Author: beaubrownmusic

Sometimes you find yourself standing at the intersection of competing priorities, unclear expectations, and frustrated people. One leader says you’re not surfacing work clearly enough. Another insists the team should be shielded from leadership noise. Meanwhile, the engineers are just trying to get their heads around what’s actually being asked. And you’re the person in the middle—expected to translate, advocate, anticipate, absorb, and somehow not combust.

After a particularly tense conversation recently, a colleague messaged me privately and said:

“You’re really stuck between a few breakdowns in communication and philosophy. It’s tough. Don’t get discouraged. This kind of thing is more common than we admit.”

I felt seen. Not fixed, not rescued—but seen. And that mattered more than I expected.

Because when you’re in a connective role—whether you’re a QA lead, a product manager, an engineering manager, or anyone else whose job is to translate complexity into clarity—you will be caught in the crossfire from time to time. It’s not a sign of failure. It’s a sign that you’re in the place where alignment still needs to happen. That can be exhausting, yes—but it’s also sacred work.

Here’s what I’m learning to do in these moments:

1. Name what’s happening. Not to blame, but to clarify. “We seem to have different assumptions about what success looks like here.” Or, “It sounds like the priorities changed, but not everyone got the memo.”
2. Slow the tempo. When things heat up, it’s tempting to go faster, fix everything, or appease the loudest voice. But wisdom often lives in the quiet, unhurried questions.
3. Seek alignment, not agreement. Agreement can be superficial. Alignment goes deeper—it’s about shared purpose, even when tactics differ.
4. Value the bridge-builders. The people who notice what’s going on beneath the surface, who check in with kindness instead of critique—these are your people. They’re holding up the invisible scaffolding that helps teams stay standing.

I don’t have a clean ending to this story yet. But I do have more clarity about my role: I’m not here to keep everyone happy. I’m here to keep the conversation going, to hold space for misalignment long enough for something better to emerge. That takes patience. It takes people who care. And it takes the occasional reminder—from someone who gets it—that this is hard, and you’re not crazy for finding it so.

So if you find yourself caught in the middle, take heart. It means you’re needed.

And if you’re someone who notices others in that space and reaches out with a word of grace—you’re doing more good than you know.

People sometimes tell me I seem calm.

I get messages like:

“I appreciate how you don’t panic.”

“You’re one of the least reactive people I’ve worked with.”

“You appear to not be freaking out right now, and you probably should be.

And while I’m grateful for the compliment (concern?), I always feel a little strange receiving it—because it’s not the full story.

The truth is, I often don’t feel calm.

Inside, I’m paddling like crazy.

Like that classic image of a duck gliding across the surface of a pond—serene and composed above water, legs churning like mad just below.

The Myth of Calm = Unbothered

In tech—and especially in quality roles—there’s often pressure to appear fast, sharp, always-on.

Urgency gets confused with competence.

Panic with passion.

Noise with leadership.

So when someone shows up quietly—steadily, thoughtfully—it can read as disinterest or disengagement.

But that’s not what’s happening here.

If I seem calm, it’s not because I don’t care.

It’s because I’ve learned the cost of frantic.

I Was Frantic for Years

Before this, I spent years as a pastor.

And I’ll be honest: I operated in a low-level state of panic for a long time.

The needs never stopped. The boundaries blurred. It was a very meaning-full existence with some amazing human beings, but the weight of holding everyone else’s fear and hope and grief was relentless.

So I got good at looking calm while my body and brain ran in high gear.

But the toll showed up—in anxiety, fatigue, and a sense that everything depended on me.

I’m still unlearning that.

Now, in software, I carry some of that same wiring. But I also carry some tools that help me live differently.

What Helps Me Match the Calm I Seem to Have

Here are a few things I lean on when I really am calm—not just appearing to be:

1. Preparation

I prepare. A lot.

Probably more than I need to.

I write things down. I map risks. I over-document. I rehearse conversations in my head.

It’s not about control—it’s about relieving my brain of the need to hold everything at once. Preparation buys me clarity in moments when I don’t have time to think.

2. Systems

I have systems. Not perfect ones, but good enough.

I use checklists. Templates. A few carefully chosen tools that keep track of what’s important.

If a task comes my way, it usually lands somewhere where I’ll see it again.

This keeps the mental paddling to a minimum.

3. Trust

This one’s harder—but I’m working on it.

I trust my team.

I trust that quality doesn’t depend solely on me.

And I trust—at least half the time—that God is at work in the mess, and that I don’t have to carry the whole thing.

This isn’t about blind optimism. It’s about loosening my grip on the illusion of total control.

4. Recovery

I’ve learned to take walks. To go quiet. To log off.

I still get wound tight, but now I try to notice it sooner.

Because calm isn’t just something I give to the team—it’s something I need for myself, too.

5. Knowing What Can Wait

Unless it’s a life-threatening or business-critical bug, it can wait until morning.

Writing it up, getting it prioritized, coordinating the fix—those are tomorrow problems.

I’ve spent too many nights treating every bump in the road like an emergency. These days, I’m learning to tell the difference.

That boundary helps me keep a clearer head. It protects my energy for the moments that do matter. And it reminds me that sometimes, rest is the most responsible thing I can do.

Calm Isn’t a Performance

If you’re in a testing or quality role and you feel like you’re drowning quietly while others assume you’re fine—just know you’re not alone.

That duck image? It’s real. And it’s okay.

But I’m also learning that I don’t want to live underwater. I want the calm to be real, not just performed. And when it is, it’s usually because I’ve done the behind-the-scenes work to create space and margin.

For preparation.

For trust.

For faith.

For breathing.

And for remembering this:

Calm isn’t about ignoring problems.

It’s about knowing which ones actually need your full attention right now—and which ones can wait a few hours while you recover your soul.

Calm isn’t the absence of motion.

It’s the presence of clarity, enough structure, and a gentler pace.

And that’s something worth working toward.

—

Beau Brown

Testing in the real world: messy, human, worth it.

I’ve only recently started digging deeper into the work of W. Edwards Deming, and the deeper I go, the more I find myself nodding along.

Though he made his name in manufacturing and management decades ago, I keep stumbling on his ideas in unexpected corners of my own work—particularly in software quality, and especially now, in an AI-heavy, fully remote environment.

There’s something refreshing about how clearly he speaks into problems we’re still facing:

Short-term thinking. Blame-shifting. Siloed teams. Sloppy systems. Metrics that create the illusion of control.

He may not have written a line of code in his life, but I think Deming still has a lot to say to testers, developers, and engineering teams in 2025.

What Deming Believed (and Why It Still Hits)

If you’re new to Deming, like I am, here’s a quick overview.

Deming was a statistician and systems thinker who helped transform industrial production in postwar Japan and later brought his ideas back to the U.S. He insisted that quality isn’t the result of catching defects—it’s the result of building better systems that prevent defects in the first place.

Some of his most compelling principles include:

• Drive out fear
• Cease dependence on inspection
• Break down barriers between departments
• Improve constantly and forever the system of production and service
• Institute leadership (not just supervision)

At the center of it all is this:

“A bad system will beat a good person every time.”

It’s a sobering idea—but also a freeing one. Because it shifts our focus from blame to design. From people to process. From fault-finding to system-shaping.

And honestly? That shift is just as relevant today as it was 50 years ago.

The PDSA Cycle: A Rhythm for Real Improvement

One of the most useful tools Deming left us is the PDSA cycle:

Plan → Do → Study → Act

This is not a checklist—it’s a mindset. A rhythm of inquiry and iteration that invites teams to slow down just enough to learn from what they’re building.

In software, I see PDSA show up in subtle but powerful ways:

• Plan – Define your hypothesis. What are we trying to change or improve? What assumptions are we making? What’s the risk?
• Do – Build it. Test it. Try it out. But keep your ears and eyes open.
• Study – Did it behave as expected? Did it solve the problem? What unintended consequences showed up?
• Act – Decide what to carry forward, what to revise, and what to scrap. Then start again.

This framework pairs beautifully with agile and lean development—but only when teams have the discipline to actually pause and study before reacting or shipping again.

PDSA is one way to build learning into the bones of your software process, instead of treating it like a fire drill.

Deming for Remote Teams

One of Deming’s core principles was this:

Break down barriers between departments.

In his context, that meant getting marketing, engineering, design, production, and leadership to stop working in silos and start seeing themselves as part of one system with a shared aim: delivering quality.

That’s still the work today—but the barriers have changed.

We’re not just talking about different floors in the same building anymore.

Now those “departments” might be:

• In different time zones
• Operating across cultural norms
• Reporting to different companies (contractors, vendors, offshore teams)
• Speaking different native languages
• Working on different platforms or backlogs

And instead of walls and office doors, the barriers are things like:

• Infrequent communication
• Misaligned definitions of “done”
• Slower feedback loops
• Friction in scheduling real-time collaboration
• Unspoken assumptions that don’t translate across cultural lines

The danger isn’t just miscommunication—it’s fragmentation.

Quality suffers when each group optimizes for its own deliverables instead of aligning around a shared vision of what good looks like.

Deming’s insight still applies, but it takes on new urgency:

Breaking down barriers now means building systems of intentional, structured connection across distance, time, and difference.

That might look like:

• Writing clear, async-friendly test plans and release notes
• Bringing QA into product planning as early as possible, no matter where they’re located
• Normalizing documentation not as overhead, but as a bridge between minds
• Creating shared rituals across time zones (e.g., end-of-day handoff notes)
• Building cultural awareness into the way we give and receive feedback

In other words: we have to build relational tissue into remote systems on purpose.

Because if we don’t, the barriers Deming warned about don’t just stay up—they harden into assumptions, rework, and mutual distrust.

And in the end, nobody owns the quality. Everyone’s just shipping their part.

A Few Deming-Inspired Practices I’m Trying

As someone still learning, I’m not here to teach Deming—I’m here to reflect on where his thought is taking me. Here are a few things I’ve started trying (or trying again) with his voice in mind:

1. Pause to ask, “What system produced this problem?” Not just, Who made a mistake? But what pressures or gaps made that mistake easy to make or hard to notice?
2. Push for clarity, not just coverage. Whether it’s test cases or team charters, I’m learning that well-defined boundaries and goals do more for quality than vague velocity ever will.
3. Use AI to reduce toil, not insight. I’m experimenting with AI to handle repetitive work—but keeping the human part (judgment, pattern recognition, nuance) where it belongs.
4. Revisit our metrics. Are we measuring what matters? Or what’s easiest to measure?
5. Name risks out loud—even if the system seems “done.” Because being a steward of quality means being willing to disrupt the comfort of “almost good enough.”
6. Try PDSA on purpose. Build time into the week—not just for building or testing, but for studying what worked and what didn’t, and making small changes accordingly.

I’m early in my journey with Deming. But already, I see how his work offers something deeper than process diagrams or test automation patterns.

It’s a mindset.

It’s a posture of care, clarity, and responsibility—for people, for systems, and for the long-term health of what we’re building together.

And I think that’s something we still need. Maybe now more than ever.

—

Beau Brown

Testing in the real world: messy, human, worth it.

Photo source: https://deming.org/w-edwards-deming-photo-gallery/

Sometimes I sit back and wonder: are we solving real problems with AI—or are we just solving expensive ones?

Lately, I’ve seen AI used in ways that feel like trying to outrun the limits of being human.

Take longevity science. A field once rooted in slow observation and human care is now buzzing with machine learning models, digital twins, and predictive “aging clocks.” In an April 2025 Forbes article by Tomoko Yokoi, the author describes how researchers are using AI to simulate decades of biological aging in silico—testing interventions without waiting for time to pass. What once took years in mice or decades in humans can now be compressed into hours of computation.

It’s impressive. It’s groundbreaking. It’s possibly even life-saving.

But part of me wonders: was it a problem that it took time?

Was it truly a flaw that understanding aging required patient, embodied presence across a human lifespan? Or was it simply expensive—and therefore labeled inefficient in a world that values scalability above all?

The Discomfort of Limits

This question has been on my mind a lot lately, not just in the context of medicine, but in tech and software as well. I’ve seen AI tools that generate code and write test plans, promising to accelerate everything from development to deployment. And they do. Until something breaks—and no one remembers how the system works.

The deeper assumption seems to be: if it takes a team of skilled humans a long time to build something, that’s a problem. But is it?

We’re increasingly told that speed is always good, and human limits are always bad. But slowness isn’t always a bug. Neither is mortality.

Sometimes the fact that things take time—that they require wisdom, conversation, conflict, or care—isn’t inefficiency. It’s reality. It’s part of what makes the work meaningful.

Not All Progress is Healing

I’m not against progress. And I’m certainly not against tools that help people live longer, healthier lives. There are parts of the longevity movement that feel hopeful—especially when they’re focused on accessibility, dignity, and care.

But when the conversation shifts from “How can we help people age well?” to “How can we prevent aging altogether?”—I start to get uneasy. Not because I fear the future, but because I care deeply about what we’re willing to call “broken” in the first place.

In testing, in engineering, in caregiving—some of the best work happens in the friction. In the waiting. In the debugging. In the time it takes to really see what’s going on.

If we train AI to skip all that, what else are we skipping?

A Different Kind of Intelligence

I use AI almost daily. It helps me generate test cases, write bug reports, explore possibilities I hadn’t considered. I think it’s a gift—when used with humility. But I don’t want to build a world where human slowness, uncertainty, or mortality are treated like defects to be patched.

What if intelligence isn’t just about speed or predictive power?

What if intelligence includes acceptance, discernment, even grief?

What if a good system isn’t just one that runs smoothly—but one that allows space for the kind of deep, messy, unscalable wisdom only humans can offer?

Staying with the Trouble

To my fellow testers, engineers, and builders: I know it’s tempting to want everything frictionless. But some things are worth the trouble.

Slowness is not failure. Collaboration is not inefficiency. Limits are not bugs.

Sometimes what the world needs most is a few good humans, working together—asking questions that AI can’t quite answer, holding space for the kind of complexity that can’t be summarized in a model.

Let’s build tools that help us become more human, not less.

Let’s not be afraid to ask whether the problem we’re solving… was ever really a problem at all.

Reference:

Yokoi, Tomoko. “How AI Is Rewriting The Future Of Aging.” Forbes, April 26, 2025. https://www.forbes.com/sites/tomokoyokoi/2025/04/26/how-ai-is-rewriting-the-future-of-aging

One of the best ways I’ve found to cut friction out of testing and release work is by embedding helpful AI prompts into the tools I already use every day. Recently, inspired by this post from Emily Maxie, I started experimenting with .cursorrules in Cursor, and honestly—it’s been kind of a game-changer.

If you haven’t used it, .cursorrules is just a file you drop into your repo. It lets you define reusable AI prompts that anyone on the team can run by typing something like:

@bugreport - login fails with 2FA enabled

Cursor picks up the @bugreport tag, finds the prompt you’ve defined in .cursorrules, and sends it along with the input to the AI. You get back a clean, consistent bug report, written in seconds—right in your IDE.

It’s simple. But surprisingly powerful.

Why this matters

If you’ve ever had to:

• write up a bug quickly but thoroughly,
• explain what changed in a release,
• spin up a test plan,
• draft internal documentation,
• or summarize a chaotic sprint into a few lines of status—

…then you know how much of your energy gets spent just figuring out how to say what needs to be said.

This system helps offload that overhead. It keeps you in flow. And it helps others do better work, faster.

A sample QA prompt library

Here’s a trimmed version of what I’m using in my .cursorrules file right now:

@bugreport - Bug Report Writing  
You are an expert QA analyst. Write a clear, concise bug report for the following issue. Include reproduction steps, expected behavior, actual behavior, and environment info.

@codereview - Code Review Checklist Generation  
You are an expert engineer. Create a checklist for code review based on the following pull request description. Include functionality, edge cases, tests, and risk assessment.

@documentation - Documentation Drafting  
You are a seasoned technical writer. Draft internal documentation for [tool/process]. Include purpose, setup steps, inputs/outputs, common issues, and troubleshooting tips.

@testplan - Exploratory Testing Plans  
You are a leading exploratory tester. Write a session-based test charter for [feature]. Include areas of focus, risks, and guiding questions.

@releasenotes - Release Note Drafting  
You know our customer base well. Turn the following merged PRs into clear, user-facing release notes, grouped by feature area.

@testcases - Test Case Generation  
You are a detail-oriented tester. Generate structured test cases with edge and negative paths. Use this format: Title, Preconditions, Divider, Steps, Divider, Expected Results.

@slackupdate - Slack/Email Summaries  
You are a clear communicator. Write a Slack update summarizing the status of [feature/release] in under 5 sentences, including progress, blockers, and ETA.

@weeklydigest - Weekly Digest  
You are writing to cross-functional stakeholders. Summarize QA status based on the following tickets and PRs. Highlight blockers, risks, and notable changes.

Where this fits in a real team

Let’s say it’s mid-sprint. A bug gets logged in Slack with three vague sentences. You’re deep in mobile automation but someone’s asking for release notes. The PM wants a testing summary for leadership by EOD.

With .cursorrules, anyone can:

• type a prompt tag and a few words of context,
• get back structured, helpful output,
• and use it as a solid starting point—fast.

It’s not magic. You still need judgment and review. But it shifts the energy from invention to refinement—and that’s a big win on a busy team.

Getting started

Here’s how to set it up:

1. Add a .cursorrules file at the root of your repo.
2. Define your prompt templates using @tags.
3. Save and restart Cursor.
4. Run a command like:

@testplan - checkout flow redesign

Start small. Add prompts as real needs come up. Let different roles contribute their own libraries—QA, docs, support, devs. You’ll end up with a living knowledge base that’s actually useful.

Bonus: connect it to release flow

With a little extra wiring (via n8n, GitHub Actions, or whatever you’re using), this system can also:

• Auto-generate release notes from merged PRs
• Summarize test run output
• Draft weekly QA digests
• Kick off post-mortem templates after incidents

The real power here is in shared structure.

It’s like team-wide muscle memory—captured in a few reusable prompts.

If you try it out, I’d love to hear how it works for you. I’m always on the lookout for ways to make QA and release work a little more humane—and a little less exhausting.

—

Beau Brown

Testing in the real world: messy, human, worth it.