Building AI agent teams isn’t about coding – it’s about managing dynamics. The same emotional intelligence that makes you good with people makes you exceptional at orchestrating agents through “vibe coding.”
After 30 years of coding, I learned AI works best when you treat it like a junior developer. Here are 5 skills that changed how I work with AI teammates.
After decades of coding and building AI solutions, I learned that picking the right LLM isn’t about finding “the best one” – it’s about matching each tool to the right job and knowing how to use them properly.
You can actually change how you experience time – not by doing more, but by slowing down inside through presence and cognitive empathy to make life feel less chaotic.
Ever feel like time is slipping away? Discover how cognitive empathy doesn’t just help you understand others – it actually slows down time and makes you present.
You know that feeling when someone gives you directions with total confidence – and you end up at a dead end? That’s AI coding assistants in a nutshell.
After years of AI prompting, I’ve figured out the key question: when do you keep tweaking versus starting over? Here’s my framework for knowing which approach works.
After 30 years of coding, I learned the secret to getting useful work from AI: break problems into small chunks instead of dumping everything into one prompt.
30 years watching AI projects fail because consultants build systems instead of workflows that work for real people. I teach teams to think differently about automation – solving actual problems, not imaginary ones.
Your AI gives mediocre answers because you’re asking it to solve complex problems all at once. Break tasks into smaller chunks instead – like building a web scraper, then analyzing data, then generating a report separately. Each piece gets the model’s full attention and drastically improves quality.
Most leaders think AI is just chatbots, but they’re missing 3x efficiency gains. After 30 years building AI workflows, I’ve learned the best systems understand people, not just data.
Breaking complex AI tasks into smaller chunks gets way better results than asking it to solve everything at once. Here’s my 30-year coding process for decomposition prompting.
After 30 years of coding, I’ve seen workflows evolve from rigid rule-based systems to adaptive AI agents that make real-time decisions and optimize themselves.
30+ years of coding taught me every tech wave was overhyped. AI is different – it’s not automating tasks, it’s making entire workflows obsolete and reshaping what’s possible.
We’re optimizing workflows that AI has already made obsolete. After 30 years of coding, I’ve learned AI doesn’t speed up old processes – it replaces them entirely.
After 30+ years in code and 8 years in AI, here’s the difference between getting stuck and getting results: knowing when to iterate vs when to start fresh.
I learned that forcing positivity actually pushes people away and makes problems worse. When we dismiss struggles with “stay positive,” trust breaks down and real issues get buried.
I spent 30 years watching companies waste money on AI tools that employees hate. The failures aren’t technical – they ignore how humans actually work and think.
After 30 years of coding, I finally figured out the real AI skill that matters: breaking problems into pieces AI can handle, not chasing the latest tools or models.
After 30 years of coding, I learned the secret to AI success: treat agents like a specialized team, not magic wands. Break problems down, write clear prompts, give them tools.
30 years of coding taught me: one AI agent is useful, but a team of specialists working together changes everything. Most people miss this completely.
Developers treating AI like a teammate instead of a search engine are pulling ahead. The gap isn’t about better models—it’s about learning to delegate effectively through skills like prompt engineering, modularization, and workflow integration.
After 30+ years coding and 8 years in AI, I’ve learned everyone focuses on which LLM is best, but misses what really matters: knowing how to talk to it properly.
AI code looks perfect but feels wrong? After 30 years coding, I’ve learned the most valuable skill isn’t getting AI to write code – it’s knowing when to ignore it.
30+ years of coding taught me this about AI prompts: it’s not about finding the “perfect” prompt, it’s knowing when to iterate vs start over completely.
Most people prompt AI once and hope for the best. The real skill is knowing when to refine versus when to scrap everything and start over.
After 30 years coding and 8 years in AI, I learned the key skill isn’t perfect prompts – it’s knowing when to keep tweaking vs starting fresh. Break prompts into pieces.
Stop asking AI giant messy questions and expecting clean answers. Break problems into small chunks first, then prompt each piece separately. 30+ year coder shares the one technique that made AI actually useful for real work.
Most teams fail at AI because they treat it like a magic button instead of a team member. Here’s how to actually build effective AI workflows that save time.
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