When we conduct moderated research, like user interviews or usability tests, for our clients, we encourage them to observe as many sessions as possible. We find when clients see us interview their users, and get real-time responses, they’re able to learn about the needs of their users in real-time and be more active participants in the process. One way we help clients feel engaged with the process during remote sessions is to establish a real-time communication backchannel that empowers clients to flag responses they’d like to dig into further and to share their ideas for follow-up questions.

There are several benefits to establishing a communication backchannel for moderated sessions:

• Everyone on the team, including both internal and client team members, can be actively involved throughout the data collection process rather than waiting to passively consume findings.
• Team members can identify follow-up questions in real-time which allows the moderator to incorporate those questions during the current session, rather than just considering them for future sessions.
• Subject matter experts can identify more detailed and specific follow-up questions that the moderator may not think to ask.
• Even though the whole team is engaged, a single moderator still maintains control over the conversation which creates a consistent experience for the participant.

If you’re interested in creating your own backchannel, here are some tips to make the process work smoothly:

• Use the chat tool that is already being used on the project. In most cases, we use a joint Slack workspace for the session backchannel but we’ve also used Microsoft Teams.
• Create a dedicated channel like #moderated-sessions. Conversation in this channel should be limited to backchannel discussions during sessions. This keeps the communication consolidated and makes it easier for the moderator to stay focused during the session.
• Keep communication limited. Channel participants should ask basic questions that are easy to consume quickly. Supplemental commentary and analysis should not take place in the dedicated channel.
• Use emoji responses. The moderator can add a quick thumbs up to indicate that they’ve seen a question.

Introducing backchannels for communication during remote moderated sessions has been a beneficial change to our research process. It not only provides an easy way for clients to stay engaged during the data collection process but also increases the moderator’s ability to focus on the most important topics and to ask the most useful follow-up questions.

In my last post, I covered what LiveView is at a high level. In this series, we’re going to dive deeper and implement a LiveView powered Markdown editor called Frampton. This series assumes you have some familiarity with Phoenix and Elixir, including having them set up locally. Check out Elizabeth’s three-part series on getting started with Phoenix for a refresher.

This series has a companion repository published on GitHub. Get started by cloning it down and switching to the starter branch. You can see the completed application on master. Our goal today is to make a Markdown editor, which allows a user to enter Markdown text on a page and see it rendered as HTML next to it in real-time. We’ll make use of LiveView for the interaction and the Earmark package for rendering Markdown. The starter branch provides some styles and installs LiveView.

Rendering Markdown

Let’s set aside the LiveView portion and start with our data structures and the functions that operate on them. To begin, a Post will have a body, which holds the rendered HTML string, and title. A string of markdown can be turned into HTML by calling Post.render(post, markdown). I think that just about covers it!

First, let’s define our struct in lib/frampton/post.ex:

Now the failing test (in test/frampton/post_test.exs):

Our render method will just be a wrapper around Earmark.as_html!/2 that puts the result into the body of the post. Add {:earmark, "~> 1.4.3"} to your deps in mix.exs, run mix deps.get and fill out render function:

Our test should now pass, and we can render posts! [Note: we’re using the as_html! method, which prints error messages instead of passing them back to the user. A smarter version of this would handle any errors and show them to the user. I leave that as an exercise for the reader…] Time to play around with this in an IEx prompt (run iex -S mix in your terminal):

Great! That’s exactly what we’d expect. You can find the final code for this in the render_post branch.

LiveView Editor

Now for the fun part: Editing this live!

First, we’ll need a route for the editor to live at: /editor sounds good to me. LiveViews can be rendered from a controller, or directly in the router. We don’t have any initial state, so let's go straight from a router.

First, let's put up a minimal test. In test/frampton_web/live/editor_live_test.exs:

This test doesn’t do much yet, but notice that it isn’t live view specific. Our first render is just the same as any other controller test we’d write. The page’s content is there right from the beginning, without the need to parse JavaScript or make API calls back to the server. Nice.

To make that test pass, add a route to lib/frampton_web/router.ex. First, we import the LiveView code, then we render our Editor:

Now place a minimal EditorLive module, in lib/frampton_web/live/editor_live.ex:

And we have a passing test suite! The ~L sigil designates that LiveView should track changes to the content inside. We could keep all of our markup in this render/1 method, but let’s break it out into its own template for demonstration purposes.

Move the contents of render into lib/frampton_web/templates/editor/show.html.leex, and replace EditorLive.render/1 with this one liner: def render(assigns), do: FramptonWeb.EditorView.render("show.html", assigns). And finally, make an EditorView module in lib/frampton_web/views/editor_view.ex:

Our test should now be passing, and we’ve got a nicely separated out template, view and “live” server. We can keep markup in the template, helper functions in the view, and reactive code on the server. Now let’s move forward to actually render some posts!

Handling User Input

We’ve got four tasks to accomplish before we are done:

1. Take markdown input from the textarea
2. Send that input to the LiveServer
3. Turn that raw markdown into HTML
4. Return the rendered HTML to the page.

Event binding

To start with, we need to annotate our textarea with an event binding. This tells the liveview.js framework to forward DOM events to the server, using our liveview channel. Open up lib/frampton_web/templates/editor/show.html.leex and annotate our textarea:

<textarea phx-keyup="render_post"></textarea>

This names the event (render_post) and sends it on each keyup. Let’s crack open our web inspector and look at the web socket traffic. Using Chrome, open the developer tools, navigate to the network tab and click WS. In development you’ll see two socket connections: one is Phoenix LiveReload, which polls your filesystem and reloads pages appropriately. The second one is our LiveView connection. If you let it sit for a while, you’ll see that it's emitting a “heartbeat” call. If your server is running, you’ll see that it responds with an “ok” message. This lets LiveView clients know when they've lost connection to the server and respond appropriately.

Now, type some text and watch as it sends down each keystroke. However, you’ll also notice that the server responds with a “phx_error” message and wipes out our entered text. That's because our server doesn’t know how to handle the event yet and is throwing an error. Let's fix that next.

Event handling

We’ll catch the event in our EditorLive module. The LiveView behavior defines a handle_event/3 callback that we need to implement. Open up lib/frampton_web/live/editor_live.ex and key in a basic implementation that lets us catch events:

The first argument is the name we gave to our event in the template, the second is the data from that event, and finally the socket we’re currently talking through. Give it a try, typing in a few characters. Look at your running server and you should see a stream of events that look something like this:

There’s our keystrokes! Next, let’s pull out that value and use it to render HTML.

Rendering Markdown

Lets adjust our handle_event to pattern match out the value of the textarea:

def handle_event("render_post", %{"value" => raw}, socket) do

Now that we’ve got the raw markdown string, turning it into HTML is easy thanks to the work we did earlier in our Post module. Fill out the body of the function like this:

If you type into the textarea you should see output that looks something like this:

Perfect! Lastly, it’s time to send that rendered html back to the page.

Returning HTML to the page

In a LiveView template, we can identify bits of dynamic data that will change over time. When they change, LiveView will compare what has changed and send over a diff. In our case, the dynamic content is the post body.

Open up show.html.leex again and modify it like so:

Refresh the page and see:

Whoops!

The @post variable will only be available after we put it into the socket’s assigns. Let’s initialize it with a blank post. Open editor_live.ex and modify our mount/3 function:

In the future, we could retrieve this from some kind of storage, but for now, let's just create a new one each time the page refreshes. Finally, we need to update the Post struct with user input. Update our event handler like this:

Let's load up http://localhost:4000/editor and see it in action.

Nope, that's not quite right! Phoenix won’t render this as HTML because it’s unsafe user input. We can get around this (very good and useful) security feature by wrapping our content in a raw/1 call. We don’t have a database and user processes are isolated from each other by Elixir. The worst thing a malicious user could do would be crash their own session, which doesn’t bother me one bit.

Check the edit_posts branch for the final version.

Conclusion

That’s a good place to stop for today. We’ve accomplished a lot! We’ve got a dynamically rendering editor that takes user input, processes it and updates the page. And we haven’t written any JavaScript, which means we don’t have to maintain or update any JavaScript. Our server code is built on the rock-solid foundation of the BEAM virtual machine, giving us a great deal of confidence in its reliability and resilience.

In the next post, we’ll tackle making a shared editor, allowing multiple users to edit the same post. This project will highlight Elixir’s concurrency capabilities and demonstrate how LiveView builds on them to enable some incredible user experiences.

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!

I get into this situation sometimes. Maybe you do too. I merge feature work into a branch used to collect features, and then continue development but on that branch instead of back on the feature branch

and have to move those commits to the feature branch they belong in and take them out of the throwaway accumulator branch

Maybe you prefer

over

Either way, that works. But it'd be nicer if we didn't have to type or even remember the branches' names and the remote's name. They are what is keeping this from being a context-independent string of commands you run any time this mistake happens. That's what we're going to solve here.

Shorthands for longevity

I like to use all possible natively supported shorthands. There are two broad motivations for that.

1. Fingers have a limited number of movements in them. Save as many as possible left late in life.
2. Current research suggests that multitasking has detrimental effects on memory. Development tends to be very heavy on multitasking. Maybe relieving some of the pressure on quick-access short term memory (like knowing all relevant branch names) add up to leave a healthier memory down the line.

First up for our scenario: the - shorthand, which refers to the previously checked out branch. There are a few places we can't use it, but it helps a lot:

We cannot use - when cherry-picking a range

and even if we could we'd still have provide the remote's name (here, origin).

That shorthand doesn't apply in the later reset --hard command, and we cannot use it in the branch -D && checkout approach either. branch -D does not support the - shorthand and once the branch is deleted checkout can't reach it with -:

So we have to remember the remote's name (we know it's origin because we are devoting memory space to knowing that this isn't one of those times it's something else), the remote tracking branch's name, the local branch's name, and we're typing those all out. No good! Let's figure out some shorthands.

@{-<n>} is hard to say but easy to fall in love with

We can do a little better by using @{-<n>} (you'll also sometimes see it referred to be the older @{-N}). It is a special construct for referring to the nth previously checked out ref.

Back in our scenario, we're on qa-environment, we switch to feature, and then want to refer to qa-environment. That's @{-1}! So instead of

We can do

Here's where we are (🎉 marks wins from -, 💥 marks the win from @{-1})

One down, two to go: we're still relying on memory for the remote's name and the remote branch's name and we're still typing both out in full. Can we replace those with generic shorthands?

@{-1} is the ref itself, not the ref's name, we can't do

because there is no branch origin/@{-1}. For the same reason, @{-1} does not give us a generalized shorthand for the scenario's later git reset --hard origin/qa-environment command.

But good news!

Do @{u} @{push}

@{upstream} or its shorthand @{u} is the remote branch a that would be pulled from if git pull were run. @{push} is the remote branch that would be pushed to if git push was run.

we can

Tacking either onto a branch name will give that branch's @{upstream} or @{push}. For example

is the branch branch-a pulls from.

In the common workflow where a branch pulls from and pushes to the same branch, @{upstream} and @{push} will be the same, leaving @{u} as preferable for its terseness. @{push} shines in triangular workflows where you pull from one remote and push to another (see the external links below).

Going back to our scenario, it means short, portable commands with a minimum human memory footprint. (🎉 marks wins from -, 💥 marks the win from @{-1}, 😎 marks the wins from @{u}.)

Make the things you repeat the easiest to do

Because these commands are generalized, we can run some series of them once, maybe

or

and then those will be in the shell history just waiting to be retrieved and run again the next time, whether with CtrlR incremental search or history substring searching bound to the up arrow or however your interactive shell is configured. Or make it an alias, or even better an abbreviation if your interactive shell supports them. Save the body wear and tear, give memory a break, and level up in Git.

And keep going

The GitHub blog has a good primer on triangular workflows and how they can polish your process of contributing to external projects.

The FreeBSD Wiki has a more in-depth article on triangular workflow process (though it doesn't know about @{push} and @{upstream}).

The construct @{-<n>} and the suffixes @{push} and @{upstream} are all part of the gitrevisions spec. Direct links to each:

• @{-<n>}
  

• @{push}
  

• @{upstream}
  

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.

Video conference tools are an indispensable part of the Plague Times. Google Meet, Microsoft Teams, Zoom, and their compatriots are keeping us close and connected in a physically distanced world.

As tech-savvy folks with years of cross-office collaboration, we’ve laughed at the sketches and memes about vidconf mishaps. We practice good Zoomiquette, including muting ourselves when we’re not talking.

Yet even we can’t escape one vidconf pitfall. (There but for the grace of Zoom go I.) On nearly every vidconf, someone starts to talk, and then someone else says: “Oop, you’re muted.” And, inevitably: “Oop, you’re still muted.”

That’s right: we’re trying to follow Zoomiquette by muting, but then we forget or struggle to unmute when we do want to talk.

In this post, I’ll share my theories for why the You’re Muted Problems are so pervasive, using Google Meet, Microsoft Teams, and Zoom as examples. Spoiler alert: While I hope this will help you be more mindful of the problem, I can’t offer a good solution. It still happens to me. All. The. Time.

Skip the why and go straight to the vidconf app keyboard shortcuts you should memorize right now.

Why we don't realize we’re muted before talking

Why does this keep happening?!?

Simply put: UX and design decisions make it harder to remember that you’re muted before you start to talk.

Here’s a common scenario: You haven’t talked for a bit, so you haven’t interacted with the Zoom screen for a few seconds. Then you start to talk — and that’s when someone tells you, “You’re muted.”

We forget so easily in these scenarios because when our mouse has been idle for a few seconds, the apps hide or downplay the UI elements that tell us we’re muted.

Zoom and Teams are the worst offenders:

• Zoom hides both the toolbar with the main in-app controls (the big mute button) and the mute status indicator on your video pane thumbnail.
• Teams hides the toolbar, and doesn't show a mute status indicator on your video thumbnail in the first place.

Meet is only slightly better:

• Meet hides the toolbar, and shows only a small mute status icon in your video thumbnail.

Even when our mouse is active, the apps’ subtle approach to muted state UI can make it easy to forget that we’re muted:

Teams is the worst offender:

• The mute button is an icon rather than words.
• The muted-state icon's styling could be confused with unmuted state: Teams does not follow the common pattern of using red to denote muted state.
• The mute button is not differentiated in visual hierarchy from all the other controls.
• As mentioned above, Teams never shows a secondary mute status indicator.

Zoom is a bit better, but still makes it pretty easy to forget that you’re muted:

• Pros:
  • Zoom is the only app to use words on the mute button, in this case to denote the button action (rather than the muted state).
  • The muted-state icon’s styling (red line) is less likely to be confused with the unmuted-state icon.
• Cons:
  • The mute button’s placement (bottom left corner of the page) is easy to overlook.
  • The mute button is not differentiated in visual hierarchy from the other toolbar buttons — and Zoom has a lot of toolbar buttons, especially when logged in as host.
  • The secondary mute status indicator is a small icon.
  • The mute button’s muted-state icon is styled slightly differently from the secondary mute status indicator.
• Potential Cons:
  • While words denote the button action, only an icon denotes the muted state.

Meet is probably the clearest of the three apps, but still has pitfalls:

• Pros:
  • The mute button is visually prominent in the UI: It’s clearly differentiated in the visual hierarchy relative to other controls (styled as a primary button); is a large button; and is placed closer to the center of the controls bar.
  • The muted-state icon’s styling (red fill) is less likely to be confused with the unmuted-state icon.
• Cons:
  • Uses only an icon rather than words to denote the muted state.
• Unrelated Con:
  • While the mute button is visually prominent, it’s also placed next to the hang-up button. So in Meet’s active state you might be less likely to forget you’re muted … but more likely to accidentally hang up when trying to unmute. 😬
    

I know modern app design leans toward minimalism. There’s often good rationale to use icons rather than words, or to de-emphasize controls and indicators when not in use.

But again: This happens on basically every call! Often multiple times per call!! And we’re supposed to be tech-savvy!!! Imagine what it’s like for the tens of millions of vidconf newbs.

I would argue that “knowing your muted state” has turned out to be a major vidconf user need. At this point, it’s certainly worth rethinking UX patterns for.

Why we keep unsuccessfully unmuting once we realize we’re muted

So we can blame the You’re Muted Problem on UX and design. But what causes the You’re Still Muted Problem? Once we know we’re muted, why do we sometimes fail to unmute before talking again?

This one is more complicated — and definitely more speculative. To start making sense of this scenario, here’s the sequence I’m guessing most commonly plays out (I did this a couple times before I became aware of it):

The crucial part is when the person tries to unmute by pressing the keyboard Volume On/Off key.

If that’s in fact what’s happening (again, this is just a hypothesis), I’m guessing they did that because when someone says “You’re muted” or “I can’t hear you,” our subconscious thought process is: “Oh, Audio is Off. Press the keyboard key that I usually press when I want to change Audio Off to Audio On.”

There are two traps in this reflexive thought process:

First, the keyboard volume keys control the speaker volume, not the microphone volume. (More specifically, they control the system sound output settings, rather than the system sound input settings or the vidconf app’s sound input settings.)

In fact, there isn’t a keyboard key to control the microphone volume. You can’t unmute your mic via a dedicated keyboard key, the way that you can turn the speaker volume on/off via a keyboard key while watching a movie or listening to music.

Second, I think we reflexively press the keyboard key anyway because our mental model of the keyboard audio keys is just: Audio. Not microphone vs. speaker.

This fuzzy mental model makes sense: There’s only one set of keyboard keys related to audio, so why would I think to distinguish between microphone and speaker? 

So my best guess is hardware design causes the You’re Still Muted Problem. After all, keyboard designs are from a pre-Zoom era, when the average person rarely used the computer’s microphone.

If that is the cause, one potential solution is for hardware manufacturers to start including dedicated keys to control microphone volume:

Video conference keyboard shortcuts you should memorize right now

Let me know if you have other theories for the You’re Still Muted Problem!

In the meantime, the best alternative is to learn all of the vidconf app keyboard shortcuts for muting/unmuting:

• Meet
  • Mac: Command(⌘) + D
  • Windows: Control + D
• Teams
  • Mac: Command(⌘) + Shift + M
  • Windows: Ctrl + Shift + M
• Zoom
  • Mac: Command(⌘) + Shift + A
  • Windows: Alt + A
  • Hold Spacebar: Temporarily unmute

Other vidconf apps not included in my analysis:

• Cisco Webex Meetings
  • Mac: Ctrl + Alt + M
  • Windows: Ctrl + Shift + M
• GoToMeeting
  • Mac: No keyboard shortcut?
  • Windows: Ctrl + Alt + A

Bonus protip from Jackson Fox: If you use multiple vidconf apps, pick a keyboard shortcut that you like and manually change each app’s mute/unmute shortcut to that. Then you only have to remember one shortcut!