Archive for the ‘Intellectual Intertia’ Category

You can’t innovate when…

Your company believes everything should always go as planned.

You still have to do your regular job.

The project’s completion date is disrespectful of the work content.

Your company doesn’t recognize the difference between complex and complicated.

The team is not given the tools, training, time and a teacher.

You’re asked to generate 500 ideas but you’re afraid no one will do anything with them.

You’re afraid to make a mistake.

You’re afraid you’ll be judged negatively.

You’re afraid to share unpleasant facts.

You’re afraid the status quo will be allowed to squash the new ideas, again.

You’re afraid the company’s proven recipe for success will stifle new thinking.

You’re afraid the project team will be staffed with a patchwork of part time resources.

You’re afraid you’ll have to compete for funding against the existing business units.

You’re afraid to build a functional prototype because the value proposition is poorly defined.

Project decisions are consensus-based.

Your company has been super profitable for a long time.

The project team does not believe in the project.

Image credit Vera & Gene-Christophe

For top line growth, think no-to-yes.

Bottom line growth is good, but top line growth is better.  But if you want to grow the bottom line, ignore labor costs and reduce material costs. Labor cost is only 5-10% of product cost. Stop chasing it, and, instead, teach your design community to simplify the product so it uses fewer parts and design out the highest cost elements.

Where the factory creates bottom line growth, top line growth is generated in the market/customer domain. The best way I know to grow the top line is to broaden the applicability of your products and services. But, before you can broaden applicability, you’ve got to define applicability as it is.  Define the limits of what your product can do – how much it can lift, how fast it can run a calculation and where it can be used.  And for your service, define who can use it, where it can be used and what elements without customer involvement. And with the limits defined, you know where top line growth won’t come from.

Radical top line growth comes only when your products and services can be used in new applications.  Sure, you can train your sales force to sell more of what you already have, but that runs out of gas soon enough. But, real top line growth comes when your services serve new customers in new ways.  By definition, if you’re not trying to make your product work in new ways, you’re not going to achieve meaningful top line growth.  And by definition, if you’re not creating new functionality for your services, you might as well be focusing on bottom line growth.

If your product couldn’t do it and now it can, you’re doing it right. If your service couldn’t be used by people that speak Chinese and now it can, you’re on your way.  If your product couldn’t be used in applications without electricity and now it can, you’re on to something.  If your service couldn’t run on a smartphone and now it can, well, you get the idea.

For the acid test, think no-to-yes.

If your product can’t work in application A, you can’t sell it to people who do that work. If your service can’t be used by visually impaired people, you’re not delivering value to them and they won’t buy it. Turning can’t into can is a big deal. But you’ve got to define can’t before you can turn it into can. If you want top line growth, take the time to define the limits of applicability.

No-to-yes is powerful because it creates clarity. It’s easy to know when a project will create no-to-yes functionality and when it won’t.  And that makes it easy to stop projects that don’t deliver no-to-yes value and start projects that do.

No-to-yes is the key element of a compete-with-no-one approach to business.

image credit – liebeslakritze

How to Avoid a Cliff

Much like living organisms continually evolve to secure their place in the future, technological systems can be thought to display similar evolutionary behavior.  Viruses mutate so some of them can defeat the countermeasures of their host and live to fight another day. Technological systems, as an expression of a company’s desire to survive, evolve to defeat the competition and live to pay another dividend.

There are natural limits to evolutionary success in any single direction.  When one trait is improved it pushes on the natural limits imposed by the environment.  For example, a bacterium let loose in a friendly Petri dish will replicate until it eats all the food in the dish. Or, on a longer timescale, if the mass of a bird increases over generations when its food source is plentiful, the bird will get larger but will also get less agile. The predators who couldn’t catch the fast, little bird of old can easily catch and eat the sluggish heavyweight. In that way, there’s an edge condition created by the environmental Petri dishes and predators.  And it’s the same with technological systems.

Companies and their technological systems evolve within their competitive environment by scanning the fitness landscape and deciding where to try to improve.  The idea is to see preferential lines of improvement and create new technologies to take advantage of them.  Like their smaller biological counterparts, companies are minimum energy creatures and want to maximize reward (profit) with minimum effort (expense) and will continue to leverage successful lines of evolution until it senses diminishing returns.

The diminishing returns are a warning sign that the company is approaching an edge condition (a Petri dish of a finite size). In landscape lingo, there’s a cliff on the horizon. In technology lingo, the rate of improvement of the technology is slowing.  In either language, the edge is near and it’s time to evolve in a new direction because this current one is out of gas.

Like the bird whose mass increases over the generations when food is readily available, companies also get fat and slow when they successfully evolve in a single direction for too long.  And like the bird, they get eaten by a more agile competitor/predator. And just as the replication rate of the bacterium accelerates as the food in the Petri dish approaches zero, a company that doesn’t react to a slowing rate of technological improvement is sure to outlive its business model.

Biology and technology are similar in that they try new things (create variants of themselves) in order to live another day.  But there’s a big difference – where biology is blind (it doesn’t know what will work and what won’t), technology is sighted (people that create use their understanding to choose the variants they think will work best).  And another difference is that biological evolution can build only on viable variants where technology can use mental models as scaffolds to skip non-viable embodiments to cross a chasm.

There’s no need to fall off the cliff.  As a leading indicator, monitor the rate of improvement of your technology.  If its rate of improvement is still accelerating, it’s time to develop the next line of evolution. If its rate is declining, you waited too long. It’s time to double down on two new lines of evolution because you’re behind the curve. And remember, like with the population of bacteria in the Petri dish, sales will keep growing right up until the business model runs out of food or a competitor eats you.

Image credit — Amanda

Technology, Technologists and Customers

Henry Ford famously said if he asked people what they wanted, they would have asked for faster horses.  And there’s a lot of truth to his statement. If you ask potential customers what they want next, they’ll give you an answer. And when you show them the prototype, they won’t like it.  Their intentions are good and their answers are truthful, but when you give them what they ask for and put the prototype in their hands, they will experience it in a way they did not expect.  It will be different than they thought.  Thinking how something will be is different than physically interacting with it.  That’s how it is with new things.

And just like with the horses, because they don’t know the emergent technologies and their radically different capabilities, they can’t ask for what’s possible.  They won’t ask for a combustion engine that eliminates their horses because all they know is horses.  They’ll ask for more horses, bigger horses or smaller ones, but they won’t ask for combustion cylinders.

The trick is to understand what people do and why they do it.  Like an anthropologist, spend time watching and understanding. And, if you can, understand what they don’t do and why they don’t do it.  The new and deeper understanding of their actions, along with the reasons for them, create an anchoring perspective from which to understand how emergent technologies can change their lives.

Technologies evolve along worn paths. And depending on the maturity of the technology, some worth paths are more preferential than others.  For example, if fuel economy is stagnant for the last ten years it means it’s likely time for a young technology to emerge that uses a different physical principle such as battery power.  Though technology’s evolutionary direction is not predictable in an exact sense, it is dispositional.  Like the meteorologist can’t pinpoint where the storm center will hit the coast or predict the maximum wind speed to within one or two miles per hour, she can say which states should hunker down and tell you if the wind will be strong enough to blow out your windows.  She cannot predict the specifics, but she knows there’s a storm on the horizon and she knows its character, disposition and tendencies.

Now, anchored in how people use the state-of-the-art technologies (ride horseback, ride in buggies, use a team of horses to pull a heavy wagon) look at what the new technologies want to become (horses to combustion engine) and image how people’s lives would be better (faster trips, longer pleasure rides, heavier payloads, no barns and cleaner streets.)  Now, using the new technology, build a prototype and show it to customers.  Put them in the driver’s seat and blow their minds.  Listen to the questions they ask so you can better understand the technology from their perspective because just as they don’t understand the technology, you don’t understand what the technology means to them, the people who will buy it.  Use their questions to improve the technology and the product.

Technologists know technology, technology knows what it wants to be when it grows up and customers know what they want after they see what could be.  And to create a new business, it takes all three working together.

Image credit — William Creswell

Moving from Static to Dynamic

At some point, what worked last time won’t work this time. It’s not if the business model will go belly-up, it’s when. There are two choices. We can bury our heads in the sands of the status quo, or we can proactively observe the world in a forward-looking way and continually reorient ourselves as we analyze and synthesize what we see.

The world is dynamic, but we behave like it’s static. We have massive intellectual inertia around what works today.  In a rearward-looking way, we want to hold onto today’s mental models and we reject the natural dynamism swirling around us.  But the signals are clear. There’s cultural change, political change, climate change and population change. And a lower level, there’s customer change, competition change, technology change, coworker change, family change and personal change. And still, we cling to static mental models and static business models. But how to move from static to dynamic?

Continual observation and scanning is a good place to start. And since things become real when resources are allocated, allocating resources to continually observe and scan sends a strong message. We created this new position because things are changing quickly and we need to be more aware and more open minded to the dynamic nature of our world.  Sure, observation should be focused and there should be a good process to decide on focus areas, but that’s not the point. The point is things are changing and we will continually scan for storms brewing just over the horizon.  And, yes, there should be tools and templates to record and organize the observations, but the important point is we are actively looking for change in our environment.

Observation has no value unless the observed information is used for orientation in the new normal.  For orientation, analysis and synthesis is required across many information sources to develop ways to deal with the unfamiliar and unforeseen. [1] It’s important to have mechanisms to analyze and synthesize, but the value comes when beliefs are revised and mental models are updated. Because the information cuts against history, tradition and culture, to make shift in thinking requires diversity of perspective, empathy and a give-and-take dialog. [1] It’s a nonlinear process that is ironed out as you go.  It’s messy and necessary.

It’s risky to embrace a new perspective, but it’s far riskier to hold onto what worked last time.

 

[1] Osinga, Frans, P.B. Science, Strategy and War, The strategic theory of John Boyd. New York: Routledge, 2007.

image credit – gabe popa

Rule 1: Allocate resources for effectiveness.

We live in a resource constrained world where there’s always more work than time.  Resources are always tighter than tight and tough choices must be made. The first choice is to figure out what change you want to make in the world. How do you want put a dent in the universe? What injustice do you want to put to rest? Which paradigm do you want to turn on its head?

In business and in life, the question is the same – How do you want to spend your time?

Before you can move in the right direction, you need a direction. At this stage, the best way to allocate your resources is to define the system as it is. What’s going on right now? What are the fundamentals? What are the incentives? Who has power?  Who benefits when things move left and who loses when things go right?  What are the main elements of the system? How do they interact? What information passes between them? You know you’ve arrived when you have a functional model of the system with all the elements, all the interactions and all the information flows.

With an understanding of how things are, how do you want to spend your time? Do you want to validate your functional model? If yes, allocate your resources to test your model. Run small experiments to validate (or invalidate) your worldview.  If you have sufficient confidence in your model, allocate your resources to define how things could be.  How do you want the fundamentals to change? What are the new incentives? Who do you want to have the power? And what are the new system elements, their new interactions and new information flows?

When working in the domain of ‘what could be’ the only thing to worry about is what’s next. What’s after the next step?  Not sure. How many resources will be required to reach the finish line? Don’t know. What do we do after the next step? It depends on how it goes with this step. For those that are used to working within an efficiency framework this phase is a challenge, as there can be no grand plan, no way to predict when more resources will be needed and no way to guarantee resources will work efficiently. For the ‘what could be’ phase, it’s better to use a framework of effectiveness.

In a one-foot-in-front-of-the-other way, the only thing that matters in the ‘what could be’ domain is effectively achieving the next learning objective. It’s not important that the learning is done most efficiently, it matters that the learning is done well and done quickly.  Efficiently learning the wrong thing is not effective. Running experiments efficiently without learning what you need to is not effective. And learning slowly but efficiently is not effective.

Allocate resources to learn what needs to be learned. Allocate resources to learn effectively, not efficiently. Allocate your best people and give them the time they need.  And don’t expect an efficient path. There will be unplanned lefts and rights. There will be U-turns. There will times when there’s lots of thinking and little activity, but at this stage activity isn’t progress, thinking is. It may look like a drunkard’s walk, but that’s how it goes with this work.

When the objective of the work isn’t to solve the problem but to come up with the right question, allocate resources in a way that prioritizes effectiveness over efficiency. When working in the domain of ‘what could be’ allocate resources on the learning objective at hand. Don’t worry too much about the follow-on learning objectives because you may never earn the right to take them on.

In the domain of uncertainty, the best way to allocate the resources is to learn what you need to learn and then figure out what to learn next.

Image credit – John Flannery

What’s an innovator to do?

Disruption, as a word, doesn’t tell us what to do or how to do it.  Disruption, as a word, it’s not helpful and should be struck from the innovation lexicon.  But without the word, what’s an innovator to do?

If you have a superpower, misuse it. Your brand’s special capability is well known in your industry, but not in others. Thrust your uniqueness into an unsuspecting industry and provide novel value in novel ways. Take it by storm. Contradict the established players. Build momentum quickly and quietly.  Create a step function improvement. Create new lines of customer goodness. Do things that haven’t been done. Turn no to yes.

Don’t adapt your special capability, use it as-is. Adaptation is good, but it’s better to flop the whole thing into the new space.  Don’t think graft, think transplant.  Adaptation brings only continuous improvement.  It’s better to serve up your secret sauce uncut and unfiltered because that brings discontinuous improvement.

Know the needs your product fulfills and meet those needs in another industry.  Some say it’s better to adapt your product to other industries, and to achieve a reasonable CAGR, adaptation is good.  But if you’re looking for an unreasonable CAGR, if you’re looking to stand things on their head, try to use your product as-is. When you can use your product as-is in another industry, you connect dots only you can connect and meet needs in ways only you can.  You bring non-intuitive solutions. You violate routines of accepted practice and your trajectory is not limited by the incumbents’ ruts of success. You’ll have a whole new space for yourself. No sharing required.

But how?

Simply and succinctly, define what your product does.  Then, make it generic and look to misapply the goodness in a different application. For example, manufacturers of large and expensive furniture wrap their products in huge plastic bags to keep the furniture dry and clean during shipping. Generically, the function becomes: use large plastic bags to temporarily protect large and expensive products from becoming wet.  Using that goodness in a new application, people who live in flood areas use the large furniture bags to temporarily protect their cars from water damage.  Just before the flood arrives, they drive their cars into large plastic bags and tie them off.  The bags keep their car dry when the water comes.  Same bag, same goodness, completely unrelated application.

And there’s another way.  Your product has a primary function that provides value to your customers. But, there is unrealized value in your product that your existing customers don’t value. For example, if your company has a proprietary process to paint products in a way that results in a high gloss finish, your customers buy your coating because it looks good. But, the coating may also create a hard layer and increase wear resistance that could be important in another application. Because your coating is environmentally friendly and your process is low-cost, new customers may want you to coat their parts so they can be used in a previously non-viable application.  There is unrealized value in your products that new customers will pay for.

To see the unrealized value, use the strength-as-a-weakness method.  Define two constraints: you must sell to new customers in a new industry and the primary goodness, why people buy your product, must be a weakness.  For example, if your product is fast, you’ve got to use unrealized value to sell a slow one. If it’s heavy, the new one must be light. If small, the new one must be large.  In that way, you are forced to rely on new lines of goodness and unrealized value to sell your product.

Don’t stop continuous improvement and product adaptation.  They’re valuable. But, start some discontinuous improvement, step function increases and purposeful misuse.  Keep selling to the same value to the same customers, but start selling to new customers with previously unrealized value that has been hiding quietly in your product for years.

Evolution is good, but exaptation is probably better.

Image credit – Sor Betto

How To Reduce Innovation Risk

The trouble with innovation is it’s risky.  Sure, the upside is nice (increased sales), but the downside (it doesn’t work) is distasteful. Everyone is looking for the magic pill to change the risk-reward ratio of innovation, but there is no pill.  Though there are some things you can do to tip the scale in your favor.

All problems are business problems.  Problem solving is the key to innovation, and all problems are business problems.  And as companies embrace the triple bottom line philosophy, where they strive to make progress in three areas – environmental, social and financial, there’s a clear framework to define business problems.

Start with a business objective.  It’s best to define a business problem in terms of a shortcoming in business results. And the holy grail of business objectives is the growth objective.  No one wants to be the obstacle, but, more importantly, everyone is happy to align their career with closing the gap in the growth objective.  In that way, if solving a problem is directly linked to achieving the growth objective, it will get solved.

Sell more.  The best way to achieve the growth objective is to sell more. Bottom line savings won’t get you there.  You need the sizzle of the top line. When solving a problem is linked to selling more, it will get solved.

Customers are the only people that buy things.  If you want to sell more, you’ve got to sell it to customers. And customers buy novel usefulness.  When solving a problem creates novel usefulness that customers like, the problem will get solved.  However, before trying to solve the problem, verify customers will buy what you’re selling.

No-To-Yes.  Small increases in efficiency and productivity don’t cause customers to radically change their buying habits.  For that your new product or service must do something new. In a No-To-Yes way, the old one couldn’t but the new one can. If solving the problem turns no to yes, it will get solved.

Would they buy it? Before solving, make sure customers will buy the useful novelty. (To know, clearly define the novelty in a hand sketch and ask them what they think.) If they say yes, see the next question.

Would it meet our growth objectives? Before solving, do the math. Does the solution result in incremental sales larger than the growth objective? If yes, see the next question.

Would we commercialize it? Before solving, map out the commercialization work. If there are no resources to commercialize, stop.  If the resources to commercialize would be freed up, solve it.

Defining is solving. Up until now, solving has been premature. And it’s still not time. Create a functional model of the existing product or service using blocks (nouns) and arrows (verbs). Then, to create the problem(s), add/modify/delete functions to enable the novel usefulness customers will buy.  There will be at least one problem – the system cannot perform the new function. Now it’s time to take a deep dive into the physics and bring the new function to life.  There will likely be other problems.  Existing functions may be blocked by the changes needed for the new function. Harmful actions may develop or some functions will be satisfied in an insufficient way.  The key is to understand the physics in the most complete way.  And solve one problem at a time.

Adaptation before creation. Most problems have been solved in another industry. Instead of reinventing the wheel, use TRIZ to find the solutions in other industries and adapt them to your product or service.  This is a powerful lever to reduce innovation risk.

There’s nothing worse than solving the wrong problem.  And you know it’s the wrong problem if the solution doesn’t: solve a business problem, achieve the growth objective, create more sales, provide No-To-Yes functionality customers will buy, and you won’t allocate the resources to commercialize.

And if the problem successfully runs the gauntlet and is worth solving, spend time to define it rigorously.  To understand the bedrock physics, create a functional of the system, add the new functionality and see what breaks.  Then use TRIZ to create a generic solution, search for the solution across other industries and adapt it.

The key to innovation is problem solving. But to reduce the risk, before solving, spend time and energy to make sure it’s the right problem to solve.

It’s far faster to solve the right problem slowly than to solve the wrong one quickly.

Image credit – Kate Ter Haar

Dismantle the business model.

When companies want to innovate, there are three things they can change – products, services and business models. Products are usually the first, second and third priorities, services, though they have a tighter connection with customer and are more lasting and powerful, sadly, are fourth priority.  And business models are the superset and the most powerful of all, yet, as a source of innovation, are largely off limits.

It’s easy to improve products. Measure goodness using a standard test protocol, figure out what drives performance and improve it. Create the hard data, quantify the incremental performance and sell the difference.  A straightforward method to sell more – if you liked the last one, you’re going to like this one. But this is fleeting. Just as you are reverse engineering the competitors’ products, they’re doing it to you. Any incremental difference will be swallowed up by their next product. The half-life of your advantage is measured in months.

It’s easy for companies to run innovation projects to improve product performance because it’s easy to quantify the improvement and because we think customers are transactional. Truth is, customers are emotional, not rational. People don’t buy performance, they buy the story they create for themselves.

Innovating on services is more difficult because, unlike a product, it’s not a physical thing. You can’t touch it, smell it or taste it.  Some say you can measure a service, but you can’t. You can measure its footprints in the sand, but you can’t measure it directly. All the click data in the world won’t get you there because clicks, as measured, don’t capture intent – an unintentional click on the wrong image counts the same a premeditated click on the right one. Sure, you can count clicks, but if you can’t count the why’s, you don’t have causation. And, sure, you can measure customer satisfaction with an online survey, but the closest you can get is correlation and that’s not good enough.  It’s causation or bust.  You’ve got to figure out WHY they like your services. (Hint – it’s the people who interface directly with your customers and the latitude you give them to advocate on the customers’ behalf.)

Where services are difficult to innovate, the business model is almost impossible. No one is quite sure what the business model actually is an in-the-trenches-way, but they know it’s been responsible for the success of the company, and they don’t want to change it. Ultimately, if you want to innovate on the business model, you’ve got to know what it is, but before you spend the time and energy to define it, it’s best to figure out if it needs changing.  The question – what does it look like when the business model is out of gas?

If you do what you did last time and you get less in return, the business model is out of gas.

Successful models are limiting. Just like with the Prime Directive, where Captain Kirk could do anything he wanted as long as he didn’t interfere with the internal development of alien civilizations, do anything you want with the business model as long as you don’t change it. And that’s why you need external help to formally define the business model and experiment with it. The resource should understand your business first hand, yet be outside the chain of command so they can say the sacrilegious things that violate the Prime Directive without being fired.  For good candidates, look to trusted customers and suppliers.

To define the business model, use a simple block diagram (one page) where blocks are labelled with simple nouns and arrows are labelled with simple verbs. Start with a single block on the right of the page labelled “Customer” and draw a single arrow pointing to the block and label it.  Continue until you’ve defined the business model.  (Note – maximum number of blocks is 12.)  You’ll be surprised with the difficulty of the process.

After there’s consensus on the business model, the next step is to figure out how the environment changed around it and to identify and test the preferred evolutionary paths. But that’s for another time.

Image credit – Steven Depolo

Imagination

If you can’t imagine it, it can’t be done.

But if it can’t be done, how can you imagine it?

No one is buying a product like the one you imagined. There’s no market.

No one can buy an imaginative product that doesn’t yet exist. There may be a market.

Imagine things are good, just as they are.

Imagine an upstart competitor will obsolete your best product.

Let’s fix what is.

Let’s imagine what isn’t, and build it.

Don’t waste time imagining radical new concepts. There’s no way to get there.

Use your imagination to create an unobtainable concept, then build a bridge to get there.

Imagine the future profits of our great recipe. Let’s replicate it.

Imagine our recipe has a half-life. Let’s disrupt it.

To be competitive, we’ve got to use our imagination to reduce the cost of our products.

To be competitive, we’ve got to use our imagination to obsolete our best work.

Put together a specification, a detailed Gannt chart and make it happen on time.

Imagine what could be, and make a prototype.

Let’s shore up our weaknesses and live to fight another day.

Let’s imagine our strength as a weakness and invent the future.

We are the best in the industry. Imagine how tough it is to be our competitor.

Imagine there’s a hungry start-up who will do whatever it takes to get the business.

We’ve got to protect our market share.

Imagine what we could create if we weren’t constrained by our success.

Imagine how productive we will be when we standardize the work.

Imagine how much fun we will have when we reinvent the industry.

Ask the customer what they want, built it and launch it.

Imagine what could be, build a prototype, show the customer, listen and refine.

Let’s follow the script. Imagine the profits.

Let’s burn the script and imagine a new one.

Image credit — Allegra Ricci

Diabolically Simple Prototypes

Ideas are all talk and no action.  Ideas are untested concepts that have yet to rise to the level of practicality.  You can’t sell an idea and you can’t barter with them. Ideas aren’t worth much.

A prototype is a physical manifestation of an idea. Where ideas are ethereal, prototypes are practical. Where ideas are fuzzy and subject to interpretation, prototypes are a sledge hammer right between the eyes.  There is no arguing with a prototype. It does what it does and that’s the end of that. You don’t have to like what a prototype stands for, but you can’t dismiss it. Where ideas aren’t worth a damn, prototypes are wholly worth every ounce of effort to create them.

If Camp A says it will work and Camp B says it won’t, a prototype will settle the disagreement pretty quickly.  It will work or it won’t.  And if it works, the idea behind it is valid.  And if it doesn’t, the idea may be valid, but a workable solution is yet-to-be discovered.  Either way, a prototype brings clarity.

Prototypes are not elegant.  Prototypes are ugly. The best ones do one thing – demonstrate the novel idea that underpins them. The good ones are simple, and the best ones are diabolically simple. It is difficult to make diabolically simple prototypes (DSPs), but it’s a skill that can be learned.  And it’s worth learning because DSPs come to life in record time. The approach with DSPs is to take the time up front to distill the concept down to its essence and then its all-hands-on-deck until it’s up and running in the lab.

But the real power of the DSP is that it drives rapid learning.  When a new idea comes, it’s only a partially formed.  The process of trying to make a DSP demands the holes are filled and blurry parts are brought into focus.  The DSP process demands a half-baked idea matures into fully-baked physical embodiment.  And it’s full-body learning.  Your hands learn, your eyes learn and your torso learns.

If you find yourself in a disagreement of ideas, stop talking and start making a prototype. If the DSP works, the disagreement is over.

Diabolically simple prototypes end arguments. But, more importantly, they radically increase the pace of learning.

Image credit – snippets101

Mike Shipulski Mike Shipulski
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