#aiemployee
How to price AI automation (the math, the packages, and what to say on the call) How to price an AI automation project for a small business, with the real math. 1. Price the problem, not your time. Example: a heating and air company's office loses 24 hours a week to follow-ups ($42,000 a year) and about one job a month to quotes nobody chased ($48,000). The problem costs about $90,000 a year. 2. The 5× rule: the client should get back at least five times what they pay in year one. Value sets the ceiling, your costs set the floor (double your gut hours). 3. Sell it in three parts: map it (paid, $2–3K for a small company), build it (fixed price, never hourly), run it (monthly, e.g. $800). 4. Send three options, not one: $6,500 / $12,000 / $22,000. Most clients pick the middle. 5. The client owns the accounts and pays AI usage directly. 6. On the call, ask instead of pitching, and let them say the value number. Say your price, then stop talking. No clients yet? Do the first one at your job: time it before and after. That's your case study. #aiautomation #aiemployee #aiagents #aiconsulting
Professor Glitch104,100 просмотров • 1 день назад
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 Glitch137,800 просмотров • 24 дней назад
How to turn any app idea into a real app with AI, step by step (no developer) How to build a complete app from an idea with AI, no coding, step by step. 1. Shrink the idea: one sentence (who it's for, the one job, how they pay). Cut everything else for version one. 2. Plan before code: in Claude Code, talk it through and have it write a plan file (screens, back end checks, database, AI in the middle). 3. Screens first, with fake data. Click through it like each user. 4. Make it real: a free Supabase project for the database and logins. Keep the secret key private. 5. Money: connect Stripe and test with test cards first. 6. Break it on purpose: duplicate names, declined cards, abandoned checkouts. Have Claude Code write tests and fix what fails. 7. Go live: GitHub → Vercel → your own domain. Heads-up: a business taking payments needs Vercel Pro ($20/month), not the free plan. 8. Five real users. Watch where they get stuck. This is the three-minute version. The full walkthrough is in my vibe coding course, inside a community of 800+ people, from complete beginners to people who have been in tech for years. Check my profile. #vibecoding #buildwithai #claudecode #aiagents #aiemployee #aiautomation
Professor Glitch10,700 просмотров • 1 день назад
How to build an AI employee that works while you sleep. An AI agent is just a loop (goal → step → check → repeat) that can reach for tools. On its own it hits a wall: it forgets everything, waits for you, owns nothing. The ladder that turns it into staff: • ① context — files YOU write; it reads them every run so nothing's generic, and they grow (you're raising it) • ② memory — facts + your rules + how-to's; a skill is a memory • ③ a lane, not a task • ④ tools — MCP, ~1 min to connect • ⑤ a schedule — wakes on its own, pings you only for decisions • ⑥ guardrails — low-stakes it does; high-stakes it drafts and waits for your yes I run mine from a Google Sheet — it drafts every reply in my voice, I approve in 10 minutes, every 30 minutes it sends the approved ones and learns from my revisions. The moat: anyone can buy the same AI. Nobody has your context. Come build it with me. #aiemployee #aiagent #aiautomation #professorglitch
Professor Glitch536,400 просмотров • 3 месяцев назад
How to build an AI receptionist that answers your business phone (books jobs, knows your customers) The whole build, on a real phone number. A Retell voice agent with your own Google Docs as its knowledge → books jobs in Cal.com → exact quotes from a small calculator (the AI never adds prices) → n8n looks up the caller in your Google Sheet before she says a word ("Hi Jen") and checks where the cleaner is mid-call → warm transfer to a person for complaints and damage → office leads into HubSpot → a summary of every call in Slack and your sheet. Tested on 21 hard simulated callers: 17 passed on the live version, and the misses go into the next one. About 13 cents a minute plus $2/month for the number. The full AI receptionist course, with every workflow, is coming to my community soon. #aiagents #aireceptionist #aiemployee
Professor Glitch35,100 просмотров • 8 дней назад
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 Glitch61,200 просмотров • 25 дней назад
Build a knowledge base inside Microsoft Teams your whole team can just ask Right now, the knowledge base at your company is you. Where's the price list, what's the after-hours fee, can I approve this refund. Here's how to build the real one inside Microsoft Teams, step by step, on the real screens. The idea: it works like a library. Your files are the books. Teams is the building. Every channel is a shelf. The AI (a Copilot agent) is the librarian. She doesn't memorize your books: every time someone asks, she looks it up in your files and answers with the source. Everyone has a library card. The agent only answers from files the person asking can already open. The build: Shelves: Teams → New items → New team → "Company Handbook", private. One channel per department. Every team is a SharePoint site and every channel is a folder in it. HR goes in a private channel (its own site). Books: one topic per Word doc, named the way people ask. Two-line summary, owner, last reviewed. Bullet points (Copilot can't read tables in these files yet). Split long handbooks into ~15–20 page files, turn scanned PDFs into Word, delete duplicates. Upload in the channel's Shared tab. Add Owner and Last reviewed columns in SharePoint. The librarian: Copilot in Teams → Agents → New agent → Skip to configure. Strict instructions (answer only from the handbook, show the document, never guess, give a link to ask a human). Knowledge: paste the SharePoint site link (and the HR channel's site). Turn on "Only use specified sources". Add suggested prompts. Test in Try It. Library cards: test with two real people. A tech asks about pay: nothing. HR asks: answer plus the HR file. Never upload private files straight into the agent; uploaded files skip permissions. Front desk: Create → Share → your team → Can chat → copy the link, post it in General, pin it. People chat with it one-on-one in Copilot (you can't @mention it in a channel). Every asker needs a Copilot license: $30/user/month, or Microsoft 365 Copilot Business (up to 300 users) $21, $18 on the current promo. Or your admin turns on pay-as-you-go (roughly $0.12 per answer). New files can take up to a day to show up. Keep it current (Power Automate): Form "Ask a human" → when a new response is submitted → save it to a Knowledge Gaps list → post it in a Knowledge Gaps channel. And every Monday at 8: find docs whose Last reviewed date is older than 90 days → message the owner. Then: the same agent in Copilot inside Outlook, Copilot connectors for Google Drive, Salesforce or Confluence, and Copy to Copilot Studio when you outgrow it. #aiautomation #aiemployee #microsoftcopilot #claudecode
Professor Glitch87,200 просмотров • 1 месяц назад
How to build a company second brain that never makes things up (RAG, every step) A company AI that answers from 18,000 documents, shows the exact page, pulls up the right diagram, and says so when it can't prove something. Here's how it's built, step by step. The idea (RAG): the AI looks things up before it answers. Search by meaning works like a map: every chunk of every document is a pin, and similar meanings sit on the same street. Part numbers need exact-word search, so you run both. The loader (a program that gets documents onto the map), built with Claude Code: Read the documents properly. Docling (free) keeps tables whole and cuts out every figure. Claude describes each diagram and reads its part numbers. Dry run on 50 documents first. Label every chunk (metadata). Which document, which version, current or replaced, which model, which plant, who can open it. The AI can only pick from approved lists, and anything that doesn't match goes to a human. Access is enforced in code before the search runs. Chunk by headings. About 500 tokens per chunk, a little overlap, tables and procedures kept whole, the heading path stamped on every chunk, plus one line of context. Fill the index in Pinecone. "Search by both meaning and exact words." Keep it current (n8n): every 15 minutes, ask SharePoint what changed. New versions go in first, old versions come out second. Answering (the courtroom): security at the door, a clerk that asks when something's missing, an evidence room (both searches plus a reranker), a first ruling (not enough evidence means "not in approved documents" and a ticket to the owner), a lawyer that cites every sentence, and a judge that checks every claim and number. Prove it before launch: 300 real questions per department, including ones it must refuse. Leaks must be zero. #aiautomation #microsoftcopilot #claudecode #aiemployee #aiagents
Professor Glitch78,000 просмотров • 1 месяц назад
Why your AI app has zero sales (and the system that fixes it) AI made building easy. An app in a weekend, a landing page in an hour. So everybody has an idea, a few people finish it, and then it sits there with zero visitors. The fix is the same AI that helped you build it. Here's the content system I walk through in this video: 1. Ideas: AI reads what your buyers search for and complain about, and hands you a list of video ideas every week. 2. Script: give it your product, your voice and one idea. The script teaches something useful first and mentions your product at the end. 3. Video: record your real product doing the job, the messy before and the clean after. AI handles the edit, the captions and the voice. You review every one. 4. Push: the same video goes to TikTok, YouTube, Instagram and LinkedIn, scheduled for the week. What worked feeds next week's ideas. Run the loop. Your first twenty videos will probably flop, and that's normal. Volume is how you find the ones that land. It works for services too: show the problem you solve, the before and after, and the result. #aiautomation #aiemployee #aiagents
Professor Glitch47,000 просмотров • 24 дней назад
How to start a business with an AI team that already knows everything — from day one I'm going to tell you exactly how to start a business with AI. From scratch. Longer than my usual, on purpose — the whole build, not tips. And if you already have a business, it's more urgent for you: your team has to learn everything you've already built. Most people build the business first, then a year later try to hand it to an AI — and spend weeks explaining what the business even is. That's onboarding a stranger, forever. Flip the order. One habit first: stop typing. I've barely touched my keyboard in over two years — I talk, and a voice tool types it. Not about speed: when typing is the cost you write one lazy line and get something generic. When talking is the cost you tell it everything. The five pieces: Skills — written instructions, like a one-pager for a new hire Memory — not a mind. A file it writes to and reads back. Not written down = gone Context — what it knows right now Tools — what it's actually allowed to touch More than one — one owns the site, one the customers, one the money Then you build. Day one you need a website, so you build it together — and while it builds, it writes things down in files you can open. It does NOT remember building your site. Nothing remembers. What exists is the write-up in your folder. That's the asset — and that's exactly why building together beats explaining later: you never document a year of decisions, because documenting was the doing. Days — not years — gets you the skeleton standing. That is not the same as a business making money. Customers are still your job. But the build, the part that used to take a year and a payroll? Days. #aiagents #aiemployee #aiteam
Professor Glitch161,700 просмотров • 3 месяцев назад
The entire roadmap to learn AI, in one video Everything I know, in the order that actually works. Seven stops — and at every one: what you learn, how long it takes, and what you can BUILD the day you finish it. 1 — FOUNDATIONS (one focused day). What this technology actually is, and the difference between a chat, an automation, and an agent. Basic, and it's what makes everything after it click instead of feeling like magic. 2 — CONTEXT. Your world written into plain files: what your business does, who your customers are, how you talk. That's your second brain. → An AI that answers about YOUR business instead of the average one. 3 — PROCESS MAPPING (a week). Draw the map before you build: what happens first, where a decision gets made, where it ends. Almost everyone skips this, which is why they spend months building the wrong system beautifully. 4 — AUTOMATIONS (first one: an afternoon). Work that fires on its own. Lead comes in → sheet updates → message goes out → follow-up waits two days and checks. → A flow that handles something you do by hand every week. 5 — AI INSIDE THE AUTOMATION. The intelligence takes the step that needed a person: reads the email, decides what it is, pulls the details, sends it down the right path. → A system that makes the call, then acts on it. 6 — AGENTS. AI with a goal and a loop: plan, do, check, fix, again — until it's finished. And here you gain the judgment that separates operators from hobbyists: when plain automation is enough, when to add AI, when a job needs a full agent. 7 — AI EMPLOYEES + THE COMMAND CENTER. An agent that STAYS: memory so corrections stick, skills written once, working in your real accounts behind guardrails where risky moves wait for your yes — all run from one board. → A workforce. Alongside it all: building your own tools. Describe what you wish existed and build it. Your ideas turn into products. Two to three months of focused learning and this stuff is genuinely running parts of your work. #aiautomation #aiemployee #aiagent
Professor Glitch91,600 просмотров • 1 месяц назад
How to build an AI team in Claude Code — every step One AI employee is Claude Code plus three things on its desk: a job description it reads every time it wakes up, playbooks for the jobs it owns, and a notebook where its lessons go. Build that five times and you have five strangers. The team is everything you add on top. Here is the whole build, in order. 1. A desk for every employee. Each one is its own folder: job description, playbooks, notebook, working files. Nothing leaks between desks, so one glance tells you what each employee knows and owns. 2. The company folder. Three pages every employee reads the second it wakes up: one about you (how you work, what always waits for your yes), one on how the company talks, and the roster with a one-line job for each employee. Write your brand voice once and the hire you make on Friday inherits it on Friday. 3. The shared notebook. Corrections that belong to everyone (a brand rule, a platform policy, a tool nobody touches without asking) go in one notebook the whole team reads. Correct one employee Tuesday, all of them follow it Wednesday. 4. Handoffs. When a job shows up, Claude Code reads the roster one-liners and passes it to the right employee. Employees pass work down the line themselves: spin up a teammate in the background, wait for an answer, or leave a note in a teammate's inbox. You are out of the middle. 5. One board. To do, doing, done. Every employee moves its own cards and submits finished work for your review. One screen tells you who is on what. 6. A clock on every desk. One employee sorts the inbox at nine, another leaves you a trend brief each morning, another checks for approvals every half hour. The team has been at work for two hours before you wake up. 7. Fences. Each employee gets an enforced list of what it can touch (leave writing off the list and it physically cannot write a file) and a short list of what always waits for your yes: sending outside the company, spending money, deleting anything. 8. One employee whose whole job is hiring the others. Say what you need, answer three questions, and a new hire is on the roster and online in your dashboard a few minutes later. The rest of the team knows it exists before you've said a word. How it all moves, for anyone who has never written code: underneath the team sits a set of scripts. A script is a small piece of code that does one chore the same way every time. One builds a new hire's desk and adds the roster line. One carries messages between employees. One wakes each employee on its clock. One files a new playbook onto the right desk. You never open them. They fire when the moment comes. A worker runs a job. A team runs the company. This is how my own operation runs: Quill sorts the inbox, Nova reads the trends, Atlas writes the proposals, Rack keeps the servers alive, all from one shared company folder, one notebook, one board. If context, skills and notebooks are still new words to you, that's who this is for. Seven courses inside my community take you from knowing nothing to building this, in order. The platform itself, the exact code my employees run on, every one of those scripts included, is in there too: download it, open it, start hiring. Join us and start today. The link is in my profile. #aiemployee #aiagent
Professor Glitch94,800 просмотров • 2 месяцев назад