Jinja Tip: Validate and check your Jinja templates for syntax errors with j2lint. Ideal for integrating into a CI pipeline. ➜ https://github.com/aristanetworks/j2lint Here is an example below 👇 $ uv tool install j2lint $ cat config.j2 hostname {%for int in interfaces%} interface ip address {{int.ip}} {{int.NetMask}} {%endfor

Introduction When you're working with structured data in Python, there are two common options: TypedDict (from the typing module) and Pydantic (a popular validation library). Both are useful, but they serve different needs depending on whether you prioritize static type checking, runtime validation, or performance. In this post,

PySentry - Scan your Python dependencies for known vulnerabilities. Built in Rust for performance, PySentry is fast, easy to use, and supports flexible output formats (Markdown, JSON, etc.). Here are some examples 👇 # Install pysentry-rs using uv (or use pip install pysentry-rs) $ uv add pysentry-rs # Run a security scan on the

If I’m honest, I feel like Jinja gets a bad rap. Its role is often thought to be only for generating network configurations. And with model-driven automation being on the rise it can feel like Jinja is a legacy, tainted tool that we should be moving away from. I’

Overview Hi all, welcome back to another blog post on Ansible. I hope most of you are familiar with spine and leaf architecture, where each leaf switch connects to every spine switch to create a non-blocking, scalable fabric. In this example, we will have two spine switches and six leaf

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A quick Panda tip for you... Import nested data into a DataFrame using json_normalize with record_path and meta. Here’s an example 👇 import pandas as pd data = [ { "network_device": { "hostname": "spine1-nxos", "os_version": "9.3.7", "interface&

When writing code, clarity and maintainability are everything. One of the most powerful design principles you can apply to achieve this is CQS - Command Query Separation. What is CQS? CQS (Command Query Separation) means: A function should either do something (a Command) or answer something (a Query) - never

Overview When working with Ansible, you often deal with structured data made up of nested dictionaries and lists. This could be anything from device configurations to network status or inventory data. Accessing the specific piece of data you need can get messy, especially when the structure is deep or repetitive.