Claude Cowork is one of the most important shifts happening in AI right now, especially for people who don't want to become programmers. The simplest way to understand it is: - Claude Chat helps you think about the work. - Claude Cowork can actually perform the work. Anthropic describes Cowork as bringing the same agentic architecture behind Claude Code to everyday knowledge work, without requiring a terminal. Instead of answering one prompt at a time, it can plan a multi-step job, break it into pieces, use tools and files, execute the work, and return a finished result. 🧠 What does that actually mean? Suppose you have a folder containing: - 20 customer invoices - several Excel files - meeting notes - customer proposals - PDFs - sales reports - With ordinary Claude Chat, you might upload documents and ask questions about them. **With Cowork, you could give it a job** 🧑‍⚖️PROMPT: Go through these files. Organize the customers. Create a spreadsheet showing revenue by customer. Identify accounts that declined. Create a management summary and put the finished files in this folder.” Cowork can plan and execute those steps rather than merely explaining how you should do them. It can directly read and write files in folders you authorize, create spreadsheets, presentations, and documents, coordinate subtasks, browse websites, and use connected applications ''' That's the fundamental change: - Prompt → Answer becomes - Goal → Plan → Actions → Finished Work ❓Why should you care? Because this is where AI starts moving from assistant toward digital worker. 🧠Imagine saying: “Every Monday, review these reports, compare them with last week, identify anything unusual, and prepare my management briefing.” Cowork supports scheduled tasks that can run remotely even when your computer isn't online. Or: “Take these meeting notes and build the presentation for Friday.” Or: “Research these competitors, organize the findings, create a comparison spreadsheet, and prepare an executive report.”

If you have been using ChatGPT but have never touched Codex, this is for you. And don't worry. You do not need to be a programmer. I think the easiest way to understand Codex is this: ChatGPT helps you think and create. Codex can actually work on things with you. That is a big difference. With ChatGPT, you might say: “Help me figure out how to build this.” With Codex, you can move closer to: “Here is the project. Help me build it, change the files, check the work, and fix what isn't working.” That is why Codex matters. 🧠 So What Exactly Is Codex? Codex is OpenAI's AI coding and software-building agent. But don't let the word coding scare you away. You can communicate with Codex in normal English. You tell it what you are trying to accomplish, give it access to the appropriate project files, and it can work through the task. It can: 🔹 Read and understand project files 🔹 Create and edit files 🔹 Build websites and applications 🔹 Find and fix problems 🔹 Run tests 🔹 Research technical issues 🔹 Work through larger projects step by step 🔹 Use reusable instructions and Skills 🔹 Connect to other tools through things like MCP The important idea is not that Codex can write code. The important idea is that Codex can take action. ** That is the shift I want you to understand. 💡 What Problem Does Codex Solve? For years, we have used AI mostly like this: Ask → Answer → Copy → Paste → Do the work yourself Codex starts moving us toward: Describe the goal → Give context → Let the agent work → Review the result You are moving from simply talking to AI toward delegating work to AI. That is a very important skill to understand as AI continues to evolve. 🚀 10 Simple Steps to Start Using Codex 1. Get Codex running Start with OpenAI's desktop ChatGPT experience and access Codex from there. 👀 Tip: Don't worry about understanding every feature. Your first goal is simply to get comfortable opening Codex and looking around. 2. Sign in with your ChatGPT account Use the same OpenAI account you already use for ChatGPT.

I just added a new section inside the AI Tools classroom called LLM Models. It has 5 short parts, and I added it because I think this is one of those things people skip when learning AI. 📊 We talk a lot about prompts, ChatGPT, Claude, Gemini, agents, and all the new AI tools coming at us. But we don't spend enough time understanding what is actually doing the work underneath all of them. That's the LLM. Think of the model as the engine. 📌Not every engine is built for the same job. AI models aren't either. Some are designed for speed and everyday tasks. Some are better for writing and communication. Others are much stronger when you need deeper reasoning, planning, analysis, or help solving a difficult problem. And this is where I think it starts becoming important to your career. Imagine two people working at the same company with access to exactly the same AI tools. One knows how to ask AI questions. The other knows which model to use, when to increase the reasoning, when to dial it back, and how to recognize when the AI is starting to lose context or logic. Same tools. Very different results. The second person isn't necessarily more technical. They have simply learned how to manage AI instead of just using it. That's what I want these 5 parts to teach: 🔥Part 1: The Models Behind the Work 🔥Part 2: Range of Model Capability 🔥Part 3: My Model Map 🔥Part 4: Find the Floor 🔥Part 5: Build Your Model Operating System 💡 Don't try to memorize all the model names. They will change. Learn something more valuable. Learn how to think about models, how to match the model to the assignment, and when you need more or less capability. As AI becomes part of everyday work, I believe this will separate people who occasionally use AI from those who really know how to put it to work. You can find all 5 parts inside AI Tools → LLM Models. 📌 Enjoy and let me know if you have any questions