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Core UX Flows of Mobile Shopping Apps

Earlier this year we began thinking about iOS and Android native application UI patterns. We were specifically interested in instances when both platforms shared conventions, and when they diverged. (For more background, read up on our methodology and marketplace findings.)

Today we're going to talk about the two most common user experience models for native apps, the conventions and trade-offs they force you to consider, and when you might chose either.

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CDN RUM and Eggnog

Welcome to the Great Mobify CDN Shootout. We have pitted several major CDN providers against each other in a RUM test for global supremacy, a Royal RUMble if you will. We want to show you how you can get the most value from what is otherwise an expensive and opaque world of complexity and hand-waving.

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Why Meetings Are A Waste Of Time And How To Run Them More Efficiently

Meetings are a waste of time

Do you ever get a meeting invite and dread going or wonder why am I here when you get there? I’ve made it a practice to not accept meetings that don’t have an agenda or clear objective.

One rule of thumb I use is the G.A.S factor (Give a Shit factor) — Why do you need me there?, Am I a collaborator?, Subject matter expert?, Decision maker? or Do I need to be informed? If it ticks any of these boxes then I feel much better accepting a meeting and will most likely not be a waste of my time.

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A Python guide to handling HTTP request failures

A lot of things can go wrong when requesting information over HTTP from a remote web server: requests timeout, servers fail, government operatives cut undersea cables. You get the picture.

Identifying and handling failures helps build fault tolerant systems that stay up even when services they rely on are down. A nice side effect is your phone is less likely to beep in the middle of the night with a message from your coworkers talking in all caps.

This guide will introduce you to the common ways HTTP requests fail and how to handle the failures.

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How to Use SQLAlchemy Magic to Cut Peak Memory and Server Costs in Half

We do a lot of A/B testing at Mobify, and that means a lot of analysis of the results. We collect and store data from many websites and need to be able to run analyses multiple times as we improve and modify our techniques.

Much of this work is done in Python, using SQLAlchemy for database access and Pandas for analysis. This post is about how we took an initial script that used a simple approach to reading data and reduced the peak memory requirements.

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