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Build your first AI agent with Claude Code (step by step) Most people's first "agent" is a chatbot they have to babysit. This one does a real job: the 40 minutes you lose every morning working out what needs you today. Here's the whole build in Claude Code, in...

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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 Aufrufe • vor 29 Tagen

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 Aufrufe • vor 2 Monaten

THIS IS ACTUAL F*CKING GOLD Anthropic just shared one of the best tricks for Claude Code. The idea: stop configuring Claude Code by hand. One prompt, 44 seconds, and your whole .claude/ folder writes itself. 7 files, 162 lines, 2 cents. you typed one line and wrote zero of them. You just launch Claude Code like this: claude --model opus "set up this repo: ..." here's the split: → 3 Haiku 5.5 agents scan the repo before a single file gets written → one reads the stack, one reads the npm scripts, one hunts for risky paths → Opus 5.5 takes those three summaries and writes the actual config → 7 files land: CLAUDE.md, 3 agents, permissions, a format hook and a /ship skill the part everyone gets wrong: they write CLAUDE.md from memory. a config written without reading the repo is a guess, and you live with that guess for months. here the risk scan came back with 5 dangerous paths and those went straight into the permissions file, so the agent is blocked from them before it ever runs once. the scripts scan found 5 npm scripts, and the agents got wired to the ones that actually exist in this repo instead of generic placeholders. one prompt from you, 7 files from it, 0 lines written by hand. Haiku reads. Opus writes. You commit. JEV engineering is the same move one layer down: 1,867 forks this run - which file, which agent, allow or ask - resolved in code at 0.97 sharpness, and the split ones (0.59 on "allow or ask") come back to you instead of being guessed the full tree ▸ you paste one prompt describing the setup you want ▸ 3 Haiku 5.5 agents scan stack, scripts and risky paths in parallel ▸ every agent returns a summary, never raw files ▸ Opus 5.5 writes CLAUDE.md, the agents, permissions, the hook and the skill ▸ anything touching permissions comes back to you before it is written ▸ JEV resolves micro-forks in code and escalates only the split ones paste the tree and this prompt into Claude Code ↓ "Set up this repo for me: 1. First scan it with 3 Haiku 5.5 agents in parallel: one reads the stack and manifests, one reads the build and npm scripts, one finds paths that are dangerous to touch. 2. Each agent returns a summary only, never raw file contents. 3. Then write CLAUDE.md, 3 subagents, a permissions file, a format hook and a /ship skill, all based on what the scan actually found, not on defaults. 4. Every risky path from the scan goes into the deny list before anything runs. 5. Ask me before writing anything that grants permissions. Show me every file as a diff first. No writes until I say go." ↳ save it, this is the first thing you run in every new repo.

Sprytix

25,468 Aufrufe • vor 21 Tagen

Why your AI gets dumber the longer you chat (context rot): the one Claude Code file, plus one habit, that fixes it How context rot works: 1. Everything in your chat sits in the AI's working memory, the context window: every message, every file it opened, every answer it gave. The model reads through all of it before every reply. 2. Anthropic, the company behind Claude, says the more tokens you pile into the context window, the worse the model gets at recalling any one thing in it. They call it an attention budget, and every new token spends a little of it. 3. When the window fills up, Claude Code compacts: it replaces the conversation with a summary. If a rule only lived in the chat, it can get lost in that summary. The fix is one file, CLAUDE.md, plus one habit. 4. CLAUDE.md is a plain text file in your project folder. Claude Code reads it at the start of every session, and after compaction it re-reads it from the file. Think of it as a sign bolted above the whiteboard: wipe the board all you want, the sign stays. 5. How to create a CLAUDE.md: open Claude Code in your project folder and type /init. It writes a first draft. Then add what it can't guess: who you are, who the client is, and your rules ("never change a headline without asking", "prices come from column D of the Google Sheet", "dates are day, month, year"). 6. Keep CLAUDE.md short. Anthropic's Claude Code guide says to aim for under 200 lines, because a bloated file gets ignored too. For every line, ask: would it make a mistake without this? If not, cut it. The other half of the fix is how you work: 7. One job, one session. Type /clear when you switch jobs: the board's wiped, and the sign (CLAUDE.md) is still up. 8. If you've corrected the AI twice on the same thing, skip round three in capitals. Clear it, put the rule in CLAUDE.md, and start again. This is context engineering for people who don't code: an AI assistant that remembers your business rules, your clients and your formats, for client work, reports, content, spreadsheets and proposals, whether you're a freelancer, an agency or a small business. #claudecode #aiagents #contextengineering #learnai

Professor Glitch

12,200 Aufrufe • vor 28 Tagen

install these before your next long Claude Code session if your Opus 5.5 bill hurts and you don’t know where to start, copy this: claude-code-router: sits in front of Claude Code and sends each request to the cheapest model that can do it. Haiku does the grep, Opus only gets the hard part. 37k stars rtk: shrinks test, build and git output before the model sees it. one binary, up to 90% less bash output in your context. 83k stars claude-mem: remembers what happened in your past sessions and feeds back only the relevant part, so you stop paying to re-explain your project every morning. 100k stars context-mode: a huge tool output goes into a local database instead of your context window, and Claude pulls out only the lines it needs. 18k stars caveman: makes Claude answer in short telegraphic sentences. code stays identical, and output is the expensive side on Opus 5.5 ($20 per million). 51k stars token-optimizer: a local dashboard that shows where your tokens actually went, and keeps your work alive across compactions. 1.9k stars token-savior: replaces whole-file reads with symbol lookups, so Claude reads the function and not the 2,000 lines around it code-review-graph: maps your repo as a graph and gives Claude only the code connected to the change you’re reviewing jev-ultrafast: a browser agent where Jev picks every click, and a text model wakes up only when something has to be typed. 16,758 stars none of them makes Opus smarter. every one stops it from paying for work it never needed to do the full setup with scripts is in the article. do the measuring step first, so you know what you actually saved

rvaniaaa

24,787 Aufrufe • vor 1 Monat

THIS MIGHT BE THE #1 OPEN-SOURCE REPO FOR CLAUDE CODE RIGHT NOW. IT GIVES CLAUDE A MEMORY AND SLASHES YOUR TOKEN COST ON EVERY QUESTION The repo is safishamsi/graphify, a free open-source skill that turns any codebase into a knowledge graph Claude Code can read instantly. Instead of grepping through your files every session, Claude gets a map of how everything connects The problem it fixes: Every time you ask Claude Code about a big repo, it does the same thing, greps through dozens of files like a brute-force Ctrl+F, blows through your context window, and sometimes still misses the answer hiding in a file nobody searched. Claude Code has no memory of how your project is structured. Every session starts from zero What it does: It maps your entire codebase into a knowledge graph, capturing not just which files exist, but which functions depend on which, which modules are central, and which files cluster around the same concern. Claude queries the map instead of scanning files How it works, three passes: 1. Code structure, free and local. Tree-sitter parses your files and pulls out classes, functions, imports and call graphs. No LLM, no tokens, just your actual code mapped deterministically 2. Audio and video, if you have them. Transcribed locally and folded into the graph 3. Docs, papers, images. Here an LLM does semantic analysis, figuring out what each document means and where it fits. Only the meaning gets sent up, never your raw source It saves you money: Normally a question about a big repo makes Claude spawn explore agents that scan file after file, eating your context window and your token budget before you get an answer. With the graph already built, Claude queries the map instead of re-reading the codebase every time. Same answer, a fraction of the tokens. The graph only gets built once, then a hook rebuilds it after each commit for free, so you never pay that scanning cost again. The bigger the repo, the bigger the gap The best parts: it's a skill, so once installed Claude knows when to use it without you memorizing commands. It works on non-code folders too, point it at docs or notes and it can spin up an Obsidian vault How to add it to your Claude: 1. Install Claude Code if you haven't: npm install -g Paul Jankura-ai/claude-code 2. Add the skill: claude skill add safishamsi/graphify 3. Open your project folder and run /graphify . to build the graph 4. Optional, make it automatic: graphify hook install so the graph rebuilds after every commit That's it. Ask Claude about your repo and it reads the map instead of burning tokens on a file hunt Bookmark this

Yarchi

56,568 Aufrufe • vor 5 Monaten