

Marc
1.8K posts

@vallver
Running ops at a tech company. Figuring out what AI changes and what it doesn't. Writing about it weekly → https://t.co/0YLns0OfB2













My Claude Cowork guide blew up this week. So I recorded the full step-by-step walkthrough (plus some extra sauce). In this video I show the exact 10 tips I use to turn Cowork into a real daily operator (as a non-developer): 1. Import your memory from tools like ChatGPT or Gemini 2. Set global instructions 3. Use plan mode before execution 4. Build the right folder/context structure 5. Install plugins 6. Connect Slack 7. Connect Google Calendar 8. Connect Gmail 9. Use skills for repeatable high-quality outputs 10. Set scheduled tasks for automated daily briefings And to make it even easier to get started, I prepared a full guide with all of my prompts you can copy/paste: github.com/JJenglert1/get… This is everything you need to not only get started with cowork for the first time, but to have it do real work for you that actually matters. If this helped you, I’d love your help spreading the word. Share it with a friend or colleague who wants to stop just talking with AI and start having AI actually do work for them.



I've been asking $100m+ company execs one question: "What is the #1 thing slowing/stopping your company's AI transformation?" A non-exhaustive list of responses: 1) Data quality and connectivity of systems. Plus systems that play nice with AI. 2) Lack of leadership buy-in and implementation 3) Data governance restrictions. 4) Willingness of staff to adopt AI. 5) Incurious culture. Lack of knowledge of the current state of AI 6) Tooling doesn't have API access; team is still learning how to use LLMs. 7) Industry regulation/privacy. 8) Data quality and lack of a comprehensive AI system across the full company. 9) Unclear ownership across teams. 10) Time to actually build solutions. 11) Mixed AI literacy levels across teams. 12) No clear strategy / I'm starting the initiative from scratch. 13) Quality output. 14) Upskilling developers. 15) Silos. 16) Data Security and Security Guideline unclear. 17) Lack of training. 18) Data quality is unclear across multi-product teams. 19) Time. What would your answer be to this question?
