It all started with a simple, everyday annoyance in the kitchen. My wife would be in the middle of cooking, hands covered in flour, oil, or whatever was being prepped for dinner, and the kitchen would start filling with smoke. Our range hood works perfectly fine, but it is completely “dumb”—controlled exclusively by a small, analog infrared remote. Having to stop, wash her hands, find the little plastic remote, and press the right buttons just to clear the air was a daily friction point.

One of the problems I found when working with my home lab is that I was lacking a solution for storing secrets. Even though there are open source solutions like Hashicorp vault to manage secrets, and managing it something requires maintenance and I didn’t want to have the operational burden. I used to manage my password with a KeePass vault file. However that solution fell short when dealing with multiple devices like phones and multiple laptops. So I chose a password manager. I ended up choosing Bitwarden as a password manager, one of the reasons I chose this solution was that it is open source and if I ever want to host it myself I can migrate to the open source version and migration will bee seamless to a hosted solution. I still chose the cloud version of it. And at some point working on my labs I decided to use my password manager to store the secrets I need for my automations. Bitwarden offers a cli tool which is pretty handy for automations, it outputs secrets in json format which is pretty useful to be used in any language. I chose to keep things simple and use it on the shell and parse it with jq tool, one downside of the bitwarden CLI tool is that they don’t offer a build for linux arm64 arch, so if you are running on a raspberry pi you will have to build the tool from source. This post is opinionated to Bitwarden, but the same concepts should work for other password managers CLI tools, of course depending on outputs a few things should be adapted to other password managers outputs.

I have been working with golang projects for quite some time and I found that the usual fork and pull request on github is a bit trickier when working with golang, as golang doesn’t have dependency management tools like python has PyPI or npm for NodeJS, etc. Golang dependency management relies on pretty much git tag or commit hashed to specific repos for using golang modules in your code, in my personal opinion I have mixed feelings about the approach, however I am not going to discuss that here, I might do that in another post. However when contributing to open source projects I found myself in trouble until I understood how things worked in managing dependencies in golang.

One of my passions in technology is networking, another passion is open source and linux. So sometimes I come across networking software or just to play around with linux standard networking tools like iptables, tc or iproute2, or even play with open source implementations of routing protocols using Quagga/Bird/FRR/go-bgp. I always found hard when testing these tools to get a proper setup on my laptop. Of course there are some tools that already help us in this regard like GNS3. However, using GNS3 I need to provide virtual machine images and configure all of them to save specific topologies. I wanted something that I can use quick and simple without the overhead of managing virtual machines and their configurations. First thing I though of was set up vagrant linux vms with extra network interfaces and use ansible to configure the virtualized linux routers. Then I thought, why not do it in docker?

One of the most popular services from cloud providers is cloud storage, to be more specific AWS S3, Google Cloud Storage, Azure Storage or any similar service from other cloud providers. This service is very convenient for developers and SREs or System Admins, it solves the problem of managing disks, storage devices , storage servers, etc at a very low cost for storing files, depending on the cloud provider, some of them even offer a staggering 99.999999999% durability. Given the benefits and low cost of such services, I have seen more and more reliance on the cloud storage services. But there is a catch on the cheap pricing, in order to make a good decision when architecting an application we need to look a all pricing items for the cloud storage service to optimize around costs. Cloud storage services usually charge for the storage used for files stored, api calls to put, retrieve and list files and network traffic. So we need to have in mind these parameters to avoid surprises on our cloud storage bills. I will not go into too much details on each cloud provider on this post but I will show something I found out recently that made an application lower the costs about 10 times just by optimizing the way the data was being accessed. These optimizations might be a key to make a feature or an app profitable. In particular I will talk about AWS S3 api calls and the behavior of some of the official AWS SDKs that needed some tweaking. Of course all findings shown in this post were done with documentation found at the time of this writing. So I suggest double check the SDKs behavior as it might change in the future.