André N. Meyer

10. September 2026

Five Myths about Measuring Developer Experience

This post appeared first in my Substack: flowlabs.substack.com. Sign-up there.

You rolled out AI tools, added a dashboard, and still can’t answer whether any of it made your developers’ lives better or improved outcomes. The gap usually isn’t your tooling. It’s that the things easiest to measure aren’t the things that matter, and more numbers haven’t made the picture any clearer.

These aren’t tooling problems, they’re beliefs about measurement that sound reasonable and quietly steer teams wrong. When I introduced the DevEx Metrics Compass in my last post, I mentioned them only in passing. This post is about the top five myths that teams trip over most often.

28. August 2026

Too Many DevEx Metrics, Too Little Guidance: Introducing the DevEx Metrics Compass

Companies are spending heavily on AI for software development, often $1,000+ per developer per month per the Stanford AI Spend Index, and naturally want to measure the impact of their investment. Is it just more output – commits, PRs, line of codes? Or also better outcomes – increased product quality, organizational effectiveness and Developer Experience (DevEx)?

Yet, measuring AI impact is hard. There are more than 120 DevEx metrics in circulation across 9+ frameworks, including SPACE, DORA, and DX Core 4. In such a fragmented landscape, the challenge is selecting the metrics that fit your goals, context, and measurement maturity, then instrumenting and interpreting them well enough to make meaningful improvements. Our new ACM Queue article explores the problem and presents the DevEx Metrics Compass, our solution for better navigating DevEx metrics.

9. August 2026

PersonalAnalytics for Researchers: Run Your Own Field Study Without Building the Infrastructure

An open-source tool for collecting privacy-friendly computer interaction data paired with self-reports, ready to fork and deploy to your own study participants.

Most researchers who want to study how people actually work, study, and spend their time at their computers face the same challenge early on: before you can collect a single data point, you need software that runs reliably on laptops and computers, logs interaction data without being a keylogger, prompts participants for self-reports, handles participant consent and privacy, and gets the data back to you without exposing sensitive information. Building that from scratch can take months of work before the research even starts.

PersonalAnalytics exists so you don’t have to. Developed and maintained by HASEL, it’s a free, open-source desktop application built specifically for researchers who want participants to run a study client on their own machines and collect computer interaction data in a privacy-protected way. It has been used with over a thousand participants across more than a dozen peer-field studies, field experiments and lab experiments at venues like CHI, CSCW, DIS, and TSE.

22. June 2026

#8: Did we automate the wrong parts? How AI is Reshaping Developers’ Worklives

AI automated what developers love most: coding. Yet collaboration and communication, the parts that consume most of a senior developer’s day, remain largely under supported.

A few months ago, Caleb Mellas posted a question on LinkedIn that stuck with me: “Why did we build AI to do coding, when most of our time as Senior+ engineers is actually spent talking in meetings?”

Our own pre-AI research confirmed this repeatedly: how developers actually spend their time differs from what they wish they could spend it on: What makes a good workday is more coding time and more agency, while meetings, interruptions, and coordination consistently appear as the key sources of friction. So the question Caleb was really asking is not just about time allocation. It is about what developers value in their work.

And yet those are the parts AI has left largely untouched. Developers now report using AI more while simultaneously spending less time on work they find meaningful. A recent article from Brian Houck picked up on this observation: of all the key dimensions of developer experience (and SPACE), collaboration and communication is the one least impacted by AI (and also the least studied one). That made me wonder more broadly: how has AI actually changed the way developers spend their days?

11. June 2026

MindfulPacer in the Oec Magazine: Pace, Don’t Push: Living with Long Covid

Around 450,000 people in Switzerland live with an energy-limiting condition (ELC) such as Long Covid or ME/CFS. For them, a seemingly normal day — doing laundry, cooking, a short walk — can trigger Post-Exertional Malaise: days of near-total exhaustion that follow any overexertion.

28. May 2026

PersonalAnalytics featured in GitHub’s Academic Maintainer Month

This May, GitHub featured PersonalAnalytics in their Maintainer Month spotlight: a series celebrating open-source maintainers in academia. I’m honored to be included.

PersonalAnalytics started as my master’s thesis and grew into a tool I use for my own research: a privacy-focused, locally-running app that helps knowledge workers reflect on their work habits — time spent, productivity, and well-being — without sending any data to the cloud. Over the past decade, it has been used in field studies with around 1,500 participants and contributed to more than a dozen peer-reviewed publications.

4. May 2026

#7: Tokenmaxxing: when a (bad) metric becomes the bill

A bad metric becomes a target, the bill comes due, and we end up paying for it twice – once in dollars, once in degraded service.

At Meta, engineers reportedly burned through 60.2 trillion AI tokens in 30 days. At Anthropic API rates, that’s around $900M. The cause: an internal “Claudeonomics” leaderboard ranking engineers by token consumption, thereby encouraging them to inflate their numbers. Meta isn’t alone – similar AI usage dashboards currently run or did run at Microsoft, Salesforce, Shopify, and others. Meta has since taken the leaderboard down.

Nvidia CEO Jensen Huang, adding fuel to such a bad metric, declared that he’d be “deeply alarmed” if a $500K engineer consumed less than $250K in tokens annually. Not without his own interest in mind – Nvidia sells the GPUs.

We have been here before.

10. April 2026

#6: Cognitive Load. The Hidden Cost of Moving Fast with AI

GenAI coding tools were supposed to free up mental capacity. Offload the boilerplate, the syntax, the repetitive work, and let developers focus on what matters. This goal was only achieved partially.

The question isn’t whether AI affects cognitive load. It’s why and how.

19. March 2026

#5: Trust is a Bottleneck for GenAI Adoption in Software Development

Why nearly half of developers *actively* distrust AI tools — and what the research says actually changes that.

According to the Stack 2025 Overflow Developer Survey with over 33,000 respondents, only 3% highly trust the accuracy of AI tools, while 46% actively distrust them. This isn’t a niche concern, it’s a large part of the developer population, and it not only impacts adoption of AI tools, but how they impact productivity.

2. March 2026

#4: Today’s 30–60% AI Productivity Gains in Software Development are an Illusion

Why productivity claims from consultants and vendors are misleading your leadership. And what early data actually shows.

Developers have been using generative AI in various stages of the SDLC for a few years now, and with mixed results. Yet, claims of 30-60% developer productivity gains continue to circulate, especially in consulting narratives.

These numbers sound compelling, but they rest on shaky grounds: Despite widespread experimentation, only 26% of companies generate tangible value from AI at scale (BCG), and the considered metrics are often flawed.

30. January 2026

MindfulPacer Launch Event

At the end of January, an online event marked the public launch of MindfulPacer, a new app focused on helping people better understand and manage their available energy in everyday life.

The event included a short introduction to the background of the project, a live demonstration of the app, and an open discussion about practical use cases. Around 65 participants joined the session and contributed questions and feedback during an interactive Q&A.

18. November 2025

Newsletter #3: How to Cut Meeting Fatigue and Reduce Multi-Tasking

If your team is multi-tasking during meetings, it’s a sign that the meetings, not the team, needs fixing.

Developers spend 8-10 hours a week, on average, in meetings. While some are useful, many are viewed as unproductive and interrupt long periods of focused work. Good meetings are short, well-prepared, and include only the right people. Scattered or unnecessary meetings disrupt deep work and increase fatigue.

23. October 2025

Newsletter #2: Achieving Focused Work in Hybrid Teams

Regular deep work – or better yet, achieving flow – is one of the hardest things to maintain in hybrid teams. What are key barriers and what can we do?

At FlowLabs, we’ve seen firsthand how even small design changes to workflows can foster team focus.

26. September 2025

Newsletter #1: Get more tasks done, GAI is not the silver bullet, but has potential, and improve DX

This post appeared first on Substack.

I’m starting an essentialist blog to share quick productivity tips, inspiring reads, and useful tools. Today, I cover tools to finally get your tasks done and GenAI in software engineering.

I haven’t sent a newsletter in a while. Why? I kept postponing it due to other priorities and uncertainty about how to set it up. If I commit to it, I want to stick with it – while making sure it’s truly valuable to you, as value your attention.

Inspired by Tim Ferriss’ and Alan Frei’s minimalist, plaintext blog that covers short inspirational reads, tools and ideas, I am finally starting mine. And no, I won’t write the newsletter with ChatGPT, promise! Also, if you have feedback on the format, feel free to email me at andre@flowlabs.com. Thanks for being here!

4. August 2025

Forbes: 3 Ways Managers Can Make Teams More Productive

This post first appeared in Forbes on June 25, 2025 and was written by Prof. Dr. Lauren Howe, one of our research collaborators.

Work today is full of interruptions. The constant influx of emails, notifications, and questions from co-workers can slow individual progress and create a sense of frustration. After all, most people want to feel productive and work toward crossing off important goals from their to-do list.

But productivity is more than a one-person show: it’s a team effort. Recognizing this can help managers to make their teams more productive.

1. Signal Team Member Availability

One solution is to let team members know when they should avoid interrupting people, so that focus time is protected. To accomplish this, a team of researchers at the University of Zurich developed the “FlowLight,” a tool that detects when a worker is focused and signals availability for interactions to others. First, the tool measures whether workers are in a state of “flow,” detecting this through computer activity like keyboard and mouse usage. Then, the tool signals via a lightbulb mounted nearby whether now is a good time for interruptions. Like a traffic light, red signals that a person should not be interrupted as they’re in the midst of deep work, while green means go ahead.

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1. Signal Team Member Availability

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