Linky #3 - How We Lose (and Regain) Our Ability to Think
This week, I explore intellectual humility, AI’s impact on critical thinking, the evolving nature of quality engineering, decision-making, and the future of our careers.
Welcome to the third edition of Linky (see Linky #1 and Linky #2 for past issues). I’ll highlight articles, essays, or books I’ve recently enjoyed, with some commentary on why they caught my attention and why I think they’re worth yours.
This week’s newsletter is packed with thought-provoking insights on quality engineering, critical thinking, and navigating uncertainty. From Adam Grant’s reframing of “I don’t know” as a strength to Duolingo’s AI-driven test automation strategy, I explore how we can foster healthier work environments and make better decisions. Plus, reflections on AI’s impact on our thinking, why returning to the office won’t magically fix broken systems, and the evolving nature of careers in tech. If you want to challenge your perspectives and rethink how you approach quality and collaboration, you’ll enjoy this week’s Linky.
Latest post from the Quality Engineering Newsletter
This week's post summarises my takeaways from a recent panel discussion about quality engineering. It covers how teams are adopting quality engineering, how testing is evolving, and the roles, pitfalls, and organisational support needed for success.
I’d love to hear more QE stories from the trenches, so if you have any to share—big or small—let me know (reply to this email or DM me on Notes or LinkedIn). You could be featured in a future QEN post!
This post discusses one of the key questions for quality engineering: Where do we build quality in? Using one of my past talks, Speed Vs. Quality, as an example, it shows how different techniques can help teams build quality into their products, processes, and people. Most importantly, it shows how they complement and support each other to create better quality outcomes for engineering teams.
Talks
Last week, I gave a talk on Psychological safety – The link between speaking up, complexity and high-performing teams for a local Manchester meet-up, Deliver Sessions. Neil Vass, one of the organisers, has a great write-up. Check it out here.
Adam Grant on I don't know
“I don’t know” is not an admission of ignorance. It’s an expression of intellectual humility. “I was wrong” is not a confession of failure. It’s a display of intellectual integrity. “I don’t understand” is not a sign of stupidity. It’s a catalyst for intellectual curiosity.
Great way to reframe uncertainty to create psychologically safer environments*. If we can normalise these phrases in our teams, it can make it easier for people to speak up. Remember, the highest-performing teams tend to be the ones that identify and correct issues the fastest—not those with the best people in them. Read here
Was Diversity, Equity, and Inclusion (DEI) Hiring Good or Bad for America?
Off-topic but thought-provoking.
In order to have a better conversation about DEI, we’re going to need to stop making sweeping statements. There’s no universal answer to the question of whether a particular company or institution is discriminatory vs. woke.
Taking a look at these three cases, it seems clear that some DEI programs may have been excessive, perhaps even to the point of impacting safety. But it’s also clear that very real hiring biases in race and gender persist. Somewhere in all of this, there are the kernels of a balanced and effective hiring strategy.
For me, this reinforces my view that you need to take the time to understand the system you’re talking about before passing judgment; otherwise, you risk mischaracterising it and possibly making things worse. We need to get more comfortable with saying, "I don’t know enough to comment." See Adam Grant’s quote from earlier on I don't know.Read the DEI post here
Dana Daher on GenAI study into critical thinking
The key insight here is the gradual tradeoff happening in workplaces: as AI tools become more capable and trusted, humans may be unconsciously trading their deep cognitive capabilities for convenience and speed.
While anecdotal, I’ve felt the same when I use these tools. I just feel like I’m thinking less critically about what I’m doing, but they sure can speed you up. One approach I've started using is getting these tools to give me feedback on what I’m doing and seeing what they offer. I've found that really useful for identifying gaps I’ve missed.
The other aspect I’ve noticed is that my skills help me assess whether the LLM’s outputs are sensible. Without that ability, I couldn’t critically evaluate if its outputs align with the context in which they’re to be used. But, to do that effectively, you need a high degree of competency in the area you're asking the LLM to assist with. Read here
If you didn’t care before, a fad won’t fix it now
The glittering promise of staff returning to the office solving all your company woes over profit, lack of growth, innovation, or politics is fool’s gold if you were not already focused heavily on removing the friction in the flow of value to customers, communication, alignment, operating model, and management.
This applies to Agile, Lean, GenAI, OKRs, and even Quality Engineering 😱. If you’ve struggled with these approaches and your culture isn’t collaboratively focused on learning and coaching, then why would returning to the office magically solve all your problems?
That's why one of my principles of QE focuses on creating healthier work environments—to help teams navigate these issues. Read the post here.
How Duolingo Reduced Manual Regression Tests by 70% Using AI Tools
If we instead wrote tests to achieve a broader goal like “Progress through the screens until you see XYZ,” GPT Driver would interpret each screen as presented with its end goal in mind and continue to progress until it could no longer interpret what to do with a screen or had achieved the aforementioned success criteria.
They also noted that this approach has drawbacks, mainly that the automation could miss issues that didn’t stop the test from achieving its outcome. I don’t think this is a replacement for regression testing but rather a new type of testing—one that gets us thinking about the outcomes we’re trying to achieve.
This links back to my automation post: it’s not about how but why you’re automating. If you've answered that question, creating tests with these AI tools is going to be much easier and will help your teams achieve their overall goals. Without a clear ‘why,’ AI-powered test automation risks becoming just noise rather than adding real value. Read Duolingo post here.
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Ethan Mollick on prompt engineering
Half of “prompt engineering” was actually just prompting LLMs to act like Reasoners
Chain-of-thought is what I’d call thinking out loud when you’re trying to work something out. I do this all the time when trying to make sense of a problem, and it’s fascinating that when LLMs do something similar, it significantly improves their output.
Next time you're working through a problem, try using this method—think aloud or write out your reasoning step by step. Does it help? Read Ethans post here
Preempting Problems in a Sociotechnical System | Honeycomb.io
People are necessary for complex technical systems to work together when all of the components weren’t designed to do so. We handle the edge cases. More than that, we can and do invent whole new solutions which make the old artifacts and their edge cases obsolete.
It’s this adaptive capacity that makes organisations work and do the useful things they do. Humans and machines have to work together in a sociotechnical system.
"Whether a decision is good or bad can change based on how you act after the choice is made.
You can't learn all the lessons beforehand. You learn a lot about what you want in a marriage after getting married. You discover what type of career you enjoy after doing a lot of work. And so it goes in nearly every area of life. In many cases, what you wish you knew ahead of time can only be learned after the decision is made.
So there is nothing left but to pay attention to what you like, continue to iterate, and commit to making the most of each opportunity. There is no perfect decision. Good decisions are made right after the fact."
This is why experimentation is so important to engineering teams. Without it, people will pre-judge approaches and either not try or half-arse them. But with an experiment, the success or failure of it takes a back seat, and instead, you're looking to see what you can learn. That learning can help you determine if the decision was good or bad. Read here.
FS Blog on the meaning of work
The meaning you give work determines its difficulty.
A coder working on a passion project works 12 hours straight and calls it energizing. That same programmer, doing maintenance on legacy code they consider meaningless, feels exhausted after 2 hours.
Your relationship with the work shapes its weight more than the work itself.
Framing is key to what meaning we attach to work, and as quality engineers, working with our teams to help better frame the work they’re doing is going to be the difference in whether they put in the effort to do it well or half-baked. Is that refactoring work to reduce tech debt or helping the team achieve their goal of healthier work environments to aid future quality outcomes? Read it here
Finding your second career
Interesting thread on Reddit where a few folks who have been in the industry for 20+ years wonder what’s next. I’m 21 years in and was thinking the same. But about 5 years ago, I came across this article by Peter Drucker called Managing Oneself, and it got me thinking about my second career:
We hear a great deal of talk about the midlife crisis of the executive. It is mostly boredom. At 45, most executives have reached the peak of their business careers, and they know it. After 20 years of doing very much the same kind of work, they are very good at their jobs. But they are not learning or contributing or deriving challenge and satisfaction from the job. And yet they are still likely to face another 20 if not 25 years of work. That is why managing oneself increasingly leads one to begin a second career.
Maybe this newsletter ends up being my second career—or maybe one of many. I don't know if I’ll be speaking and writing for the next twenty years, but I’m excited about the future and where this will take me. I've found framing the future positively like this opens me up to more possibilities and opportunities, whereas a doom-and-gloom approach tends to keep me stuck where I am. Read the thread here.
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That concludes the second Linky. What do you think? Is this format appealing to you? Would you prefer more content like this or something different? Please share your feedback in the comments or reply to this email.
Great content that you shared. I liked this format. Keep up the good work :)