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Phantom APIs: The Security Nightmare Hiding in Your AI-Generated Code (Mon, 22 Dec 2025)
The call came at 2:47 AM on a Tuesday in October 2024. I'd been following API security incidents for fifteen years, but this one made my coffee go cold as the CISO walked me through what happened. Their fintech had discovered attackers extracting customer financial data through /api/v2/admin/debug-metrics — an endpoint that shouldn't exist. No developer remembered building it. Their OpenAPI specs contained zero references to it. Yet there it was, quietly serving PII to anyone who stumbled across the URL.
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LLMOps Explained: How It Works, Key Benefits, and Best Practices (Mon, 22 Dec 2025)
Let us say you are interacting with a bank’s AI assistant regarding a payment issue. All of a sudden, it reflects the incorrect answer and freezes your account. Disgusting, yeah? Now, can you imagine this situation happening with numerous customers simultaneously? Such failures can destroy trust and attack the company’s expenditure if the systems are misplaced. It is exactly where LLMOps marks its entry. It is the backdoor process ensuring large language models operate seamlessly, reliably, and safely. In other terms, LLMOps helps to keep the AI agents beneficial instead of harmful. Still figuring out the answer to - “what is LLMOps”?  Let us explore in depth through this blog.  What Is LLMOps? Large language model operations (LLMOps) refer to the practices and workflows involved in the management of large language models (LLMs). It is done through their development, deployment, monitoring, and maintenance. This refers to a type of model, such as OpenAI's GPT series or Google's BERT. It represents the latest advancement in AI technology, which is trained on large datasets to handle tasks, including but not limited to text generation, translation, question answering, etc. 
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Recent Linux Kernel Features Relevant to System Design (Mon, 22 Dec 2025)
Every new version of the Linux kernel provides changes that have an immediate influence on hardware interaction, memory efficiency, system speed, and security. These developments are very relevant not just to huge business servers but also to embedded systems, multimedia platforms, and real-time gadgets. The need to support increasingly complex hardware, provide more secure abstractions, and lower overhead drives kernel development. A technical overview of a number of new features that meet these needs is given in this document: SOF Dynamic Pipeline Support, io_uring, DAMON, Landlock LSM,memfd_secret, and IOMMU FD API. Each is described along with its purpose, relevant system design scenarios, and possible real-world applications.
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Pandera: The Open-Source Framework for Data Validation (Mon, 22 Dec 2025)
Pandera is an open-source library for validating DataFrame-like objects (such as pandas, Polars, and Dask) by defining schemas that specify column names, data types, and statistical properties. At runtime, Pandera checks these schemas to ensure data quality. Its syntax is similar to Pydantic, providing a class-based API for defining DataFrameModels with column definitions and constraints. Pandera also integrates seamlessly with data processing pipelines by offering decorators that automatically validate function inputs and outputs. Pandera is a lightweight and expressive data validation library for Python that helps ensure the reliability and robustness of data processing pipelines. It provides an API for defining "schemas" that describe the expected properties of dataframe-like objects, including column names, data types, and statistical properties. By explicitly defining and enforcing these assumptions at runtime, Pandera catches data quality issues early, preventing silent data corruption from propagating downstream to analytical models or production applications. It offers two main ways to define schemas: an object-based API for simpler validation and a class-based, Pydantic-style API that uses Python type hints for more complex cases.
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DevOps Cafe Podcast

DevOps Cafe Ep 79 - Guests: Joseph Jacks and Ben Kehoe (Mon, 13 Aug 2018)
Triggered by Google Next 2018, John and Damon chat with Joseph Jacks (stealth startup) and Ben Kehoe (iRobot) about their public disagreements — and agreements — about Kubernetes and Serverless. 
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DevOps Cafe Ep 78 - Guest: J. Paul Reed (Mon, 23 Jul 2018)
John and Damon chat with J.Paul Reed (Release Engineering Approaches) about the field of Systems Safety and Human Factors that studies why accidents happen and how to minimize the occurrence and impact. Show notes at http://devopscafe.org
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DevOps Cafe Ep. 77 - Damon interviews John (Wed, 20 Jun 2018)
A new season of DevOps Cafe is here. The topic of this episode is "DevSecOps." Damon interviews John about what this term means, why it matters now, and the overall state of security.  Show notes at http://devopscafe.org
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