I've used my Synology NAS for some time now - about two years and counting, and it's been a great tool to backup information locally (e.g. from my phones or shared computers). Then, I got to thinking - it's pretty much a mini-computer. It has a quad-core 1.4Ghz CPU, a whopping 2GB RAM, and _plenty_ of storage. I can do more with it than just use it for occasional data dumps. That is - I could use it for frequent data dumps.
I had a chat with a friend the other day, and he mentioned off-hand that in his life there is a very unique problem - lack of a short username for their Twitter account. Seems like everything good is already taken, which makes sense considering that Twitter itself is 14 years old. You can bet that in 14 years, a lot of people did get very creative with usernames.
I am naturally curious about the APIs that the devices in my house use, so when I got an air quality monitor, one of the first things I did was fiddle with the REST APIs that were made available through the device.
TL;DR: Check the source code out on GitHub for the project. It’s a demonstration of how you can use simple components to build awesome tools. That’s right, you don’t need Kubernetes for this! Table of contents Introduction The basics Ingress Data store Rendering layer Building the tools SQLite database Ingress script Analysis notebook Conclusion Introduction I’m one of those people that needs data around the things that I do - there is just something fun about being able to quantify and analyze things.
One of the things that I am really curious about is analysis of publicly-available data. There is a lot of useful context that can shed a lot of light on some important happenings and trends. I’ve started with one of the resources that has a lot of rich, user-created content: Reddit. I also wanted to focus on a local implementation, that does not require me to sign up for a big data service, such as BigQuery.
Whenever we talk about documentation infrastructure, one of the most common pieces of feedback I hear from developers is that it’s too complicated to set up. There is just too much configuration, fiddling around and trying to make sure that the output is produced in way that is expected. That’s why back in June I set out to build a documentation CLI that allows one to produce docs with one liners.
Yet another one of those times where people kept asking about something on Twitter, and I thought that it would be easier to write a blog post explaining the inner workings of things instead of responding in 280 characters. This time, this relates to "How can I build my own little docs.microsoft.com", so let's tackle the problem head-on.
We are all about GitHub on the docs.microsoft.com team. We host documentation there and just recently we launched content feedback that's storing comments in GitHub issues as well. Today, we moved all site feedback to GitHub as well.
This weekend I’ve spent some time to rework foggycam, the open-source tool to record Nest camera footage locally and to the cloud.
I previously talked about connecting to Instagram without the browser as part of a little hobby project I am working on. Another component of this project involves tracking the popularity of individual hashtags on Instagram, and how those grow over time. There are two approaches that I will describe here, each with its own merits, that can solve the problem. NOTE: This is a just-for-fun project - no private information is collected, or shared through it.
For one of the hobby projects, that I am working on, I thought I would leverage the Instagram API. It deals with an automation scenario, so the choice was obvious - I can put together a Python script that runs at scheduled intervals. Lucky for me, I’ve also learned that there is already a Python library that can help me access the API - it is archived and no longer maintained, but should at least give me some leverage over what I wanted to do.