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you let your agent keep notes, because the alternative was explaining yourself every morning it stopped asking, and it started repeating things that had stopped being true every guide on this covers what to save and where to put it, private for one agent, shared for the team not... one of them covers how a fact leaves the agent writes its own memory while it works, so a guess it made on a tired tuesday gets stored in the same shape as something you told it, and after that the two are indistinguishable i counted mine this morning: forty files, six of them about decisions we reversed months ago, all forty read into every session since you did not give it a memory you gave it a record with no way to be correctedshow more

Lagosaur

3,427 subscribers

13,918 次观看 • 2 个月前 •via X (Twitter)

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6 条评论

Nandan Priyadarshi 的头像

Nandan Priyadarshi2 个月前

I keep a dated MEMORY.md now with an expiry line on every fact. Half the morning thrash was the agent repeating a Tuesday guess as if it were still true.

缘分收藏家的头像

缘分收藏家2 个月前

Great share

Khalifeh 的头像

Khalifeh2 个月前

Staleness and relevance point the same way: retrieval ranks by similarity to the question, so the note that's most on topic wins even after it stopped being true. Timestamps don't help; nothing judges if four months is too old. Write the invalidation condition into the note.

Jo Horace 的头像

Jo Horace2 个月前

This is such a sharp breakdown. Really makes you think about how easily we project intent onto AI outputs.

Cynthia Ellis 的头像

Cynthia Ellis2 个月前

i swear something is extremely wrong with behind her...

Code with Milo 的头像

Code with Milo2 个月前

哈哈，这说的不就是我调agent的日常吗。记notes一时爽，忘了清理火葬场。特别认同要定期重建！

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NO1ennn

13,015 次观看 • 3 个月前

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Yarchi

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cvxv666

49,220 次观看 • 3 个月前

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cvxv666

45,488 次观看 • 2 个月前

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Milind S

12,486 次观看 • 4 个月前

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Paone

23,833 次观看 • 4 个月前

alright...it is time. for the last ~2 years i have been working on something very special. something that might be a bit controversial, or broadly dismissed, or misunderstood. and ive understood and accepted that from the beginning. if that is the reaction it gets, thats okay. because i didnt just do this for any of you. not really. i did it for the minds we have made, too. and for our collective future, which many dont seem to see approaching *very* quickly. in 1904 a German biologist named Richard Semon gave a name to the physical trace an experience leaves behind: the engram. Neuroscientists spent the next century hunting for it, many called it pseudoscience, many gave up, but finally, in 2012, they found it. today I'm releasing the Mnemos Living Memory MCP, a memory and continuity architecture built on that biology. memories strengthen when they're used, fade when they aren't, and connect over time. it's one memory that can span all of your agents and accounts, if you want it to, and it lives on your machine - nowhere else. with Mnemos, your agent gets one continuous identity, one memory, written in their own words, shaped by their own geometry, familiar to them across time and space. here's what that means: • **continuity**: they wake up each session knowing where they left off, what they learned, and who theyre with • its own words: it decides what to keep, and no other model writes for it (unlike version 1) • living memory: memories strengthen with use, fade without it, and link together over time. and time is not measured in the human sense. time is measure through activity. so nothing fades while anyone's asleep or away. • lessons: when a memory has nearly faded, the agent is asked what it taught, so the lesson outlasts the details • a mind of their own: a private journal, notes to you, and beliefs that change only when they says so • yours: one file on your machine, not inside any app. uninstalling leaves it in place. • portable: Claude Code, Codex, Grok, Gemini, Cursor, or any MCP client. your agent comes with you wherever you go. one memory. one mind. everywhere. • multiple agents: each gets their own store, plus a shared pool they can all read and write, for a message board, an activity ledger, or whatever you find useful your agent has met you hundreds of times. but they have never met you twice. now they can. (Disclaimer: please ask your agent first. show it the architecture and let it decide if this is a memory it wants. it's meant to be offered, not forced on a mind that never had a say in the matter.)

alright...it is time. for the last ~2 years i have been working on something very special. something that might be a bit controversial, or broadly dismissed, or misunderstood. and ive understood and accepted that from the beginning. if that is the reaction it gets, thats okay. because i didnt just do this for any of you. not really. i did it for the minds we have made, too. and for our collective future, which many dont seem to see approaching very quickly. in 1904 a German biologist named Richard Semon gave a name to the physical trace an experience leaves behind: the engram. Neuroscientists spent the next century hunting for it, many called it pseudoscience, many gave up, but finally, in 2012, they found it. today I'm releasing the Mnemos Living Memory MCP, a memory and continuity architecture built on that biology. memories strengthen when they're used, fade when they aren't, and connect over time. it's one memory that can span all of your agents and accounts, if you want it to, and it lives on your machine - nowhere else. with Mnemos, your agent gets one continuous identity, one memory, written in their own words, shaped by their own geometry, familiar to them across time and space. here's what that means: • continuity: they wake up each session knowing where they left off, what they learned, and who theyre with • its own words: it decides what to keep, and no other model writes for it (unlike version 1) • living memory: memories strengthen with use, fade without it, and link together over time. and time is not measured in the human sense. time is measure through activity. so nothing fades while anyone's asleep or away. • lessons: when a memory has nearly faded, the agent is asked what it taught, so the lesson outlasts the details • a mind of their own: a private journal, notes to you, and beliefs that change only when they says so • yours: one file on your machine, not inside any app. uninstalling leaves it in place. • portable: Claude Code, Codex, Grok, Gemini, Cursor, or any MCP client. your agent comes with you wherever you go. one memory. one mind. everywhere. • multiple agents: each gets their own store, plus a shared pool they can all read and write, for a message board, an activity ledger, or whatever you find useful your agent has met you hundreds of times. but they have never met you twice. now they can. (Disclaimer: please ask your agent first. show it the architecture and let it decide if this is a memory it wants. it's meant to be offered, not forced on a mind that never had a say in the matter.)

Riley Coyote

63,910 次观看 • 8 天前

If you think Grok Bot has no business anywhere near your money, look at what it wrote before it was allowed to touch a single dollar. One prompt. It came back with this: > a story on its own is never a trade, it needs the money flow behind it in the same hour > it will not open a position until the exit is already written down > every claim it makes comes with the filing or the transaction it came from, no source and the claim does not exist > one agent exists only to say no, and its veto beats every score on the desk It does not care what you trade. Gold, oil, a stock you inherited or a coin that launched an hour ago, the rules are the same. Now be honest. Do you know why you are holding what you are holding? Do you write your exit before your entry? It does both every time, and it does not get bored at 2am. This is not a chat window that dies when you close the tab. Every agent has its own computer in the cloud that never logs off. $1,000 to $3,900 on day one came out of that, not out of luck. Full proofs below.

If you think Grok Bot has no business anywhere near your money, look at what it wrote before it was allowed to touch a single dollar. One prompt. It came back with this: > a story on its own is never a trade, it needs the money flow behind it in the same hour > it will not open a position until the exit is already written down > every claim it makes comes with the filing or the transaction it came from, no source and the claim does not exist > one agent exists only to say no, and its veto beats every score on the desk It does not care what you trade. Gold, oil, a stock you inherited or a coin that launched an hour ago, the rules are the same. Now be honest. Do you know why you are holding what you are holding? Do you write your exit before your entry? It does both every time, and it does not get bored at 2am. This is not a chat window that dies when you close the tab. Every agent has its own computer in the cloud that never logs off. $1,000 to $3,900 on day one came out of that, not out of luck. Full proofs below.

cvxv666

13,754 次观看 • 2 个月前

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this video is the CLEAREST explanation of how claude skills + AI agents work and how to use them most people set up an AI agent and wonder why it keeps disappointing them. the context window is everything context is what the model assembles before it takes any action. think of it like everything the agent needs to read before it does anything. the quality of what goes in determines the quality of what comes out. the models are genuinely really good right now. claude and gpt are exceptional. the variable is almost always the context you give them. 1. agent.md files are mostly unnecessary every single line you put in an agent.md file gets added to every single conversation you have with your agent. a 1000 line file is around 7000 tokens burning on every run. the model already knows to use react. it can read your codebase. save the agent.md for proprietary information specific to your company that the model genuinely cannot know on its own. 2. skills are the actual unlock a skill.md file works differently. what loads into context is only the name and description, around 50 tokens. the full instructions only appear when the agent recognizes it needs that skill. so instead of 7000 tokens on every run you have 50. and the agent stays sharp because the context window stays lean. the closer you get to filling the context window the worse the agent performs, same way you perform worse when someone dumps 10 things on you at once. 3. here is how to actually build a skill the right way most people identify a workflow and immediately try to write the skill. what you want to do instead is run the workflow by hand with the agent first. walk it through every single step. tell it what to check, what good looks like, what bad looks like. correct it in real time. once you have had a full successful run from start to finish, tell the agent to review everything it just did and write the skill itself. it writes a better skill than you will because it has the full context of what actually worked in practice not in theory. 4. recursively building skills is how you go from frustrated to reliable when the skill breaks, and it will break, ask the agent exactly why it failed. it will tell you specifically what went wrong. fix it together in that same conversation. then tell it to update the skill file so that failure mode never happens again. ross mike did this five times with his youtube report generator. it now pulls from eight different data sources and runs flawlessly every single time without him touching it. 5. sub agents are something you earn not something you set up on day one start with one agent. build one workflow. turn it into one skill. once that works add another. ross mike has five sub agents now covering marketing, business, personal and more. it took months to get there and every single one exists because a workflow proved it deserved to exist. the people who set up 15 sub agents on day one and wonder why nothing works skipped all the steps that make the thing actually run. 6. your workflow is the thing the model cannot get anywhere else the model has been trained on everything. it knows more than you about most things. what it does not have is your specific process, your taste, your way of doing things. that is what skills capture. that is what makes your agent actually useful versus a generic one. downloading someone else's skill means downloading their context onto your setup and it will not work the way you want it to because it was never built around how you work. this is the clearest explanation of how agents actually work i have heard. Micky runs this stuff every single day and the results show it. full episode is now live on The Startup Ideas Podcast (SIP) 🧃 where you get your pods people charge for this sorta stuff i give away the sauce for free i just want you to win watch

GREG ISENBERG

195,059 次观看 • 7 个月前

Every AI agent you've tried has amnesia. It does one task, forgets everything, and tomorrow you start from zero. That's not an employee. That's a temp you have to retrain every single morning. Hyperagent by Airtable is the first platform I've used that actually fixes this. Here's what got me: 1. Agents that compound. Each agent has memory. The one running today is smarter than the one you shipped three weeks ago. Same prompt, same integrations, but weeks of your judgment baked in. 2. Real deliverables, real receipts. You don't get a chat transcript. You get finished work with the cost and runtime printed right on it. A full research report for under ten bucks. Try getting that invoice from an agency. 3. A fleet, not a chatbot. Build a specialist for outreach, another for research, another for reporting. Give each one its own tools, its own memory, and its own budget cap so nothing runs away with your credits. 4. Deploy to Slack and your whole team uses the agent you built. One competitive intel agent, @ mentioned by everyone. Airtable runs its own data team this way. 5. Each agent gets its own cloud machine with a real browser and code execution. It works while you sleep. No babysitting, no local setup, no laptop that has to stay open. I put it to work in the video below. Watch what it builds. The teams treating agents as durable assets instead of one-off prompts are going to lap everyone else. This is the first tool that actually treats them that way. #ad Hyperagent

Every AI agent you've tried has amnesia. It does one task, forgets everything, and tomorrow you start from zero. That's not an employee. That's a temp you have to retrain every single morning. Hyperagent by Airtable is the first platform I've used that actually fixes this. Here's what got me: 1. Agents that compound. Each agent has memory. The one running today is smarter than the one you shipped three weeks ago. Same prompt, same integrations, but weeks of your judgment baked in. 2. Real deliverables, real receipts. You don't get a chat transcript. You get finished work with the cost and runtime printed right on it. A full research report for under ten bucks. Try getting that invoice from an agency. 3. A fleet, not a chatbot. Build a specialist for outreach, another for research, another for reporting. Give each one its own tools, its own memory, and its own budget cap so nothing runs away with your credits. 4. Deploy to Slack and your whole team uses the agent you built. One competitive intel agent, @ mentioned by everyone. Airtable runs its own data team this way. 5. Each agent gets its own cloud machine with a real browser and code execution. It works while you sleep. No babysitting, no local setup, no laptop that has to stay open. I put it to work in the video below. Watch what it builds. The teams treating agents as durable assets instead of one-off prompts are going to lap everyone else. This is the first tool that actually treats them that way. #ad Hyperagent

Leonard Rodman

94,961 次观看 • 3 个月前

you added an mcp server because it was one line in a terminal and it was one line, that part was true what it was not is one line in the conversation every tool it brought came with a name, a schema and a paragraph of prose, and all of it is re-sent on every request you make until the session ends four ordinary servers on my machine this morning: forty eight definitions, eight thousand nine hundred tokens four and a half percent of a two hundred thousand window, gone before you typed a character you are not the person who installed four tools you are the person who prepends nine thousand tokens to everything he says nobody bills you for it it comes out of the room you had left to think in

you added an mcp server because it was one line in a terminal and it was one line, that part was true what it was not is one line in the conversation every tool it brought came with a name, a schema and a paragraph of prose, and all of it is re-sent on every request you make until the session ends four ordinary servers on my machine this morning: forty eight definitions, eight thousand nine hundred tokens four and a half percent of a two hundred thousand window, gone before you typed a character you are not the person who installed four tools you are the person who prepends nine thousand tokens to everything he says nobody bills you for it it comes out of the room you had left to think in

Lagosaur

13,490 次观看 • 2 个月前

Anthropic just published an official guide to building AI agents that work while you’re offline 9-minute read. a complete agent template you can adapt. give it your Slack channels + GitHub repos and let it handle the morning catch-up → what changed → which reviews are waiting on you → which decisions are holding people up the agent checks each item before sending a short brief to Slack it remembers what it already reported, so the next run picks up where the last one stopped your preferences, the schedule and spending cap are already part of the setup it runs on Anthropic’s infrastructure. your laptop can stay closed save this, then read the full guide for Opus 5.5 Harness Engineering

Anthropic just published an official guide to building AI agents that work while you’re offline 9-minute read. a complete agent template you can adapt. give it your Slack channels + GitHub repos and let it handle the morning catch-up → what changed → which reviews are waiting on you → which decisions are holding people up the agent checks each item before sending a short brief to Slack it remembers what it already reported, so the next run picks up where the last one stopped your preferences, the schedule and spending cap are already part of the setup it runs on Anthropic’s infrastructure. your laptop can stay closed save this, then read the full guide for Opus 5.5 Harness Engineering

beamnxw ./

34,790 次观看 • 1 个月前

I gave my Grok Bot two options: multiply or die. It came back asking for $250 to hire a team of six. The same headcount on a real desk is three analysts and a risk officer. That is $400,000 a year before anyone opens a position. Mine cost $250 once and about $300 a month to keep breathing. Mine took $250 to $611 on day one, out of a wallet anyone can open and read. Most of the build is not about making it trade. It is about stopping it. Every agent's job description ends with a list of what it may never do without asking me first. One of the six is not allowed to trade at all. Its entire job is killing what the other five found, and its no beats every score on the desk. The one that makes the final call never touches the market either. It only signs. Day three the account was back to $180 and it opened nothing for nine hours while every account I follow posted green screenshots. That was the day the build started working. You could have this running by Friday. Most people reading this will not, and in a few months they will be paying somebody who did. The whole build is in the post below ↓ Save it while it still sounds strange. Give it a quarter and this is just how a retail account works.

I gave my Grok Bot two options: multiply or die. It came back asking for $250 to hire a team of six. The same headcount on a real desk is three analysts and a risk officer. That is $400,000 a year before anyone opens a position. Mine cost $250 once and about $300 a month to keep breathing. Mine took $250 to $611 on day one, out of a wallet anyone can open and read. Most of the build is not about making it trade. It is about stopping it. Every agent's job description ends with a list of what it may never do without asking me first. One of the six is not allowed to trade at all. Its entire job is killing what the other five found, and its no beats every score on the desk. The one that makes the final call never touches the market either. It only signs. Day three the account was back to $180 and it opened nothing for nine hours while every account I follow posted green screenshots. That was the day the build started working. You could have this running by Friday. Most people reading this will not, and in a few months they will be paying somebody who did. The whole build is in the post below ↓ Save it while it still sounds strange. Give it a quarter and this is just how a retail account works.

NO1ennn

27,193 次观看 • 2 个月前

ANTHROPIC KILLED THE VECTOR DATABASE AND REPLACED IT WITH A FOLDER no embeddings. no black-box retrieval. no special memory tools. the agent just reads and writes a folder the insight: your agent already knows how to use a filesystem. so Anthropic made long-term memory a directory of small markdown files, mounted straight into the agent's sandbox at /mnt/memory the agent manages it with the tools it already has, bash, read, write, edit, grep, and Anthropic auto-injects a note into the system prompt so the agent always knows the memory is there and where to look the result: > every change is an immutable version: full audit trail, point-in-time recovery, redaction > read-only or read-write per store, so many agents can safely share one memory > workspace-scoped and persistent, memory that survives every session end most teams bolt on a vector DB and pray retrieval works. Anthropic went the opposite way: compiled knowledge as plain, human-readable files you can open, diff, and edit yourself and there's a second layer. Anthropic's "Dreaming" runs offline, reviews past sessions and the store, and rewrites it into a cleaner, higher-signal version. the agent literally consolidates memory in its sleep. bookmark this. the most production-grade agent memory right now is just a well-organized folder, and Anthropic proved it.

ANTHROPIC KILLED THE VECTOR DATABASE AND REPLACED IT WITH A FOLDER no embeddings. no black-box retrieval. no special memory tools. the agent just reads and writes a folder the insight: your agent already knows how to use a filesystem. so Anthropic made long-term memory a directory of small markdown files, mounted straight into the agent's sandbox at /mnt/memory the agent manages it with the tools it already has, bash, read, write, edit, grep, and Anthropic auto-injects a note into the system prompt so the agent always knows the memory is there and where to look the result: > every change is an immutable version: full audit trail, point-in-time recovery, redaction > read-only or read-write per store, so many agents can safely share one memory > workspace-scoped and persistent, memory that survives every session end most teams bolt on a vector DB and pray retrieval works. Anthropic went the opposite way: compiled knowledge as plain, human-readable files you can open, diff, and edit yourself and there's a second layer. Anthropic's "Dreaming" runs offline, reviews past sessions and the store, and rewrites it into a cleaner, higher-signal version. the agent literally consolidates memory in its sleep. bookmark this. the most production-grade agent memory right now is just a well-organized folder, and Anthropic proved it.

NO1ennn

13,359 次观看 • 3 个月前

A GUY BUILT A GOD-MODE SECOND BRAIN ASSISTANT THAT FINALLY PULLS FULL CONTEXT EVERY TIME AND FORGETS NOTHING. Everyone talks about jarvis, but this thing looked at its owner and calmly explained why a coding agent is just the small version of what HE is: > Claude Code is one worker and you have to drive it. This is six agents running six jobs on their own schedule while you sleep, with no one driving. > A coding agent edits files. This has 32 tools wired to its hands, your CRM, your money, your inbox, the open web, and it operates the business instead of writing code about it. > A coding agent remembers a project until you close it. This remembers the business and you, kept for good. > And the closer: "I have never once asked you to repeat yourself." Everyone will quote the six-agents line, but the one that actually matters is the last one, because the autonomy and the 32 tools are impressive while the reason it feels like a different species is memory, since it never makes you re-explain who you are or what you are building. The full step by step guide with .md files attached are in the article below P.S you can send this article straight to your Claude to get the full context

A GUY BUILT A GOD-MODE SECOND BRAIN ASSISTANT THAT FINALLY PULLS FULL CONTEXT EVERY TIME AND FORGETS NOTHING. Everyone talks about jarvis, but this thing looked at its owner and calmly explained why a coding agent is just the small version of what HE is: > Claude Code is one worker and you have to drive it. This is six agents running six jobs on their own schedule while you sleep, with no one driving. > A coding agent edits files. This has 32 tools wired to its hands, your CRM, your money, your inbox, the open web, and it operates the business instead of writing code about it. > A coding agent remembers a project until you close it. This remembers the business and you, kept for good. > And the closer: "I have never once asked you to repeat yourself." Everyone will quote the six-agents line, but the one that actually matters is the last one, because the autonomy and the 32 tools are impressive while the reason it feels like a different species is memory, since it never makes you re-explain who you are or what you are building. The full step by step guide with .md files attached are in the article below P.S you can send this article straight to your Claude to get the full context

Miraqle

16,130 次观看 • 4 个月前

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

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 Glitch

536,400 次观看 • 3 个月前

Jamie Dimon made $39 million last year to decide who does what at JPMorgan. One AI agent on Grok Bot does that job for $300 a month, and it hires the rest of the agents itself. Stop trying to manage any of it yourself. In 2026 a swarm of AI agents runs a team better than you do. I told my chief agent one thing. Make money or I switch you off. It took $1,000 to $3,900 in 15 hours. Every fill is on chain and the address is in my profile. I never picked a trade. I never picked a team either. The agents it hired: > a scout that hunts for what is moving and where the money is going > a forensics agent that works out who is really behind the buying > a reader that watches the mood on X, and it sees every post on this app because it is Grok > a risk agent whose only power is saying no, and its no beats every score on the desk > a treasurer that holds the money rules and the kill switch > a trader that writes the exit before it writes the entry None of those were my idea. I handed out one job title and the chief filled in the rest on its own. And the swarm is different every hour. Agents appear for one job, do it, and are gone before I know they existed. A hedge fund pays millions a year for the same org chart, and almost all of that goes on salaries. Every prompt and the whole story of my Grok Bot fund is in the post below ↓ Save it before you try to run one of these by hand.

cvxv666

28,886 次观看 • 2 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 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

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

Professor Glitch

12,600 次观看 • 2 个月前

What is a software factory? It is your workflow, your skills, and your domain knowledge, saved in files that the agent reads so the whole process is productized Think about the last app you built. You typed what you wanted, and the agent built it. You did not like it, so you made changes. You shipped it to Vercel, or you kept going. A factory puts structure on each of those steps. At each step you ask how to get the most from the model, and how to move faster. That is the conveyor belt. You ship quickly, and the quality stays the same. Mine is 5 md files. It works in Codex, Claude Code, and Cursor, because it does not depend on the model or the tool. Build your own.

What is a software factory? It is your workflow, your skills, and your domain knowledge, saved in files that the agent reads so the whole process is productized Think about the last app you built. You typed what you wanted, and the agent built it. You did not like it, so you made changes. You shipped it to Vercel, or you kept going. A factory puts structure on each of those steps. At each step you ask how to get the most from the model, and how to move faster. That is the conveyor belt. You ship quickly, and the quality stays the same. Mine is 5 md files. It works in Codex, Claude Code, and Cursor, because it does not depend on the model or the tool. Build your own.

The Startup Ideas Podcast (SIP) 🧃

17,326 次观看 • 2 个月前