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SPECIAL EDITION!

“THE HUMAN AI DETECTORS - And Other Familiar Enemies of Progress”

Today’s post is a little different from my usual deep dives into engineering culture, architecture, and leadership.
This one is a Special Commentary Edition - because sometimes the funniest (and most revealing) lessons about modern work come from outside the codebase.

We’ve all lived through the same old warnings:
🧮 “Calculators will make you forget math.”
📱 “SMS will make you forget grammar.”
👥 “Social media will destroy real friendships.”

And now the 2025 classic:
🤖 “AI will make your brain stop working.”

Meanwhile, anyone who has actually used an LLM knows the opposite is true:
👉 AI is a productivity multiplier.

But here’s where the story gets interesting…
There’s a growing group I call THE HUMAN AI DETECTORS - often in recruiting and headhunting - who live in a perfect paradox:

“We help you improve your LinkedIn with AI!”
…but also…
“We reject applications if they used AI!”

It’s like telling a finance candidate not to use Excel,
an engineer not to use CAD,
or a driver not to use power steering because “real drivers use muscle.”

In this commentary piece, I explore why this mindset exists, why it’s flawed, and what it reveals about our relationship to technology, truth, and talent.

This one is humorous - but with a serious message underneath.

👉 Read the full Special Commentary Edition here:
THE HUMAN AI DETECTORS - And Other Familiar Enemies of Progress

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SAFe can bring structure and alignment across large organizations.
But applied mechanically, it often feels less agile than Scrum — and resistance follows.

In this article, I argue that real success comes from:

• Anchoring SAFe in mindset over mechanics
• Adapting it to context, not copying a framework
• Empowering Scrum Masters and Release Train Engineers as true change agents

🔎 Does SAFe amplify agility in your organisation — or add unnecessary overhead?

👉 Read the full article here: 
 

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Vibe Coding is here. And it’s impressive -but also risky.

We’re entering a new era where developers describe what they want, and AI generates entire applications: logic, UI, tests… everything.

So the big question becomes:
Is this the breakthrough we’ve been waiting for -or just the latest silver bullet?

To find out, I built a real example using only natural language.
It worked -but it also revealed important risks:

➡️ Vibe coding jumps directly from prompt to code.
➡️ It skips design and architecture completely.
➡️ Without those, systems become fragile and impossible to oversee.

In the podcast, I cover:

• what vibe coding actually is
• a hands-on example (user story → code → tests)
• the architectural risks nobody talks about
• how engineering discipline restores safety
• a simple hybrid model for real-world use
• platforms worth exploring

If you’re curious about how AI will fit into modern R&D -this podcast hopefully will give you some answers.

👉 Read the full article here: https://www.linkedin.com/pulse/vibe-coding-speed-hype-engineering-reality-alessandro-guida-t7jsf/
 
 

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Most Agile models assume teams can work fully independently.
But in real R&D organisations — especially those with shared platforms, legacy systems, or compliance constraints — inter-team dependencies are everywhere.

Ignoring them doesn’t make you more “agile.”
It just makes the blockers invisible.

In this podcast, I explain how light-weight discipline (not heavy frameworks) can dramatically improve flow, quality, and predictability — even without going full SAFe.

You’ll find:

• The real challenges hidden inside dependencies
• Practical, discipline-based techniques that work in any organisation
• Common pitfalls most teams fall into
• A more pragmatic, engineering-driven view of agility

If you want to read the full article it is here.

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This edition examines Agile Transformations — not as a process rollout, but as an organizational maturity journey.

If Agile is to help us build better software, not just follow rituals, we need to understand how culture, architecture, leadership, and context interact.

I hope you’ll enjoy this updated edition — and join the newsletter if you haven’t already.

You can read the full article here.

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From Moonshot to Sustainable Innovation

Most companies celebrate their first big idea — the moonshot that gets them off the ground.
But very few manage to repeat it.

Why?
Because real, continuous innovation isn’t luck.
It’s a system — built on people, environment, and structure.

In this new article, I share the simple 3-part framework I’ve used across multiple companies to transform “once-in-a-while creativity” into a repeatable innovation engine:

🔎  Hire for curiosity
 🎬  Feed the creative engine
 🔁  Institutionalize the innovation process

These ideas were shaped over many years: from early startup environments to Inspiration Days, innovation sprints, and team cultures obsessed with learning.

If your organization wants more than a single breakthrough — if it wants sustainable innovation — this is for you.

If you want to read the full article it is here.

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It’s time to treat software engineering seriously.

For years, Agile has relied on playful metaphors — tribes, squads, trains, sprints, even pigs and chickens.
These made the frameworks easier to sell, but they also made our profession look less like engineering and more like a playground.

Today our systems run hospitals, railways, satellites, and financial infrastructure.

The language, mindset, and expectations must match that responsibility.

In this second issue of the Agile Software Engineering newsletter, I argue for a shift toward clarity, engineering discipline, and frameworks rooted in Lean, DevOps, and architecture — without losing creativity or innovation.

If you would like to read the full article it is here.

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This Podcast is an audio version of the written Agile Software Engineering newsletter.  If you want to go deeper, don't forget to subscribe the newsletter too.