This is a retelling of a presentation I gave at work. In it, I describe a mechanism I've started using to raise the quality of artifacts I check into version control.

For a long while now I've been brainstorming how I could leverage the API that's present in the Cisco Spark collaboration platform to create a bot. There are lots of goofy and fun examples of bots (ie, Gifbot) that I might be able to draw inspiration from, but I wanted to create something that would provide high value to myself and anyone else that choose to download and use it. The idea finally hit me after I started using Zabbix for system monitoring. Since Zabbix also has a feature-rich API, all the pieces were in place to create a bot that would act as a bit of middle-ware between Zabbix and Spark. I call the bot: Zpark.

For the benefit of readers who haven't worked with Flask or don't know what Flask is, it's a so-called microframework for writing web-based applications in Python. Basically, the framework takes care of all the obvious tasks that are needed to run a web app. Things like talking HTTP to clients, routing incoming requests to the appropriate handler in the app, and formatting output to send back to the client in response to their request. When you use a framework like this, you as the developer can concentrate a lot more on the application logic and worry a lot less about hooking the app into the web.

As you may have guessed from the title of this post, one of the other tasks that Flask manages is logging within the application. For example, if you want to emit a log message when a user logs in or when they upload a new photo, Flask provides a simple interface to generate those log messages.

Flask has a large community of active users built around it and as a result, there's tons of best practice information out there on scaling, talking to a database, and even whole tutorials on how to build full applications. But it's been my experience that there is very little information devoted to the topic of logging. Granted logging isn't very fun and does nothing to get your app to market quicker or increase user counts, but it is really important if you want to get any sort of feedback from the app about what it's doing during normal operation or what exactly happened if something goes wrong.

In this post, I'll look very briefly at how logging works in Flask and then examine three common methods used to record log files within Flask and how each of them can shoot you in the foot. I'll close by offering some recommendations that should keep you on safe ground.

I'm a big fan of Let's Encrypt (free, widely trusted SSL certificates) but not a big fan of most of the client software available for requesting and renewing certificates. Unlike a typical certificate authority, Let's Encrypt doesn't have a webui for requesting/renewing certs; everything is driven via an automated process that is run between a Let's Encrypt software client and the Let's Encrypt web service.

Since the protocols that Let's Encrypt uses are standards-based, there are many open source clients available. Being security conscious, I have a few concerns with most of the clients:

• Complication. Many of the clients are hundreds of lines long and unnecessarily complicated. This makes the code really hard to audit and since this code is playing with my crypto key material, I do want to audit it.
• Elevated privilege. At least one of the clients I saw required root permission. That's a non starter.

This post is about finding and fixing a memory leak I discovered in the SNMP daemon, snmpd(8), in OpenBSD. This sort of analysis is foreign territory for me; I'm not a software hacker by day. However, using instructions written by Otto Moerbeek as my Rosetta stone and Google to fill in the blanks when it came to usage of the GNU debugger, gdb(1), I was able to find and fix the memory leak.

I'm documenting the steps I used for my future self and for others.