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How a $50K AI project actually starts (process mapping, step by step) Before anyone builds anything, a real AI project starts with a stopwatch and a wall of sticky notes. Here's the whole method on one company: a 60-person parts distributor where 400 orders a day arrive as PDF... attachments and six people retype them into the ERP. Interview the people who do the work, not the owner. The owner says taking an order is 5 steps. The person who's done it for nine years walks you through 41, including the sticky note on her monitor. Sit next to them with a stopwatch. Measure touch time (6 minutes of actual work) against lead time (3 hours from email to confirmation). The minutes cost wages. The hours cost customers. Break every box into atomic steps. One person, one system, one action. "Enter the order" is 11 steps. AI can't do "enter the order", but it's great at "match their part number to ours", and it tells you how sure it is. Hunt the exceptions. Old part numbers, price mismatches, backorders, credit holds, duplicate POs. Log them for two weeks. That's where the time goes. Give every step one of three colors. Blue is a rule, so automate it. Purple is judgment on messy input, so AI does it and a person approves when it's unsure. Orange is money or a relationship, so it stays human. Fix broken steps before you automate them. Score and sequence. Frequency × minutes × cost of a mistake gives you the roadmap. Track one number from day one: the share of orders nobody had to touch. Then the $50K has a shape: paid discovery, then phases that each pay for the next. Nobody loses their job. They stop retyping and handle the exceptions and the customers. #aiautomation #aiagentshow more

Professor Glitch

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20,900 次观看 • 1 个月前 •via TikTok

教育财经科学技术 #aiautomation #aiagent

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How to finish an AI project and make your first dollar (instead of starting 14 more) How to finish an AI project and turn one idea into your first dollar. With AI, starting is free: a landing page in ten minutes, a logo in ten seconds, a whole app over a weekend. So starting feels like progress, but finishing is still hard. Selling it, testing it, delivering it to real people. Most of us end up with 14 half-built projects, and nobody has paid us for any of them. The rule: one idea, all the way to its first dollar, before you touch the next one. Done has one meaning: a stranger paid you. Here is how to build a small AI business step by step: 1. Test it before you build it. Write the offer in two sentences: who it's for and what it does for them. Let AI build one page for it in an hour, then spend the week talking to people, not building. If ten people hear it and nobody wants to pay, you just saved yourself three months. 2. Pick the shape of the money, because each shape needs AI in a different place. A small price for lots of people needs attention, so AI handles the sales and delivers automatically. A big price for a few clients needs trust, so you take the calls and AI does the prep and delivery work. A monthly price needs people to stay, so AI keeps delivering something useful every month. 3. Once it sells, take yourself out of it one job at a time: the follow-ups, the onboarding, the delivery. Each one becomes an AI workflow with a briefing, checks, and your approval where it matters. That is how you automate a business with AI without losing control. Then ask the one question that tells you when to start the next idea: could you take a week off and it would still sell and deliver without you? If yes, start the next one. If no, you're not done yet. Every finished idea pays for the next one, and every system you built carries over, so the second one takes half the time. Ideas are free now. Finishing is the skill. This is for side hustlers, solo founders, freelancers and small business owners who use AI tools and keep starting instead of finishing. The Thinker's Mind, inside my community, ends with choosing your project, the one you'll actually finish. And the AI Command Center teaches you to build the AI employees that run it once it works. Seven courses, in order. If you're interested, the link is in my profile. #aiautomation #ai #aiagents

Professor Glitch

10,200 次观看 • 1 个月前

People are making stupid money with AI — and they're no geniuses. Here is how All respect to them, I mean that. But most of them are regular people who learned ONE skill in a short amount of time and ran one recipe with it. You've probably seen it — somebody around you quietly earning with AI while you wonder what the secret is. Luck. Genius. Timing. It's a recipe with two parts, and almost everybody only ever does part one. PART ONE — BUILD something people actually want. With AI this has never been easier: an automation that saves a business owner ten hours a week, a tool for one specific job, a service delivered faster than anyone charging double. A few focused months for the skills, a couple of weeks for the build. Part one is necessary. And part one, alone, pays you nothing. Because that's the beautiful store you open in the desert. Perfect design, real work, months of grind — nobody walks past. That's where almost everyone quits, convinced it was luck after all. PART TWO — DISTRIBUTION. The actual secret. Put the valuable thing in front of the RIGHT people, AT SCALE. Two equations: MATCHING — speak your buyer's language. Built a tool for fitness trainers? Talk to trainers about their clients, their programs, their day, in trainer words. The algorithm reads your language and hands your videos to the people who speak it. SCALE — one video is a hope. Hundreds are a system. The content engine: make three videos, it turns them into fifteen posts across every platform, every week. One person shows up like a media company. Look at anyone actually winning with AI: they built something worth wanting, then made sure thousands of the right people saw it. Both halves, every time. It was never luck. It's build, times distribution. #aiagent #aiautomation #makemoneywithai

People are making stupid money with AI — and they're no geniuses. Here is how All respect to them, I mean that. But most of them are regular people who learned ONE skill in a short amount of time and ran one recipe with it. You've probably seen it — somebody around you quietly earning with AI while you wonder what the secret is. Luck. Genius. Timing. It's a recipe with two parts, and almost everybody only ever does part one. PART ONE — BUILD something people actually want. With AI this has never been easier: an automation that saves a business owner ten hours a week, a tool for one specific job, a service delivered faster than anyone charging double. A few focused months for the skills, a couple of weeks for the build. Part one is necessary. And part one, alone, pays you nothing. Because that's the beautiful store you open in the desert. Perfect design, real work, months of grind — nobody walks past. That's where almost everyone quits, convinced it was luck after all. PART TWO — DISTRIBUTION. The actual secret. Put the valuable thing in front of the RIGHT people, AT SCALE. Two equations: MATCHING — speak your buyer's language. Built a tool for fitness trainers? Talk to trainers about their clients, their programs, their day, in trainer words. The algorithm reads your language and hands your videos to the people who speak it. SCALE — one video is a hope. Hundreds are a system. The content engine: make three videos, it turns them into fifteen posts across every platform, every week. One person shows up like a media company. Look at anyone actually winning with AI: they built something worth wanting, then made sure thousands of the right people saw it. Both halves, every time. It was never luck. It's build, times distribution. #aiagent #aiautomation #makemoneywithai

Professor Glitch

10,200 次观看 • 2 个月前

5 real ways to make money with AI (and which one to start with) Nobody can promise you a number. What I can show you is where the money actually is, and how to get there faster with AI doing half the work. Five real ways, from fastest to slowest: Sell a service you already know, ten times faster. Build small AI systems for local businesses: answer the website at night, follow up every quote on day 2 and day 5. Become the AI person at your job. Automate the Monday report and show the hours you saved. Build one small product. Slowest on its own, because a product with no audience earns nothing. Content. Show the problem you solve, over and over. It makes every other way work. Start with one. Get good, make money with it, then add the second. The part everybody skips is how people find out you exist: show the work, let AI do the hunting (local listings, each website, what's missing, a drafted message you fix and send), and post every day. #aiagents #aiautomation #aiemployee #claude

5 real ways to make money with AI (and which one to start with) Nobody can promise you a number. What I can show you is where the money actually is, and how to get there faster with AI doing half the work. Five real ways, from fastest to slowest: Sell a service you already know, ten times faster. Build small AI systems for local businesses: answer the website at night, follow up every quote on day 2 and day 5. Become the AI person at your job. Automate the Monday report and show the hours you saved. Build one small product. Slowest on its own, because a product with no audience earns nothing. Content. Show the problem you solve, over and over. It makes every other way work. Start with one. Get good, make money with it, then add the second. The part everybody skips is how people find out you exist: show the work, let AI do the hunting (local listings, each website, what's missing, a drafted message you fix and send), and post every day. #aiagents #aiautomation #aiemployee #claude

Professor Glitch

61,200 次观看 • 27 天前

How people actually make money with AI right now: 5 honest ways (and where to start) How to make money with AI, the honest version: the five ways people are really making money with AI right now, who each one is for, the catch, and which one to start with. 1. AI consulting: business owners know they need AI and don't know where to start, so they pay someone to look at how the business runs, put AI in the right places, and build it. The catch: if you can't build it yet, don't sell it yet. 2. The builder: build one AI system that fixes one problem, like answering every new lead in two minutes, and install it for business after business. Setup fee, then a monthly fee. The catch: keep it one system with small changes, or twenty clients turns into twenty different jobs. 3. The freelancer: keep selling the skill you already have (writing, design, bookkeeping, video editing) and let AI do the first draft and the boring parts, so you deliver faster and take on more clients. The catch: your name is on it, so check everything. 4. A digital product plus an AI content system (for people who aren't technical): turn what you already know from your own work into a product, like a bookkeeper's month-end checklist or a salon owner's pricing playbook. Film one tip a week, and let AI turn it into a week of posts and emails that point back at the product. The product takes a weekend. Getting found takes months. 5. AI at your job (the one to start with): time a task that eats your week, automate it with AI, time it again, and write both numbers down. Do three tasks, bring one before/after sheet to your boss, and offer the same for other teams. That's how people get the new title and the raise: they become the AI person on the team. How they connect: after a few months at your job you have real builds, real numbers and real proof, and the business down the road has the exact same problem. Sell them the build (consulting) or install it for every business like them (the builder). AI even helps you find them by scanning local business websites and drafting the first message for you. Start with one, get good at it, make money, then add the second. If you don't know how to build any of this yet, I teach it step by step, from complete beginner to real AI systems you can use at work or sell. Check my profile. #aiautomation #aiconsulting #aiagent #learnai

Professor Glitch

10,700 次观看 • 5 天前

How to make money with AI: the 7 laws most people break How to make money with AI: the 7 laws, and the five that most people break. Break them and AI keeps you busy and broke. Follow them and every month gets easier than the last. Most people chase the newest AI tool, build a cool demo and wonder why nobody pays. The people getting paid follow a short list of rules about systems, problems and results, not tools. Here they are, each one shown on a screen in the video. The 7 laws of making money with AI: 1. Leverage. You can't scale your hours, you can scale a system. The second time you do the same task by hand, that's an AI workflow you haven't built yet. One person with the right systems can serve 100 clients. 2. The boring problem. Nobody pays for a cool demo. They pay to make a painful job disappear: invoices, follow-ups, data entry, the inbox nobody wants to touch. That's where AI automation for small business makes money. 3. The system. AI multiplies whatever you plug it into, so a mess becomes a faster mess. Fix the process first, write down the facts, the rules and the edge cases, then add the AI. 4. Depth. Learn one tool all the way down. Once you truly understand one, switching to the next takes about an hour. 5. Reliability. You get paid for what an AI agent does right the 400th time, not once. That means tests, human in the loop checks and a plan for when it's unsure. 6. Outcomes. Nobody wants an AI agent. They want leads answered in five minutes, invoices paid on time and their evenings back. Sell the hours, the money and the customers, in their words. 7. Compounding. Every system you build makes the next one faster. The briefing, the skills and the checks carry over. Month one you build one system, month six you build one a week. This is for freelancers, agency owners, consultants and anyone building AI automation for small businesses or for clients, and for business owners who want AI to actually pay off. Most of these laws are whole courses inside my community. The Thinker's Mind covers laws two and three: finding the boring problem and fixing the system before AI touches it. The AI Automations course is law five: tests, error handling and human checks. The AI Command Center covers depth and compounding: one tool, Claude Code, all the way down, with skills you build once and reuse. Seven courses, in order. If you're interested, the link is in my profile. #aiautomation #aiagents #learnai

Professor Glitch

28,900 次观看 • 1 个月前

AI agents vs automation Chatbot, automation, AI workflow, AI agent, AI employee. Five words everybody mixes up, explained with one business and one email. A dental office gets: "Hi, my tooth hurts, can I come in this week, after 3?" 1. Chatbot. ChatGPT or Claude in a chat window. The receptionist copies the email in, asks for a nice reply, pastes it back. Smart, but it only moves when you type. Every step is still you. 2. Automation. A fixed recipe: when this happens, do that. Tools like Zapier, Make or n8n. New email arrives, save it to the patient sheet, send the "we got your message" reply. No thinking at all, the same steps a thousand times a day. And when something unexpected comes in, it does the wrong thing, perfectly. 3. AI workflow. The same recipe with a brain in the middle. AI reads the email and sorts it: booking, question or emergency. A second AI step pulls out "this week, after 3". Then plain automation checks the calendar, finds Thursday at 4:00 and sends it to the patient to confirm. Two AI steps, still a workflow: you drew every road ahead of time. So when the next patient adds "my insurance changed", the booking road has no stop for it, and that part goes nowhere. 4. AI agent. You stop drawing the road. You give it a goal (get this patient booked), tools (the calendar, the insurance list, the email) and rules (never book over lunch, never promise a price). It decides the steps: checks the calendar, sees Thursday is open, notices the insurance change, looks up the new plan, drafts one reply that covers both. A different email gets different steps. 5. AI employee. An agent with a job: a job description, a memory of yesterday and a start time. Every morning at 7:00 it goes through the inbox, books what it can and leaves the front desk one list: here's what I booked, here's what needs you. The one question that sorts all five: who decides the next step? Chatbot: you, every time. Automation: you, once, ahead of time. AI workflow: you drew the path, AI fills in the blanks. Agent: AI picks the steps inside your rules. If the steps are the same every time, use automation. If the input is messy, add one AI step. Only when the steps really change every time do you need an agent. Most businesses need more automation and fewer agents. #aiautomation #aiagent #aiemployee

AI agents vs automation Chatbot, automation, AI workflow, AI agent, AI employee. Five words everybody mixes up, explained with one business and one email. A dental office gets: "Hi, my tooth hurts, can I come in this week, after 3?" 1. Chatbot. ChatGPT or Claude in a chat window. The receptionist copies the email in, asks for a nice reply, pastes it back. Smart, but it only moves when you type. Every step is still you. 2. Automation. A fixed recipe: when this happens, do that. Tools like Zapier, Make or n8n. New email arrives, save it to the patient sheet, send the "we got your message" reply. No thinking at all, the same steps a thousand times a day. And when something unexpected comes in, it does the wrong thing, perfectly. 3. AI workflow. The same recipe with a brain in the middle. AI reads the email and sorts it: booking, question or emergency. A second AI step pulls out "this week, after 3". Then plain automation checks the calendar, finds Thursday at 4:00 and sends it to the patient to confirm. Two AI steps, still a workflow: you drew every road ahead of time. So when the next patient adds "my insurance changed", the booking road has no stop for it, and that part goes nowhere. 4. AI agent. You stop drawing the road. You give it a goal (get this patient booked), tools (the calendar, the insurance list, the email) and rules (never book over lunch, never promise a price). It decides the steps: checks the calendar, sees Thursday is open, notices the insurance change, looks up the new plan, drafts one reply that covers both. A different email gets different steps. 5. AI employee. An agent with a job: a job description, a memory of yesterday and a start time. Every morning at 7:00 it goes through the inbox, books what it can and leaves the front desk one list: here's what I booked, here's what needs you. The one question that sorts all five: who decides the next step? Chatbot: you, every time. Automation: you, once, ahead of time. AI workflow: you drew the path, AI fills in the blanks. Agent: AI picks the steps inside your rules. If the steps are the same every time, use automation. If the input is messy, add one AI step. Only when the steps really change every time do you need an agent. Most businesses need more automation and fewer agents. #aiautomation #aiagent #aiemployee

Professor Glitch

10,500 次观看 • 14 天前

The 6 levels of AI (and why you can't skip one) Most people think they're good at AI because they use it every day. That's level one. There are six. You don't use it. You tried it once, got something generic, and decided it's overhyped. Chat. Ask, get an answer, copy it somewhere. It saves minutes, but every chat starts from zero. Context. Write one page about your work: who you are, your customers, how you write, what good looks like. Stop rewriting the prompt. Projects and skills. The page lives in a project with your files. Your repeatable jobs become skills, so the AI follows your steps the same way every time. Automations. A new lead gets a drafted reply before you open your laptop. A late invoice gets a reminder on day three. You stop being the trigger. Agents. Give it a goal, not steps. It plans, uses your tools, checks its own work, and asks you before anything important. A team of AI employees. Each one has a lane, its own instructions, tools and memory. They hand work to each other and tell you what needs you. The part that matters: every level sits on the one under it. Context makes skills work. Skills make automations reliable. Reliable automations are what agents use. Find your level honestly, then do the next one. Not the top one. Inside my community I have every level, in order, in eight courses, from your first chat to a team of AI employees. If you're interested, the link is in my profile. #aiautomation #aiemployee #aiagents

Professor Glitch

137,800 次观看 • 25 天前

somebody just torched their entire career for us someone inside Anthropic wrote down exactly how they wire their AIs to work as a team instead of one at a time, every number, and let the whole thing walk out the door it's called Graph Engineering: you stop asking one AI to do the whole job and start running a crew. one plans, a few go and look, one checks the others for lies their own measurement: the crew came back with 96% of the quality at 46% of the price. same models, same question. they only changed who is allowed to talk to whom Anthropic raised $65,000,000,000 to figure this out. it costs you $0 run one tonight in a normal chat, 3 moves: 1. give it the job and make it write the plan before it touches anything 2. run each step in its own fresh chat so nothing bleeds into the next 3. in one clean chat at the end, make it check the work against sources and mark whatever it could not confirm save this, then open the piece below: how many workers a job actually needs, and where one more starts making it worse ↓

somebody just torched their entire career for us someone inside Anthropic wrote down exactly how they wire their AIs to work as a team instead of one at a time, every number, and let the whole thing walk out the door it's called Graph Engineering: you stop asking one AI to do the whole job and start running a crew. one plans, a few go and look, one checks the others for lies their own measurement: the crew came back with 96% of the quality at 46% of the price. same models, same question. they only changed who is allowed to talk to whom Anthropic raised $65,000,000,000 to figure this out. it costs you $0 run one tonight in a normal chat, 3 moves: 1. give it the job and make it write the plan before it touches anything 2. run each step in its own fresh chat so nothing bleeds into the next 3. in one clean chat at the end, make it check the work against sources and mark whatever it could not confirm save this, then open the piece below: how many workers a job actually needs, and where one more starts making it worse ↓

Argona

1,388,887 次观看 • 3 个月前

The 4 levels of AI in a business: how to go from AI tasks to an AI-run operation How to use AI in your business: there are four levels, and most owners never leave level one. At level four, a whole part of the business runs without you in the room. Here is the ladder, using Fernway, a fictional cleaning business, as the example: 1. Tasks. You open the chat, ask, and AI writes the email, the post, the quote. You save minutes a day, but you still decide what to do, still start it, and still carry every answer into your other apps. You got faster, and your plate is just as full. 2. Processes (AI workflow automation). A process is a job that happens the same way every time: a lead comes in, you reply, send prices, offer a time. Write the steps down once and connect them with an automation tool like n8n. Now the lead arriving is the trigger, the steps run by themselves, and the AI in the middle writes the reply. Fernway now answers new leads in 2 minutes instead of 4 hours, and the first business to answer often wins the job. How to pick your first one: it happens every week, and the rules fit on a sticky note. 3. Departments. This is where most people go wrong: ten automations that don't know about each other. A department is those processes sharing one memory. Answer the lead, qualify it, book the visit, follow up on day 3 and day 7, update the deal, all reading and writing the same customer record in your CRM. Nobody gets asked the same question twice and nothing falls through the cracks. Add rules, like a discount over 10 percent waits for your yes. That is human in the loop where it matters. 4. The operation (AI agents and AI employees). Sales, delivery, invoices, reviews: each department gets its own AI employee, and they hand work to each other through one board. A job closes in sales and delivery picks it up. Delivery finishes and invoicing sends the bill. Your job becomes managing it: approving, reading the numbers, fixing what breaks. How to tell where you are: if AI only works when you open it, you're on level one. If one job runs without you, you're on level two. Each level saves you more than the one before, and most owners are stuck on tasks. This fits service businesses and small teams: cleaning companies, agencies, home services, clinics, any business that gets leads and follows up on them. #aiautomation #aiagents #aiemployee

The 4 levels of AI in a business: how to go from AI tasks to an AI-run operation How to use AI in your business: there are four levels, and most owners never leave level one. At level four, a whole part of the business runs without you in the room. Here is the ladder, using Fernway, a fictional cleaning business, as the example: 1. Tasks. You open the chat, ask, and AI writes the email, the post, the quote. You save minutes a day, but you still decide what to do, still start it, and still carry every answer into your other apps. You got faster, and your plate is just as full. 2. Processes (AI workflow automation). A process is a job that happens the same way every time: a lead comes in, you reply, send prices, offer a time. Write the steps down once and connect them with an automation tool like n8n. Now the lead arriving is the trigger, the steps run by themselves, and the AI in the middle writes the reply. Fernway now answers new leads in 2 minutes instead of 4 hours, and the first business to answer often wins the job. How to pick your first one: it happens every week, and the rules fit on a sticky note. 3. Departments. This is where most people go wrong: ten automations that don't know about each other. A department is those processes sharing one memory. Answer the lead, qualify it, book the visit, follow up on day 3 and day 7, update the deal, all reading and writing the same customer record in your CRM. Nobody gets asked the same question twice and nothing falls through the cracks. Add rules, like a discount over 10 percent waits for your yes. That is human in the loop where it matters. 4. The operation (AI agents and AI employees). Sales, delivery, invoices, reviews: each department gets its own AI employee, and they hand work to each other through one board. A job closes in sales and delivery picks it up. Delivery finishes and invoicing sends the bill. Your job becomes managing it: approving, reading the numbers, fixing what breaks. How to tell where you are: if AI only works when you open it, you're on level one. If one job runs without you, you're on level two. Each level saves you more than the one before, and most owners are stuck on tasks. This fits service businesses and small teams: cleaning companies, agencies, home services, clinics, any business that gets leads and follows up on them. #aiautomation #aiagents #aiemployee

Professor Glitch

20,700 次观看 • 1 个月前

75 days left in 2026: the AI plan, two weeks at a time There are 75 days left in 2026. Next year, people who chat with AI will be competing with people who have AI doing real work for them. They aren't smarter. They have a system. Here's how to build one in five moves, one every two weeks. Days 1–15: Give AI context. Write one page: what you do, who it's for, how you like things done, what good looks like. Use it every day. By day three the answers stop sounding generic. Days 16–30: Automate one task. Pick the task you hate most. Write the steps down once, let AI do it while you check. Two hours becomes ten minutes. Days 31–45: Make it run. Connect it to your email or spreadsheet and put it on a schedule, so it runs without you starting it. Days 46–60: Build one thing. A small app, an AI that answers your website at night, or a system that turns one idea into a week of posts. It has to work when someone else uses it. Days 61–75: Show it. Post it, record it working, send it to five business owners or your boss. That's when the opportunities start. You won't master AI by January. Nobody will. If you start today, day 75 is New Year's Eve. If you start next week, you've lost a tenth of the time. Inside my community I have eight courses that teach every part of this, in order. If you're interested, the link is in my profile. #learnai #aiautomation #aitools #aiagents

Professor Glitch

11,900 次观看 • 26 天前

been testing teamily ai on a website project and ngl, the way the agents work together is pretty interesting but the part i didn't expect: a partner i'm working with jumped into the same group and worked right alongside them, same context. it's not an ai tool for one person, it's humans + agents: a few people and a few agents on one project gave it a rough idea for an ai developer workspace and built this with website builder: the interesting part: → midway my partner asked to update the pricing section → one agent planned 3 plans, another built them into the existing site, a third reviewed the design → watched the live site adapt, same project from rough idea to finished site & teamily AI is a human-agent collaborative workspace, great for small businesses and teams feels pretty different from just opening another chatbot and prompting it step by step. more like a small team that keeps building on each other's work Teamily AI try it out:

been testing teamily ai on a website project and ngl, the way the agents work together is pretty interesting but the part i didn't expect: a partner i'm working with jumped into the same group and worked right alongside them, same context. it's not an ai tool for one person, it's humans + agents: a few people and a few agents on one project gave it a rough idea for an ai developer workspace and built this with website builder: the interesting part: → midway my partner asked to update the pricing section → one agent planned 3 plans, another built them into the existing site, a third reviewed the design → watched the live site adapt, same project from rough idea to finished site & teamily AI is a human-agent collaborative workspace, great for small businesses and teams feels pretty different from just opening another chatbot and prompting it step by step. more like a small team that keeps building on each other's work Teamily AI try it out:

Aditi

12,935 次观看 • 1 个月前

How to make AI run your business AI running your business doesn't mean you disappear. It means the follow-ups, the reports, the replies and the reminders get done every day without you pushing them. You stay the owner. You stop being the bottleneck. The six pieces, in the order you build them: 1. Context: a folder of plain documents (what you sell, your prices, your customers, how you talk, what needs your OK, your top 10 questions). Every AI job reads it first. 2. Skills: every job you do more than twice, written down once, step by step. 3. Memory: a running record the AI reads and updates, so tomorrow it knows what happened today. 4. Tools: email, calendar, spreadsheets, invoicing, and you decide exactly what it may touch. 5. Schedules: every morning at 7, every Monday, every 3-day-old quote. It just happens. 6. A board: what the AI did, what waits for your yes, what got stuck. The rule: money, contracts and angry customers wait for you. AI drafts. You decide. Start with context, one skill, one job, then put that job on a schedule. Run it two weeks, fix what breaks, add the next one. #aiagents #aiautomation #aiemployee

How to make AI run your business AI running your business doesn't mean you disappear. It means the follow-ups, the reports, the replies and the reminders get done every day without you pushing them. You stay the owner. You stop being the bottleneck. The six pieces, in the order you build them: 1. Context: a folder of plain documents (what you sell, your prices, your customers, how you talk, what needs your OK, your top 10 questions). Every AI job reads it first. 2. Skills: every job you do more than twice, written down once, step by step. 3. Memory: a running record the AI reads and updates, so tomorrow it knows what happened today. 4. Tools: email, calendar, spreadsheets, invoicing, and you decide exactly what it may touch. 5. Schedules: every morning at 7, every Monday, every 3-day-old quote. It just happens. 6. A board: what the AI did, what waits for your yes, what got stuck. The rule: money, contracts and angry customers wait for you. AI drafts. You decide. Start with context, one skill, one job, then put that job on a schedule. Run it two weeks, fix what breaks, add the next one. #aiagents #aiautomation #aiemployee

Professor Glitch

10,600 次观看 • 26 天前

The roadmap to learn AI automation — from zero Everyone wants AI running their work. Almost nobody knows the order to learn it in. So here's the whole map: you start with plain automation (form → email → CRM → team, running itself in n8n). Then you do the one thing that changes everything — you drop a brain into that automation. Now it reads each customer and writes the custom email, sorts the message, decides what's next. Chain a few brains together and one writes the copy, one makes the image, one turns it into a video, one builds the report. That's the jump from a conveyor belt to an actual employee inside your workflow. Automation first. Then AI automation. That's the order. Want the full path? It's in my profile. #aiautomation#aiskills #aiagents #

Professor Glitch

80,700 次观看 • 3 个月前

BREAKING: Zuckerberg just described the near-future of computing, and it has nothing to do with screens. His call: within 5 years, every person on Earth has a personal AI that knows their life, understands their goals, and does the work for them. Not a chatbot. Not a search box. An agent that is deeply, intimately embedded in how you live. When the person building the social layer for 3 billion people says this is where we're going, it's not a prediction. It's a roadmap. Is the personal AI agent the most valuable product anyone can build right now?

BREAKING: Zuckerberg just described the near-future of computing, and it has nothing to do with screens. His call: within 5 years, every person on Earth has a personal AI that knows their life, understands their goals, and does the work for them. Not a chatbot. Not a search box. An agent that is deeply, intimately embedded in how you live. When the person building the social layer for 3 billion people says this is where we're going, it's not a prediction. It's a roadmap. Is the personal AI agent the most valuable product anyone can build right now?

Guillermo Flor

6,382,179 次观看 • 1 个月前

I'm making $8,000 a month building Claude skills for other people's businesses. I call it the AI Concierge. I have 5 clients paying $1,200 to $2,000 a month each. My effective rate is about $1,200 an hour. Every engagement runs on one framework I call AOA: Audit, Optimize, Automate. Here's the entire model: 1) Most people skip straight to Automate. That's the fun part. It's also how you end up automating a broken process. 2) Audit first. The client shares their screen and walks the process in painful detail while I take notes in a Google Doc and interrupt with a million questions. 3) Capture the plumbing, not just the steps. Who owns each step, which tool it runs in, where the data lives, and whether that tool can connect to Claude. 4) Hand the audit doc to Claude and ask it to find a better way. Then push back. Claude's first synopsis is usually partly wrong, and the client catches it. 5) Cut the fat. Most processes are 15 steps and only need to be 11. Most clients use 3 tools where 2 would do. 6) One home per fact. One client stored customer data in ClickUp and the CRM. We killed the ClickUp copy. Duplicates are just human error waiting to happen. 7) Have Claude write the build plan. It splits the process into skills, scaffolds each one, and lists next steps. Then you lock the doc. 8) A skill is a markdown file plus reference files. A reference file is just an example of what good looks like. Claude builds all of it. 9) Build one skill at a time from the build plan. If the audit was done right, Claude one shots it. Test on a real run, let Claude interview you, iterate. Version 2 is usually the keeper. 10) Draft first, never auto send. A speed to lead skill drafts replies for review on day one. It earns auto send later. Two things that make this work: 1) Audit and Optimize are 80% of the work. Automate is the 20%. You have to earn the right to build. 2) Skills do one thing well. Split at every point a human or a wait interrupts the chain, then let an orchestrator run them back to back. Full step-by-step playbook for starting a profitable AI Concierge business (free): Full breakdown below. Enjoy!

I'm making $8,000 a month building Claude skills for other people's businesses. I call it the AI Concierge. I have 5 clients paying $1,200 to $2,000 a month each. My effective rate is about $1,200 an hour. Every engagement runs on one framework I call AOA: Audit, Optimize, Automate. Here's the entire model: 1) Most people skip straight to Automate. That's the fun part. It's also how you end up automating a broken process. 2) Audit first. The client shares their screen and walks the process in painful detail while I take notes in a Google Doc and interrupt with a million questions. 3) Capture the plumbing, not just the steps. Who owns each step, which tool it runs in, where the data lives, and whether that tool can connect to Claude. 4) Hand the audit doc to Claude and ask it to find a better way. Then push back. Claude's first synopsis is usually partly wrong, and the client catches it. 5) Cut the fat. Most processes are 15 steps and only need to be 11. Most clients use 3 tools where 2 would do. 6) One home per fact. One client stored customer data in ClickUp and the CRM. We killed the ClickUp copy. Duplicates are just human error waiting to happen. 7) Have Claude write the build plan. It splits the process into skills, scaffolds each one, and lists next steps. Then you lock the doc. 8) A skill is a markdown file plus reference files. A reference file is just an example of what good looks like. Claude builds all of it. 9) Build one skill at a time from the build plan. If the audit was done right, Claude one shots it. Test on a real run, let Claude interview you, iterate. Version 2 is usually the keeper. 10) Draft first, never auto send. A speed to lead skill drafts replies for review on day one. It earns auto send later. Two things that make this work: 1) Audit and Optimize are 80% of the work. Automate is the 20%. You have to earn the right to build. 2) Skills do one thing well. Split at every point a human or a wait interrupts the chain, then let an orchestrator run them back to back. Full step-by-step playbook for starting a profitable AI Concierge business (free): Full breakdown below. Enjoy!

Corey Ganim

71,827 次观看 • 2 个月前

If I had to learn AI automation from scratch in three months, here's exactly what I'd do, in order. Three months. October, November, December. Enough to go from nothing to AI employees doing real work, and to enter 2027 with a skill maybe one person in a thousand actually has. OCTOBER — two parts. Short one: the concepts you keep hearing and never quite get (model, context, automation, agent, employee, and the difference). Long one: get clear on what you actually want AI to do for you, map your system (what starts a job, what happens next, where somebody decides, where it ends), and make it ready: the files, the rules, the pieces written down. You can't hand off what you can't see. NOVEMBER — automation, then AI automation. Plain paths first (form → sheet, invoice → filed, reminder → sent). Then AI in the middle so the paths can read and decide: email in, AI works out what it is, drafts the reply, only the judgment calls reach you. One automation, then ten, then a hundred. DECEMBER — the team. Agents that loop until a whole job is done. The apps and tools your business needs, written by AI. Then the command center: AI employees, each with a job, context, memory, a schedule, and a gate so risky decisions wait for your yes. By New Year's Eve they work while you sleep and you approve instead of doing. #professorglitch #aiautomation #aiagent

If I had to learn AI automation from scratch in three months, here's exactly what I'd do, in order. Three months. October, November, December. Enough to go from nothing to AI employees doing real work, and to enter 2027 with a skill maybe one person in a thousand actually has. OCTOBER — two parts. Short one: the concepts you keep hearing and never quite get (model, context, automation, agent, employee, and the difference). Long one: get clear on what you actually want AI to do for you, map your system (what starts a job, what happens next, where somebody decides, where it ends), and make it ready: the files, the rules, the pieces written down. You can't hand off what you can't see. NOVEMBER — automation, then AI automation. Plain paths first (form → sheet, invoice → filed, reminder → sent). Then AI in the middle so the paths can read and decide: email in, AI works out what it is, drafts the reply, only the judgment calls reach you. One automation, then ten, then a hundred. DECEMBER — the team. Agents that loop until a whole job is done. The apps and tools your business needs, written by AI. Then the command center: AI employees, each with a job, context, memory, a schedule, and a gate so risky decisions wait for your yes. By New Year's Eve they work while you sleep and you approve instead of doing. #professorglitch #aiautomation #aiagent

Professor Glitch

42,200 次观看 • 1 个月前

How Claude actually gets to know you (7 layers) Every new chat, Claude starts fresh. It only knows what's in front of it. So most people are talking to a stranger every morning. Here's how you change that, one layer at a time: 1. Memory: Claude keeps notes from your chats, and you can open them, read them and fix them. 2. A project: your instructions and your files in one place. Every chat inside it starts with all of it. 3. Skills: write each weekly job down once. Claude reads every skill's name and opens the one that matches. 4. Connectors: your email, calendar and drive, so "prep me for tomorrow" uses your real meetings. 5. Claude Code: a folder on your computer with a CLAUDE.md file. Your business, your rules. Then it makes the files and checks its own work. 6. Schedules: the morning brief is ready at 7. 7. A team: each job gets its own lane, its own tools, its own rules. Then the deep part: leads, sales, onboarding, support and invoicing as one system. One source of truth, events that start the next step, automations for fixed steps, an agent for the messy ones, and guardrails: logs, approvals, alerts. The people who get the most out of Claude are the ones who set it up. #aiagents #claudecode #aiautomation #aiemployee

How Claude actually gets to know you (7 layers) Every new chat, Claude starts fresh. It only knows what's in front of it. So most people are talking to a stranger every morning. Here's how you change that, one layer at a time: 1. Memory: Claude keeps notes from your chats, and you can open them, read them and fix them. 2. A project: your instructions and your files in one place. Every chat inside it starts with all of it. 3. Skills: write each weekly job down once. Claude reads every skill's name and opens the one that matches. 4. Connectors: your email, calendar and drive, so "prep me for tomorrow" uses your real meetings. 5. Claude Code: a folder on your computer with a CLAUDE.md file. Your business, your rules. Then it makes the files and checks its own work. 6. Schedules: the morning brief is ready at 7. 7. A team: each job gets its own lane, its own tools, its own rules. Then the deep part: leads, sales, onboarding, support and invoicing as one system. One source of truth, events that start the next step, automations for fixed steps, an agent for the messy ones, and guardrails: logs, approvals, alerts. The people who get the most out of Claude are the ones who set it up. #aiagents #claudecode #aiautomation #aiemployee

Professor Glitch

14,300 次观看 • 19 天前

How to put an AI agent to work this week: AI agents explained for beginners, n8n vs Claude Code How to put an AI agent to work this week, drawn start to finish: what an AI agent is, what it's made of, and which tool to build it in. AI agent vs chatbot vs automation: - An automation is a sprinkler on a timer. Six in the morning, every day, same water, even when it's raining. That's a workflow: same trigger, same steps. - A chatbot is a smart friend on the phone. Great advice, but you're still the one under the sink holding the wrench. - An AI agent is the handyman you let into the house. You hand over the job and he does the work, in a loop: goal, plan, act, check, repeat, until the job is done or he needs you. The four pieces of every AI agent you'd trust with real work: 1. The brain: the AI model inside Claude, ChatGPT or Gemini. It reads, reasons and picks the next step. 2. Context: instructions (the job sheet), memory (notes from last time) and knowledge (your price list, services and documents). This is the piece people skip, and it's why their agent writes like a stranger. 3. Tools: the keys you hand over, like your email, calendar and files. Only the keys this job needs. 4. Guardrails: house rules. Anything that leaves the house (an email, a payment, a deletion) waits for your approval. Human in the loop. One real example, end to end. The goal, in one sentence: follow up on today's client call. The AI agent reads the call notes in Google Drive, checks Google Calendar for an open slot (your client notes say she likes mornings), drafts the follow-up email in Gmail with the price sheet linked, and updates your client sheet in Google Sheets. Then it stops, so you can read it and hit send. n8n vs Claude Code, the doorbell and the desk: - n8n is the doorbell: a visual workflow automation tool that connects your apps and waits for a trigger. A call note lands in a Google Drive folder and the follow-up gets drafted, day or night. For the step that needs judgment, the n8n AI Agent node holds the same four pieces and runs the same loop. - Claude Code is the desk: an AI agent that works next to you, on your computer, in your files. It reads CLAUDE.md (the job sheet), uses skills (your steps written down once) and reaches Gmail and your calendar through MCP (the keys). - Doorbell jobs go in n8n: something happens, same steps, every time. Desk jobs go to Claude Code: messy, different every time, or living in your files. They work together, too: n8n has a built-in MCP server, so Claude Code can build n8n workflows for you. How to put an AI agent to work this week: 1. Pick one job you do every week that starts the same way (follow-ups, quote requests, the Monday report). 2. Write the context: one page with the job, how you like it done, and three good examples. 3. Hand it one key, drafts only. It reads and drafts, and nothing sends. 4. Two weeks of yeses. It drafts, you send, and every fix goes into that one page. That's your first agent: one job, one page, one key, two weeks of yeses. The only part left is the setup: n8n, Claude Code, and the keys wired into your real tools. I teach all of it inside my community, course by course, in that order. If you're interested, the link is in my profile. #claudecode #aiagents #n8n #aiautomation

Professor Glitch

18,500 次观看 • 1 个月前

Anthropic ex-engineer runs an internal graph that costs $6 a month and catches what a $300,000 eval suite misses. No retrieval layer. Seven nodes and one rule about who's allowed to change their mind. He published the whole schema. His version starts from the opposite idea. A graph is not an execution order. It's a memory of why. Seven nodes. Every edge carries the reason it exists: > INTENT - states what the task is for. Never how > DECOMPOSE - splits it into steps, each with a stated assumption > WORKER - executes one step. Sees nothing else > AUDIT - checks the output against the assumption, not the goal > DRIFT - compares the current step to INTENT and flags divergence > LEDGER - stores every decision with the assumption that justified it > ROOT - holds the graph, and when an assumption breaks, re-runs every step built on it Six nodes act. One node remembers why they acted. Every step carries the assumption that made it correct, so a false one only reruns what stood on it. That's the entire design. A pipeline that forgets its reasons has to redo all of it or trust all of it. He replayed a month of agent runs. 4,100 steps, 380 built on an assumption that was wrong by day three. The old pipeline shipped all 380 and linked none of them. Everyone else builds graphs where output moves forward and the reasoning evaporates. He built one where the reason travels with the result. The blast radius is the output nobody else produces. The article below is the full build - node prompts, the assumption format, the invalidation rule that finds every step downstream of a broken one. Save it. You'll want it open in the other

Anthropic ex-engineer runs an internal graph that costs $6 a month and catches what a $300,000 eval suite misses. No retrieval layer. Seven nodes and one rule about who's allowed to change their mind. He published the whole schema. His version starts from the opposite idea. A graph is not an execution order. It's a memory of why. Seven nodes. Every edge carries the reason it exists: > INTENT - states what the task is for. Never how > DECOMPOSE - splits it into steps, each with a stated assumption > WORKER - executes one step. Sees nothing else > AUDIT - checks the output against the assumption, not the goal > DRIFT - compares the current step to INTENT and flags divergence > LEDGER - stores every decision with the assumption that justified it > ROOT - holds the graph, and when an assumption breaks, re-runs every step built on it Six nodes act. One node remembers why they acted. Every step carries the assumption that made it correct, so a false one only reruns what stood on it. That's the entire design. A pipeline that forgets its reasons has to redo all of it or trust all of it. He replayed a month of agent runs. 4,100 steps, 380 built on an assumption that was wrong by day three. The old pipeline shipped all 380 and linked none of them. Everyone else builds graphs where output moves forward and the reasoning evaporates. He built one where the reason travels with the result. The blast radius is the output nobody else produces. The article below is the full build - node prompts, the assumption format, the invalidation rule that finds every step downstream of a broken one. Save it. You'll want it open in the other

wast3

307,838 次观看 • 2 个月前

This is how we streamline AI implementation across 26 companies in a PE portfolio. Each company has 10 departments, and each department has 10 workflows. If you do them one at a time, you need "50,000 forward deployed engineers across three years." Our method is to group the companies by system of record. For finance, the 5 companies on NetSuite are one group. The 4 companies on Dynamics are a different group. Then you find what NetSuite can do and what it cannot do. You build only for the gaps. In one group, the processes are still different. The 5 NetSuite companies used 12, 9, 15, 18 and 13 steps for their finance process. The target is 6 steps, with agents at specific points. You will not often make all 5 exactly equal. You can come close if you map each process with the stakeholders before you change it. A single company or 26, sort the work by its system before you build.

This is how we streamline AI implementation across 26 companies in a PE portfolio. Each company has 10 departments, and each department has 10 workflows. If you do them one at a time, you need "50,000 forward deployed engineers across three years." Our method is to group the companies by system of record. For finance, the 5 companies on NetSuite are one group. The 4 companies on Dynamics are a different group. Then you find what NetSuite can do and what it cannot do. You build only for the gaps. In one group, the processes are still different. The 5 NetSuite companies used 12, 9, 15, 18 and 13 steps for their finance process. The target is 6 steps, with agents at specific points. You will not often make all 5 exactly equal. You can come close if you map each process with the stakeholders before you change it. A single company or 26, sort the work by its system before you build.

The Startup Ideas Podcast (SIP) 🧃

17,379 次观看 • 1 个月前