Everli Cart Assistant

Reading Time: 6 minutes

Your AI-based grocery helper

Have you ever dreamt going in a supermarket and finding all your favorite products ready for you on a single shelf?
Are you the kind of person who’d like to get the grocery done as fast as possible, and move onto more interesting stuffs?
Or would you like finding all the things you usually buy ready for you, and spend more time exploring offers and new products?
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A Strategy for Scalable Business Logic

Reading Time: 6 minutes

Or: How to kill off the if branch.

Everli has changed a lot in the past years! Not only by growth in raw numbers, but structurally.

  • From just Italy into multiple countries.
  • New kinds of business partnerships and customer relationships.
  • New types of retailers.

(And that’s not even mentioning external changes like new government regulations)

Feature Creep Can’t Be Stopped

So imagine you’ve got some code. It’s readable, it met all the requirements, very elegant!
”Okay code, now meet the real world.”

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What on earth do I know about delivering groceries?

Reading Time: 5 minutes


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.

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Design System Android – A Problematic Situation

Reading Time: 5 minutes

Prologue

Picture this: you are an Android dev, and a new feature needs to be implemented. It’s a small screen, fairly straightforward layout, some inputs, nothing fancy.

And here you are, on one screen you got the Figma document open, in the other screen your IDE. You see this new button, seems a little bit larger than what you have in the rest of the app. It’s probably just a minor mistake on the designer’s part. You decide to use the regular size for the button.

And here’s where the fun begins.

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When your server needs an Ad Server

Reading Time: 4 minutes

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 🙂

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Uppy Updates!

Reading Time: 2 minutes

Do you remember our project: “Uppy” ? Here’s our previous article! It has grown and now also the backend is open source!

The backend is (also) yours!

It took some time in making it ready for the public but, the very heart of the project is now open on Github for everybody to perform surgery on! 💉 😜

Go grab it here: github.com/everli/uppy

Of course, it’s fully dockerized and running it locally or on premises is just a breeze plus it follows the standard Laravel guidelines so that you can feel at home from the beginning.

Try out new features!

During this time we added also a few new features that made our PM(s) really happy, let us know what you think of:

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Han Solo: a Feed Processing Story

Reading Time: 5 minutes

Some questions that anyone using Everli might wonder about: how do we get all the products onto the web and mobile apps? And how do we keep our catalog and prices daily updated?

This process has taken a bit of work and, for now, a couple of iterations. In this post, we’ll focus on the first iteration. Let’s try to shed some light on it!

The mission

Our mission is to provide the best catalog for our users with the most accurate prices, item details and availability.

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A Brief History of Price Updates (Part 3)

Reading Time: 5 minutes

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.

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