The classic adage is “good design speaks for itself.” Which would mean that if something’s as good of an idea as you think it is, a client will instantly see that it’s good too, right?

Here at Viget, we’re always working with new and different clients. Each with their own challenges and sensibilities. But after ten years of client work, I can’t help but notice a pattern emerge when we’re trying to get approval on especially cool, unconventional parts of a design.

So let’s break down some of those patterns to hopefully better understand why clients hesitate, and what strategies we’ve been using lately to help get the work we’re excited about approved.

Imagine this: the parallax homepage with elements that move around in surprising ways or a unique navigation menu that conceptually reinforces a site’s message. The way the content cards on a page will, like, be literal cards that will shuffle and move around. Basically, any design that feels like an exciting, novel challenge, will need the client to “get it.” And that often turns out to be the biggest challenge of all.

There are plenty of practical reasons cool designs get shot down. A client is usually more than one stakeholder, and more than the team of people you’re working with directly. On any project, there’s an amount of telephone you end up playing. Or, there’s always the classic foes: budgets and deadlines. Any idea should fit in those predetermined constraints. But as a project goes along, budgets and deadlines find a way to get tighter than you planned.

But innovative designs and interactions can seem especially scary for clients to approve. There’s three fears that often pop up on projects:

The fear of change. 

Maybe the client expected something simple, a light refresh. Something that doesn’t challenge their design expectations or require more time and effort to understand. And on our side, maybe we didn’t sufficiently ease them into our way of thinking and open them up to why we think something bigger and bolder is the right solution for them. Baby steps, y’all.

The fear of the unknown. 

Or, less dramatically, a lack of understanding of the medium. In the past, we have struggled with how to present an interactive, animated design to a client before it’s actually built. Looking at a site that does something conceptually similar as an example can be tough. It’s asking a lot of a client’s imagination to show them a site about boots that has a cool spinning animation and get meaningful feedback about how a spinning animation would work on their site about after-school tutoring. Or maybe we’ve created static designs, then talked around what we envision happening. Again, what seems so clear in our minds as professionals entrenched in this stuff every day can be tough for someone outside the tech world to clearly understand.

The fear of losing control. 

We’re all about learning from past mistakes. So lets say, after dealing with that fear of the unknown on a project, next time you go in the opposite direction. You invest time up front creating something polished. Maybe you even get the developer to build a prototype that moves and looks like the real thing. You’ve taken all the vague mystery out of the process, so a client will be thrilled, right? Surprise, probably not! Most clients are working with you because they want to conquer the noble quest that is their redesign together. When we jump straight to showing something that looks polished, even if it’s not really, it can feel like we jumped ahead without keeping them involved. Like we took away their input. They can also feel demotivated to give good, meaningful feedback on a polished prototype because it looks “done.”

So what to do? Lately we have found low-fidelity prototypes to be a great tool for combating these fears and better communicating our ideas.

What are low-fidelity prototypes?

Low fidelity prototypes are a tool that designers can create quickly to illustrate an idea, without sinking time into making it pixel-perfect. Some recent examples of prototypes we've created include a clickable Figma or Invision prototype put together with Whimsical wireframes:

A rough animation created in Principle illustrating less programatic animation:

And even creating an animated storyboard in Photoshop:

They’re rough enough that there’s no way they could be confused for a final product. But customized so that a client can immediately understand what they’re looking at and what they need to respond to. Low-fidelity prototypes hit a sweet spot that addresses those client fears head on.

That fear of change? A lo-fi prototype starts rough and small, so it can ease a client into a dramatic change without overwhelming them. It’s just a first step. It gives them time to react and warm up to something that’ll ultimately be a big change.

It also cuts out the fear of the unknown. Seeing something moving around, even if it’s rough, can be so much more clear than talking ourselves in circles about how we think it will move, and hoping the client can imagine it. The feature is no longer an enigma cloaked in mystery and big talk, but something tangible they can point at and ask concrete questions about.

And finally, a lo-fi prototype doesn’t threaten a client’s sense of control. Low-fidelity means it’s clearly still a work in progress! It’s just an early step in the creative process, and therefore communicates that we’re still in the middle of that process together. There’s still plenty of room for their ideas and feedback.

Lo-fi prototypes: client-tested, internal team-approved

There are a lot of reasons to love lo-fi prototypes internally, too!

They’re quick and easy. 

We can whip up multiple ideas within a few hours, without sinking the time into getting our hearts set on any one thing. In an agency setting especially, time is limited, so the faster we can get an idea out of our own heads, the better.

They’re great to share with developers. 

Ideally, the whole team is working together simultaneously, collaborating every step of the way. Realistically, a developer often doesn’t have time during a project’s early design phase. Lo-fi prototypes are concrete enough that a developer can quickly tell if building an idea will be within scope. It helps us catch impractical ideas early and helps us all collaborate to create something that’s both cool and feasible.

Stay tuned for posts in the near future diving into some of our favorite processes for creating lo-fi prototypes!

Viget is full of outdoor enthusiasts and, of course, technologists. For this year's Pointless Weekend, we brought these passions together to build TrailBuddy. This app aims to solve that eternal question: Is my favorite trail dry so I can go hike/run/ride?

While getting muddy might rekindle fond childhood memories for some, exposing your gear to the elements isn’t great – it’s bad for your equipment and can cause long-term, and potentially expensive, damage to the trail.

There are some trail apps out there but we wanted one that would focus on current conditions. Currently, our favorites trail apps, like mtbproject.com, trailrunproject.com, and hikingproject.com -- all owned by REI, rely on user-reported conditions. While this can be effective, the reports are frequently unreliable, as condition reports can become outdated in just a few days.

Our goal was to solve this problem by building an app that brought together location, soil type, and weather history data to create on-demand condition predictions for any trail in the US.

We built an initial version of TrailBuddy by tapping into several readily-available APIs, then running the combined data through a machine learning algorithm. (Oh, and also by bringing together a bunch of smart and motivated people and combining them with pizza and some of the magic that is our Pointless Weekends. We'll share the other Pointless Project, Scurry, with you soon.)

The quest for data.

We knew from the start this app would require data from a number of sources. As previously mentioned, we used REI’s APIs (i.e. https://www.hikingproject.com/data) as the source for basic trail information. We used the trails’ latitude and longitude coordinates as well as its elevation to query weather and soil type. We also found data points such as a trail’s total distance to be relevant to our app users and decided to include that on the front-end, too. Since we wanted to go beyond relying solely on user-reported metrics, which is how REI’s current MTB project works, we came up with a list of factors that could affect the trail for that day.

First on that list was weather.

We not only considered the impacts of the current forecast, but we also looked at the previous day’s forecast. For example, it’s safe to assume that if it’s currently raining or had been raining over the last several days, it would likely lead to muddy and unfavorable conditions for that trail. We utilized the DarkSky API (https://darksky.net/dev) to get the weather forecasts for that day, as well as the records for previous days. This included expected information, like temperature and precipitation chance. It also included some interesting data points that we realized may be factors, like precipitation intensity, cloud cover, and UV index. 

But weather alone can’t predict how muddy or dry a trail will be. To determine that for sure, we also wanted to use soil data to help predict how well a trail’s unique soil composition recovers after precipitation. Similar amounts of rain on trails of very different soil types could lead to vastly different trail conditions. A more clay-based soil would hold water much longer, and therefore be much more unfavorable, than loamy soil. Finding a reliable source for soil type and soil drainage proved incredibly difficult. After many hours, we finally found a source through the USDA that we could use. As a side note—the USDA keeps track of lots of data points on soil information that’s actually pretty interesting! We can’t say we’re soil experts but, we felt like we got pretty close.

Putting our design hats on.

From the very first pitch for this app, TrailBuddy’s main differentiator to peer trail resources is its ability to surface real-time information, reliably, and simply. For as complicated as the technology needed to collect and interpret information, the front-end app design needed to be clean and unencumbered.

We thought about how users would naturally look for information when setting out to find a trail and what factors they’d think about when doing so. We posed questions like:

• How easy or difficult of a trail are they looking for?
• How long is this trail?
• What does the trail look like?
• How far away is the trail in relation to my location?
• For what activity am I needing a trail for?
• Is this a trail I’d want to come back to in the future?

By putting ourselves in our users’ shoes we quickly identified key features TrailBuddy needed to have to be relevant and useful. First, we needed filtering, so users could filter between difficulty and distance to narrow down their results to fit the activity level. Next, we needed a way to look up trails by activity type—mountain biking, hiking, and running are all types of activities REI’s MTB API tracks already so those made sense as a starting point. And lastly, we needed a way for the app to find trails based on your location; or at the very least the ability to find a trail within a certain distance of your current location.

Using machine learning to predict trail conditions.

As stated earlier, none of us are actual soil or data scientists. So, in order to achieve the real-time conditions reporting TrailBuddy promised, we’d decided to leverage machine learning to make predictions for us. Digging into the utility of machine learning was a first for all of us on this team. Luckily, there was an excellent tutorial that laid out the basics of building an ML model in Python. Provided a CSV file with inputs in the left columns, and the desired output on the right, the script we generated was able to test out multiple different model strategies, and output the effectiveness of each in predicting results, shown below.

We assembled all of the historical weather and soil data we could find for a given latitude/longitude coordinate, compiled a 1000 * 100 sized CSV, ran it through the Python evaluator, and found that the CART and SVM models consistently outranked the others in terms of predicting trail status. In other words, we found a working model for which to run our data through and get (hopefully) reliable predictions from. The next step was to figure out which data fields were actually critical in predicting the trail status. The more we could refine our data set, the faster and smarter our predictive model could become.

We pulled in some Ruby code to take the original (and quite massive) CSV, and output smaller versions to test with. Now again, we’re no data scientists here but, we were able to cull out a good majority of the data and still get a model that performed at 95% accuracy.

With our trained model in hand, we could serialize that to into a model.pkl file (pkl stands for “pickle”, as in we’ve “pickled” the model), move that file into our Rails app along with it a python script to deserialize it, pass in a dynamic set of data, and generate real-time predictions. At the end of the day, our model has a propensity to predict fantastic trail conditions (about 99% of the time in fact…). Just one of those optimistic machine learning models we guess.

Where we go from here.

It was clear that after two days, our team still wanted to do more. As a first refinement, we’d love to work more with our data set and ML model. Something that was quite surprising during the weekend was that we found we could remove all but two days worth of weather data, and all of the soil data we worked so hard to dig up, and still hit 95% accuracy. Which … doesn’t make a ton of sense. Perhaps the data we chose to predict trail conditions just isn’t a great empirical predictor of trail status. While these are questions too big to solve in just a single weekend, we'd love to spend more time digging into this in a future iteration.

We have a lot of traditions here at Viget, many of which you may have read about - TTT, FLF, Pointless Weekend. There are others, but you have to be an insider for more information on those.

Pointless Weekend is one of our favorite traditions, though. It’s been around over a decade and some pretty fun work has come out of it over the years, like Storyboard, Baby Bookie, and Short Order. At a high level, we take 48 hours to build a tool, experiment, or stunt as a team, across all four of our offices. These projects are entirely separate from our client work and we use them to try out new technologies, explore roles on the team, and stress-test our processes.

The first step for a Pointless Weekend is assembling the teams. We had two teams this year, with a record number of participants. You can read about TrailBuddy, what the other team built, here.

Once we were assembled, we set out to understand the constraints and the goals of our Pointless Project. We went into this weekend with an extra pep in our step, as we were determined to build something for the upcoming Viget 20th anniversary TTT this summer. Here’s what we knew we wanted:

1. An activity all Vigets could do together, where they could create memories, and share broadly on social
2. Something that we could use in a spotty network at C Lazy U Ranch in Colorado
3. A product we can share with others: corporate groups, families and friends, schools, bachelor/ette parties

We landed on a scavenger hunt native app, which we named Scurry (Scavenger + Hurry = Scurry. Brilliant, right?). There are already a few scavenger apps available, so we set out to create something that was

• Quick and easy to set up hunts
• Free and intuitive for users
• A nice combination of trivia and activities
• Social! We wanted to enable teams to share photos and progress

One of the main reasons we have Pointless Weekends is to test out new technologies and processes. In that vein, we tried out Notion as our central organizing tool - we used it for user journeys, data modeling, and even writing tickets, which we typically use Github for.

When we built the app, we needed to prepare for spotty network service, as internet connectivity isn’t guaranteed at C Lazy U Ranch – where our Viget20 celebration will be. A Progressive Web Application (PWA) didn't make sense for our tech requirements, so we chose the route of creating a native application.

There are a number of options available to build native applications. But, as we were looking to make as much progress as possible in 48-hours, we chose one of our favorite frameworks: React Native. React Native allows developers to build true, cross-platform native applications, using some of our favorite technologies: javascript, the React framework, and a native-specific variant of CSS. We decided on the turn-key solution Expo. Expo has extra tooling allowing for easy development, deployment, and debugging.

Our frontend developers were able to immediately dive in making screens and styling components, and quickly made the mockups in Whimsical a reality.

On the backend, we used the supported library to connect to the backend datastore, Firebase. Firebase is a hosted solution for data storage, with key features built-in like authentication, realtime updates, and offline support. Our backend developer worked behind the frontend developers hooking those views up to live data.

Both of these tools, Expo and Firebase, were easy to use and allowed us to focus on building a working application quickly, rather than being mired in setup or bespoke solutions to common problems.

We made impressive progress in our 48-hour sprint, but there’s still some work to do. We have some additional features we hope to add before TTT, which will require additional testing and refining. For now, stay tuned and sign up for our newsletter. We’ll be sure to share when Scurry is ready for the world!

Yesterday (Apr 22, 2020) Phoenix 1.5 was officially released ????

There’s a long list of changes and improvements, but the big feature is better integration with LiveView. I’ve previously written about why LiveView interests me, so I was quite excited to dive into this release. After watching this awesome Twitter clone in 15 minutes demo from Chris McCord, I had to try out some of the new features. I generated a new phoenix app with the —live flag, installed dependencies and started a server. Here are five new features I noticed.

1. Database actions in browser

Oops! Looks like I forgot to configure the database before starting the server. There’s now a helpful message and a button in the browser that can run the command for me. There’s a similar button when migrations are pending. This is a really smooth UX to fix a very common error while developing.

2. New Tagline!

Peace-of-mind from prototype to production

This phrase looked unfamiliar, so I went digging. Turns out that the old tagline was “A productive web framework that does not compromise speed or maintainability.” (I also noticed that it was previously “speed and maintainability” until this PR from 2019 was opened on a dare to clarify the language.)

Chris McCord updated the language while adding phx.new —live. I love this framing, particularly for LiveView. I am very excited about the progressive enhancement path for LiveView apps. A project can start out with regular, server rendered HTML templates. This is a very productive way to work, and a great way to start a prototype for just about any website. Updating those templates to work with LiveView is an easier lift than a full rebuild in React. And finally, when you’re in production you have the peace-of-mind that the reliable BEAM provides.

3. Live dependency search

There’s now a big search bar right in the middle of the page. You can search through the dependencies in your app and navigate to the hexdocs for them. This doesn’t seem terribly useful, but is a cool demo of LiveView. The implementation is a good illustration of how compact a feature like this can be using LiveView.

4. LiveDashboard

This is the really cool one. In the top right of that page you see a link to LiveDashboard. Clicking it will take you to a page that looks like this.

This page is built with LiveView, and gives you a ton of information about your running system. This landing page has version numbers, memory usage, and atom count.

Clicking over to metrics brings you to this page.

By default it will tell you how long average queries are taking, but the metrics are configurable so you can define your own custom telemetry options.

The other tabs include process info, so you can monitor specific processes in your system:

And ETS tables, the in memory storage that many apps use for caching:

The dashboard is a really nice thing to get out of the box and makes it free for application developers to monitor their running system. It’s also developing very quickly. I tried an earlier version a week ago which didn’t support ETS tables, ports or sockets. I made a note to look into adding them, but it's already done! I’m excited to follow along and see where this project goes.

5. New LiveView generators

1.5 introduces a new generator mix phx.gen.live.. Like other generators, it will create all the code you need for a basic resource in your app, including the LiveView modules. The interesting part here is that it introduces patterns for organizing LiveView code, which is something I have previously been unsure about. At first glance, the new organization makes sense and feels like a good approach. I look forward to seeing how this works on a real project.

Conclusion

The 1.5 release brings more changes under the hood of course, but these are the first five differences you’ll notice after generating a new Phoenix 1.5 app with LiveView. Congratulations to the entire Phoenix team, but particularly José Valim and Chris McCord for getting this work released.