controls a huge factory. Those things have objective functions, that is they have a function that they are designed to maximize and that is why they are used in those particular applications. Or in military terms, you could say the objective is to hit the target. You might say the objective is to hit the target unless some thing specified, but it's a specified thing comes up in which case don't hit the target and so on. This is, as I said, almost the opposite of what humans do when humans think. For a start, the AI has to be obedient, that is it has to actually do the things it is programmed to do, whereas a human is fundamentally disobedient, especially when being creative. When a human plays chess, they are performing a completely different kind of computation. They don't do the same things, they don't investigate the same possibilities that the artificial chess playing machine does, because the artificial one is capable of looking at billions and billions of possibilities, whereas the human can only look at hundreds or something. They are doing something completely different. Another difference is that the human can explain, can write a book later, having become world champion, can write a book saying how I did it, as the computer program that beats the world champion can write no such book, because it has no idea how it did it. It was just following a program. I was doing this and that and that and none of that is illuminating. Also, third thing, the chess player can decide I don't want to play chess anymore, from now on I will play Go or from now on I will play tennis. If commanded to play chess, the functionality will deteriorate completely. Those things are different. What we want in an AGI is that it behaves in a way that cannot be specified in advance, because if you specified it, you would already have the answer. The AGI program has to give unexpected answers, answers to questions we didn't even know how to ask."

Deutsch Explains

72,455 Aufrufe • vor 1 Jahr