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Most people think prompting is just writing better sentences Anthropic shows why that's wrong This 32-minute workshop breaks down how prompts are built for production Not prompt hacks Not "act as" Not another $300 course Actual systems Evals Edge cases Model migration Agent loops Good prompt is not one... show more

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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

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Anthropic Engineer: "Fable 5 is back, and most people will use it wrong" "You can set up Sonnet 5 and Fable 5 properly in one afternoon and stop overpaying every single day" In 29 minutes, he breaks down each Claude model, why setup matters, and where Fable 5 fits Not every task needs the strongest model Some jobs need speed. Some need reasoning. Some need long agent loops Fable 5 is where the loop work gets interesting: planning, checking, fixing, and running again Most people will upgrade and keep the same messy workflow Power users will route the right task to the right model and save money daily Bookmark and watch this before everyone starts wasting Fable 5

rari

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Boris Cherny, the creator of Claude Code at Anthropic, just explained why he stopped prompting Claude entirely in this talk he breaks down exactly how the future is teams of agents, not better prompts: - the 14% you lose to CLAUDE.md before typing a word - the features that change how Claude thinks before you type a word - the features most Claude users don't know exist - why typing prompts one at a time is already the slow lane Boris Cherny: "Now I don’t prompt Claude anymore - I have loops that are running. My job is to write loops." if you've been using Claude for more than a month and never left the chat window, you have at least 23 untouched features. probably 25 instead of another show tonight, watch this make sure to bookmark it before it gets lost in your feed my breakdown of all 25 features is below

Boris Cherny, the creator of Claude Code at Anthropic, just explained why he stopped prompting Claude entirely in this talk he breaks down exactly how the future is teams of agents, not better prompts: - the 14% you lose to CLAUDE.md before typing a word - the features that change how Claude thinks before you type a word - the features most Claude users don't know exist - why typing prompts one at a time is already the slow lane Boris Cherny: "Now I don’t prompt Claude anymore - I have loops that are running. My job is to write loops." if you've been using Claude for more than a month and never left the chat window, you have at least 23 untouched features. probably 25 instead of another show tonight, watch this make sure to bookmark it before it gets lost in your feed my breakdown of all 25 features is below

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Boris Cherny, Head of Claude Code at Anthropic: "I do not prompt Claude anymore. I have loops running that prompt Claude. My job is to write loops." The person who built the most used AI coding tool stopped prompting entirely. Most people using it every day are still typing one request at a time. This Github repo changes that. ↳ Seven production loop patterns with real token cost estimates ↳ CLI that scaffolds your entire loop in five minutes ↳ Starters for Grok, Claude Code, Codex, and Opencode The repo gives you the framework. My article covers the mental shift that makes it actually work.

Boris Cherny, Head of Claude Code at Anthropic: "I do not prompt Claude anymore. I have loops running that prompt Claude. My job is to write loops." The person who built the most used AI coding tool stopped prompting entirely. Most people using it every day are still typing one request at a time. This Github repo changes that. ↳ Seven production loop patterns with real token cost estimates ↳ CLI that scaffolds your entire loop in five minutes ↳ Starters for Grok, Claude Code, Codex, and Opencode The repo gives you the framework. My article covers the mental shift that makes it actually work.

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Respan Launch Week, Day 2: Evals Evals are one of the hardest parts of building AI applications. It is not because teams cannot run them. It is because they are hard to structure, hard to compare, and even harder to improve over time. So teams end up guessing. Did this prompt actually get better? Is this model really an improvement? We built Evals in Respan to make this systematic. You define: - what you want to test, such as prompts, models, or configs - the dataset, from production logs or test cases - the evaluators, whether LLM, code, human, or a mix Then you run experiments and compare results side by side. Same inputs. Same evaluation. Clear answers. No more guessing. Start running your first eval.

Respan Launch Week, Day 2: Evals Evals are one of the hardest parts of building AI applications. It is not because teams cannot run them. It is because they are hard to structure, hard to compare, and even harder to improve over time. So teams end up guessing. Did this prompt actually get better? Is this model really an improvement? We built Evals in Respan to make this systematic. You define: - what you want to test, such as prompts, models, or configs - the dataset, from production logs or test cases - the evaluators, whether LLM, code, human, or a mix Then you run experiments and compare results side by side. Same inputs. Same evaluation. Clear answers. No more guessing. Start running your first eval.

Respan

127,656 Aufrufe • vor 3 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.

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Sam Altman said on camera at Stripe Sessions that we are in an insane area, stuck with old devices and old operating systems. The OS and the browser are built for humans clicking. Both get rewritten around agents that do not click. Two years deep building agents. Sharpest framing I have heard a frontier lab CEO put on the next interface. ↓ Read it before the gap locks in The part nobody is sitting with: >the chat window is the old interface >the new one is an agent in a loop with your machine >the gap between people prompting and people running agents is forming right now >in six months it will be permanent Open Claude, type a question, copy the answer, paste it into VS Code. The average dev runs this loop every day. They think they are using AI. That is 5% of what these tools can do. The people pulling ahead do not prompt. They build harnesses. They run agents in loops. Same model. Different output. Full 2026 agent playbook in the article below here:

Sam Altman said on camera at Stripe Sessions that we are in an insane area, stuck with old devices and old operating systems. The OS and the browser are built for humans clicking. Both get rewritten around agents that do not click. Two years deep building agents. Sharpest framing I have heard a frontier lab CEO put on the next interface. ↓ Read it before the gap locks in The part nobody is sitting with: >the chat window is the old interface >the new one is an agent in a loop with your machine >the gap between people prompting and people running agents is forming right now >in six months it will be permanent Open Claude, type a question, copy the answer, paste it into VS Code. The average dev runs this loop every day. They think they are using AI. That is 5% of what these tools can do. The people pulling ahead do not prompt. They build harnesses. They run agents in loops. Same model. Different output. Full 2026 agent playbook in the article below here:

Rohit

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Claude Code creator: "100% of our pull requests at Anrtopic are run by Claude Code. 80–90% of code review too. The feature I’m using the most today is /loops. I’m not prompting Claude anymore - I’m building loops" in this interview he breaks down exactly how a system changes everything: - the memory and context features that turn Claude into a second brain - the knowledge architecture most users don't know Claude can build - the integration layer that connects Claude to your actual workflow - why typing one question at a time is the most expensive way to use Claude if you've been using Claude for months and still start every conversation from scratch with zero context, you don't have a Claude problem. you have a system problem instead of another show tonight, watch this make sure to bookmark it before it gets lost in your feed full guide in the article below

Khairallah AL-Awady

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Google just released a free 2-hour+ course on full agent engineering. How to go from one prompt to a system that runs while you sleep: 38:46 - Build your first AI agent 54:46 - Connect MCP tools 1:12:43 - Run agents with four loop patterns 1:20:57 - Turn agent loops into graphs 2:22:31 - Build the complete autonomous system Most people are still building one agent and stopping there. Google is already teaching the full stack: Agents → Tools → Loops → Graphs → Autonomous Systems Single agents are the old workflow. Systems that run without you are the new one. This course is worth more than most paid agent engineering bootcamps. Bookmark and watch it today Then read the full graph engineering playbook below

Google just released a free 2-hour+ course on full agent engineering. How to go from one prompt to a system that runs while you sleep: 38:46 - Build your first AI agent 54:46 - Connect MCP tools 1:12:43 - Run agents with four loop patterns 1:20:57 - Turn agent loops into graphs 2:22:31 - Build the complete autonomous system Most people are still building one agent and stopping there. Google is already teaching the full stack: Agents → Tools → Loops → Graphs → Autonomous Systems Single agents are the old workflow. Systems that run without you are the new one. This course is worth more than most paid agent engineering bootcamps. Bookmark and watch it today Then read the full graph engineering playbook below

Lunar

190,139 Aufrufe • vor 3 Tagen

Creator of agent loops: "Anthropic also using my agent systems formula and still the biggest mistake in agents: is skipping Graphs" his 3 rules for every agent: rule 1 → loop it, don't run once - think, act, observe, repeat - cut the context and it forgets why it started rule 2 → an agent is memory + tools + reasoning - drop one and it breaks rule 3 → stop fine-tuning the model - the real alpha is the infrastructure around it the formula behind every AI agent - explained by the one who wrote it one agent → a loop → a graph of loops → a system that never forgets watch & bookmark - then read how to wire it into a graph ↓

Creator of agent loops: "Anthropic also using my agent systems formula and still the biggest mistake in agents: is skipping Graphs" his 3 rules for every agent: rule 1 → loop it, don't run once - think, act, observe, repeat - cut the context and it forgets why it started rule 2 → an agent is memory + tools + reasoning - drop one and it breaks rule 3 → stop fine-tuning the model - the real alpha is the infrastructure around it the formula behind every AI agent - explained by the one who wrote it one agent → a loop → a graph of loops → a system that never forgets watch & bookmark - then read how to wire it into a graph ↓

codila

23,915 Aufrufe • vor 11 Tagen

The creator of Claude Code deleted his IDE in November 2024. In the last 30 days, 100% of his contributions were written by Claude Code. 259 PRs. His job is not prompting anymore. His job is writing loops that prompt Claude for him. Here is what that progression actually looks like: → stage 1: write code by hand with autocomplete → stage 2: run 5-10 claude sessions in parallel, prompt each one yourself → stage 3: write loops that prompt claude — you become the author, the model becomes a subroutine → the ralph loop (2025): bash one-liner, fixed anchor files, built a programming language for $297 → the 2026 loop: loops supervising loops, scheduling on infrastructure time, git-backed durable state → five building blocks: automations, durable state, validator split, loop orchestration, crash recovery Most developers are still in stage 2, prompting sessions by hand. The ones at stage 3 are writing the loops that do the prompting for them. Read this article to be ahead of the curve! Follow NeilXbt for more AI engineering intelligence that shows you exactly where the leverage moved and what building at that level actually looks like.

The creator of Claude Code deleted his IDE in November 2024. In the last 30 days, 100% of his contributions were written by Claude Code. 259 PRs. His job is not prompting anymore. His job is writing loops that prompt Claude for him. Here is what that progression actually looks like: → stage 1: write code by hand with autocomplete → stage 2: run 5-10 claude sessions in parallel, prompt each one yourself → stage 3: write loops that prompt claude — you become the author, the model becomes a subroutine → the ralph loop (2025): bash one-liner, fixed anchor files, built a programming language for $297 → the 2026 loop: loops supervising loops, scheduling on infrastructure time, git-backed durable state → five building blocks: automations, durable state, validator split, loop orchestration, crash recovery Most developers are still in stage 2, prompting sessions by hand. The ones at stage 3 are writing the loops that do the prompting for them. Read this article to be ahead of the curve! Follow NeilXbt for more AI engineering intelligence that shows you exactly where the leverage moved and what building at that level actually looks like.

NeilXbt

11,556 Aufrufe • vor 1 Monat

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.

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.

kocer

25,573 Aufrufe • vor 1 Monat

Cat Wu, head of product for Claude Code at Anthropic: "you can build almost anything with Claude from a single prompt now" one prompt gets you a working screen, not YOUR product Claude Code has never seen your design system, so it guesses the look fresh every time. how to hand Claude Code that system is in the article. better than the $300 design course in your tabs, and this one's free.

Cat Wu, head of product for Claude Code at Anthropic: "you can build almost anything with Claude from a single prompt now" one prompt gets you a working screen, not YOUR product Claude Code has never seen your design system, so it guesses the look fresh every time. how to hand Claude Code that system is in the article. better than the $300 design course in your tabs, and this one's free.

Mnimiy

113,663 Aufrufe • vor 1 Monat

Anthropic's managed agents team just showed the Fable 5 workflow most people haven't seen Their line was simple: Fable 5 is our best model for running self-improving agent systems" Fable 5 + loops + dynamic workflows + dreaming = self-improving agents 13 minutes Live from Anthropic's Japan stage Built from scratch Worth more than a $500 agent building course Bookmark and watch it before this becomes the default setup

Anthropic's managed agents team just showed the Fable 5 workflow most people haven't seen Their line was simple: Fable 5 is our best model for running self-improving agent systems" Fable 5 + loops + dynamic workflows + dreaming = self-improving agents 13 minutes Live from Anthropic's Japan stage Built from scratch Worth more than a $500 agent building course Bookmark and watch it before this becomes the default setup

rari

32,304 Aufrufe • vor 1 Monat

Boris Cherny, the creator of Claude Code, just explained why the features most users never find are the ones that matter most in this podcast he breaks down not just how to use Claude, but what it all means for the next few years: > the right way to give Claude context before it starts working > why giving it too much information is just as bad as too little > how Claude Code is already changing how engineers build things > how the people who built Claude actually use it day to day based on this podcast and months of using Claude myself, I put together a guide on everything Claude can do that most people have no idea exists you can find it below

Boris Cherny, the creator of Claude Code, just explained why the features most users never find are the ones that matter most in this podcast he breaks down not just how to use Claude, but what it all means for the next few years: > the right way to give Claude context before it starts working > why giving it too much information is just as bad as too little > how Claude Code is already changing how engineers build things > how the people who built Claude actually use it day to day based on this podcast and months of using Claude myself, I put together a guide on everything Claude can do that most people have no idea exists you can find it below

Anatoli Kopadze

22,474 Aufrufe • vor 2 Monaten

Boris Cherny (creator of Claude Code, Anthropic): "My entire CLAUDE.md is two lines. With every new model you need less instruction, not more, most people are over-engineering the one thing that should stay tiny." in a 7-minute interview, Boris Cherny breaks down why the smallest AI-coding setup wins, and the one moment you should delete your config and start from scratch most engineers do the opposite: they pile on rules, build scaffolding around the model… then the next model ships and makes all of it useless. what he actually keeps in those two lines, and why he tells his team to throw the rest away. Watch the talk, then read the article below. That’s worth more than a $800 course on agent engineering.

Boris Cherny (creator of Claude Code, Anthropic): "My entire CLAUDE.md is two lines. With every new model you need less instruction, not more, most people are over-engineering the one thing that should stay tiny." in a 7-minute interview, Boris Cherny breaks down why the smallest AI-coding setup wins, and the one moment you should delete your config and start from scratch most engineers do the opposite: they pile on rules, build scaffolding around the model… then the next model ships and makes all of it useless. what he actually keeps in those two lines, and why he tells his team to throw the rest away. Watch the talk, then read the article below. That’s worth more than a $800 course on agent engineering.

Prometheus

238,034 Aufrufe • vor 1 Monat

Nobel Prize winner and Google DeepMind CEO Demis Hassabis just revealed the real AI skill gap: “The next generation will do things that used to take teams of 10, 20, 30, 50 people” Most people heard that and thought: “Cool, AI makes me faster” Wrong. AI does not just make people faster. It changes what one person can become. A startup used to need: > researcher > designer > developer > marketer > analyst > support team > ops manager Now one AI-native person can wire all of that into workflows. > Agents that research. > Tools that write. > Workflows that test. > Automations that publish. > Memory that compounds. This is not prompt engineering. This is leverage. The next 5 years will not belong to people who write better prompts. They will belong to people who build better workflows. Watch the full interview. Bookmark this. It is one of the most important ideas of the decade.

Nobel Prize winner and Google DeepMind CEO Demis Hassabis just revealed the real AI skill gap: “The next generation will do things that used to take teams of 10, 20, 30, 50 people” Most people heard that and thought: “Cool, AI makes me faster” Wrong. AI does not just make people faster. It changes what one person can become. A startup used to need: > researcher > designer > developer > marketer > analyst > support team > ops manager Now one AI-native person can wire all of that into workflows. > Agents that research. > Tools that write. > Workflows that test. > Automations that publish. > Memory that compounds. This is not prompt engineering. This is leverage. The next 5 years will not belong to people who write better prompts. They will belong to people who build better workflows. Watch the full interview. Bookmark this. It is one of the most important ideas of the decade.

spect

132,349 Aufrufe • vor 1 Monat

THIS GUY BUILT AN AUTONOMOUS AI AGENT OUT OF CLAUDE CODE + OBSIDIAN and this is way more interesting than another “use AI to take notes” demo the trick is simple: Obsidian is not the writing app here. it becomes the agent’s memory, task board, and context folder. Claude Code is not just answering prompts. it reads the vault, edits files, follows instructions, and keeps moving through the work like a junior operator with a filesystem. the reusable setup looks like this: 1. create an Obsidian vault for one project 2. keep goals, rules, tasks, decisions, and references as markdown files 3. point Claude Code at the folder 4. give it a clear operating loop: read context → choose next task → execute → write back what changed 5. use the notes as persistent memory instead of re-explaining the project every chat that’s the part people miss. the “agent” is not magic. it’s the boring combination of: - local files - explicit rules - task state - write access - a model that can run through the repo/vault Obsidian makes the memory human-readable. Claude Code makes the memory executable. that combo is why the video worked: it turns a notes app into an operating surface for actual work. best use cases: - content systems - research vaults - coding projects - client ops docs - personal knowledge bases that need actions, not just storage the caveat: if your vault is messy, your agent becomes messy too. folders, naming, “done” criteria, and forbidden actions matter more than the prompt. but once the structure is clean, this is one of the easiest ways to build an agent that remembers what happened yesterday without paying for a full custom app.

THIS GUY BUILT AN AUTONOMOUS AI AGENT OUT OF CLAUDE CODE + OBSIDIAN and this is way more interesting than another “use AI to take notes” demo the trick is simple: Obsidian is not the writing app here. it becomes the agent’s memory, task board, and context folder. Claude Code is not just answering prompts. it reads the vault, edits files, follows instructions, and keeps moving through the work like a junior operator with a filesystem. the reusable setup looks like this: 1. create an Obsidian vault for one project 2. keep goals, rules, tasks, decisions, and references as markdown files 3. point Claude Code at the folder 4. give it a clear operating loop: read context → choose next task → execute → write back what changed 5. use the notes as persistent memory instead of re-explaining the project every chat that’s the part people miss. the “agent” is not magic. it’s the boring combination of: - local files - explicit rules - task state - write access - a model that can run through the repo/vault Obsidian makes the memory human-readable. Claude Code makes the memory executable. that combo is why the video worked: it turns a notes app into an operating surface for actual work. best use cases: - content systems - research vaults - coding projects - client ops docs - personal knowledge bases that need actions, not just storage the caveat: if your vault is messy, your agent becomes messy too. folders, naming, “done” criteria, and forbidden actions matter more than the prompt. but once the structure is clean, this is one of the easiest ways to build an agent that remembers what happened yesterday without paying for a full custom app.

kocer

30,403 Aufrufe • vor 1 Monat

🚨do you understand what two Anthropic engineers just explained in 16 minutes. Barry and Mahesh built Claude Skills from scratch. here's the part nobody is talking about: > Skills are just folders. > folders that teach Claude your job. > your workflow. your expertise. your domain. Claude on day 30 is a completely different tool than day one. watch this before you write another prompt. before you build another agent. before you touch another tool. 16 minutes. bookmark it. watch it today. and if you want to learn everything about Claude from scratch the full 4 hour guide is waiting below.

🚨do you understand what two Anthropic engineers just explained in 16 minutes. Barry and Mahesh built Claude Skills from scratch. here's the part nobody is talking about: > Skills are just folders. > folders that teach Claude your job. > your workflow. your expertise. your domain. Claude on day 30 is a completely different tool than day one. watch this before you write another prompt. before you build another agent. before you touch another tool. 16 minutes. bookmark it. watch it today. and if you want to learn everything about Claude from scratch the full 4 hour guide is waiting below.

Kirill

5,345,864 Aufrufe • vor 3 Monaten

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.

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14,833 Aufrufe • vor 1 Monat