It was Monday, 1st of July, 2019. I’d recently traveled from the Portuguese island of Madeira to the sweltering inland heat of Verona in the north of Italy.
The objective of my trip was to join Supermercato24: a relatively new, small startup with big ambitions.
I was feeling conflicted.
On the one hand, the interview process had been extensive, and I could feel the startup, go-getter, “move fast” energy I so desperately needed at the time. There were no red flags during the initial approach, and I felt like I had struck gold.
Advertisement placement is a double-edged sword. You would like to avoid ruining your fancy design, but at the same time, you could leverage advertisement content to foster discovery, and you might find new ways to scale your business. You have to improve both customers’ and brands’ experience, as all things should be. There is nothing worse than sharing an intrusive Ad that does not add value to the user. An example? I am bald, so please don’t waste your money on advertising shampoos to me 🙂
Here we go again. This is the third episode of our series on how we are ingesting retailers’ data for our beloved customers. If you did not read our previous post about this topic, consider spending a few minutes catching up on it.
In the following paragraphs, we will show you how our new process to update the assortment of our stores works and our journey to get there.
From the previous episode
In the previous episode, we showed you how we reached coordination in our price update flow. We achieved it by the mean of a semaphore stored on Redis: each time it gets accessed, we decrease its value. Once it reaches 0, the next stage of our pipeline begins. We also covered our Conversion Optimizer, a machine learning algorithm.
As soon as prices are updated, the next step of our pipeline can start.
It was over two years ago when I joined Everli. Besides usual duties, I was given the tremendous opportunity to migrate the frontend of a complex e-commerce system from Angular to Vue. It’s this type of a challenge you wait most of your life as a developer to be able to use all of your past experience and build something new and as close to “perfection” as it can be.
The most crucial requirement, at that time, was to avoid big rollouts and make every release as much valuable and smooth as possible. Back then, It sounded pretty complex, but it was vital for the company, which was about to double the number of users in the next months. We were growing fast, and new markets were about to be conquered. With my buddy Nicola, we decided to take the small steps and take the pressure off our shoulders by replacing Angular with Vue bit by bit.
EverliiOS apps get new amazing features every week, constantly growing bigger and more complex, this makes Xcode build times grow proportionally.
Of course, one solution might be to regularly update your hardware, but here we’re going to talk about a free software alternative to that. In the Everli iOS team, we decided to give PodBuilder a spin, and we did right.
DISCLAIMER: this article is the transcription of an internal talk. You can find the presentation here. In Everli we have recurring internal moments that we use for sharing knowledge, as you could see from the previous post. We are going to integrate the key points of the book we are reviewing as we move forward and then we are going to share our learnings and outcomes.
Recently, the R&D Team at Everli embraced a super challenging initiative that we call Firebreaks! 🔥🔥🔥🚒
This served as a natural breakpoint as teams wind down their old missions and prepare to start their new ones. It’s an excellent opportunity to pursue other work that’s of interest to them and of value to the organization.
Our colleagues ventured into several workshops and tested themselves in a five days sprint!
Here we go again. This is the second episode of our docuseries about how we are managing retailers’ data for our beloved customers. If you did not read our previous post about this topic, you should spend a few minutes catching up on it. With this part, we would like to introduce the hidden aspect of our product and I promise, it is not a buzzword: Machine Learning. In the following paragraphs, we are going to show new technical improvements and the analytics aspects under the hood.