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RAG vs the second brain — which memory system your AI needs. Two ways to give an AI your knowledge — and the difference decides whether your AI actually knows your business. Way one is RAG. Your documents get chopped into fragments and filed into a searchable pile. Every...

17,600 просмотров • 2 месяцев назад •via TikTok

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How to build a second brain that updates itself (like a librarian) How to build a second brain that updates itself: an AI knowledge base for your business that knows your clients, your prices and how you work, and keeps itself up to date every time you give it something new. The setup, in plain words: 1. One folder on your computer with two shelves: an inbox where you dump everything (call notes, emails, PDFs, voice memos, ideas) and the brain, with clean pages, one per client, project, pricing, and one about you. 2. One rules file that turns Claude into your librarian: never change an original, every fact points back to its source, mark old facts outdated instead of erasing them, and link everything related. 3. File it: drop a note in, and the AI updates every page it affects (often five or six) and links them back to the note. 4. When something changes, the brain updates itself. A client cuts their budget from $8,000 to $6,000 on a call: the client page, the timeline and the revenue page all update, and the old number stays, marked outdated, with the date and the source. 5. Ask it anything and it answers from your own pages, with the source. 6. Once a week, it checks the shelves: pages that disagree, facts that went stale, pages nothing links to. It's all plain text (markdown) files on your computer, and you own them. Open the folder in Obsidian to watch the connections grow. The whole job in four moves: you drop, it files, you ask, it checks. If you want to build yours, I teach it file by file, plus full courses from complete beginner to building real AI systems. Check my profile. #secondbrain #claude #aiagents #claudecode #professorglitch

Professor Glitch

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MCP: how one person runs the jobs a whole team used to do MCP is how your AI connects to your real stack — and it's one of four pieces. I run my business on all four. 1 — YOUR TOOLS. Your AI gets connected to your real stack: email, CRM, marketing platform, files. There's a standard way to do this now — it's called MCP — and connecting one takes minutes. Log in, grant access, done. From then on your AI can read and write in that tool the way an employee with a login would. 2 — YOUR SECOND BRAIN. Your business written down where the AI can read it: what you sell, your prices, your customers, your policies, how you talk, what happened last quarter. Files and folders, organized so it finds the right one. Tools give it hands; this gives it knowledge. 3 — SKILLS. The operating procedure for one job, written once. How to pull a customer's history from the CRM. How to build a branded proposal. How your marketing emails are structured. Steps, rules, one good example — the same thing you'd write for a new hire, except you write it once and it's followed exactly, forever. 4 — MEMORY. Every correction you make gets written down and never comes back. Your AI gets more like you every week. What that setup actually does: a support email lands → it searches your knowledge base, looks the customer up in your CRM, sees their real history, and writes a personal reply with the right facts. Marketing emails get drafted and the list gets handled. A client asks for a proposal → a branded PDF comes back built from your actual prices. Product images generated. Video generated. These are the jobs you'd post a listing for. And it stays supervised: you review, you correct what's off, the correction becomes a rule, the next round is better. Close to 80% of the work in my business runs this way — with me checking and approving instead of producing. Building a company used to mean hiring for every function you couldn't personally cover. Now one person sets up the functions, and the roles run. The people I teach are building exactly this, most of them for businesses they run alone. #aiautomation #aiemployee #aiagent

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How to Build second Brain My entire second brain is on a hard drive — not in ChatGPT, not in Claude, not in anybody's app. It's folders and files. I can unplug it, hand it to a completely different AI tomorrow, and it still knows everything about me and my business. The whole build: • raw/ — yours. Every note, meeting transcript, voice memo, screenshot. You dump, you don't organise. • wiki/ — the AI's. It reads raw, works out what matters, writes clean pages, and links them together. That's the part that matters — most "chat with your documents" tools just find chunks that look similar and hope. They have no idea two documents are related. Yours does, so it walks from one page to the ones it's tied to and picks up the context it was missing. • index.md — the map. Without it, the AI reads your entire brain every time you ask one question. • log.md — the memory. So a new document six weeks later updates the old page instead of duplicating it. • A hook or a schedule keeps it running without you. And here's why it's the foundation: when you build AI employees later — the one answering your email, the one going through your numbers — they don't start from zero. They feed from this. They already know how you talk, what you charge, who your customers are. Because the best model on earth still doesn't know your business. This is the thing that closes that gap. Full step-by-step build + templates this week. Check my profile. #aiagent #aiautomation #secondbrain

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How to create AI employees — start to finish, everything included The complete build of an AI employee, start to finish — the whole path in under four minutes. The agent underneath — every AI employee starts as an agent: a brain (the AI model), tools (real connections into your email, calendar, spreadsheets so it acts instead of only talking), and a loop (it works the job, checks itself, and keeps going until it's done). Feed the brain context and it works on YOUR business. The four upgrades that make it an employee — memory (corrections get written down and read back before every job — explain a thing once and it stays explained), permanent context (your world lives with it and changes as your business changes), skills (your way of doing a job, taught once, followed every time), and a schedule (an agent runs when you call it — an employee clocks in on its own, seven in the morning, inbox already sorted). The build tool — an AI coding assistant: Claude Code or Codex. That class of tool IS the agent machinery — it navigates your context, picks its next move, loops until the job is done, and carries memory between runs. You set it up in plain English, and when a piece you need exists nowhere, it writes that piece itself. The full assembly — pick one job, give it context, wire the tools, teach the skill, add memory, set the schedule. One rule built in: high-stakes moves wait for your yes; everything else runs around the clock. The ceiling — one employee becomes a pattern, patterns become a team, and the team becomes a department passing work to each other. This video is the skeleton of my entire curriculum — every step is a full course, taught from zero, step by step, about four months front to back. It's all inside my community. #aiemployee #aiagent #aiautomation

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12,600 просмотров • 2 месяцев назад

If I started over as an AI operator, here's exactly what I'd build Receipts first: I run five AI employees across my businesses — they handle roughly 80% of what I used to do myself. An AI operator is someone whose operations actually RUN on AI, at any size of business. Starting from zero, I'd build five layers, in this exact order — each one feeds the next: LAYER 1 — THE SECOND BRAIN (yes, first). A set of folders and files holding everything about the business, kept by an AI librarian: every new document gets read, summarized, linked to everything related, and written into one index that knows the whole operation. It stays alive — every decision gets filed the day it happens. Knowledge first, because everything else drinks from it. LAYER 2 — CONTEXT. Out of the library come the files every AI reads before it works: who we serve, what we sell, pricing, voice, what we never promise. Context is the difference between hiring an intern and hiring a partner. LAYER 3 — THE DOORS. Connect the tools your business lives in: inbox, calendar, sheets, customer list, storage. Every connection = a thing your AI can DO instead of describe. LAYER 4 — THE EMPLOYEES. One per lane. Each is a small bundle: instructions (owns/escalates), skills (jobs as recipes), memory (corrections stick — sharper every week), schedule (the lane runs without you). And the design rule that makes it genius: a skill never carries its own copy of your business — it REFERENCES the library. "Write in our voice" points at the voice file. "Quote current prices" points at the price list. Change how you talk or update your pricing once, and every skill in the whole operation updates instantly. One file changed, every employee changed with you. LAYER 5 — CHAINS. Skills call tools, and skills call other skills. One command — "monthly promo" — pulls the right customers, drafts the campaign in your voice from your context, builds the emails, schedules the posts, logs every reply. Whole projects shipping from one sentence. And the part that makes it a SYSTEM: every layer updates every other. A correction lands in memory. A closed deal lands in the library, the librarian re-links it, and every employee touching sales is smarter by the afternoon. The stack is alive. Your day becomes judgment: read the sheets, type send / edit / skip, make the calls only a human should make. That's 80% handled — and the 20% left is the part you actually love. Users try AI. Operators run on it. #aiagent #aiautomation #aiemployee

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