Anthony Corletti cloud computing. startups. music. etc.

Trust the Process

The past ten months have felt like a couple of years – so I’ve decided to stop, reflect, and share some of my learnings.

I’ve built up two companies; one from the ground up regarding technology and infrastructure alone, and the other from absolutely nothing. One of those companies has since closed, and the other is still puttering along in my free time.

I made a tough decision to commit my startup journey to moonlighting hours only and find a more stable company to join that’s just as exciting and could help me grow my skills and network. It was time to start a job search.

I’d like to share some of my thoughts and experiences specifically around this job search; what went well, what didn’t go well, warning signs, and when the universe slaps you in the face with something great.

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AI Infrastructure on Kubernetes

The rise in usage of cloud computing resources and container management platforms for executing AI (Artificial Intelligence) and ML (Machine Learning) workloads has led many engineers and companies to question the suitability and effectiveness of Kubernetes’ resource management and scheduling to meet the growing requirements of these workloads.

So why’s that? What patterns, architectures, and procedures has led these companies and engineers to this problem of scaling ML platforms on Kubernetes? And what kind of solution could we apply to help solve those problems?

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How DNS Works

It’s easy to take for granted the ability to type google.com into a web browser, press Enter, and be served a powerful search engine.

The DNS, or Domain Name System, is mostly responsible for getting that data from somewhere on earth to your screen.

So how does it work?

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Text Processing in Ruby

I’ve come across a lot of text based processing and analytics software is written in python, but not so much in ruby, so I’ve decided to share an example about how to algorithmically parse lots of text with plain ruby code – no dependencies required!

The problem statement is simple.

Given a new-line delimited text file, with each line being field separated by commas, determine the $m$ most common string of length $n$, accounting for multiple most common strings.

Let’s get hacking 🤓

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Passwordless Logins in Python

Many internet services use OAuth and email based protocols for sending login links and tokens.

I think it’s a pretty slick way to log users in and reduces overhead for them by adding yet another credential to their password managers.

I hadn’t found a python implementation I liked so I decided to implement a login credential issuer in pure python that I could use to send login links to users, similar to a slack-like login flow.

It didn’t take much code to implement! I’ll walk you through it.

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