How I Find Problems to Solve as a Staff Engineer

Mike's Notes

Discovered this in a recent Arc Notes Weekly. My method as well.

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Last Updated

09/10/2026

How I Find Problems to Solve as a Staff Engineer

By: Lalit Maganti
Lalit Maganti: 25/07/2026

Lalit Maganti: I'm a founding engineer of Perfetto, a tool for understanding where time goes in software. I've been building it at Google since 2017, helping engineers track down performance problems in Android, Chrome, and beyond. I share what I learn in articles and notes.

...

Discussed on Hacker News and lobste.rs.

Note: this post was revised after publishing for increased clarity, based on reader feedback.

...

“How do you find problems worth working on?” a senior engineer I mentor asked me recently. He’s trying to make the jump to staff engineer and realized that the role isn’t just about doing the work he’s assigned. He also needs to get involved in figuring out what his team and org should be building.

Someone else had suggested blocking out time in his calendar to think about the bigger picture. He’d tried that, but hadn’t found it productive, so he asked if I had any alternatives.

I told him I rarely find good problems by staring at a blank page and trying to “think strategically.” Instead, I act like a sponge. I listen to the stream of day-to-day noise, absorb the problems people are having and let them sit in the back of my mind. Over time, some fade away while connections begin to appear between others that initially seemed unrelated. Eventually, I start to see what’s really slowing people down and what my team or I can do about it.

I’ve worked with many engineers who’ve never really tried this. They wait for managers or leads to identify opportunities, then demonstrate their value by solving the hardest assigned problems. That can absolutely lead to promotion. But the projects that have made the biggest impression in my career were the ones where I found and solved an important problem my leaders did not yet realize existed.

One caveat: my experience comes mainly from working on infrastructure and developer tools at large companies, on teams where engineers have a lot of bottom-up autonomy to influence their roadmaps. In a more top-down environment, there may simply be less room to work this way.

Absorb problems, not requests

People love talking about the problems they are facing: in meetings, chat threads, presentations and email. They explain why their work is hard, complain about what slows them down and describe what they wish they could do.

When something overlaps with my area, I start pulling on the thread. I might ask, “If X existed, would it solve your problem?” or point them at an existing feature in a product I own and ask how much of their use case it covers.

Users often ask for a particular solution instead of explaining their root issue. Rather than taking the request at face value, I keep digging until I understand what they are trying to accomplish and why existing products do not work for them.

As a natural introvert, this sort of ambient listening works particularly well for me. I don’t need to fill my calendar with speculative meetings just to find ideas; there is already an enormous amount of useful information flowing around me during a normal week.

When a problem seems worth exploring, though, I become more active; I need to see how it affects the team’s day-to-day work. I’ll sit with them as they walk me through their workflows and the bugs they’re investigating. When I can, I’ll try working through some of those bugs myself. Seeing the problem firsthand makes it easier to separate what the team actually needs from the solution they asked for.

I also seek out people who see more of the organization than I do: those who own critical systems, work across several teams or have particularly deep insight into the work downstream of my team. I’ll arrange a 1:1 or coffee chat and ask about interesting problems they’ve come across. They may have already seen the same issue in several places and started connecting the dots, giving me a head start on patterns I might otherwise have taken much longer to notice.

Let problems accumulate

Several times, I’ve been burned by moving too fast. I became excited by a request from a vocal team, built the feature and watched them barely use it. Their priorities had changed, or the request had come from a one-off investigation that no longer mattered. How eager a team was in that moment wasn’t the same as how important the feature was relative to everything else my product needed to support. By hyperfocusing on their request, I lost sight of the bigger picture.

That taught me to let potential problems pile up. Listening the way I do leaves me with far more of them than I could possibly solve, and not all deserve action. Most don’t need to turn into projects the first time I hear about them; waiting can be a superpower.

Waiting means the same problem might pop up independently in different teams, making it a higher priority to solve. Or problems that look different on the surface might turn out to have the same shape, so I can address several use cases in one shot. Or, as I’ve learned painfully, the requesting team didn’t even care that much in the first place.

Instead, I make a mental note and revisit the problem if it comes up again. Other engineers I know write this sort of thing down more systematically. The mechanism is a personal choice: everyone has to figure out what works for them. What matters is keeping unresolved problems around long enough for more evidence to accumulate.

Find the common shape

Waiting helps me collect evidence, but that alone doesn’t tell me what to build. I still need to work out whether the problems I’ve retained are genuinely related and what, if anything, could address them together.

Perfetto, the performance debugging tool I work on, is a good example. It displays recordings of system activity on a timeline made up of rows called “tracks.” Over a couple of years, teams kept asking for small, specific additions to the UI. One wanted a command to keep their preferred tracks pinned to the top of the screen; the next team wanted the same, but for a completely different set of tracks. Others wanted Perfetto to open already zoomed in on a particular part of a recording, or to show a custom aggregation tuned to what they cared about. A few had stopped waiting for us and built elaborate workarounds with bookmarklets.[1]

By the time enough of these had piled up, my head was the usual tangle: the requests themselves, the constraints on each and a handful of half-formed solutions. I’ve learned not to force a solution by just sitting at a desk and thinking. Instead, my best untangling happens on long, aimless walks around London, where connections come more easily when I’m not trying to force them.

What I eventually realized was that none of these teams really wanted the specific feature they’d asked for. Each wanted to personalize Perfetto for their own workflow without imposing their choices on everyone else. The underlying need wasn’t any one feature but rather the ability to extend the UI. When a connection like that finally clicks, it’s one of the best feelings in the job: several awkward requests collapse into a single idea, and possibilities open up that none of them hinted at on their own.

That feeling, though, is exactly when I have to be careful, because a common shape is only a hypothesis and elegance is not evidence. When it happened with extending the UI it turned out to be real, but I’ve been fooled before.

In another recent case I was convinced that building a transparent caching system for querying Perfetto traces would solve issues with sharing large traces and repeated queries. It was only as I wrote the RFC and built a prototype that I realized the elegance was a lie: the two problems wanted genuinely different solutions. I reluctantly split the design in two, both halves of which have since shipped.[2]

Pressure-test before building

You’d think this would be the moment I start building, but it usually isn’t. How far I go depends on how sure I am that the idea works and that people actually want it.

If something is useful and low-risk enough, I act straight away: I send the change and let my manager know. When I’m unsure whether an idea will work or how much effort it will take, I build a throwaway prototype instead; it exposes the failure points and gives me something concrete for others to react to. And when an idea is big but I’m convinced by it, I commit to the full effort: weeks or months of work and the hard yards of building support across other engineers and teams.

Through all of it, I’m not only trying to convince other people; I’m also trying to convince myself. Sometimes the honest answer is to stop: if people don’t see the value I do, or we hit a major technical wall, I’d rather drop the idea now than build something no one uses or that becomes a maintenance nightmare. And sometimes it holds up but the timing is wrong, so I park it, ready to spring into action the day it becomes an org priority.

When an idea does hold up, I don’t necessarily need to be the person who builds it. I might implement it, someone else on my team might, or it might change what the org focuses on. Finding and shaping the right problem can have an impact even when I don’t own the implementation.

The Perfetto extensions idea was worth that full effort. We were already building plugins to modularize the UI, but they weren’t enough: teams had to open source all their plugin code, which wasn’t an option for many internal use cases. So before building anything new, I took the problem and my proposal to my manager, teammates and the client teams. I ended up writing two RFCs, having several 1:1s and giving a couple of talks, refining it as the feedback came in.

In the end, I designed and implemented macros as “lightweight extensions”: a way to automate actions in the UI without writing a plugin. Extension servers took the idea further by letting teams share their macros.

Instead of implementing every requested feature ourselves, we gave teams ways to adapt Perfetto to their own needs. Dozens of teams inside Google now use macros and extension servers, and several other companies use extension servers internally too.

Solving useful problems helps me find the next one

The more often I go through this process, the easier it becomes. When I show genuine interest in someone’s problem, ask useful questions or help solve it, they remember. They start coming to me earlier and bring me into conversations with other people facing related issues.

That gives me a wider view of what is happening across the organization, making it easier to spot patterns and build things people actually need. Solving one of those problems brings me into more conversations, and the loop continues.

Those successes build the kind of trust that comes from long-term stewardship. Early on, I had to turn many of these ideas into something real myself to prove that my judgment was sound. Over time, my manager and org gave more weight to my assessment of what mattered. That allowed me to influence the roadmap without needing to own every project.

This differs from the idea that becoming a staff engineer means replacing technical work with meetings and coordination. For me, conversations are inputs into what I build, not the end result.

Conclusion

That is what I wanted my mentee to understand: finding problems worth solving isn’t separate from the rest of the job. It comes from staying engaged with people’s work long enough to see what no single request can show you.

References

  1. These workarounds used bookmarklets to run JavaScript against Perfetto’s internal UI APIs.
  2. The original proposal was to use a transparent cache for repeated queries and faster reopening of large traces. As I worked through it, I realized repeated queries were better served by keeping sessions warm in memory, whereas reopening was better served by explicitly exporting a trace into a format designed to load quickly. A transparent disk cache could also retain multi-gigabyte files without the user realizing and would need a new system to manage their lifetime. The proposal was ultimately replaced by warm sessions and streaming table export. 

The Tech Talent We’re Not Looking For Because It’s Already Here

Mike's Notes

Dorenda's dyslexic brother, John Britten, made the carbon-fibre elbow crutches that enabled my Tracy to complete the NZ Coast-to-Coast. The only paraplegic to ever do so.

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Last Updated

08/10/2026

The Tech Talent We’re Not Looking For Because It’s Already Here

By: Dorenda Britten
Tech New Zealand: 05/10/2026

Dorenda Britten: Co-Founder and Co-Developer, Peppr | Co-Founder, Unlock Innovation.

...

Dyslexia Awareness Week, 6–12 October 2026

As New Zealand’s technology sector grapples with AI, rapidly changing skills requirements and the challenge of finding the people it needs to innovate, there is another question worth asking: do we actually know what talent we already have?

For Dyslexia Awareness Week, perhaps it is time to look at dyslexia through a different lens. Dyslexia is usually discussed in terms of reading, writing and workplace accommodations. These things matter. But focusing only on the difficulties can mean overlooking something potentially very valuable to New Zealand’s technology sector: a different way of thinking.

Because 1 in 5 people are dyslexic whether they know it or not, the chances are you already employ people with hidden talents. Research by EY and Made By Dyslexia identified a range of capabilities associated with dyslexic thinking, including creativity, visualisation, cognitive flexibility, logical reasoning, systems analysis, complex problem solving and empathy.

Significantly for the technology sector, the research also makes connections with areas including programming, technology and user-experience design and customer relations.

Not every dyslexic person will have the same strengths. But the broader picture raises an interesting question for employers.

Are we recognising these capabilities when they are already sitting inside our teams and peppered through your job application processes?

AI makes the question more urgent. The World Economic Forum’s Future of Jobs Report 2025 found that analytical thinking remains the number-one core skill identified by employers. Creative thinking is fourth, while systems thinking is also among the core capabilities employers identify as important.

Looking towards 2030, the report expects AI and big data, analytical thinking, creative thinking, resilience and flexibility, technological literacy, curiosity, systems thinking and empathy all to continue increasing in importance.

There is an interesting convergence here. As organisations invest heavily in AI some of the human capabilities they increasingly need, alongside that technology, are also capabilities frequently associated with dyslexic thinking. That should matter to the technology sector. AI can process enormous quantities of information and increasingly undertake routine and repeatable work. But organisations still need people who can ask different questions, recognise patterns, imagine alternatives, make unexpected connections and see a problem from another perspective.

What did we find in New Zealand? Our interest in this began through work undertaken by Unlock Innovation (now Peppr) in New Zealand’s technology sector. Our 2022 Tech Skills Pilot, supported by MBIE, involved New Zealand technology businesses and revealed a significant presence of people who identified as dyslexic, including people whose dyslexia had not necessarily been visible or declared within their workplace. 

It prompted a much bigger question for us. If organisations don’t know who their different thinkers are, how can they intentionally include their thinking when they are trying to innovate?

The answer is not simply to identify dyslexic employees or put another label on People. It is to examine the systems around them. How do we run meetings? How do we communicate ideas? Who gets heard? How do we recruit and promote people? How do we form innovation teams? And do our processes favour people who communicate and process information in conventional ways?

An organisation can employ brilliant different thinkers and still unintentionally design them out of the innovation process.

From awareness to advantage

This is why we believe the conversation around dyslexia needs to move beyond awareness alone. Awareness is important, but recognition and inclusion are where organisations begin to see value. This work is now continuing with the University of Canterbury to further investigate the relationship between dyslexia, different thinking and innovation. We want to better understand what happens when organisations deliberately create conditions in which different thinkers can contribute their capabilities.

For New Zealand’s technology sector, this could become increasingly important.

The conversation about our future workforce often begins with a skills shortage: Where will we find the people with the capabilities we need?

During Dyslexia Awareness Week, we’d like technology leaders to consider a different starting point: what if some of the talent you are looking for is already in your organisation, but you haven’t yet learned how to see it? This may be one of New Zealand’s most overlooked opportunities for innovation.

Skeptic vs. 'Doomer': How Scared Should We be of Rogue AI?

Mike's Notes

I agree with Gary Marcus.

The insight I got from watching this interview is that hallucinations in LLM results are generally statistical because they rely on probabilistic vectors. But statistics apply to populations, not individuals. Everything about an individual is a fact.

Resources

References

  • Algebraic Mind, The: Integrating Connectionism and Cognitive Science, by Gary Marcus. 2001.

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Last Updated

07/10/2026

Sceptic vs. 'Doomer': How Scared Should We Be of Rogue AI?

By: Jonathan Kay
Quillette: 02/10/2026

 Jonathan Kay: Mike invented and designed Pipi and founded Ajabbi.

Gary Marcus: Cognitive scientist Gary Marcus, author of The Algebraic Mind and Taming Silicon Valley and writer of the Substack Marcus on AI

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Quillette podcast host Jonathan Kay interviews AI expert Gary Marcus about the possibility that a rogue artificial intelligence will turn us all into paper clips.

...

This week, I’m going to tackle a big and important subject—artificial intelligence and the risks it could pose to society.

I’m going to be honest here. I’ve been putting this subject off because it’s so big and so technical that I felt intimidated by it.

But the subject is increasingly dominating the news, and I’m a journalist, so it’s time to face the music.

Maybe you’ve been putting off educating yourself about this subject for the same reason.

If so, we’re in this together. So let’s dive in.

The good news is that my guest this week, Gary Marcus, is a real expert—someone who’s been theorising and writing about AI—including its dangers—for more than a quarter century. He’s a well-known American psychologist, cognitive scientist, and book author, who also runs the popular AI blog, Marcus on AI.

But before I run that interview, which I recorded last week, let’s review some key terms, so that the discussion you’re about to hear makes the maximum amount of sense.

First of all, and I’m grossly simplifying here, AI comes in two big categories.

The first, which you’ll hear Gary often refer to as “Symbolic AI,” basically operates like a very big and very sophisticated version of a conventional computer program, with lots of rules, and decision trees, and if-then statements—just like you remember from middle-school computer class, except much, much bigger.

This kind of AI is great for, say, creating a database, or setting out rigid decision trees for an airline pilot to follow. But it’s really bad at a lot of basic tasks that even small children can do, like, say distinguish a dog from a cat, or a plate from a frisbee.

The second kind of AI, which you’ll hear us refer to as “neural networks,” or machine learning, or deep learning, which are overlapping concepts, is completely different.

This kind of AI, which became ascendant in the early 2010s, isn’t created with human instructions. Rather, these systems teach themselves by reference to massive troves of pre-existing data—including through a technical process you’ll hear me refer to as backpropagation.

By way of example, let’s say you want to create a neural network to identify a cat. Instead of writing thousands of lines of code relating to fur and whiskers and meowing noises, you just show the neural network millions of pictures of cats and millions of pictures of things that aren’t cats; and then instruct the computer to teach itself the difference between the two by a computational process of trial and error.

The idea here is that the neural network will start with a completely useless algorithm, then compare the results to the data set, and then try again with tweaked parameters, teaching itself—though a process of machine learning—to refine its self-created algorithms so that it better aligns with the data you’ve already given it.

The humans aren’t giving the computer rules about what a cat is or isn’t. They’re just providing the data trove and then hitting the go button and the computer teaches itself.

These systems are easy to scale, by supplying them with more data and computing power—which is one of their big selling points to investors.

But the problem is that the iterative, self-constructed nature of these mechanisms means they aren’t grounded by predictable rules, and so can sometimes hallucinate artificial realities—a problem that Gary predicted in a 2001 book. Even something as basic as a simple database of public figures, the sort of thing human-resources department at large companies were creating on 1970s-era mainframes, is alien to the neural network information architecture.

As a result, purely neural-network-driven AI systems aren’t very good at following user-supplied rules, even fairly basic ones, like, “write me an academic paper with citations, but make sure those citations aren’t made up,” or “find and download some information for me, but please don’t hack into any proprietary servers in the process,” or, more speculatively, “make a trillion paper clips for me, but don’t make any of them from the bodies of humans.”

I know that last one sounds weird, but I promise it will make sense when you listen to the podcast,

All of this has set off a movement known as the AI doomers, who don’t just think AI will take our jobs and access our banking information—but that it will cause the extinction of humanity itself, by launching nuclear weapons, or creating and unleashing some kind of super powerful bioweapon.

While my guest is not a doomer, he does call himself a sceptic, especially when it comes to what he regards as the allegedly irresponsible and dangerous business practices of certain companies—OpenAI, in particular.

Unlike U.S. senator Bernie Sanders, he doesn’t want to ban hyper-advanced AI systems that greatly surpass human capabilities (which is sometimes called superintelligence). But Gary also doesn’t approve of Donald Trump’s apparently laissez-faire attitude, either.

And he’d like to see a more balanced approach from government, an approach that holds AI companies to account if they don’t place appropriate safeguards on their products, and align those products with human values.

And what would those safeguards look like?

Well, this gets us back into the technical sphere. Gary has long advocated something called neurosymbolic AI, which combines the extraordinary power of neural networks with some of the safeguarding and fact-checking features that can be implemented through the rules-based, predictably algorithmic features of so-called “classic AI”.

It’s all very complicated, I know, but I’m confident I have the guest who can walk us through it.

Please enjoy my interview with Gary Marcus, AI expert extraordinaire.



Stealth Mode continues

Mike's Notes

After a recent trip to Christchurch, NZ, recruiting for the mission is underway, and Ajabbi and Pipi will stay in stealth mode until they are strong enough to rapidly scale without limits.

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Last Updated

06/10/2026

Stealth Mode continues

By: Mike Peters
On a Sandy Beach: 25/09/2026

Mike Peters: Mike invented and designed Pipi and founded Ajabbi.

...

4-18 September 2026 Christchurch visit

While on holiday in Christchurch, opportunities and openness were confirmed by visiting the following;

  • KiwiSaaS Meetup
  • KiwiSaaS Leaders Breakfast
  • IT Curry Meetup
  • AI Meetup
  • Westpac Smarts
  • WordPress Meetup
  • BDI
  • Interested people
  • ChristchurchNZ
  • Business Canterbury
  • NZTE
  • Ministry of Awesome
  • Epic Innovation
  • Collaboration with University of Canterbury Faculty of Engineering
  • Libraries, legal, commercial office, printing, banking, transport options, data centre, and electrical services

The CBD is compact, with good public transport that makes walking easy and simplifies collaboration and access to resources. Much of the built infrastructure is new, and Christchurch has become a magnet for technical talent.

One thing Mike learned was the importance of being surrounded by good people, and that getting the culture right and building a great team will take time. This is a critical foundation for scaling.

A big hard problem

Global IT waste from project failure exceeds $100 billion annually (IEEE).

AI slop will only add to the problem.

Why Ajabbi

Ajabbi exists to reduce failure in massive enterprise systems for socially useful critical infrastructure. Ajabbi is a bootstrapped, mission-led organisation with no investors. All future profits will go to a yet-to-be-established non-profit foundation to fund open research, science communication, and support for the Pipi user community.

Origins

Pipi originated as a national platform for community-led ecological restoration in New Zealand, built and operated by NZERN. Mike founded NZERN and served as National President and software architect. The NZ government funded Pipi version 4 and later valued it at $3M, with a great team, 300K lines of code, and a 3-rack data centre. A bad business model, the Christchurch earthquakes, and a change of government funding ended it all.

In Invercargill in 2017, Mike rebuilt Pipi from memory as version 6, then refactored and merged it with open-source biological cell simulation software, eventually making it a self-managing complex adaptive system (CAS). The intent was to make Pipi self-funding by being useful in multiple domains, solving the business model problem. Invercargill was a great place to do the initial work because of the city's obvious critical infrastructure problems.

10 years later, at version 9, it has become a non-LLM SaaS platform that runs 3,000K lines of code on CPU, is constrained by the laws of physics, and can self-manage, learn, evolve, and replicate; it is designed to host large, complex systems and gives them the same properties and real-world model context. It has a large closed core, and Pipi can automatically generate many open-source SaaS applications from ontologies and configuration.

Pipi also has many other unique capabilities to explore, offering other possible income streams to support the mission.

Stealth mode

Ajabbi and Pipi operate in stealth mode, with 99% of the system hidden. Mike keeps research notes on the build-out for his own use in an engineering blog, "On a Sandy Beach". Still, a community has grown, with a waitlist. Blog traffic is growing rapidly. Initial community testing with a closed beta has been roughly successful. It's good enough to move forward confidently, solving issues as you go. Because "an exit" isn't the goal, the biggest threat is getting swamped by rapid growth, so stay in stealth mode.

Now prove it

Pipi makes a big claim: Developers will need to prove the black box works by testing, using, and then telling others. With a long runway from upfront usage fees, it's cash-flow positive from day one and covers its very low costs. It's ready to go live, staying 100% reliable, secure, and safe while slashing customer costs. It is a SaaS platform for developers to build reliable, self-managing enterprise systems using no-code, plugins and API.

Slow and steady, community-driven, it can expand to many cloud platforms, human languages, and writing systems, with a highly adaptive, accessible UI for everyone. Eventually, the enterprise layer can be donated to the Cloud Native Foundation.

Next steps

This is a rough sketch that will no doubt change along the way.

  1. Carefully complete setup of legal, tax, banking, etc.
  2. Lead with developer docs, training videos, recorded live demos, polished Ajabbi websites, recorded slide talks, white papers, newsletters, logo, business cards, and recruiting for the mission.
  3. Stay in stealth mode.
  4. Move Ajabbi onto an Ajabbi enterprise account to become self-managed using Pipi (Dogfooding).
  5. Go live for a single customer carefully chosen from the waitlist. Prove it. Refine. Repeat. Grow by one customer at a time so that they all succeed.
  6. Growth by word-of-mouth referrals among developers while staying in stealth mode. Accumulate funds. Grow the ecosystem. Build a great team.
  7. Resource and configure Ajabbi and Pipi to rapidly scale without limits.
  8. Escape from stealth mode.
  9. ...

MedGemma: Google's open models for Health AI development

Mike's Notes

I recently attended the excellent Google Startup School: HealthTech. Most video sessions were in the early hours (NZ Time), so I watched them on demand.

A virtual series exploring cutting-edge technology, cloud infrastructure, and the future of care. Held September 24, 2026-October 1, 2026.

Looks great, lots of possibilities for integrating MedGemma with Pipi in the future.

Resources

References

  • MedGemma 1.5 Technical Report, [v2] Fri, 1 May 2026, arXiv:2604.05081.

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Last Updated

05/10/2026

MedGemma: Google's open models for Health AI development

By: Daniel Golden
YouTube: 24/09/2026 53:32

Daniel Golden: Dan is a Software Engineering Manager in Google Research, where he has worked since 2019 (including one year at Verily) on AI diagnostic and generative systems for ophthalmology, radiology, pathology, and healthcare more broadly. He leads the Health AI Developer Foundations (HAI-DEF) program in Google Research, which focuses on developing MedGemma and other open multimodal foundation models for healthcare, as well as tools that help developers build medical AI applications more effectively.

...

Explore Google’s family of open medical AI models, including MedGemma, and learn how they are purpose-built for healthcare workloads. Discover how engineering and clinical research teams fine-tune and deploy these models securely on Google Cloud to accelerate diagnostic workflows and build next-generation health applications.

YouTube: 24/09/2026
Play length: 53:32

Inside interoception: The hidden sense of how you feel inside

Mike's Notes

I discovered this article in MIT Technology Review by reading a recent note about AI by John F. Sowa on the Ontolog Forum.

"The guys from Anthropic have ZERO understanding of the complexity and power of the human brain-body system.   For a better perspective, I recommend a recent article from MIT Technology Review.  It shows that our conscious information is a tiny fraction of the immense amount that the brain is processing every second.   The languages we speak express a tiny fraction of what is conscious.  But the part outside of consciousness is immensely larger .  

I copied a few paragraphs that emphasize the immense amount of non-linguistic information that LLMs (Large Language Models) ignore. ..." - John F. Sowa 

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Last Updated

04/10/2026

Inside interoception: The hidden sense of how you feel inside

By: Katherine W. Isaacs 
MIT Technical Review:12/05/2026

Katherine W. Isaacs: Katherine W. Isaacs is a writer and senior lecturer at the MIT Sloan School of Management. Her teaching and research focus on the intersection of psychology, technology, and innovation. Originally trained as a biologist and later as a social psychologist, she is currently working on a book called Gut Feel, about intuition, interoception, and embodied decision-making.

...

Researchers are decoding how signals move between body and brain, with implications for how we understand and treat conditions from obesity to anxiety.

Your brain lives in the dark space of your skull. Yet it knows when the wind lifts the hairs on your skin, when your heart is racing, when your gut tightens with fear.

It’s also, right now, predicting what you’ll read next as your eyes move across this page. It’s picking up signals that help it make sense of what’s happening around you and prepare you to act if you need to stay safe. You aren’t usually aware that your brain is doing all that.

Our senses take in information at a staggering rate—roughly 11 million bits flood in every second from our skin, eyes, ears, and more. That’s nearly three paperback novels’ worth of data every second. Only a sliver reaches our conscious awareness. Researchers estimate that our conscious minds can process roughly 10 to 60 bits of information per second, about the rate at which you’re reading this sentence. That’s a ratio of about one conscious bit to hundreds of thousands of unconscious bits.

And that’s a mercy. As Moriah Thomason, a neuroscientist at NYU Langone, says, “Thank goodness we’re built like this. There’s a layer of what we have access to in conscious awareness. And then we have a right-under-the-surface amount. There is only a certain amount we are meant to ‘hold in mind’ in order to function successfully.” 

What you are aware of: Your stomach growling when you’re hungry. Your palms sweating before you speak in public. The breath you just took, if you pay attention to it. Even your heartbeat, which some people can sense from the inside without feeling their pulse in their wrist.

Scientists have a word for how we sense ourselves from the inside: interoception. 

The term was coined in 1906 by the British neurophysiologist Charles Sherrington. For most of the 20th century it remained largely confined to textbooks. Today, thanks to a 2021 Nobel Prize and new tools that can map the interoceptive system across the body, the study of this facility is suddenly quite hot. As researchers decode how signals move between body and brain, a clearer picture is starting to take shape—with implications for how we understand and treat conditions from obesity to chronic pain to anxiety.

The field began to take off in the 1990s. In 1994, the neurologist Antonio Damasio published a book with a pointed title: Descartes’ Error. He challenged the historical separation of thinking and feeling, arguing that our ability to choose and act is driven by feelings, and those feelings in turn are shaped by the body’s signals, such as your gut clenching or your skin going clammy. When we lose that connection between feeling and thinking, as one of Damasio’s patients did after surgery to treat a brain tumor, we may still be able to reason with perfect logic about the pros and cons of traveling on a Tuesday or a Wednesday. But without the emotional signals that help us predict what a choice will feel like, our reason spins and circles, and we cannot decide.

A contemporary of Damasio’s, the neuroscientist Bud Craig, spent his career asking one question: How do you feel? He charted how the brain builds an inner map of the body and updates it in real time every moment you are alive.

Think of the captain’s bridge on the USS Enterprise, where a live map displays the status of the ship’s critical systems: oxygen levels, energy availability, hull integrity, shield strength. Another set of indicators senses things outside the ship: asteroid belts, enemy ships, radiation, life signs, and spatial anomalies not yet understood.

Your brain, only about the size of your two fists pressed together, creates a map like this for your entire body, along with a map of the outside world, from data streaming in through your five senses. Together, they feed into your brain’s working model of you in the world, now and across time—where you are, who you are, your expectations for what’s about to happen (based on everything you know), and what all that means for you.

When someone asks “How are you doing?” we consult our maps and report back on our status. We might say we’re happy, depleted, anxious, or energetic. These feelings are always a braid of emotional and physical sensations. They’re what your interoceptive navigational system serves up to your awareness when you sense yourself from the inside.

As we grow up, we learn to interpret what these sensations mean—interpretations that, in turn, can alter our physiology, emotions, and behavior. Research by the psychologist Alia Crum shows that people who embrace a “stress is enhancing” mindset produce more growth hormones than people who have a “stress is debilitating” mindset. They also experience more positive emotions and greater cognitive flexibility.

Language also matters. We learn words for the textures of our feelings—words that then shape how we feel and act. The psychologist Marc Brackett points out that people low in "emotional granularity"—the ability to distinguish between closely related feelings—react more impulsively under stress and are less able to find meaning in difficult experiences.But mindsets and emotional intelligence are malleable. We can learn that “anxious” is different from “terrified,” and we can even reframe how we interpret our body’s sensations. Instead of thinking of the butterflies in our bellies as annoying, we can welcome them as our body’s way of preparing us for a peak performance.

Research by the neuroscientist Lisa Feldman Barrett, who discovered and named emotional granularity, suggests that it is not about describing or labeling emotions. It's about how our brains construct emotions with more or less specificity, often outside of awareness.

Scientists have long understood that the interoceptive information informing these lived experiences travels via two major systems: nerves and humors (blood and lymph). Now they’re actively studying a third system—the “interstitium,” a network of fluid-filled spaces woven throughout the body’s connective fascia that may also play a role in communication.

But until recently, scientific understanding of this interoceptive system looked like a high-level schematic that left out vital details—how information travels from the outside environment in, how it moves from your body to your brain, and how it is integrated and interpreted within your brain. Researchers are now racing to explore what the neuroscientist Catherine Tallon-Baudry calls this “new continent of awareness.”

The wandering highway

One of the most active areas of research centers on the vagus nerve, the main component of the parasympathetic nervous system and an information highway carrying news from your organs up to your brain and back down to your body. The vagus has become a celebrity nerve, ubiquitous in wellness podcasts and trauma therapy. “Tone your vagus nerve.” “Activate your parasympathetic system.” The language suggests a single thing you can target, like a muscle. The reality, as Steve Liberles at Harvard Medical School is discovering, is far more interesting.

Liberles has spent most of his career mapping what he calls “the great wide unknown” of one of our largest and longest nerves. He speaks the way he works—methodically, without overselling. But the questions driving him are huge. How do we sense our body’s inner state? What information flows through which channels? And how does the brain decide what to do with it?

“When I’m nervous giving a talk in front of a thousand people,” he says, “my heart might race. I might get butterflies in my stomach. I might get goosebumps on my skin.” We all know what he’s talking about.

“It’s bizarre,” he muses. “Your brain has to send a signal to the gut, and then the gut back to the brain, to tell you you’re nervous?” He pauses. “This just shows there is this intimate connectivity between the brain and the body that’s real.”

The vagus is often called the calming nerve, because it controls “rest and digest” functions that quiet our body after the sympathetic nervous system revs us up with “fight or flight” impulses to handle danger or stress. 

But it is also doing something else: It’s listening to us inside. Anatomists have known for over a century that roughly 80% of its fibers carry information upward, from body to brain. Think of it as a two-lane highway with far more traffic headed north. What scientists are just beginning to understand in detail is what those signals are saying. 

Liberles is decoding the vagus with molecular precision and finding that its messaging system is unexpectedly diverse. So far, his research has uncovered dozens of types of vagus nerve cells, each wired to a specific organ. Team Red relays information about the heart; Team Blue, the gut.

Within those teams, each courier has a unique job that’s different from those all its teammates perform. Liberles found 10 types in the lungs alone. Until then, only one lung reflex had ever been identified, in 1868. One nerve courier carries information about breathing rate; another the stretch of your lungs; yet another information about airway threats, like food going down the wrong pipe.

“It’s super exciting to think about what each of these neurons is doing,” he told me in a conversation last fall, a flash of intensity breaking through the calm. “Where does it go in the body? What is it sensing? What is it controlling?”

The doors of the cell

Liberles is mapping the vagus information highways. But highways need on-ramps for signals to enter. For years, one of neurobiology’s biggest mysteries was the molecular on-ramp for our sense of touch.

Somewhere, something in our bodies was converting physical force into an electrical signal that the nervous system could understand. But no one knew how. 

Solving that mystery required a scientist willing to trust a hunch when the data couldn’t show the way. 

Ardem Patapoutian grew up in Lebanon and fled the country’s civil war at 18, landing in Los Angeles, where he delivered pizzas and wrote horoscopes for a local newspaper before falling in love with science at UCLA.

In the 1990s, as a postdoc at the University of California, San Francisco, he became fascinated with our sense of touch—the last of the five major senses not yet understood at the molecular level. The lung stretch signal that Liberles’s vagus neurons carry to the brain? No one had ever figured out how that signal began.

“How do you feel the embrace of a loved one? How do your fingers distinguish one texture of hair from another?” Patapoutian invites us to wonder in his 2021 Nobel Prize lecture. The problem: Most cellular communication works through chemistry. But mechanical force offers no molecule to bind. How does the body translate physical pressure into the electrochemical language that neurons speak?

Scientists knew that the answer had to be an ion channel—a protein gate embedded in cell membranes that opens to let electrically charged particles into the cell. But tracking down the one responsible for touch turned out to be absurdly difficult. Ion channels are a hundred thousandth the size of a cell, invisible to ordinary microscopes. Worse, they don’t resemble each other. You can’t recognize one by its shape or its sequence of amino acids. Even with one right in front of you, nothing would tell you it was there.

At Scripps, where he works now, Patapoutian decided to try an unusual approach. He’d try to find cells that showed sensitivity to touch and destroy their internal genetic blueprint one gene at a time—hunting for the move that would make the cell go numb. It was tedious, expensive, and possibly a dead end. “A lot of people made fun of us,” he says.

Two years in, Patapoutian’s collaborator Bertrand Coste had burned through half his postdoctoral appointment with no results. Patapoutian said: Another 30 genes, and then we decide whether to continue.

What kept them going, Patapoutian told me, was informed intuition. “As you gain more experience, you have this sense of what’s going to work, what’s not going to work. Sometimes the data cannot answer the question of when to stop or when to continue. There has to be another process. If you start trusting it, it gives you an avenue to continue.”

Coste knocked out candidate gene 72. Flatline. The cell had gone numb.

They’d found it—the mechanism behind something you feel every day.

They named the protein they identified PIEZO, from the Greek piezi, meaning pressure. There are two variations, PIEZO1 and PIEZO2, each responsible for sensing different kinds of pressure in the body. They’re elegant in their design—over 2,500 amino acids folded into a three-bladed propeller-shaped gate embedded in cell membranes. When pressure stretches the membrane, the gate opens and electrically charged ions flood through, translating physical pressure into an electrical signal that the brain can understand—all within milliseconds.

Patapoutian calls scientific discovery a dream that survives reality. He won the Nobel Prize in medicine in 2021 for his discovery of PIEZO, sharing the award with David Julius of UCSF for his work on how cells sense temperature. Now researchers are finding PIEZO proteins everywhere—skin, organs, blood vessels, and even red blood cells, where they help the cells squeeze through narrow capillaries. They’re how your brain knows where your hand is in space without looking at it, a sense called proprioception. They’re in plants too, enabling roots to sense pressure as they push down into the earth.

PIEZO was just the beginning. With a $14.5 million grant from the US National Institutes of Health, Patapoutian and his collaborators are now mapping the body’s entire interoceptive system—as many internal senses as he can find, he says.

Patapoutian has translated his discovery into a unique form of public outreach. At scientific conferences, he sometimes rolls up his sleeve mid-lecture to reveal half his arm covered in ink—a gigantic PIEZO protein in exquisite anatomical detail, its blades spreading across his biceps. Then he flexes. The tattoo flexes with him, the structure bending exactly as the real protein does when pressure opens the gate.

“At a pub or a party,” he explains, smiling, “how else would I demonstrate this beautiful structure?”

Orchestrating the field

Steve Liberles is mapping a major interoception highway. Ardem Patapoutian discovered the gates of touch. Meanwhile, Wen Chen at the National Institutes of Health is pulling the field together, putting neuroscientists, immunologists, physiologists, and clinicians into the same room. The demand, she says, has been enormous.

She tested her pitch at a dinner party with NIH colleagues a few years ago. You’re hungry right now—that’s interoception. You’re thirsty—that’s interoception. Heads nodded as she pointed around the table.

“We can’t have just the brain or just the body,” she told me. “We need to look at the whole person.”

In 2018 she organized a symposium on interoception where Liberles was one of the invitees, along with researchers and practitioners of meditation and yoga. “It was not their thing,” she says, laughing as she recalls how uncomfortable some of the researchers looked. But the practitioners were excited to finally meet scientists who were studying the inner mechanisms of what they did.

That was followed by a series of NIH workshops on interoception that spanned topics from basic science to clinical practice. Patapoutian was the keynote speaker for the first one. 

The NIH began funding scientists to chart the neural circuits of interoception and bringing them together to talk about their findings. Partway through one of these meetings, the equipment failed for an hour. More than 1,000 people stayed online, waiting for it to come back.

“We were shocked at the turnout,” she says. “There was much bigger interest than we could have imagined.”

Chen is now building infrastructure to match the demand: a formal community, funding mechanisms, a venue where cardiologists and neuroscientists and clinicians can all find each other. And she’s redefining the field as she goes; interoception is not a one-way signal from body to brain but a continuous two-way communication system, each direction shaping the other in real time. 

Liberles’s nervousness on stage is that two-way loop in action. Signals from his racing heart and belly butterflies travel up to the brain, which weaves them into an interpretation: This is anxiety, and this is what to do to handle it. His actions produce fresh signals that the brain reads in light of its ongoing predictions about what will happen next. In the body-brain communication loop, each player constantly updates the other.

I asked Wen what her work on interoception might mean for another inner sense: intuition. “People talk about ‘gut feelings,’” I said. “How does that relate to interoception?”

“Intuition might be the bridge where interoception moves from unconscious processing to conscious awareness,” she answered. “If that’s true, then intuition is not magic. It’s physiology.”

But it depends on how we read the signals. Intuition is like pain. It tells you something, but it’s not always clear what. “Perhaps we can treat intuition as a source of data,” she says. “Meaningful, but probably not complete.”

“Maybe we can be grounded in both—in feeling and fact.”

Which raises a more personal question: What do you do with the signals your body is sending?

One avenue for exploration is therapeutic intervention—both pharmacological and neural stimulation. Vagal nerve stimulation has treated epilepsy and depression for four decades, but as Liberles puts it, it’s like pressing all the keys on the piano to hit one note. Weight-loss drugs like Ozempic act in part through vagal pathways but can cause nausea as a side effect, because the targeting isn’t precise enough. Map the body’s circuits with enough accuracy and you might hit the note you actually want.

Another area of active research is psychological and behavioral—teaching people how to detect and even shape interoceptive signals. Low interoceptive awareness is linked to mental-health disorders and stress-related physical conditions. But like emotional intelligence, it’s not fixed. Researchers are finding that people can boost their body awareness by, for example, learning to detect their heartbeats from the inside—now a common measure of interoceptive awareness. Other interventions focus on body-based therapies and conscious activation of the parasympathetic “rest and digest” system to improve emotional and physical well-being. The placebo effect is another example of the mind acting on the body through expectation alone.

The signals we once dismissed as vague feelings—when your gut tightens before you know why, when your body says yes or no before your mind catches up—those are real. How we interpret them and whether we act on them is another frontier.

It’s clear that gut feelings play a role in scientific research, especially when the path forward looks foggy. Patapoutian’s informed intuition kept him and his colleagues going long enough to find PIEZO, a reminder that major discoveries often start with a hunch that is later tested against evidence. Chen puts it well: Maybe we can be grounded in both feeling and fact.

Update

This story was updated to include information on Lisa Feldman Barrett's work.

...

Katherine W. Isaacs is a writer and senior lecturer at the MIT Sloan School of Management. Her teaching and research focus on the intersection of psychology, technology, and innovation. Originally trained as a biologist and later as a social psychologist, she is currently working on a book called Gut Feel, about intuition, interoception, and embodied decision-making.

Designing Nested Tables: The UX of Showing Complex Data Without Creating Chaos

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This gem was listed in a recent Smashing Magazine article. Will be included in a future update to the Pipi Design System.

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Last Updated

03/10/2026

Designing Nested Tables: The UX of Showing Complex Data Without Creating Chaos

By: Damilola Bamgbelu
Medium: 20/05/2025

Damilola Bamgbelu: Product Designer, best known for crafting great experiences that deliver audacious results for both users and stakeholders.

...

Nested tables have a bit of a reputation. They’re often seen as messy or overwhelming, but when used thoughtfully, they can actually help users make sense of complex, layered information, without jumping between screens.

UI comparison of single vs. multi-level nested tables showing category data, with notes on usability benefits for each approach

I remember the first time I designed a nested table. It looked amazing in Figma: beautiful rows expanding neatly into sub-rows. I thought I was a genius.

Until user testing.

One participant squinted at the screen, clicked a few times, then said:

“Wait… where am I now? What row was I just looking at?”

That’s when it hit me:

Nested tables aren’t just about organizing data. They’re about organizing thinking.

…If you’ve ever struggled to design tables that show parent and child data together , without overwhelming users, this article is for you. Let’s walk through it step by step.

1. Always Start with “Why Nest This?”

Imagine you’re packing for a trip. Do you cram everything into your backpack just because you might need it? Or do you only pack what you’ll actually use?

Nested tables are the same. If users don’t need the child data right now to make a decision or take action, don’t show it inline.

Always ask:

  • Why does this need to be visible right now?
  • What task does seeing both parent and child data help users complete faster?
  • Could a modal, tab, or progressive disclosure do it better?

If you don’t have a strong reason, nesting is clutter, not clarity.

As Steve Krug puts it in Don’t Make Me Think,

“Every element you add competes with every other element for your user’s attention.” [1]

The more things you show on screen at once, the harder it is for users to know what to focus on.

A designer processing thoughts on what design decisions to make. Source: Freepik

2. Keep It Shallow: Two Levels Max

Picture this: you’re standing at the edge of a swimming pool. Jump into the shallow end? Great. Dive straight into the deep end with no bottom in sight? Panic.

Deep nesting feels like drowning in data.

  • One level of nesting (Parent > Child) is good.
  • Two levels (Parent > Child > Grandchild) is manageable — if truly needed.
  • Three or more? Stop. Redesign.

Humans aren’t built to track endless layers while scanning a screen. This aligns with George Miller’s Law, which says:

“Humans can comfortably hold 5 to 9 items in working memory.” [2]

If you must use multi-level nesting:

  • Visually differentiate each layer (indentation, color bands, labels)
  • Limit nesting to 2 levels
  • Provide expand/collapse all and clear context cues

To borrow a thought Jared Spool and Steve Krug might both agree on:

“The deeper the nesting, the higher the cognitive load. Use with restraint, and always design for collapse.” [3]

3. Make Expanding and Collapsing Feel Effortless

Ever opened a drawer and it jammed halfway? Now compare that to one that slides open smoothly. That’s how expanding a nested row should feel: smooth, responsive and satisfying.

When someone clicks to expand a row, they should get immediate, clear feedback, such as:

  • A smooth animation
  • A skeleton loader
  • A rotated chevron

These are microinteractions, small signals that say, “Yes, that worked.”

According to Jared Spool:

“Good UX reduces uncertainty at every interaction.” [4]

If there’s silence, delay, or confusion, users hesitate or worse, assume it’s broken. Every tiny motion helps users trust what’s happening.

UI showing nested tables with chevron icons to expand/collapse rows and a skeleton loader for loading child data illustrating that expanding and collapsing should feel effortless

4. Prioritize Scanning, Not Cramping

One of the biggest mistakes with nested tables is trying to show everything at once. The result? A wall of numbers, labels, and rows that’s hard to parse.

You know that feeling you get when you walk into a store that’s so packed you can’t even move? That’s how users feel when a table is over-stuffed with information.

Your goal: Make it insanely easy to scan. If someone can’t scan your table in 2–3 seconds and find what they need, it’s too dense.

How to improve scan-ability:

  • Clearly indent child rows to show hierarchy
  • Use soft shading or dividers to separate levels
  • Keep typography clean and consistent
  • Add sticky headers to maintain context

As Edward Tufte famously said:

“Clutter isn’t about the information itself. It’s a failure of layout.” [5]

Table UI showing clear row indentation for hierarchy, soft shading to separate levels, clean typography, and sticky headers to maintain context while scrolling

5. Never Leave Users with a Blank Expansion

Expanding a nested table row should feel purposeful, not like a dead end. Think of it like opening a fridge expecting leftovers, only to find it empty. No note, no label, no clue what happened.

That’s what it feels like when a user clicks to expand a row and sees nothing. When there’s no child data to display, it’s not enough to show an empty cell. A blank space can confuse users and make them wonder:

Instead:

  • Explain the state: e.g. “No related tasks found”
  • Suggest the next step: e.g. “+ Create Task”
  • Make it visually distinct: muted icons, faded backgrounds, or subtle illustrations

This tiny design choice adds clarity.

As Luke Wroblewski advises:

“Empty space should guide users toward their next action, not leave them wondering what went wrong.” [6]

Nested table UI showing an expanded row with an empty state message and a clear call-to-action button

6. Build Accessibility in from Day One

You’ve designed a slick, responsive table. It expands smoothly. It works in dark mode. But here’s the test:

  • Can a screen reader user understand the structure?
  • Can a keyboard-only user navigate the table easily

If not, you’re unintentionally excluding users. As Whitney Quesenbery puts it:

“Accessibility is not a feature. It’s a foundation.” [7]

Nested tables add visual hierarchy and relationships that are obvious to sighted users, but completely invisible to users relying on assistive technologies.

Without proper semantic markup, screen readers can’t tell what’s expandable, what’s nested inside what, or even what content is currently visible. And for keyboard users, expanding a row should be as easy as clicking, but often isn’t.

To make a nested table accessible;

  • Use semantic HTML

    Use actual <table>, <tr>, <td>, and <th> elements — not divs styled to look like tables.

  • Use aria-expanded and aria-controls

    These attributes help screen readers announce whether a row is open or collapsed, and which section it controls.

  • Ensure keyboard navigation

    Users should be able to tab through parent and child rows, expand/collapse with Enter or Space, and not lose focus in the process.

  • Design an alternate flat view

    For users who don’t benefit from inline nesting (e.g. screen reader users), offer a simplified version — a grouped list or summary view that doesn’t require expanding/collapsing.

Accessibility doesn’t only benefit users with disabilities.

  • Clear structure improves usability for everyone.
  • Keyboard-friendly interfaces help power users navigate faster.
  • Screen-reader compatibility often translates to better semantic structure and more maintainable code.

Side-by-side nested table designs, one accessible with semantic HTML, ARIA support, keyboard navigation, where the other lacks these features.

Final Thoughts

Nested tables aren’t the enemy. Careless nesting is. When designed intentionally, they:

  • Reveal relationships
  • Enable fast decisions
  • Prevent unnecessary page hopping

But if poorly designed, they:

  • Confuse users
  • Increase cognitive load
  • Exclude those who rely on assistive tech

Designers must treat nested tables not just as containers of data, but as tools for clarity and action. That means:

  • Start with why
  • Design for scanning and accessibility
  • Plan microinteractions
  • Never leave users wondering what’s happening

Good UX isn’t about making data visible.

It’s about making it useful, usable and usable by everyone. Nested tables are powerful. Use them with care.

References

  1. Steve Krug, Don’t Make Me Think, 2000.
  2. George A. Miller, “The Magical Number Seven, Plus or Minus Two,” Psychological Review, 1956.
  3. Paraphrased synthesis of ideas from Jared Spool and Steve Krug on cognitive load and interface clarity.
  4. Jared Spool, quoted in multiple UX talks and writings on reducing uncertainty in interactions.
  5. Edward Tufte, The Visual Display of Quantitative Information, 1983.
  6. Luke Wroblewski, “Designing for Empty States,” blog and presentations on interface design.
  7. Whitney Quesenbery, quoted in A Web for Everyone: Designing Accessible User Experiences, 2014.