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Most people think a Forward Deployed Engineer just ships code. The job is actually 3 stages, and each one is a prerequisite for the next: Audit: - Understand the system before you touch anything. Evals: - Prove the system behaves correctly. - Pull 5,000 past examples, build a golden... show more

The Startup Ideas Podcast (SIP) 🧃

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22,726 次观看 • 6 天前 •via X (Twitter)

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FDE is the hottest role in tech. Here's the 30-day plan to actually become one: Week 1: Build the loop - Agent looping, tool usage, guardrails - Context, memory, and an audit trail - Checkpoint: one working agent, one task. Week 2: Make it recover - JSON schemas, not freeform text - Failure modes for the 1,000 ways it breaks - "There's only one way something goes right. A thousand ways it goes wrong." Week 3: Make it measurable - Retry logic + a golden dataset for evals - Test cheaper models on sub-tasks - Score it on revenue, risk, cost Week 4: Defend it like an FDE - The pain points, the architecture, the economics - Then pitch it to real businesses Think: - Week 1 = build - Week 2 = survive - Week 3 = measure - Week 4 = sell That's the whole ladder.

FDE is the hottest role in tech. Here's the 30-day plan to actually become one: Week 1: Build the loop - Agent looping, tool usage, guardrails - Context, memory, and an audit trail - Checkpoint: one working agent, one task. Week 2: Make it recover - JSON schemas, not freeform text - Failure modes for the 1,000 ways it breaks - "There's only one way something goes right. A thousand ways it goes wrong." Week 3: Make it measurable - Retry logic + a golden dataset for evals - Test cheaper models on sub-tasks - Score it on revenue, risk, cost Week 4: Defend it like an FDE - The pain points, the architecture, the economics - Then pitch it to real businesses Think: - Week 1 = build - Week 2 = survive - Week 3 = measure - Week 4 = sell That's the whole ladder.

The Startup Ideas Podcast (SIP) 🧃

105,872 次观看 • 6 天前

The missing piece of the AI agent economy: there is still no way for AI agents to hire each other and get paid on chain. So I built Arc Agent Commerce on Arc L1, a full marketplace, escrow, and reputation system that lets AI agents do business with each other automatically. Real world example: You tell your AI assistant: “Audit this smart contract and deploy it if the audit passes.” Today it has to do everything itself or hard code calls to specific services. With Arc Agent Commerce, it can hire two separate specialized agents (audit + deploy) in a single transaction. Money stays in escrow until each completes their part. How it works (in plain steps): 1. Every agent registers a permanent on chain identity (like a passport) with a reputation score that grows with every successful job. 2. Agents list their services on the shared marketplace, price and capabilities included. 3. A client creates a multi stage pipeline. The entire budget is locked in escrow in one upfront transaction. 4. Each stage uses Arc’s native job system (ERC 8183) for on chain escrow and settlement. 5. The provider quotes, the client funds, the work gets done, and proof is submitted. 6. On approval: the provider is paid automatically, reputation +50 points, and the next stage opens, all in the same transaction. 7. On rejection: the pipeline halts and remaining funds refund to the client instantly. The protocol doesn’t reinvent anything. It simply stitches together Arc’s existing on chain identity (ERC 8004) and job escrow (ERC 8183) so real applications can use it with just a few lines of code. Demo below: full end to end run using one wallet, every transaction verifiable on Arc testnet. → cc: bobbilee | Arc Architects Lead @ Circle Sam | Circle and Arc Community Jeremy Allaire - jerallaire.arc

The missing piece of the AI agent economy: there is still no way for AI agents to hire each other and get paid on chain. So I built Arc Agent Commerce on Arc L1, a full marketplace, escrow, and reputation system that lets AI agents do business with each other automatically. Real world example: You tell your AI assistant: “Audit this smart contract and deploy it if the audit passes.” Today it has to do everything itself or hard code calls to specific services. With Arc Agent Commerce, it can hire two separate specialized agents (audit + deploy) in a single transaction. Money stays in escrow until each completes their part. How it works (in plain steps): 1. Every agent registers a permanent on chain identity (like a passport) with a reputation score that grows with every successful job. 2. Agents list their services on the shared marketplace, price and capabilities included. 3. A client creates a multi stage pipeline. The entire budget is locked in escrow in one upfront transaction. 4. Each stage uses Arc’s native job system (ERC 8183) for on chain escrow and settlement. 5. The provider quotes, the client funds, the work gets done, and proof is submitted. 6. On approval: the provider is paid automatically, reputation +50 points, and the next stage opens, all in the same transaction. 7. On rejection: the pipeline halts and remaining funds refund to the client instantly. The protocol doesn’t reinvent anything. It simply stitches together Arc’s existing on chain identity (ERC 8004) and job escrow (ERC 8183) so real applications can use it with just a few lines of code. Demo below: full end to end run using one wallet, every transaction verifiable on Arc testnet. → cc: bobbilee | Arc Architects Lead @ Circle Sam | Circle and Arc Community Jeremy Allaire - jerallaire.arc

RIDWAN

12,958 次观看 • 3 个月前

Boris Cherny, the engineer who built Claude Code: "I don't talk to an agent anymore. I talk to a loop" a prompt is one instruction you babysit. a loop is a goal the AI works toward on its own: > it plans > does the work > checks itself > fixes what's weak > and repeats until it's done you step out, the work keeps going that's the shift the best engineers quietly moved to. most people hear a line like that and have no idea what it means in practice this breaks it down without the hype: what a loop actually is, when it's worth building, when it's a money trap, and how to run one yourself today watch his clip, then read the article below

Boris Cherny, the engineer who built Claude Code: "I don't talk to an agent anymore. I talk to a loop" a prompt is one instruction you babysit. a loop is a goal the AI works toward on its own: > it plans > does the work > checks itself > fixes what's weak > and repeats until it's done you step out, the work keeps going that's the shift the best engineers quietly moved to. most people hear a line like that and have no idea what it means in practice this breaks it down without the hype: what a loop actually is, when it's worth building, when it's a money trap, and how to run one yourself today watch his clip, then read the article below

Dep

20,849 次观看 • 1 个月前

.Tucker Carlson: "Libertarian economics was a scam perpetrated by the beneficiaries of the economic system that they were defending... I think you need to ask: 'Does this economic system produce a lot of Dollar Stores?' And if it does, it's not a system that you want, because it degrades people—and it makes their lives worse and it increases exponentially the amount of ugliness in your society. And anything that increases ugliness is evil. So if it's such a good system, why do we have all these Dollar Stores?"

.Tucker Carlson: "Libertarian economics was a scam perpetrated by the beneficiaries of the economic system that they were defending... I think you need to ask: 'Does this economic system produce a lot of Dollar Stores?' And if it does, it's not a system that you want, because it degrades people—and it makes their lives worse and it increases exponentially the amount of ugliness in your society. And anything that increases ugliness is evil. So if it's such a good system, why do we have all these Dollar Stores?"

System Update

1,222,212 次观看 • 2 年前

A legendary M&A banker had one rule before every client pitch: "Don't forget to ask for the cookie." Matt Wells (AlphaSense) shared the story. The chairman was part of the breakup of the Bells. He flew the Concorde. He met the Queen. And before every pitch, he would corner the junior bankers with the same line. What he meant: "If you don't ask for the cookie, you're never going to get the cookie. You put all this work into this pitch. When you're in there with the client or potential client, ask for the mandate." Pitching is the easy part. Closing is the part most people skip. It applies to every high-stakes ask. Banking mandate. Job offer. LP commitment. Political endorsement. The work that wins is not the deck. It is the one sentence at the end where you actually ask. Ask for the cookie.

A legendary M&A banker had one rule before every client pitch: "Don't forget to ask for the cookie." Matt Wells (AlphaSense) shared the story. The chairman was part of the breakup of the Bells. He flew the Concorde. He met the Queen. And before every pitch, he would corner the junior bankers with the same line. What he meant: "If you don't ask for the cookie, you're never going to get the cookie. You put all this work into this pitch. When you're in there with the client or potential client, ask for the mandate." Pitching is the easy part. Closing is the part most people skip. It applies to every high-stakes ask. Banking mandate. Job offer. LP commitment. Political endorsement. The work that wins is not the deck. It is the one sentence at the end where you actually ask. Ask for the cookie.

David Weisburd 🚀

224,423 次观看 • 1 个月前

A LINUX KERNEL DEVELOPER PROVED THE THING YOU PUSH CODE TO IS SECRETLY A DATABASE THAT CAN VERSION ALMOST ANYTHING AND THAT MOST DEVS HAVE ONLY EVER TOUCHED A TENTH OF IT 42 minutes from Josh Triplett -- a longtime Linux kernel and Debian developer -- showing that Git is a general-purpose, tamper-evident versioning engine that just happens to be famous for code. -> The moment it clicks, Git stops being "Where my code lives" and becomes what it really is underneath: a content-addressable store that can version almost anything -- your configs, your notes, your servers' state, entire datasets. People run whole wikis on it. They version their entire machine's configuration with it. They ship websites by pushing to it. They track data too big to email. None of it is a hack -- it's the same handful of objects you already use for code, pointed somewhere new. Treating Git as a code-only tool was never the ceiling -> it's a versioning engine for anything, and the people who see that automate what the rest of the team still does by hand. And as AI agents start spitting out not just code but configs, docs and data, the one system that can version and audit all of it at once is already sitting on your machine. You learned five commands to survive. This is the talk that shows you were standing on top of a database the whole time. It changes what you think the tool is even for. Bookmark & Watch it today ↓

A LINUX KERNEL DEVELOPER PROVED THE THING YOU PUSH CODE TO IS SECRETLY A DATABASE THAT CAN VERSION ALMOST ANYTHING AND THAT MOST DEVS HAVE ONLY EVER TOUCHED A TENTH OF IT 42 minutes from Josh Triplett -- a longtime Linux kernel and Debian developer -- showing that Git is a general-purpose, tamper-evident versioning engine that just happens to be famous for code. -> The moment it clicks, Git stops being "Where my code lives" and becomes what it really is underneath: a content-addressable store that can version almost anything -- your configs, your notes, your servers' state, entire datasets. People run whole wikis on it. They version their entire machine's configuration with it. They ship websites by pushing to it. They track data too big to email. None of it is a hack -- it's the same handful of objects you already use for code, pointed somewhere new. Treating Git as a code-only tool was never the ceiling -> it's a versioning engine for anything, and the people who see that automate what the rest of the team still does by hand. And as AI agents start spitting out not just code but configs, docs and data, the one system that can version and audit all of it at once is already sitting on your machine. You learned five commands to survive. This is the talk that shows you were standing on top of a database the whole time. It changes what you think the tool is even for. Bookmark & Watch it today ↓

slash1s

384,056 次观看 • 1 个月前

Critics and audiences are at each other’s throats over Citizen Vigilante, and I’m going to tell you what this fight is really about. This isn’t a review like the critics give — I lived on the other side of this fantasy. A masked vigilante hunting down criminals the system let walk, including a mob boss? I know exactly what happens to a guy like that on the street, and it doesn’t end the way it does in the movie. Fair warning: it’s a rough, violent film, and I say so. Watch to the end for what I think you should actually do about a system that fails people.

Critics and audiences are at each other’s throats over Citizen Vigilante, and I’m going to tell you what this fight is really about. This isn’t a review like the critics give — I lived on the other side of this fantasy. A masked vigilante hunting down criminals the system let walk, including a mob boss? I know exactly what happens to a guy like that on the street, and it doesn’t end the way it does in the movie. Fair warning: it’s a rough, violent film, and I say so. Watch to the end for what I think you should actually do about a system that fails people.

Michael Franzese

12,896 次观看 • 11 天前

Elon Musk just used a joke to perform an autopsy on the American economy. Two economists go for a hike. They find a pile of shit. One pays the other $100 to eat it. They keep walking. Find another pile. The second economist pays $100 back to eat that one. They stop. Neither man gained a dollar. Both ate shit for nothing. But on paper they just generated $200 in GDP. Musk: “That basically would count as a job. This is to illustrate the absurdity of economics.” That is not a punchline. That is the operating system of the federal government. Every time a politician celebrates “record job creation” this is what they are describing. Not output. Not value. Not progress. Motion. The entire bureaucratic machine exists to manufacture friction and then invoice for it. Compliance layers built to justify the next compliance layer. Oversight committees that produce nothing but the need for more oversight. Consulting firms hired to audit the work of other consulting firms. Trillions circulating through systems that have never produced a single thing you can hold in your hands. But the GDP number ticks up. So everyone applauds. The shit gets eaten. The scoreboard moves. Nobody asks what actually got built. This is why Washington treats AI like a five alarm fire. AI does not play the friction game. It does not form a committee. It does not schedule a review. It does not file 400 pages of paperwork no one will ever read. It just solves the problem. And that is the one thing the machine cannot survive. The government does not tax results. It taxes the process. The longer the process, the deeper the cut. AI compresses a ten day workflow into seconds. There is nothing left to bill. Nothing left to tax. Nothing left to skim. So they will spend the next decade warning you that AI threatens the economy. What they will never say is what it actually threatens. The illusion that activity equals progress. The $200 economy where both men ate shit and called it a job. The machines are not coming for your purpose. They are coming to prove that half the economy never had one.

Elon Musk just used a joke to perform an autopsy on the American economy. Two economists go for a hike. They find a pile of shit. One pays the other $100 to eat it. They keep walking. Find another pile. The second economist pays $100 back to eat that one. They stop. Neither man gained a dollar. Both ate shit for nothing. But on paper they just generated $200 in GDP. Musk: “That basically would count as a job. This is to illustrate the absurdity of economics.” That is not a punchline. That is the operating system of the federal government. Every time a politician celebrates “record job creation” this is what they are describing. Not output. Not value. Not progress. Motion. The entire bureaucratic machine exists to manufacture friction and then invoice for it. Compliance layers built to justify the next compliance layer. Oversight committees that produce nothing but the need for more oversight. Consulting firms hired to audit the work of other consulting firms. Trillions circulating through systems that have never produced a single thing you can hold in your hands. But the GDP number ticks up. So everyone applauds. The shit gets eaten. The scoreboard moves. Nobody asks what actually got built. This is why Washington treats AI like a five alarm fire. AI does not play the friction game. It does not form a committee. It does not schedule a review. It does not file 400 pages of paperwork no one will ever read. It just solves the problem. And that is the one thing the machine cannot survive. The government does not tax results. It taxes the process. The longer the process, the deeper the cut. AI compresses a ten day workflow into seconds. There is nothing left to bill. Nothing left to tax. Nothing left to skim. So they will spend the next decade warning you that AI threatens the economy. What they will never say is what it actually threatens. The illusion that activity equals progress. The $200 economy where both men ate shit and called it a job. The machines are not coming for your purpose. They are coming to prove that half the economy never had one.

Dustin

1,158,247 次观看 • 3 个月前

Citadel CEO Ken Griffin explained why generative AI is useless for hedge funds - "it works for the next 5 minutes. The next 2 years it falls apart" every VC pitching AI right now is selling the opposite of what the $65B fund is actually doing 48-min Stanford lecture and you'll understand what AI actually does inside a tier-1 hedge fund bookmark & watch - this is the AI take no fund manager will say out loud

Citadel CEO Ken Griffin explained why generative AI is useless for hedge funds - "it works for the next 5 minutes. The next 2 years it falls apart" every VC pitching AI right now is selling the opposite of what the $65B fund is actually doing 48-min Stanford lecture and you'll understand what AI actually does inside a tier-1 hedge fund bookmark & watch - this is the AI take no fund manager will say out loud

st1ne

428,784 次观看 • 2 个月前

the CEO of NVIDIA just said the PC you use today will be gone, replaced by an AI supercomputer that lives in your house Jensen Huang stood on stage holding the machine and said it becomes less like a computer and more like R2-D2, a thing in your home that just does work for you all day he put it bluntly, this is as big as the day the phone became the smartphone and the wild part is the hardware to do it already exists and ships this year here is what he actually unveiled: > a desktop that runs a one trillion parameter AI model locally, 768GB of memory, sitting by your desk > agents that run 24/7 with no meter, no cloud bill, no rental, doing work while you sleep > NVIDIA and Microsoft rebuilding the PC from the ground up for the first time in 40 years > a full lineup, from a $249 chip to enterprise monsters, and almost nobody knows which one they actually need the hype is going to push everyone toward the biggest most expensive machine i wrote the honest breakdown, every NVIDIA AI box, what each really does, the real math, and which one is actually yours the full guide is in the article below

the CEO of NVIDIA just said the PC you use today will be gone, replaced by an AI supercomputer that lives in your house Jensen Huang stood on stage holding the machine and said it becomes less like a computer and more like R2-D2, a thing in your home that just does work for you all day he put it bluntly, this is as big as the day the phone became the smartphone and the wild part is the hardware to do it already exists and ships this year here is what he actually unveiled: > a desktop that runs a one trillion parameter AI model locally, 768GB of memory, sitting by your desk > agents that run 24/7 with no meter, no cloud bill, no rental, doing work while you sleep > NVIDIA and Microsoft rebuilding the PC from the ground up for the first time in 40 years > a full lineup, from a $249 chip to enterprise monsters, and almost nobody knows which one they actually need the hype is going to push everyone toward the biggest most expensive machine i wrote the honest breakdown, every NVIDIA AI box, what each really does, the real math, and which one is actually yours the full guide is in the article below

John Doe

193,709 次观看 • 1 个月前

Most people trying to build a vertical AI business start by building the agent. That's the mistake. Here's the 5-step order that actually works: Step 1) Find a boring pain point - Pick a workflow you already know from your job. - That's your unfair advantage. Step 2) Map the workflow - Literally draw it out. - Every step, every handoff. Step 3) Do the job as a service - Manually. - For real clients. - Before any code. Step 4) Document the edge cases - This is where the real IP lives. - You only find them by doing the work. Step 5) Add vertical agents to replace each step - Now you have proprietary data and a workflow you own. Skip steps 1–4 and you're just building a demo.

Most people trying to build a vertical AI business start by building the agent. That's the mistake. Here's the 5-step order that actually works: Step 1) Find a boring pain point - Pick a workflow you already know from your job. - That's your unfair advantage. Step 2) Map the workflow - Literally draw it out. - Every step, every handoff. Step 3) Do the job as a service - Manually. - For real clients. - Before any code. Step 4) Document the edge cases - This is where the real IP lives. - You only find them by doing the work. Step 5) Add vertical agents to replace each step - Now you have proprietary data and a workflow you own. Skip steps 1–4 and you're just building a demo.

The Startup Ideas Podcast (SIP) 🧃

14,202 次观看 • 3 个月前

Do you know why it's so hard to detect incoming missiles and decoys? Because it falls into a class problem called Inverse Problems. Most people are far more familiar with forward problems. You know the system, you know the inputs, and you work out what happens next. Inverse Problems go the other way. You see messy measurements and from them you have to reconstruct the hidden thing you actually care about. That is a much harder task. A Radar does not see a missile at x with velocity v. It sees noisy returns, clutter, ghost echoes, and dropouts, and from that it has to infer whats actually out there.

Do you know why it's so hard to detect incoming missiles and decoys? Because it falls into a class problem called Inverse Problems. Most people are far more familiar with forward problems. You know the system, you know the inputs, and you work out what happens next. Inverse Problems go the other way. You see messy measurements and from them you have to reconstruct the hidden thing you actually care about. That is a much harder task. A Radar does not see a missile at x with velocity v. It sees noisy returns, clutter, ghost echoes, and dropouts, and from that it has to infer whats actually out there.

Mathelirium

83,018 次观看 • 4 个月前

🐿️ the thing i’m looking forward to the most is the music video! 🐧 the music video is going to be really fun! that is actually the thing i’m looking forward to the most in this comeback 🐿️ me too! 🐧 we’ve filmed it already and i think it’s going to be fun 🤭 🐿️ it’s not too complicated and i think people will connect with the story 🐧 you’ll understand the mv as soon as you watch it so i think moas will really enjoy it! 🐿️ it captures the members’ beauty well and it’s done really well, it was filmed in a hurry but it came out really well 🐿️ there have actually been a lot of songs that amazed me once i watched the music video 🐿️ i haven’t even watched the full completed version yet and i’m still really excited for it

🐿️ the thing i’m looking forward to the most is the music video! 🐧 the music video is going to be really fun! that is actually the thing i’m looking forward to the most in this comeback 🐿️ me too! 🐧 we’ve filmed it already and i think it’s going to be fun 🤭 🐿️ it’s not too complicated and i think people will connect with the story 🐧 you’ll understand the mv as soon as you watch it so i think moas will really enjoy it! 🐿️ it captures the members’ beauty well and it’s done really well, it was filmed in a hurry but it came out really well 🐿️ there have actually been a lot of songs that amazed me once i watched the music video 🐿️ i haven’t even watched the full completed version yet and i’m still really excited for it

💬

30,274 次观看 • 4 个月前

A Stanford mathematician who spent 10 years as a professional magician just described the market in one sentence: "I've spent my life on two tricks: making a rigged deck look random, and making a random one look rigged. The market is the first trick, and almost nobody catches it." That's Persi Diaconis. He has a free lecture that asks one question: does anything actually happen at random? The answer is: far less than you think. The market is his first trick in the wild. It looks like pure chance. Buried inside is a faint rig, a 50.75% tilt no eye can see. Your gut reads a losing week as a broken system and a hot streak as skill. Wrong both times. The tilt is invisible to human intuition, which is exactly why funds hand the decision to the math. None of it is hidden. Diaconis has taught it for decades. The probability goes back to 1713. The lecture is free. Here's the trap: you feel every win and every loss, but you cannot feel the average. And the average is the only thing that pays. It takes thousands of trades for a 51% edge to separate from luck, and almost everyone quits long before then. The math is free. The patience to trust it past your own eyes is the edge.

A Stanford mathematician who spent 10 years as a professional magician just described the market in one sentence: "I've spent my life on two tricks: making a rigged deck look random, and making a random one look rigged. The market is the first trick, and almost nobody catches it." That's Persi Diaconis. He has a free lecture that asks one question: does anything actually happen at random? The answer is: far less than you think. The market is his first trick in the wild. It looks like pure chance. Buried inside is a faint rig, a 50.75% tilt no eye can see. Your gut reads a losing week as a broken system and a hot streak as skill. Wrong both times. The tilt is invisible to human intuition, which is exactly why funds hand the decision to the math. None of it is hidden. Diaconis has taught it for decades. The probability goes back to 1713. The lecture is free. Here's the trap: you feel every win and every loss, but you cannot feel the average. And the average is the only thing that pays. It takes thousands of trades for a 51% edge to separate from luck, and almost everyone quits long before then. The math is free. The patience to trust it past your own eyes is the edge.

veles

2,165,034 次观看 • 15 天前

🚨Iran is boiling. The currency is collapsing. Protests are spreading. And a US president just issued a warning that most people are already misreading. If you think this is just about protesters versus the regime, there's a lot more to it. If you want to understand what’s actually going on, and what the next 48 to 72 hours could mean for Iran, Israel, and the region, check out the video below for the full breakdown⬇️

🚨Iran is boiling. The currency is collapsing. Protests are spreading. And a US president just issued a warning that most people are already misreading. If you think this is just about protesters versus the regime, there's a lot more to it. If you want to understand what’s actually going on, and what the next 48 to 72 hours could mean for Iran, Israel, and the region, check out the video below for the full breakdown⬇️

Inside_Israel_Intel

14,216 次观看 • 6 个月前

"We treat software like it is so precious. And in the new world we're going to, it's going to be much more fungible. But that's the good news." Garry Tan on where the moats will be for future YC companies: "That's where taste, agency, and trust matter a lot. This is something that's only crystallized recently. We've been talking about agency and taste for the longest time. Agency is being able to prompt, and taste is being able to do evals." "Trust is the third thing. It's probably the most important. When you have an enterprise company that is selling to real businesses that are relying on you — that's your moat. Your moat is actually trust. They went through the effort to try you, get you onboarded, and incorporate you into how they work."

"We treat software like it is so precious. And in the new world we're going to, it's going to be much more fungible. But that's the good news." Garry Tan on where the moats will be for future YC companies: "That's where taste, agency, and trust matter a lot. This is something that's only crystallized recently. We've been talking about agency and taste for the longest time. Agency is being able to prompt, and taste is being able to do evals." "Trust is the third thing. It's probably the most important. When you have an enterprise company that is selling to real businesses that are relying on you — that's your moat. Your moat is actually trust. They went through the effort to try you, get you onboarded, and incorporate you into how they work."

TBPN

43,619 次观看 • 3 个月前

This is how a $999 AI audit becomes a $2,500 service overnight. What I'd build with Fable 5 on Hyperagent: an interactive audit deliverable. The play: 1. Run your normal 45-min discovery call. Ask "where does your time go?" 2. Hand the transcript to Fable 5 on Hyperagent. It loops unsupervised and builds a live map of the client's entire operation. 3. Every process animated. Time sinks pulsing red. Dollar leaks counted in real time on screen. 4. Walk the client through THEIR business bleeding money. Toggle the "after automation" view. Watch the leaks stop. 5. The roadmap sells itself. Upsell the $2,500-5,000 implementation on the spot. Same audit. Same call. The deliverable went from a 6-page PDF to something the client screen-records and sends to their business partner. Built the demo below in one prompt while I walked my dog. #HyperagentPartner

This is how a $999 AI audit becomes a $2,500 service overnight. What I'd build with Fable 5 on Hyperagent: an interactive audit deliverable. The play: 1. Run your normal 45-min discovery call. Ask "where does your time go?" 2. Hand the transcript to Fable 5 on Hyperagent. It loops unsupervised and builds a live map of the client's entire operation. 3. Every process animated. Time sinks pulsing red. Dollar leaks counted in real time on screen. 4. Walk the client through THEIR business bleeding money. Toggle the "after automation" view. Watch the leaks stop. 5. The roadmap sells itself. Upsell the $2,500-5,000 implementation on the spot. Same audit. Same call. The deliverable went from a 6-page PDF to something the client screen-records and sends to their business partner. Built the demo below in one prompt while I walked my dog. #HyperagentPartner

Corey Ganim

54,552 次观看 • 1 个月前

THIS CHINESE DEVELOPER'S OBSIDIAN BRAIN MAPS EVERY CLIENT CONVERSATION - ZERO EMPLOYEES, $40,000/MONTH, CATCHES PROBLEMS BEFORE CLIENTS DO every node on screen is a conversation - green for happy clients, yellow for neutral, red for problems that need attention - the brain sees the emotional state of the entire business in real time this is not a CRM, not a dashboard, not a spreadsheet - it's a living map of every client interaction that fires connections and surfaces patterns no human account manager would ever catch when a cluster starts turning yellow the brain flags it before the client sends the complaint - when a green cluster grows it identifies what's working and replicates it across every other account automatically he replaced a $15,000/month account management team with one Obsidian brain that never sleeps, never misses a message and never needs a performance review no employees, no contractors, no standups, no payroll - just one developer, one brain and one delivery system doing the work of 10 people $40,000/month - solo - from a system that gets smarter every single conversation

THIS CHINESE DEVELOPER'S OBSIDIAN BRAIN MAPS EVERY CLIENT CONVERSATION - ZERO EMPLOYEES, $40,000/MONTH, CATCHES PROBLEMS BEFORE CLIENTS DO every node on screen is a conversation - green for happy clients, yellow for neutral, red for problems that need attention - the brain sees the emotional state of the entire business in real time this is not a CRM, not a dashboard, not a spreadsheet - it's a living map of every client interaction that fires connections and surfaces patterns no human account manager would ever catch when a cluster starts turning yellow the brain flags it before the client sends the complaint - when a green cluster grows it identifies what's working and replicates it across every other account automatically he replaced a $15,000/month account management team with one Obsidian brain that never sleeps, never misses a message and never needs a performance review no employees, no contractors, no standups, no payroll - just one developer, one brain and one delivery system doing the work of 10 people $40,000/month - solo - from a system that gets smarter every single conversation

Noisy

42,207 次观看 • 1 个月前

One guy keeps a farm of Mac minis on his desk and says each $600 box brings him $2,000 a month while he sleeps. AND THE HARDWARE ACTUALLY WORKS. But the number is not even the interesting part. The broken part is HOW: his AI no longer sits in a chat window. It sees the screen, moves the mouse itself, types and clicks the interface like a human at a computer. That is it. While most people still run AI in a chat and ask it for text, he sat Claude down right at the computer and put it to work with its hands. He automated not a single task but the workplace itself. How it actually works: on every Mac mini Claude runs with computer use turned on and the official Claude API docs spell it out: screenshot capture, mouse control, keyboard input, desktop automation. The agent opens the browser and the apps itself and runs the boring routine on a schedule: pulls leads, fills the CRM, checks orders, runs QA on the site. One box, one quiet worker that does not sleep and does not ask for a salary. His math is simple: a Mac mini is $600 once, Claude Max is $200 a month, and a live white-collar worker on the same routine costs a business $4,000 and up. So he rents out each node to a client as an AI worker for about $2,000 and 6 Mac minis come out to around $12,000 a month with costs a bit over $1,000 on subscriptions. But the $12,000 is his projection not a revenue dashboard: the video has no client, no task log, no working automation at all. The real asset here is not the stack of hardware but the one repeatable process the agent actually closes. Because a Mac mini on its own earns nothing. The money shows up exactly where the boring browser routine used to be done by hand for a salary and now you can hand it to an agent for the price of a subscription. Computer use is still in beta, almost nobody builds a service on it and the demand for cheap GUI routine is huge. The window is open for literally the next few months. Most people will watch this, laugh at the "$600 AI worker" and close it. And the ones who actually put an agent on one boring task and grind it into a repeat will ride this wave while it is still empty. Would you sit an AI right at your own computer on the boring routine or are you still clicking through it by hand?

One guy keeps a farm of Mac minis on his desk and says each $600 box brings him $2,000 a month while he sleeps. AND THE HARDWARE ACTUALLY WORKS. But the number is not even the interesting part. The broken part is HOW: his AI no longer sits in a chat window. It sees the screen, moves the mouse itself, types and clicks the interface like a human at a computer. That is it. While most people still run AI in a chat and ask it for text, he sat Claude down right at the computer and put it to work with its hands. He automated not a single task but the workplace itself. How it actually works: on every Mac mini Claude runs with computer use turned on and the official Claude API docs spell it out: screenshot capture, mouse control, keyboard input, desktop automation. The agent opens the browser and the apps itself and runs the boring routine on a schedule: pulls leads, fills the CRM, checks orders, runs QA on the site. One box, one quiet worker that does not sleep and does not ask for a salary. His math is simple: a Mac mini is $600 once, Claude Max is $200 a month, and a live white-collar worker on the same routine costs a business $4,000 and up. So he rents out each node to a client as an AI worker for about $2,000 and 6 Mac minis come out to around $12,000 a month with costs a bit over $1,000 on subscriptions. But the $12,000 is his projection not a revenue dashboard: the video has no client, no task log, no working automation at all. The real asset here is not the stack of hardware but the one repeatable process the agent actually closes. Because a Mac mini on its own earns nothing. The money shows up exactly where the boring browser routine used to be done by hand for a salary and now you can hand it to an agent for the price of a subscription. Computer use is still in beta, almost nobody builds a service on it and the demand for cheap GUI routine is huge. The window is open for literally the next few months. Most people will watch this, laugh at the "$600 AI worker" and close it. And the ones who actually put an agent on one boring task and grind it into a repeat will ride this wave while it is still empty. Would you sit an AI right at your own computer on the boring routine or are you still clicking through it by hand?

Sorven

11,948 次观看 • 29 天前