@vodafoneNL : connectie problemen vanuit Austria en Slovenia? is er iets gaande of doe ik wat fout? (zie PM).
Data werkt prima, maar telefonie lijkt niet te werken?
Key ideas I got from DevTalksRomania:
- While AI can boost productivity on some specific tasks, great teamwork improves productivity on even more activities. This is like setting up an orchestra, where everyone needs to be tuned and work in synergy.
- Writing tests manually can help you add determinism to your project. Generating both the app code and the test with AI may lead to AI writing tests on hallucinations. This is similar to OSS, where we'd write tests for a bug before implementing the fix.
- When LLMs regress towards the mean, and everyone uses LLMs, the output would be of average quality, but clients don't want to pay for average, so expertise will be very valuable to differentiate from the rest.
- When prototyping with a new tech, do it on an app that's unrelated to your business so that management doesn't promote the prototype to production-ready.
- You can use the Maven daemon, parallel tests, and caching to speed up your build significantly.
- Use linters and verifiers (e.g., ArchUnit) to speed up reviews, especially now that AI can easily generate lots of changes
- Set up a rule in your team so that a PR cannot exceed a threshold of changes (e.g., at most 400 lines of changes), so that it's easier to review and approve one PR at a time.
China published the most uncomfortable paper on vibe coding.
ETH Zurich tested 100 developers in a controlled, commercial-grade vibe coding environment to see who actually succeeds.
The findings are brutal.
The researchers tracked computer science achievement, written communication skills, and general cognitive reasoning.
They wanted to see what actually predicts vibe coding proficiency when you never touch a line of source code yourself.
Two major predictors emerged.
Written communication proficiency mattered. The ability to structure thoughts and articulate intent unambiguously in text directly impacts what the AI builds.
But that wasn't even the main takeaway.
Computer science achievement was a massive, dominant predictor of success.
Even when researchers controlled for general intelligence and reasoning skills, CS background still heavily dictated who built working software and who completely crashed.
In fact, CS knowledge contributed roughly twice the unique predictive variance of writing skills alone.
Why? Because vibe coding isn't about writing code. It’s about debugging logic.
When an AI agent builds a complex application and quietly breaks an edge case under the hood, a non-technical user looks at the glowing UI and assumes it works.
They don't know what questions to ask. They don't know what logic to challenge. They lack the mental models to recognize architectural catastrophe.
You can prompt your way past syntax.
You cannot prompt your way past a fundamental lack of engineering intuition.
The hype told us that learning to code is dead because language is all you need.
The data just proved the opposite.
To truly master the vibe, you still need to understand how the machine thinks.
Nicknames for lazy coworkers:
Cordless - only works for 2 hrs
E.T. - always wants to go home
KitKat - always taking a break
Muffler - always exhausted
Seaweed - just floats around all day
Sensor light - only works if someone walks by
Wheelbarrow - only works when pushed
—expect a visit from HR
@lazy_IT_guy It is true: we pay our software- and SaaS providers to test their products for bugs.
And we pay Extra when we find one, extra time, extra cost, extra workarounds...
Clever vendors.