Legit Security this week added a threat feed that DevSecOps teams can use to instantly determine if a newly discovered vulnerability impacts their software supply chain. Built using the Legit VibeGuard tool, the threat feed added to the company’s application security posture management (ASPM) platform also enables DevSecOps teams to take advantage of “vibecoding” techniques […]
Author: drweb
Redgate recently released SQL Compare v16, which included a new feature to work with Redgate Data Modeler. I decided to give it a try in this post. I’ll take a model and compare it to a database, and deploy my model.There’s a video of this post at the bottom if you’d rather watch me work.This is part of a series on Redgate Data Modeler. This is also part of a series of posts on SQL Compare.A New ModelI started a new project, mostly as an experiment to help me practice with some technologies, with the idea that this will become…
FIPS compliance is a great idea that makes the entire software supply chain safer. But teams adopting FIPS-enabled container images are running into strange errors that can be challenging to debug. What they are learning is that correctness at the base image layer does not guarantee compatibility across the ecosystem. Change is complicated, and changing complicated systems with intricate dependency webs often yields surprises. We are in the early adaptation phase of FIPS, and that actually provides interesting opportunities to optimize how things work. Teams that recognize this will rethink how they build FIPS and get ahead of the game.…
# Syntax element in sequence element not in sequence # Example fruits = [‘apple’, ‘banana’, ‘orange’] print(‘apple’ in fruits) # True print(‘grape’ not in fruits) # True The Python in operator checks whether a value exists inside a sequence. The Python not in operator confirms whether a value does not exist in the sequence. Both return boolean values. That’s the entire concept, but the practical applications get interesting once you see how they behave across different data types. Checking membership in Python lists Lists are the most common place you’ll use membership operators. The syntax reads naturally, almost like English,…
import pandas as pd df = pd.read_csv(‘data.csv’) print(df.head()) That single line reads your CSV file and automatically detects the header row. Pandas assumes the first row contains column names by default, which is exactly what you want 99% of the time. But let me show you what’s actually happening under the hood, because understanding the mechanics makes everything else click into place. Understanding how pd.read_csv handles headers When you call pd.read_csv(), Pandas scans the first row of your CSV file and treats it as column names. This behavior is controlled by the header parameter, which defaults to header=0. That zero…
Discover top DevOps job opportunities this week from JP Morgan Chase, SpaceX, BBR Partners, Fox Corp, and Veeva Systems, designed to help professionals advance their careers.
Large Language Models(LLMs) have been in the tech news for quite some time and every day, we learn about new advancements in this field. These models are being used in finance, management, content creation, and analysis. While these perform well by themselves, some additional advanced techniques can help increase the reliability and accuracy of LLM-based applications. One such framework is Retrieval Augmented Generation(RAG) which allows the language model to access a knowledge base, thereby increasing the quality of responses generated by llms. I have already covered a basic introduction to LLMs in the previous posts. In this one, I will…
Testlio enhances its testing platform with AI-generated dashboards that correlate over 100 signals, helping DevOps and business leaders spot quality, risk, and AI-related issues faster.
import re text = “Contact us at [email protected] or [email protected]” pattern = r’\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,}\b’ emails = re.findall(pattern, text) print(emails) # [‘[email protected]’, ‘[email protected]’] That code extracts email addresses from text using Python regex. The pattern looks cryptic at first, but each piece serves a specific purpose in matching the structure of an email address. Regular expressions give you a tiny language for describing text patterns, and Python’s re module makes them practical to use. Understanding Python regex syntax Python regex patterns combine ordinary characters with special metacharacters that represent broader matching rules. The character \d matches any digit…
Autoptic launches AI-powered change resilience agents that analyze DevOps telemetry to detect root causes, prevent incidents, and boost confidence before production changes.