I’ve built a little web-based tool called ForceNPM that makes it super easy to add NPM packages to Salesforce. Check it out:

Give ForceNPM a try!

The code is on GitHub so if you’d like to deploy this on your own Heroku app or other infrastructure, you can do that. Let me know how it goes!

Back when I first started building web apps we’d just “do it in production” by vi’ing Perl & PHP files on the server. This was fine because the risks and expectations were low. No big deal if I broke the app for a few hours. Good thing I made an app.php-bak copy!

As software became more critical to businesses, the risks of making changes to production systems increased. To cope with these risks we slowed down delivery through processes. Today many enterprises are so bogged down by risk aversion that they may only deploy to production once a year or less. The rate of change in businesses and software continues to increase and the expectations are even higher. Downtime is not an option but that change also needs to go out now!

At first glance it seems silly to do batching in the reactive world. When I first started with reactive programming I thought I wouldn’t have to worry about things like resource starvation. After all, the reactive magic bullet was *magical*! But my magic bullet fizzled when it hit downstream resource constraints causing me to need batching.

With a reactive web client library like Play Framework’s, I can easily spin up tens of thousands of web requests, in parallel, using very little resources. But what if that saturates the server I’m making requests to? In an ideal world I could get backpressure but most web endpoints don’t provide a way to do that. So we just have to be nicer to the server and batch the requests. (Sidenote: How do you know how nice you should be to the service, e.g. batch size?)

Last week at the TrailheaDX Salesforce Dev Conference we launched the DreamHouse sample application to showcase the Salesforce App Cloud and numerous possible integrations. I built an integration with the open source PredictionIO Machine Learning framework. The use case for ML in DreamHouse is a real estate recommendation engine that learns based on users with similar favorites. Check out a demo and get the source.

For the DreamHouse PredictionIO integration to work I needed to get the PredictionIO service running on Heroku. Since it is a Scala app everything worked great! Here are the steps to get PredictionIO up and running on Heroku.

Heroku recently announced early access to the new Heroku Kafka service and while I’ve heard great things about Apache Kafka I hadn’t played with it because I’m too lazy to set that kind of stuff up on my own. Now that I can setup a Kafka cluster just by provisioning a Heroku Addon I figured it was time to give it a try.

If you aren’t familiar with Kafka it is kinda a next generation messaging system. It uses pub-sub, scales horizontally, and has built-in message durability and delivery guarantees. Originally Kafka was built at LinkedIn but is now being used by pretty much every progressive enterprise that needs to move massive amounts of data through transformation pipelines.

Update: I’ve added instructions for a cooling system at the bottom of this post.

Recently I needed to create a mock HVAC system so that I could have a portable smart thermostat for various demos. I searched around but couldn’t find any such thing. So with some sleuthing and the help of my friend Bruce Eckel I was able to build a simple system that powers a smart thermostat and simulates a heating system. This post will document how to do this in case anyone else ever needs such a thing.

Great developer experiences allow you go from nothing to something amazing in under ten minutes. So I’m always trying to see how much I can minimize getting started experiences. My latest attempt is to deploy a Spring Boot app on Heroku, download the source to a developer’s machine, setup & run the app locally, make & test changes, and then redeploy those changes — all in under ten minutes (assuming a fast internet connection). Here is that experience in about six minutes:

This weekend I’m at the Go Code Colorado Challenge Weekend event in Durango. The purpose of Go Code Colorado 2016 is for teams to build something useful for businesses using one or more of the Colorado Public Datasets. Some teams are using Salesforce for the back-office / business process side of the app they are building. So I decided to see if I could pull a Colorado Public Dataset into Salesforce. Turns out it’s super easy! Just follow these steps:

Recently I blogged about a toolchain that quickly gets you going with the Salesforce REST APIs. I believe developers should be able to get started with new technologies without having to install tons of stuff and struggle for days. That blog used Quick Force Node for those who want to use JavaScript / Node.js. I’ve had a number of requests for a Java version of this toolchain so I created Quick Force Java.

When getting acquainted with new technologies I believe that users shouldn’t have to spend more than 15 minutes getting something simple up and running. I wanted to apply this idea to building an app on the Salesforce REST APIs so I built Quick Force (Node). In about 12 minutes you can deploy a Node.js app on Heroku that uses the Salesforce REST APIs, setup OAuth, then pull the app down to your local machine, make and test changes, and then redeploy those changes. Check out a video walkthrough: