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Python code optimization may seem easy or hard depending on the performance target. If the target is “best effort”, carefully choosing the algorithm and applying well-known common practices is usually enough. If the target is dictated by the UX, you have to go down a few abstraction layers and hack the system sometimes. Or rewrite the underlying libraries. Or change the language, really. This post is about our experience in Python code optimizations when whatever you do is not fast enough.

Many.NET applications and frameworks support a plugin based model. Also known as “add-in” or “extension” model. A plugin model allows extension or customization of functionality by adding assemblies and config files to a directory that is scanned at application startup. For example.

For most of my career I’ve worked with health and wellness startups. Most of these companies have a wearable that tracks movement, heart rate, body weight or stimulates a body part. The common denominator between these apps is their use of sensor data to determine physiological progress an athlete is making. Problem is, your Bluetooth Low Energy (BLE) device does not have an internet connection and cannot send diagnostics anywhere if there are errors.

Between planning, triaging tickets, negotiating requirements with external stakeholders, and actually building software, it’s hard to take the time to make dashboards or even think about the most important metrics your team needs to track. To make it easier for you to get insights into your team effectiveness and project health, we made a few updates to Dashboards and Stats that you just might like.

Despite several widely acknowledged flaws, Python remains one of the most popular development languages worldwide. The sole fact that for years Python had two different and incompatible versions existing in parallel should have spelled the end for Python given the numerous alternatives available in the market. But Python overcame this conflict. Developers also criticized Python’s design and functionalities. Python is known to be slow and inadequate at dealing with memory-intensive operations.