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.

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.

Professional certifications have become increasingly popular in this age of career switchers and the freelance gig economy. A certification can be a useful way to advance your skill set quickly or make your resume stand out, which can be especially important for those trying to break into a new industry or attract business while self-employed. Whatever your reason may be for pursuing a professional certificate, there is one question only you can answer for yourself: is it worth it?

Finding first-hand experiences from professionals with similar career goals and passions was the most helpful research I used to answer that question for myself. So, here’s mine; why I decided to get Scrum certified, how I evaluated my options, and if it was really worth it.

A shift in mindset

My background originates in brand strategy where it’s typical for work to follow a predictable order, each step informing the next. This made linear techniques like water-fall timelines, completing one phase of work in its entirety before moving onto the next, and documenting granular tasks weeks in advance helpful and easy to implement. When I made the move to more digitally focused work, tasks followed a much looser set of ‘typical’ milestones. While the general outline remained the same (strategy, design, development, launch) there was a lot more overlap with how tasks informed each other, and would keep informing and re-informing as an iterative workflow would encourage.

Trying to fit a very fluid process into my very stiff linear approach to project planning didn’t work so well. I didn’t have the right strategies to manage risks in a productive way without feeling like the whole project was off track; with the habit of account for granular details all the time, I struggled to lean on others to help define what we should work on and when, and being okay if that changed once, or twice, or three times. Everything I learned about the process of product development came from learning on the job and making a ton of mistakes—and I knew I wanted to get better.

I was fortunate enough to work with a group of developers who were looking to make a change, too. Being ‘agile’-enthusiasts, this group of developers were desperately looking for ways to infuse our approach to product work with agile-minded principles (the broad definition of ‘agile’ comes from ‘The Agile Manifesto’, which has influenced frameworks for organizing people and information, often applied in product development). This not only applied to how I worked with them, but how they worked with each other, and the way we all onboarded clients to these new expectations. This was a huge eye opener to me. Soon enough, I started applying these agile strategies to my day-to-day— running stand-ups, setting up backlogs, and reorganizing the way I thought about work output. It’s from this experience that I decided it may be worth learning these principles more formally.

The choice to get certified

There is a lot of literature out there about agile methodologies and a lot to be learned from casual research. This benefitted me for a while until I started to work on more complicated projects, or projects with more ambitious feature requests. My decision to ultimately pursue a formal agile certification really came down to three things:

1. An increased use of agile methods across my team. Within my day-to-day I would encounter more team members who were familiar with these tactics and wanted to use them to structure the projects they worked on.
2. The need for a clear definition of what processes to follow. I needed to grasp a real understanding of how to implement agile processes and stay consistent with using them to be an effective champion of these principles.
3. Being able to diversify my experience. Finding ways to differentiate my resume from others with similar experience would be an added benefit to getting a certification. If nothing else, it would demonstrate that I’m curious-minded and proactive about my career.

To achieve these things, I gravitated towards a more foundational education in a specific agile-methodology. This made Scrum the most logical choice given it’s the basis for many of the agile strategies out there and its dominance in the field.

Evaluating all the options

For Scrum education and certification, there are really two major players to consider.

1. Scrum Alliance - Probably the most well known Scrum organization is Scrum Alliance. They are a highly recognizable organization that does a lot to further the broader understanding of Scrum as a practice.
2. Scrum.org - Led by the original co-founder of Scrum, Ken Schwaber, Scrum.org is well-respected and touted for its authority in the industry.

Each has their own approach to teaching and awarding certifications as well as differences in price point and course style that are important to be aware of.

SCRUM ALLIANCE

Pros

• Strong name recognition and leaders in the Scrum field
• Offers both in-person and online courses
• Hosts in-person events, webinars, and global conferences
• Provides robust amounts of educational resources for its members
• Has specialization tracks for folks looking to apply Scrum to their specific discipline
• Members are required to keep their skills up to date by earning educational credits throughout the year to retain their certification
• Consistent information across all course administrators ensuring you'll be set up to succeed when taking your certification test.

Cons

• High cost creates a significant barrier to entry (we’re talking in the thousands of dollars here)
• Courses are required to take the certification test
• Certification expires after two years, requiring additional investment in time and/or money to retain credentials
• Difficult to find sample course material ahead of committing to a course
• Courses are several days long which may mean taking time away from a day job to complete them

SCRUM.ORG

Pros

• Strong clout due to its founder, Ken Schwaber, who is the originator of Scrum
• Offers in-person classes and self-paced options
• Hosts in-person events and meetups around the world
• Provides free resources and materials to the public, including practice tests
• Has specialization tracks for folks looking to apply Scrum to their specific discipline
• Minimum score on certification test required to pass; certification lasts for life
• Lower cost for certification when compared to peers

Cons

• Much lesser known to the general public, as compared to its counterpart
• Less sophisticated educational resources (mostly confined to PDFs or online forums) making digesting the material challenging
• Practice tests are slightly out of date making them less effective as a study tool
• Self-paced education is not structured and therefore can’t ensure you’re learning everything you need to know for the test
• Lack of active and engaging community will leave something to be desired

Before coming to a decision, it was helpful to me to weigh these pros and cons against a set of criteria. Here’s a helpful scorecard I used to compare the two institutions.

For me, the four areas that were most important to me were:

• Affordability - I’d be self-funding this certificate so the investment of cost would need to be manageable.
• Self-paced - Not having a lot of time to devote in one sitting, the ability to chip away at coursework was appealing to me.
• Reputation - Having a certificate backed by a well-respected institution was important to me if I was going to put in the time to achieve this credential.
• Access - Because I wanted to be a champion for this framework for others in my organization, having access to resources and materials would help me do that more effectively.

Ultimately, I decided upon a Professional Scrum Master certification from Scrum.org! The price and flexibility of learning course content were most important to me. I found a ton of free materials on Scrum.org that I could study myself and their practice tests gave me a good idea of how well I was progressing before I committed to the cost of actually taking the test. And, the pedigree of certification felt comparable to that of Scrum Alliance, especially considering that the founder of Scrum himself ran the organization.

Putting a certificate to good use

I don’t work in a formal Agile company, and not everyone I work with knows the ins and outs of Scrum. I didn’t use my certification to leverage a career change or new job title. So after all that time, money, and energy, was it worth it?

I think so. I feel like I use my certification every day and employ many of the principles of Scrum in my day-to-day management of projects and people.

• Self-organizing teams is really important when fostering trust and collaboration among project members. This means leaning on each other’s past experiences and lessons learned to inform our own approach to work. It also means taking a step back as a project manager to recognize the strengths on your team and trust their lead.
• Approaching things in bite size pieces is also a best practice I use every day. Even when there isn't a mandated sprint rhythm, breaking things down into effort level, goals, and requirements is an excellent way to approach work confidently and avoid getting too overwhelmed.
• Retrospectives and stand ups are also absolute musts for Scrum practices, and these can be modified to work for companies and project teams of all shapes and sizes. Keeping a practice of collective communication and reflection will keep a team humming and provides a safe space to vent and improve.

Parting advice

I think furthering your understanding of industry standards and keeping yourself open to new ways of working will always benefit you as a professional. Professional certifications are readily available and may be more relevant than ever.

If you’re on this path, good luck! And here are some things to consider:

• Do your research – With so many educational institutions out there, you can definitely find the right one for you, with the level of rigor you’re looking for.
• Look for company credits or incentives – some companies cover part or all of the cost for continuing education.
• Get started ASAP – You don’t need a full certification to start implementing small tactics to your workflows. Implementing learnings gradually will help you determine if it’s really something you want to pursue more formally.

So you’ve just written a blog post or tweet about why wireframes are becoming obsolete, the dangers of “too accessible” design, or how a certain style of icon creates “cognitive fatigue.”

Your post went viral, but now you’re getting ratioed by rude people on the Internet. That sucks! You were just trying to start a conversation and you probably didn’t deserve all that negativity (except for you, “too accessible” guy).

Most likely, you made one of these common mistakes:

1. You made generalizations about “design”

You, a good user-centered designer, know that you are not your user. Nor are you every designer.

First of all, let's acknowledge that there is no universal definition of design. Even if we narrow it down to software design, it’s still hard to make generalizations. Agency, in-house, product, startup, enterprise, non-profit, website, app, connected hardware, etc. – there are a lot of different work contexts and cultures for people with “designer” in their titles.

"The Design Industry" is not a thing, but even if it were, you don't speak for it. Don’t assume that the kind of design work you do is the universal default.

2. You didn’t share enough context

There are many great design books and few great design blog posts. (There are, to my knowledge, no great design tweets, but I am open to your suggestions.) Writing about design is not well suited to short formats, because context plays such an important role and there’s always a lot of it to cover.

Writing about your work should include as much context as you would include if you were presenting your portfolio for a job interview. What kind of organization did you work for? Who was your client and/or your stakeholders? What was the goal of the project? Your timeline? What was the makeup of your team? What were the notable business rules and constraints? How are you defining effectiveness and success?

Without these kinds of details, it’s not possible for other designers to know if what you’ve written is credible or applicable to them.

3. You were too certain

A blog post doesn’t need to be a dissertation. It’s okay to share hunches and anecdotes, but give the necessary caveats. And if you're making claims about science, bruh, you gotta cite your sources.

Be humble in your takes. Your account of what worked for you and why is more valuable to your peers than making sweeping claims and reheating the same old arguments. Be prepared to be told you’re wrong, and have the humility to realize that your perspective is just your perspective. Real conversations, like good design, are built on feedback and diverse viewpoints.

—

Together, we can improve the discourse in our information ecosystems. Don't generalize. Give context. Be humble.

During the winter 2020 Pointless Weekend, we built TrailBuddy (working app coming soon). Our team consisted of four developers, two project managers, two front-end developers, a digital-analyst, a UXer, and a designer. In about 48 hours, we took an idea from Jeremy Field’s head to a (mostly) working app. We broke up the project in two parts:. First, a back-end that crunches trail, weather, and soil data. That data is exposed via a GraphQL API for a web app to consume.

While developers built the API, I built a static front end using Next.js. Famously, static front-ends don’t have a database, or a concept of “users.” A bit of functionality I wanted to add was saving favorite trails. I didn’t want to be hacky about it, I needed some way to add users and a database. I knew it’d be hard for the developers to set this up as part of the API, they had their hands full with all the #soil-soil-soil-soil-soil (a slack channel dedicated solely to figuring out our soil data problem—those were plentiful.) I had been looking for an excuse to use Userbase, and this seemed like as good a time as any.

A textbook Userbase use case

“When would I use it?” The Usebase site lists these reasons:

• If you want to build a web app without writing any backend code.
• If you never want to see your users' data.
• If you're tired of dealing with databases.
• If you want to radically simplify your GDPR compliance.
• And if you want to keep things really simple.

This was a perfect fit for my problem. I didn’t want to write any more backend code for this. I didn’t want to see our user’s data, I don’t care to know anyone’s favorite trails.* A nice bonus to not having users in our backend was not having to worry about keeping their data safe. We don’t have their data at all, it’s end-to-end encrypted by Userbase. We can offer a reasonable amount of privacy for free (well for the price of using Userbase: $49 a year.) I am not tired of dealing with databases, but I’d rather not. I don’t think anyone doesn’t want to simplify their GDPR compliance. Finally, given our tight timeline I wanted nothing more than to keep things really simple.

Using Userbase

Userbase can be tried for free, so I set aside thirty minutes or so to do a quick proof of concept to make sure this would work out for us. I made an account and followed their Quickstart. Userbase is a fundamentally easy tool to use, but their quickstart is everything I’d want out of a quickstart:

• Written in the most vanilla way possible (just HTML and vanilla JS). This means I can adapt it to my needs, in this case React using Next.js
• Easy to follow, it does the most barebones tour of the functionality you can expect to get out of the SDK (software development kit.) In other words it is quick and it is a start
• It has a live demo and code samples you can download and run yourself

It didn’t take long after that to integrate Userbase into our app with more help from their great docs. I debated whether to add code samples of what we did here, and I didn’t because any reader would be better off using the great quickstart and docs Userbase provides—they are that clear, and that good. Depending on your use case you’ll need to adapt the examples to your needs, for us the trickiest things were creating a top level authentication context to manage users in the app, and a custom hook to encapsulate all the logic for setting, updating, and deleting favourite trails in the app. Userbase’s SDK worked seamlessly for us.

Is Userbase for you?

Maybe. I am definitely a fan, so much so that this blog post probably reads like an advert. Userbase saved me a ton of time in this project. It reminded me of “The All Powerful Front End Developer” talk by Chris Coyer. I don’t fully subscribe to all the ideas in that talk, but it is nice to have “serverless” tools like Userbase, and all the new JAMstacky things. There are limits to the Userbase serverless experience in terms of scale, and control. Obviously relying on a third party for something always carries some (probably small) risk—it’s worth noting Usebase includes a note on their pricing page that says “You can host it yourself always under your control, or we can run it for you for a full serverless experience”—Still, I wouldn’t hesitate this to use in future projects.

One of the great things about Viget and Pointless Weekend is the opportunity to try new things. For me that was Next.js and Userbase for Trailbuddy. It doesn’t always work out (in fact this is my first pointless weekend where a risk hasn’t blown up in my face) but it is always fun. Getting to try out Userbase and beginning to think about how we may use it in the future made the weekend worthwhile for me, and it made my job on this project much more enjoyable.

*I will write a future post about privacy conscious analytics in TrailBuddy when I’ve figured that out. I am looking into Fathom Analytics for that.