Coleman McCormick

Archive of posts with tag 'Ideas'

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March 29, 2024 • #

Possibility space →

Gordon Brander:

There is something powerful about this notion of possibility spaces, both from a theory standpoint, and as a way of seeing. It causes you to approach challenges in a different way. You don’t need to be creative. The creative breakthrough already exists out there in the space of possibility. It’s just waiting to be discovered.

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January 22, 2024 • #

How to Walk-and-Talk →

Kevin Kelly’s “walk and talk” is something I’ve gotta try. At least more often.

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On Validating Product Ideas

January 19, 2023 • #

Building new things is an expensive, arduous, and long path, so product builders are always hunting for means to validate new ideas. Chasing ghosts is costly and deadly.

The “data-driven” culture we now live in discourages making bets from the gut. “I have a hunch” isn’t good enough. We need to hold focus groups, do market research, and validate our bets before we make them. You need to come bearing data, learnings, and business cases before allowing your dev team to get started on new features.

Validating ideas

And there’s nothing wrong with validation! If you can find sources to reliably test and verify assumptions, go for it.

But these days teams are compelled to conduct user testing and research to vet a concept before diving in.

This push for data-driven validation I see as primarily a modern phenomenon, for two reasons:

First, in the old days all development of any new product was an enormously costly affair. You were in it for millions before you had anything even resembling a prototype, let alone a marketable finished product. Today, especially in software, the costs of bringing a new tool to market are dramatically slashed from 20 or 30 years ago. The toolchains and techniques developed over the past couple decades are incredible. Between the rise of the cloud, AWS, GitHub, and the plethora of open source tools, a couple of people have superpowers to do what used to take teams of dozens, and thousands of hours before you even had anything beyond a requirements document.

Second, we have tools and the cultural motivation to test early ideas, which weren’t around back in the day. There’s a rich ecosystem of easy-to-use design and prototyping tools, plus a host of user testing tools (like UserTesting, appropriately) to carry your quick-and-dirty prototypes out into the world for feedback. Tools like these have contributed to the push for data-driven-everything. After all, we have the data now, and it must be valuable. Why not use it?

I have no problem with being data-driven. Of course you should leverage all the information you can get to make bets. But data can lie to you, trick you, overwhelm you. We’re inundated with data and user surveys and analytics, but how do we separate signal from noise?

One of my contrarian views is that I’m a big defender of gut-based decision making. Not in an “always trust your gut” kind of way, but rather, I don’t think listening to your intuition means you’re “ignoring the data.” You’re just using data that can’t be articulated. It’s hard to get intuitive, experiential out of your head and into someone else’s. You should combine your intuitive biases with other objective data sources, not attempt to ignore them completely.

I’m fascinated by tacit knowledge (knowledge gained through practice, experience). It differentiates between what can be learned only through practice from what can be read or learned through formal sources — things we can only know through hands-on experience, versus things we can know from reading a book, hearing a lecture, or studying facts. Importantly, tacit knowledge is still knowledge. When you have a hunch or a directional idea about how to proceed on a problem, there’s always an intrinsic basis for why you’re leaning that way. The difference between tacit knowledge and “data-driven” knowledge isn’t that there’s no data in the former case; it’s merely that the data can’t be articulated in a spreadsheet or chart.1

I’ve done research and validation so many ways over the years — tried it all. User research, prototype workshopping, jobs-to-be-done interviews, all of these are tools in the belt that can help refine or inform an idea. But none of them truly validate that yes, this thing will work.

One of the worst traps with idea validation is falling prey to your own desire for your idea to be valid. You’re looking for excuses to put a green checkmark next to an idea, not seeking invalidation, which might actually be the more informative exercise. You find yourself writing down all the reasons that support building the thing, without giving credence to the reasons not to. With user research processes, so many times there’s little to no skin in the game on the part of your subject. What’s stopping them from simply telling you want you want to hear?

Paul Graham wrote a post years ago with a phenomenal, dead-simple observation on the notion of polling people on your ideas. A primitive and early form of validation. I reference this all the time in discussions on how to digest feedback during user research (emphasis mine):

For example, a social network for pet owners. It doesn’t sound obviously mistaken. Millions of people have pets. Often they care a lot about their pets and spend a lot of money on them. Surely many of these people would like a site where they could talk to other pet owners. Not all of them perhaps, but if just 2 or 3 percent were regular visitors, you could have millions of users. You could serve them targeted offers, and maybe charge for premium features.

The danger of an idea like this is that when you run it by your friends with pets, they don’t say “I would never use this.” They say “Yeah, maybe I could see using something like that.” Even when the startup launches, it will sound plausible to a lot of people. They don’t want to use it themselves, at least not right now, but they could imagine other people wanting it. Sum that reaction across the entire population, and you have zero users.

The hardest part is separating the genuine from the imaginary. Because of the skin-in-the-game problem with validation (exacerbated by the fact that the proposal you’re validating is still an abstraction), you’re likely to get a deceitful sense of the value of what you’re proposing. Would you pay for this? is a natural follow up to a user’s theoretical interest, but is usually infeasible at this stage.

It’s very hard to get early critical, honest feedback when users people have no reason to invest the time and mental energy thinking about it. So the best way to solve for this is to reduce abstraction — give the user a concrete, real, tangible thing to try. The closer they can get to a substantive thing to assess, the less you’re leaving to their imagination as to whether the thing will be useful for them. When an idea is abstract and a person says “This sounds awesome, I would love this”, you can safely assume they’re filling in unknowns with their own interpretation, which may be way off the mark. Tangibility and getting hands-on with a complete, usable thing to try, removes many false assumptions. You want their opinion on what it actually will be, not what they’re imagining it might be.

In a post from a couple years ago, Jason Fried hit the mark on this:

There’s really only one real way to get as close to certain as possible. That’s to build the actual thing and make it actually available for anyone to try, use, and buy. Real usage on real things on real days during the course of real work is the only way to validate anything.

The Agile-industrial complex has promoted this idea for many years: moving fast, prototyping, putting things in market, iterating. But our need for validation — our need for certainty — has overtaken our willingness to take some risk, trust in our tacit knowledge, and put early, but concrete and minimal-but-complete representations out there to test fitness.

De-risking investments is a critical element of building a successful product. But some attempts to de-risk actively trick us into thinking an idea is better than it is.

So I’ll end with two suggestions: be willing to trust your intuition more often on your ideas, and try your damnedest to smoke test an idea with a complete representation of it, removing as much abstraction as possible.

  1. I’d highly recommend Gerd Gigerenzer’s book Gut Feelings, which goes deep on this topic. For a great précis on the topic, check out his conversation from a few years back on the EconTalk podcast. 

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Steve Jobs on Ideas vs. Products

September 19, 2022 • #

A lot of Steve Jobs content is hagiography at this point, but this clip is fantastic:

There’s an enormous delta between idea and execution. Someone can take a great idea and squander it. Or conversely, someone could take a middling and obvious idea and execute so well they build a billion dollar business. From the first part of the clip:

One of the things that really hurt Apple was after I left John Sculley got a very serious disease. And that disease, I’ve seen other people get it, too, it’s the disease of thinking that a really great idea is 90 percent of the work, and that if you just tell all these other people “here’s this great idea” then of course they can go off and make it happen. And the problem with that is that there’s a just a tremendous amount of craftsmanship in-between a great idea and a great product, and as you evolve that great idea it changes and grows it never comes out like it starts because you learn a lot more as you get into the subtleties of it. And you also find there’s tremendous trade-offs that you have to make. There are there are just certain things you can’t make electrons do, there are certain things you can’t make plastic do or glass do or factories do or robots do. And as you get into all these things, designing a product is keeping 5,000 things in your brain, these concepts, and fitting them all together and kind of continuing to push to fit them together in new and different ways to get what you want and every day you discover something new that is a new problem or a new opportunity to fit these things together a little differently. It’s that process that is the magic.

The idea of “making to know”, or of starting the work in order to figure out the specific contours of the work, these are fascinating concepts to me. So many of the great innovations of our time are the function of the college dropout, or the less-educated craftsperson, experimenting through years of trial and error to make something happen. Often the only way to know the true bottlenecks, challenges, and chokepoints of bringing an idea to consumers (buyers, audiences, customers) is to get started. Make the map of the territory along the way.

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Daily Journaling with Morning Pages

September 6, 2022 • #

About a year ago I started experimenting with the idea of a daily journal. From someone within the Roam community, I heard about the concept of Morning Pages, which is a tool for creative writers to build a muscle for generating ideas. Author Julia Cameron defined it in her book The Artist’s Way:

Morning Pages are three pages of longhand, stream of consciousness writing, done first thing in the morning. There is no wrong way to do Morning Pages—they are not high art. They are not even “writing.” They are about anything and everything that crosses your mind– and they are for your eyes only. Morning Pages provoke, clarify, comfort, cajole, prioritize and synchronize the day at hand. Do not over-think Morning Pages: just put three pages of anything on the page… and then do three more pages tomorrow.

Freeform journaling is something I used to do years ago with Day One, but not with a longer free space to ruminate. I mostly used that to document personal events, versus thoughts and ideas. My methodology with Morning Pages has been even more loose than as Cameron defines it. I don’t necessarily get my journaling done in the morning; I just have a goal to do it sometime once per day. The first-thing-in-the-morning writing sessions are definitely the most creative and interesting, but my plan collides with reality and makes it hard to do consistently. The only constraint I set are to write for at least 15 minutes, but my default timer is 25 (more on that in a minute). No topic is off-limits. Often I’ll take some event that happened the previous day and riff on it, or take from something I recently read or a podcast I listened to, or I’ll take a trigger off of something from my Writing Ideas page and expand on existing ideas.

The Artist’s Way’s canonical method is to write longhand, which I agree affords a benefit in mental stimulation that isn’t the same as typing. I’ve experimented a little bit with this and it’s alright — definitely good for the focus and flexibility. Because you don’t need a computer or tablet, you can write anywhere you’ve got paper, and you don’t need access to a particular application. But there are too many advantages to journaling digitally to use the analog method, for me. The key determinant for whether analog or digital is better is: _which one will get you to journal more regularly? Or more deeply?

My tool of choice these days, and for the past 6 months or so, is Logseq, a networked note-taking tool that’s gotten popular in the tools-for-thought space. It’s essentially an open source Roam look-a-like, with a sprinkling of unique aspects.

But the tool itself is irrelevant beyond the fact that I write my journal entries digitally, and that the graph-based model makes for some interesting additional features for the journaling flow.

Logseq has built-in “Journals” — a function that auto-generates a new date-stamped page for each day (like Roam’s Daily Notes). I use the day’s journal for any running activities for the day, things like a scratchpad for meeting notes, reflections on my daily Readwise highlights, general passing thoughts, todos, and my Morning Pages.

I start by creating a block called [[Morning Pages]] and nest the journal entry as blocks underneath. Because that’s a link and a page itself, I can go to the Morning Pages page and see a list of every entry in the linked references. I’ll also add a word count at the top block so I can see my progress. My favorite thing about writing in a zettelkasten-style system like this is the ability to link from within my journaling to other ideas in my notes library.

Then I just write. Sometimes not even in full sentences. And without fail, every time I wall off the time to do this, with no commitments on word counts or topics or boundaries, I can pour out a thousand words easily. My daily average word count is in the 500-1000 words range, but some entries really spark the brain and go close to 1500. Since mid-August I’ve written around 14,000 words in journal entries, and it doesn’t even feel that hard. Mixing in the personal stuff also leaves a nice trail of my thinking about family life and what we were doing each day that I enjoy going back and looking at later. Seeing my old entries in Day One from 7 or 8 years ago is always enjoyable. I’d love to look back in years and have a daily record of my stream of consciousness.

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Concept-based Notes and Composable Ideas

November 12, 2021 • #

If a note is an idea, we want to make the idea as atomic as possible, so we can find and stitch them together into an interconnected web of ideas. We want composable building blocks.

Composability helps us stack, mix, and repurpose ideas. To correlate them and find the relationships between them. Prose is an excellent medium for consumption, for diving deep on a particular topic. But with a prose format for documenting ideas (through notes), it’s harder to relate shared ideas across domains. Prose makes ideas easy to expand on and consume, but difficult to decompose into reusable parts. Decompose too far, though, say into individual words and letters, and the information is meaningless. We want a middle ground that can effectively convey ideas, but is also atomic enough to be decomposed and reused. We want idea Legos.

In Self-Organizing Ideas, Gordon Brander contrasts the linear, difficult to break down expansiveness of prose with something more like an index card. With index card-level division, ideas can now be expounded on at the atomic level, but also cross-referenced and remixed more easily than long-form prose. With the Zettelkasten, Luhmann devised a system of just that: numbered index cards that could reference one another. If you use a system like this for note taking, it’s a fun exercise to actually take a batch of 3-5 permanent notes at random and look for relationships. When I’ve done this, pulling out 2 arbitrary permanent notes, it often sparks new thoughts on them, and in the best cases, entirely new atomic notes.

Within our knowledge systems, we should strive for that right altitude of scope for a particular note or idea. Andy Matuschak says “evergreen notes should be atomic.” In my system, I make atomic notes that are concept-based, with a declarative format that prompts me to keep the note focused around a specific idea. Just scrolling through the list now, I see ones like:

  • “Traditions are storehouses of trial and error”
  • “Novelty in startups is higher than predicted”
  • “Knowledge is the biggest constraint in product management”

With a format like this, each note is structured as a claim or idea, so it’s densely linkable inline within other notes. So when reading a note, the cross-link to another idea can appear seamlessly within the text. Using a concept-based approach, we might find serendipitous connections we weren’t looking for. Andy says:

If we read two books about exactly the same topic, we might easily link our notes about those two together. But novel connections tend to appear where they’re not quite so expected. When arranging notes by concept, you may make surprising links between ideas that came up in very different books. You might never have noticed that those books were related before—and indeed, they might not have been, except for this one point.

Novel ideas spring from concocting new recipes from existing ideas. Composable, atomic ideas make it more manageable to toss several disparate ones together to experiment with new combinations.

Gordon has been writing lately about his work on Subconscious, and the possibility of software-assisted self organization of ideas. This is a super intriguing idea, and exactly the sort of reason I’m interest in computers and software — for their ability to help us think more creatively, do more building, and less rote information-shuffling.

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Exapting Technologies

September 9, 2021 • #

New forms of technology tend not to materialize from thin air. The nature of innovation takes existing known technologies and remixes, extends, and co-opts them to create novelty.

Gordon Brander refers to it in this piece as “exapting infrastructure.” As in the case of the internet, it wasn’t nonexistent one day then suddenly connecting all of our computers the next. It wasn’t purposely designed from the beginning as a way for us to connect our millions of computers, phones, and smart TVs. In fact, many types of computers and the things we do with them evolved as a consequence of the expansion of the internet, enabled by interconnection to do new things we didn’t predict.

Former railroad corridors are regularly reused as cycling trails
Former railroad corridors are regularly reused as cycling trails

“Exaptation” is a term of art in evolutionary biology, the phenomenon of an organism using a biological feature for a function other than it was adapted for through natural selection. Dinosaurs evolved feathers for insulation and display, which were eventually exapted for flight. Sea creatures developed air bladders for buoyancy regulation, later exapted into lungs for respiration on land.

In the same way, technologies beget new technologies, even seemingly-unrelated ones. In the case of the internet, early modems literally broadcast information as audio signals over phone lines intended for voice. Computers talked to each other this way for a couple decades before we went digital native. We didn’t build a web of copper and voice communication devices to make computers communicate, but it could be made to work for that purpose. Repurposing the existing already-useful network allowed the internet to gain a foothold without much new capital infrastructure:

The internet didn’t have to deploy expensive new hardware, or lay down new cables to get off the ground. It was conformable to existing infrastructure. It worked with the way the world was already, exapting whatever was available, like dinosaurs exapting feathers for flight.

Just like biological adaptations, technologies also evolve slowly. When we’re developing new technologies, protocols, and standards, we’d benefit from less greenfield thinking and should explore what can be exapted to get new tech off the ground. Enormous energy is spent trying to brute force new standards ground-up when we often would be better off bootstrapping on existing infrastructure.

Biology has a lot to teach us about the evolution of technology, if we look in the right places.

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