What's new?

Managing Gen Z at the Workplace | TPM Guide Most managers are getting Gen Z wrong. It’s not about managing harder — it’s about managing differently. In this video, I break down a practical playbook for Technical Program Managers (TPMs) and Resource Managers to effectively manage, engage, and retain Gen Z talent. 🚀 What you’ll learn: The core formula that drives Gen Z performance Proven techniques like 3C Task Framing, Short Execution Loops, and Visible Impact How to design work for ownership, growth, and motivation Practical do’s and don’ts every manager should follow A simple execution model you can apply immediately This is not theory — this is actionable management strategy for modern teams. 👉 If you're a: Technical Program Manager Engineering Manager Resource Manager Team Lead This video is for you. 📌 Key takeaway: Managing Gen Z is not about control. It’s about clarity, autonomy, feedback, and impact. 👍 Like | 💬 Comment | 🔔 Subscribe 👉 Follow for more content on AI-native project management & leadership #GenZ #ProgramManagement #TPM #Leadership #FutureOfWork #EngineeringManagement #AINative #WorkplaceStrategy My Linkedin: https://www.linkedin.com/in/jacinthp/ --------------------------------------------- Visit our website: https://www.programstrategyhq.com/ --------------------------------------------- Books I read and Refer for Project Management & Agile: --------------------------------------------- Headfirst Agile: https://amzn.to/4aiMfoK Agile Estimating and Planning: https://amzn.to/4jiUjKf SAFe 5.0 Distilled: https://amzn.to/3WkUAmn Inspired - Product Management: https://amzn.to/3WoHowO

📌 𝗚𝗲𝗻 𝗭 𝗶𝘀𝗻’𝘁 𝗵𝗮𝗿𝗱 𝘁𝗼 𝗺𝗮𝗻𝗮𝗴𝗲. 𝗬𝗼𝘂’𝗿𝗲 𝗷𝘂𝘀𝘁 𝘂𝘀𝗶𝗻𝗴 𝘁𝗵𝗲 𝘄𝗿𝗼𝗻𝗴 𝗺𝗼𝗱𝗲𝗹. 𝗠𝗼𝘀𝘁 𝗺𝗮𝗻𝗮𝗴𝗲𝗿𝘀 𝗮𝗿𝗲 𝗴𝗲𝘁𝘁𝗶𝗻𝗴 𝗚𝗲𝗻 𝗭 𝘄𝗿𝗼𝗻𝗴. It’s not about managing harder. It’s about managing 𝗱𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁𝗹𝘆. → 𝗖𝗹𝗮𝗿𝗶𝘁𝘆 over control → 𝗙𝗮𝘀𝘁 𝗳𝗲𝗲𝗱𝗯𝗮𝗰𝗸 over annual reviews → 𝗚𝗿𝗼𝘄𝘁𝗵 over static roles → 𝗜𝗺𝗽𝗮𝗰𝘁 over tasks The winning formula👇 𝗖𝗹𝗮𝗿𝗶𝘁𝘆 + 𝗔𝘂𝘁𝗼𝗻𝗼𝗺𝘆 + 𝗙𝗮𝘀𝘁 𝗙𝗲𝗲𝗱𝗯𝗮𝗰𝗸 + 𝗩𝗶𝘀𝗶𝗯𝗹𝗲 𝗜𝗺𝗽𝗮𝗰𝘁 = 🚀 𝗧𝗵𝗶𝘀 𝗶𝘀𝗻’𝘁 𝗮 𝗽𝗲𝗼𝗽𝗹𝗲 𝗽𝗿𝗼𝗯𝗹𝗲𝗺. 𝗙𝗶𝘅 𝘁𝗵𝗲 𝘀𝘆𝘀𝘁𝗲𝗺, 𝗻𝗼𝘁 𝘁𝗵𝗲 𝗽𝗲𝗼𝗽𝗹𝗲. If you're a 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲 𝗠𝗮𝗻𝗮𝗴𝗲𝗿 or 𝗧𝗣𝗠 — this is your playbook 📌 𝗦𝗮𝘃𝗲 𝗶𝘁! 👉 𝗙𝗼𝗹𝗹𝗼𝘄 𝗝𝗮𝗰𝗶𝗻𝘁𝗵 𝗣𝗮𝘂𝗹 𝗭 𝗳𝗼𝗿 𝗺𝗼𝗿𝗲 #𝗚𝗲𝗻𝗭 #𝗟𝗲𝗮𝗱𝗲𝗿𝘀𝗵𝗶𝗽 #𝗣𝗿𝗼𝗴𝗿𝗮𝗺𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 #𝗙𝘂𝘁𝘂𝗿𝗲𝗢𝗳𝗪𝗼𝗿𝗸 #𝗔𝗜𝗡𝗮𝘁𝗶𝘃𝗲

🚀 7 Rungs of Claude Code Features | From Prompts to Agent Teams Most developers and teams are still using AI at Level 1 (chat + copy-paste)… But the real transformation happens when you move toward AI-native engineering systems. In this episode, we break down the 7 Rungs of Claude Code Features — a practical maturity model that shows how teams evolve from simple AI usage to fully orchestrated agent-driven development. 🎯 What you’ll learn Why most teams get stuck at early stages of AI adoption How to move from individual productivity → team standardization → system-level automation The role of CLAUDE.md, workflows, hooks, and agents How Technical Program Managers (TPMs) can drive this transformation Real-world approach to scaling AI safely with guardrails 🔄 The real shift This isn’t about better prompts. It’s about building repeatable, scalable AI systems. 👉 Repeatability → Automation → Orchestration 👥 Who is this for? Software Engineers Technical Program / Project Managers Engineering Leaders Anyone building AI-native workflows 💬 Where is your team today on this ladder? Let me know in the comments 👇 👉 Follow for more content on AI-native software engineering, TPM leadership, and system thinking #AINativeSoftwareEngineering #AIEngineering #ClaudeCode #TechLeadership #DeveloperProductivity #TPM #ArtificialIntelligence My Linkedin: https://www.linkedin.com/in/jacinthp/ --------------------------------------------- Visit our website: https://www.programstrategyhq.com/ --------------------------------------------- Books I read and Refer for Project Management & Agile: --------------------------------------------- Headfirst Agile: https://amzn.to/4aiMfoK Agile Estimating and Planning: https://amzn.to/4jiUjKf SAFe 5.0 Distilled: https://amzn.to/3WkUAmn Inspired - Product Management: https://amzn.to/3WoHowO

🚀 The 7 Rungs of Claude Code Features — Every Developer & PM Must Know 🔹 The difference between one-shot AI usage and agent orchestration 🔹 All 7 rungs : 1️⃣ Chat + Copy Paste 2️⃣ CC in Terminal 3️⃣ CLAUDE.md 4️⃣ Skills + Slash Commands 5️⃣ Hooks + MCP 6️⃣ Subagents + Plugins 7️⃣ Agent Teams 🔹 When to use each level (this is where most people fail) 🔹 How to move from manual workflows → automated systems → autonomous agents 🔥 Why This Matters Most people stop at ChatGPT-style usage. But the real power of AI comes when you: ✅ Encode workflows ✅ Automate execution ✅ Orchestrate multiple agents That’s how top teams are getting 10x productivity gains. 🎯 Who Should Watch? ✔️ Software Engineers ✔️ Technical Program Managers ✔️ AI Builders & Enthusiasts ✔️ Product Managers working with AI 📌 Pro Tip: Don’t jump to Agent Teams immediately. 👉 Match the rung to the problem — that’s the real skill. 👍 If this helped you, don’t forget to: ✅ Like ✅ Share with your team ✅ Subscribe for more AI + Project Management content 🔔 Follow for more: I share practical insights on AI-native project management, developer productivity, and real-world AI systems #AI #ClaudeCode #SoftwareEngineering #AIEngineering #AgenticAI #Productivity #TechLeadership #ProjectManagement #Developers #Automation

The 7 Rungs of Claude Code Features — Every Developer & PM Must Know Are you still using AI like a basic chatbot while others are building full AI-powered development systems? In this video, we break down The Claude Code Feature Ladder — a powerful framework that shows how AI usage evolves from simple prompts to fully orchestrated agent teams. Whether you're a developer, technical program manager, or AI enthusiast, understanding these 7 rungs will completely change how you build with AI. 💡 What You'll Learn: 🔹 The difference between one-shot AI usage and agent orchestration 🔹 All 7 rungs explained clearly: 1️⃣ Chat + Copy Paste 2️⃣ CC in Terminal 3️⃣ CLAUDE.md 4️⃣ Skills + Slash Commands 5️⃣ Hooks + MCP 6️⃣ Subagents + Plugins 7️⃣ Agent Teams 🔹 When to use each level (this is where most people fail) 🔹 How to move from manual workflows → automated systems → autonomous agents 🔥 Why This Matters Most people stop at ChatGPT-style usage. But the real power of AI comes when you: ✅ Encode workflows ✅ Automate execution ✅ Orchestrate multiple agents That’s how top teams are getting 10x productivity gains. 🎯 Who Should Watch? ✔️ Software Engineers ✔️ Technical Program Managers ✔️ AI Builders & Enthusiasts ✔️ Product Managers working with AI 📌 Pro Tip: Don’t jump to Agent Teams immediately. 👉 Match the rung to the problem — that’s the real skill. 👍 If this helped you, don’t forget to: ✅ Like ✅ Share with your team ✅ Subscribe for more AI + Project Management content 🔔 Follow for more: I share practical insights on AI-native project management, developer productivity, and real-world AI systems #AI #ClaudeCode #SoftwareEngineering #AIEngineering #AgenticAI #Productivity #TechLeadership #ProjectManagement #Developers #Automation My Linkedin: https://www.linkedin.com/in/jacinthp/ --------------------------------------------- Visit our website: https://www.programstrategyhq.com/ --------------------------------------------- Books I read and Refer for Project Management & Agile: --------------------------------------------- Headfirst Agile: https://amzn.to/4aiMfoK Agile Estimating and Planning: https://amzn.to/4jiUjKf SAFe 5.0 Distilled: https://amzn.to/3WkUAmn Inspired - Product Management: https://amzn.to/3WoHowO

🚀 𝗪𝗵𝗮𝘁 𝗮𝗿𝗲 𝗥𝗮𝗹𝗽𝗵 𝗟𝗼𝗼𝗽𝘀? 𝗛𝗲𝗹𝗽𝘀 𝗔𝘂𝘁𝗼𝗻𝗼𝗺𝗼𝘂𝘀 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 What if you could give AI a goal… and it keeps working until it’s done? That’s exactly what 𝗥𝗮𝗹𝗽𝗵 𝗟𝗼𝗼𝗽𝘀 (by Geoffrey Huntley) enable. 🧠 𝗧𝗵𝗲 𝗖𝗼𝗿𝗲 𝗜𝗱𝗲𝗮 Instead of: 👉 Prompt → Response → Human → Repeat You get: 👉 Prompt → AI → Self-iterate → Repeat until ✅ No micromanagement. Just outcomes. ⚙️ 𝗪𝗵𝗮𝘁 𝗶𝘀 𝗮 𝗥𝗮𝗹𝗽𝗵 𝗟𝗼𝗼𝗽? At its simplest: while true: run AI take output feed it back But in practice, it includes: ✔️ Exit conditions ✔️ Error detection ✔️ Logging & monitoring ✔️ Goal-based completion 🔁 𝗛𝗼𝘄 𝗜𝘁 𝗪𝗼𝗿𝗸𝘀 1️⃣ Define the goal clearly 2️⃣ Run the AI agent 3️⃣ Evaluate the result 4️⃣ Feed output back (including errors) 5️⃣ Repeat automatically 💡 Failures aren’t mistakes — they’re fuel for the next iteration. 🤖 𝗪𝗵𝘆 𝗜𝘁’𝘀 𝗣𝗼𝘄𝗲𝗿𝗳𝘂𝗹 ✨ Autonomous development ✨ Continuous improvement ✨ Scales across large codebases ✨ Works while you’re AFK ⚠️ 𝗪𝗵𝗲𝗿𝗲 𝗱𝗼 𝗥𝗮𝗹𝗽𝗵 𝗟𝗼𝗼𝗽𝘀 𝘀𝘁𝗿𝘂𝗴𝗴𝗹𝗲? • Vague goals (“make it better”) • No clear success criteria • Tasks needing human judgment 🔥 𝗧𝗵𝗶𝘀 𝗶𝘀 𝘄𝗵𝗲𝗿𝗲 𝘄𝗲 𝗺𝗼𝘃𝗲 𝗳𝗿𝗼𝗺 “𝗔𝗜 𝗮𝘀 𝗮𝘀𝘀𝗶𝘀𝘁𝗮𝗻𝘁” → “𝗔𝗜 𝗮𝘀 𝗮𝘂𝘁𝗼𝗻𝗼𝗺𝗼𝘂𝘀 𝘄𝗼𝗿𝗸𝗲𝗿” 🧩 𝗜𝗻 𝗢𝗻𝗲 𝗟𝗶𝗻𝗲 👉 Turn AI into a self-improving system by looping its output until it reaches a defined goal. #AgenticAI #AINativeSoftwareEngineering #RalphLoop #AutonomousDevelopment #SDD