How We Produce the MS Dev Show Podcast

We've gotten a lot of questions about how we get great sound on the MS Dev Show. From the start, Carl and I knew that great sound wouldn't make us successful, but bad sound could definitely hurt us.

A lot of podcasts have just... ended. It seems to happen somewhere between episode 20 and 100. I knew how important it was to make the podcast take the absolute minimum time commitment.

Today, I'm sharing our entire process.

Many podcasts won't talk about this. I'm not sure if it's too much inside baseball, or if they think they are trade secrets. Well, I'm all about sharing.

From a high-level, we find guests, prepare, record, edit, and publish.


Guests are a big part of our show. We've always wanted to have interesting guests. We occasionally have some names you've heard of, but we also love to have guests that haven't been on a podcast before.

I believe that everyone has a story, an we want to hear it.

Since guests have to take time out of their important schedule for us, our goal is to make it as easy as possible, with a minimal time commitment. Once a guest accepts and we work out a time slot, we have a template invite we send them. Templates are absolutely key to our communications, and allow us to be clear and concise. As we improve our process, we evolve our templates.

Email Template Example

Around 24 hours before the episode is scheduled to record, we send another email template that contains a rough idea of the questions Carl and I want to ask, and everything the guest needs to know to get set up. More on that later.


OneNote is what powers the MS Dev Show. All of our processes, templates, and episode details are in a OneNote notebook that Carl and I share.

OneNote Screenshot

As Carl and I come across stories we think would be worth discussing, we put links into a OneNote page for the associated episode.

We also use OneNote while we're recording, back to that in just a bit.


You might think this is the most important part, and you'd be partially right. For our mics, we copied TWiT, and use the Heil PR-40. At ~$330, it's pricey relative to other mics, but cheap compared to the computer you're plugging it into.

Heil PR-40

Lesson time. The Heil is a dynamic microphone. Some people use condenser mics, but condenser mics do a horrible job cutting out background sound. Dynamic mics do a good job of only picking up the sound right in front of them. This is key for Carl and I since we're recording from our home offices and have kids and pets.

If you want to hear the difference between a USB headset, and the mics Carl and I use, check out this track that I recorded shortly after getting the Heil:

Our mics are connected to the Alesis IO2 Express. This is what converts the signal from our mics to USB to connect to our computers.


Using an insert cable, we route the mic sound through the Behringer MDX1600 compressor/gate/limiter. This primary serves to "gate" our audio, or essentially turn it off when we're not speaking. This is our first line of defense against barking dogs, screaming kids, and loud keyboards. I now believe this equipment is optional thanks to improved software processing that I'll describe later.

Behringer Compressor/Gate/Limiter


Around 24 hours before we record, we send the guest an email reminder with additional details.

Pre-Show Email

We use Skype to talk to our guests. One lesson learned with Skype is that it auto adjusts your microphone volume by default. It has a tendency to boost the gain if you don't talk for a while. When you start talking again, it blows out the audio, and sounds terrible. I manually adjust this setting by speaking at a normal level, allow it to auto-adjust, and then uncheck the box.

Skype Auto-Adjust Mic

Carl and I both use Callburner to record all sides of the conversation. Since we both have complete copies of the call, we can be fairly confident that even if we have a technical failure, we'll still be able to fall back to a second copy.


We always record our tracks in raw WAV format:

Callburner Settings

Additionally, we ask our guests to record their own microphone input. We include simple instructions to make it as painless as possible. When everything goes according to plan, we have a separate track for every person on the call. One track for me, one track for Carl, and one track for the guest.

As we go through the episode, we use OneNote as a guide. We use it to make sure we don't forget any big, important questions, and we use it to mark off questions that were already asked. Since OneNote updates on both sides in near real-time, it allows us to run the show as we go, without stopping or IM'ing.


Short version: trim, Auphonic processing, truncate silence, finishing touches.

First, I use Audacity to trim the tracks. Audacity is free software that works amazingly well. There is always some pre-show and post-show chat, so I cut them down to the meat, and make them all the same length.

Next is noise reduction. This is an area that can eat up a lot of time if you let it. In the early episodes, I did minimal manual edits. As I started to desire higher quality, I found myself spending more and more time on editing. We're not talking about major edits, it was more about removing breaths, clicks, etc. I was getting desperate to cut this down. I was willing to try anything. I even tried Adobe Audition, but it is obvious that it wasn't really designed to edit a podcast. Don't get me wrong, it's fully capable, it's just not optimized for a podcast workflow.

Then, a miracle. I found a website called Auphonic. It's unbelievable at processing audio. It has amazing noise reduction, which is key for guests since they don't have gates. It also intelligently focuses on the track of the person that is speaking, and attenuates the other voices. This is amazingly effective. Other than our gate, this is the only audio processing we do. This software is good enough that you could skip the compressor/gate/limiter completely. It works fine if you have 1 track, but even better if you have separate tracks for each speaker. All of the settings we use for the show have been saved as a preset, so it takes less than 60 seconds to submit a processing job.

Auphonic Output

After Auphonic works its magic, I bring it back into Audacity. Then, I use a feature called truncate silence (under the "Effect" Menu). This is one of our best kept secrets. It takes out pauses in the audio that are longer than a certain duration, and shortens them up. The end result is that even if someone takes a moment to answer a question, it will sound like they answered without pausing. In a typical hour long episode, this takes out around 4 full minutes.

Truncate Silence Menu Item

Adding the Intro/Outro

I record the intro text directly into audacity. "Welcome to the MS Dev Show, episode number....". I place this track below the intro music track. Then, I use the "Auto Duck" option under the "Effect" menu. This automatically turns down the volume while I'm talking. If I had a long pause in the intro voice, the theme music would actully come back up and fill it in. Lastly, I use the Envelope Tool to make the intro sound fade in. I started doing this since the riff at the start of the intro can be a bit glaring.

Ducking the Audio

The outro is pre-recorded for convenience, and I just put it at the end.


The easiest way to publish your podcast is to use Libsyn. It's fairly inexpensive, and you pay monthly for new episodes. The great part is that you don't pay for old episodes. They handle everything for you from hosting the files to providing the feeds that you can submit to aggregators like iTunes and Stitcher. Make sure you check on those services to ensure the feed is set up the way you want.

Carl handles the shownotes, and these are created by exporting them from OneNote to an mht document, and then using PanDoc to convert to markdown to publish to our website.

Our website is completely open source. You can see all of the code in GitHub. You can even fork the site, create your own, or issue pull requests. The website itself is hosted in Azure, and automatically redeploys when we check in a change to GitHub.

Feel free to watch the commit log. You might even get a sneak peek at an episode before it's published!

GitHub Changelog


I made a quick video showing the editing process:


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IoT Data in Manufacturing - My Thoughts

I've been spending a lot of time recently thinking and literally dreaming about IoT (Internet of Things) applications. I wanted to share some of my current thinking on where we're at, what is happening, and what things might look like in the future.

Manufacturing Data Today (the boring part)

Within a manufacturing plant today, we can categorize the software into 3 high-level layers, which are not necessarily easy to delineate:

These systems are all extremely complex, and I'll never fully understand them. I'm primarily concerned the MES/SCADA portion dealing with the raw plant sensor data, how it flows, and how we turn that into meaningful information.

Optical sensors, pressure sensors, temperature sensors, and any other sensor you can imagine probably already exists and is in use within Manufacturing today. Manufacturing generates far more data than any other vertical. I suspect manufacturing has been one of the key drivers behind the dropping prices of sensors over the past few decades.

The communication network within your typical plant is based on standards that were defined over 4 decades ago. These networks exist to multiplex and centralize all of the data in the plant. Of course you'll find siloed subsystems that work independently, and aggregate data is sent to the central location. You'll find pockets of newer TCP/IP networks, but you'll also find a lot of low-speed serial communications.

Centralized Network Pattern

At the center of this system is a high-performance, time series database, known as a Historian. This is the center of the universe. All data is stored here. Security is handled through virtue of being only internally accessible.

For corporate-wide reporting, data needs to be aggregated from this historian, either through additional software, or through the ERP system and processes. This tends to be expensive, difficult, and incomplete due to the delta between the vast amount of data collected from the source, and the aggregated enterprise data.

The IoT / Cloud Transition (the fun part)

We have all of this data at the source, great. Now what? The real power is in unlocking the data.

There is some very low hanging fruit that is driving change today. Thanks to falling storage and compute costs in cloud environments, there is a big incentive to centralizing our data. Having all of our data aggregated in the cloud means that we can run massive, scalable jobs and generate reports at a scale that used to be difficult and costly. We can not only start to benchmark multiple facilities, we can drill down to any level. Slicing and dicing the data moves from being the job of a report writer, to that of the report viewer.

The cloud is where we aggregate storage and compute

Machine learning is the new frontier, and has far reaching implications. Previously, we had to know exactly what questions to ask, and having enough compute power to explore the data was expensive. Today, the cloud provides the massive horsepower we need to not just explore the data quickly, but to also glean insights that we never thought of.

Throughout history, we've spent a significant amount of time analyzing data, looking for reasons why and when things fail, trying to predict order volumes, trying to figure out how to maximize employee productivity, and the list goes on. These are questions that can be explored, and potentially optimized by data scientists and machine learning. Machine learning as a service makes it a commodity, available to any sized business, on-demand.

The Future (the exciting part)

I hope that once the dust settles, we'll have standards that allow devices from various companies to inter-operate in a reliable, secure manner.

Plummeting device costs are a given, so it's safe to assume we'll have more computing power available to us almost universally. To really get value from the data, we first need to allow devices to share it. If device A knows what device B is up to, Device A can operate more intelligently. This is a collective intelligence. This collective intelligence will also require a management hierarchy. A management hierarchy allows higher levels to have a greater understanding of what should be accomplished, and less about how it should be accomplished.

Device Collaboration & Supervision

Does this sound familiar? This is how employees are traditionally organized within an organization. As you go up the management chain, the goals become more focused on the overall organizational goals. As you go down the management chain, you get to where the real work happens, and there may be far less context in the larger organization.

Organizations are starting to evolve into a more networked design, and so will devices. Devices will have a roughly hierarchical organization, but will realize advantages to direct communications. Features like high availability can exist at lower levels.

In other words, we'll have redundancy, inter-device communication, but we'll also have a logical model that defines how the system operates. As a simple example, imagine if we had 3 temperature sensors measuring the same thing. Now imagine one of the sensors fails, or starts to report irregular values. Using a logical model that overlays the physical model allows us to define operation separating the concerns of the low-level details.

Virtual Sensor

Now, we want to get data from any point in the hierarchy to where it is needed. A machine operator needs to know what is happening in the machine in real-time. The supervisor needs to know how multiple lines are operating in real-time. The plant manager needs to know how the overall plant is running, again, in real-time. We'll also need to store historical data, operational reporting data, and so on.

Further Reading

Also check out my manufacturing projects on GitHub.

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Creating a Hackfest Culture

Software development is moving at an insane rate. Keeping up with a single technology area keeps getting more difficult. Just look at web technology. Grunt, Gulp, Yeoman, Angular, Bower... verb.js. How do we balance the need to be agile and continuously produce, with the productivity increases we get with new technology? There is a definite possibility that the latest library/framework/technique can change the way you write, built, or deliver code. You could understand it today, or allow your competition to understand it first.

I've seen a simple practice that can change how your organization researches new technologies or explores new concepts.

It's as simple as this:

Get a group of your developers together, throw out all of the rules, and just create something new.

That's it. That's all it takes, and I'm here to tell you firsthand that it absolutely works.


It sounds simple, but it's one of the most powerful tools you can use. Set aside a block of time like a week or a weekend, come up with a goal, and just work together to see what you can build. The space will be a mess of cords and the discussions will be crazy, but it's all part of the process. It's like a startup where the results are more important than process.

Hackfest vs Hackathon

I often see the term Hackfest and Hackathon used interchangeably. While the loose definitions overlap, I think there is some value in making a distinction.

Hackathons are intense and exhausting, and they’re meant to be. They’re usually a whole weekend of focused work, often with insufficient sleep, and too much encouragement to use masses of caffeine to stay awake and coding for 48 hours.

Sorry, but I’m not going to do that for my projects, let alone yours.

-Alex Bayley

Hackfest vs Hackathon

Hackathons is a combination of hack and marathon. Hackathons I've seen are typically competitions, which makes them much different than a hackfest.

Include Everyone

Feel free to include everyone. This might be a good time for testers to provide feedback early on in a new process. Managers can learn a lot at these events, and they often were full-time programmers at one point. I've found they get the most excited about getting a chance to get their hands dirty and create something again.

The best part of including everyone is that you can learn from each other. A group of attendees with a diverse set of backgrounds is ideal. Hacking with JavaScript developers made me go out an use Angular.js on a project. A IoT hackfest got me interested in using devices for collecting sensor data with Azure. Hacking alongside a Technical Fellow gave me a vision of the future. I have a vivid memory of all of these experiences.

We're too formal for a "hackfest"

The second I say hackfest, I'll occasionally get the person that starts rolling their eyes. Interestingly enough, if I call it an application accelerator, it starts to sound like a great idea. Call it whatever makes sense for your company and project. It's the concept that is most important.

Why they Work

Hackfests work amazingly well for a number of reasons:

  • There is little to no stress to produce anything. This could lead to something amazing, or fail miserably. Failing still means we've learned something important. I've never seen a person leave without having learned something significant.
  • The environment is different. Just developing in a different context can change the emotional state of the developer.
  • There are no irrelevant interruptions. A good hackfest will provide isolation from the steady stream of calls, emails, and other distractions that force everyone to switch contexts.
  • If nothing else, consider this a team building exercise.

The best part is that this doesn't have to be an isolated event. Run a hackfest, see if it works, and then try to replicate the success. The more regular you make your hackfests, the easier they become. The overhead of planning meals, hardware, and instructions become minimal.

Don't Stop Now!

If you run a successful hackfest, don't stop there! Schedule the next one. Keep the rhythm of innovation going. The amount of overhead at each one will decrease, and the value will increase.

Don't forget to share the results with everyone up your org chart. Showing that this is a powerful tool will give you buy-in for the next hackfests.


7 Lessons from Running a Hackathon


Summer of Tech 2013 Hackfest Image is Creative Commons

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Jason Young I'm Jason Young, software developer at heart, technical evangelist for Microsoft by day. This blog contains my opinions, of which my employer may not share.

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