GitLab today made generally available an agentic artificial intelligence (AI) platform that automates software engineering tasks ranging from planning to application security. Coinciding with the release of version 18.8 of the core GitLab platform, the GitLab Duo Agent Platform initially provides access to seven AI agents that DevOps teams can assign a range of tasks […]
Author: drweb
Security Is a Developer Experience Problem, Rooted in Our Foundations If we want better security outcomes without slowing teams down, we should start where software actually starts. That requires secure foundations, like hardened images, that are safe by default. With better foundations, security becomes quieter, development becomes smoother, and the entire system works the way it should.
Let’s get the obvious out of the way right up front. AI isn’t a person. Thank you, Captain Obvious. We’re all on the same page. And yet, when we announced that AI is Techstrong’s “Person of the Year” for our Predict 2026 virtual event on January 15, a few folks felt compelled to remind us […]
Why a “protected repo”? Modern teams depend on public container images, yet most environments lack a single, auditable control point for what gets pulled and when. This often leads to three operational challenges: Inconsistent or improvised base images that drift across teams and pipelines. Exposure to new CVEs when tags remain unchanged but upstream content does not. Unreliable workflows due to rate limiting, throttling, or pull interruptions. A protected repository addresses these challenges by evaluating images at the boundary between public sources and internal systems, ensuring only trusted content is available to the build process. Routing upstream pulls through a Nexus…
Sklearn’s LogisticRegression is great for pure prediction tasks, but when I want p-values, confidence intervals, and detailed statistical tests, I reach for Statsmodels instead.The library gives you two main options for binary classification: Logit and Probit. Both model the probability that an observation belongs to class 1, but they use different link functions to transform the linear combination of predictors into probabilities between 0 and 1.Statsmodel Beginner’s Learning PathWhy Statsmodels When Sklearn Exists?Statsmodels comes from the statistics world, not the machine learning world. When I fit a model with statsmodels.api.Logit(), I get back an object that behaves like what you’d…
Decorators are a concept that can trip up new Python users. You may find this definition helpful: A decorator is a function that takes in another function and adds new functionality to it without modifying the original function. Functions can be used just like any other data type in Python. A function can be passed to a function or returned from a function, just like a string or integer. If you have jumped on the type-hinting bandwagon, you will probably want to add type hints to your decorators. That has been difficult until fairly recently. Let’s see how to type…
Austin, TX / USA, 14th January 2026, CyberNewsWire
We use cookies This website uses cookies in order to enhance the overall user experience. Take a look at our privacy policy for more information.
Explore the challenges of AI agents in DevOps pipelines, highlighting the importance of model-aware detection to improve security and reduce vulnerabilities.
Modern business runs on data. But raw information is useless. It requires pipelines, structure, and transformation. This is data engineering, the critical bridge between chaotic data sources and clear analytical insight. Without this foundation, analytics cannot scale. Decisions remain guesses. This list looks at providers who build that essential infrastructure, turning data into a genuine business asset.Why Data Engineering Is the Backbone of Data-Driven CompaniesA dashboard is only as good as the pipeline feeding it. A broken data flow means bad decisions, period. Data engineering creates reliable, scalable systems that make analytics possible at all. It is not a support function.…