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From Zero to Local AI in 10 Minutes With Ollama + Python (Tue, 18 Nov 2025)
Why Ollama (And Why Now)? If you want production‑like experiments without cloud keys or per‑call fees, Ollama gives you a local‑first developer path: Zero friction: Install once; pull models on demand; everything runs on localhost by default. One API, two runtimes: The same API works for local and (optional) cloud models, so you can start on your laptop and scale later with minimal code changes. Batteries included: Simple CLI (ollama run, ollama pull), a clean REST API, an official Python client, embeddings, and vision support. Repeatability: A Modelfile (think: Dockerfile for models) captures system prompts and parameters so teams get the same behaviour. What’s New in Late 2025 (at a Glance) Cloud models (preview): Run larger models on managed GPUs with the same API surface; develop locally, scale in the cloud without code changes. OpenAI‑compatible endpoints: Point OpenAI SDKs at Ollama (/v1) for easy migration and local testing. Windows desktop app: Official GUI for Windows users; drag‑and‑drop, multimodal inputs, and background service management. Safety/quality updates: Recent safety‑classification models and runtime optimizations (e.g., flash‑attention toggles in select backends) to improve performance. How Ollama Works (Architecture in 90 Seconds) Runtime: A lightweight server listens on localhost:11434 and exposes REST endpoints for chat, generate, and embeddings. Responses stream token‑by‑token. Model format (GGUF): Models are packaged in quantized .gguf binaries for efficient CPU/GPU inference and fast memory‑mapped loading. Inference engine: Built on the llama.cpp family of kernels with GPU offload via Metal (Apple Silicon), CUDA (NVIDIA), and others; choose quantization for your hardware. Configuration: Modelfile pins base model, system prompt, parameters, adapters (LoRA), and optional templates — so your team’s runs are reproducible. Install in 60 Seconds macOS / Windows / Linux 1. Download and install Ollama from the official site (choose your OS).
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How to Create a Responsive Filter Component on React Guide (Tue, 18 Nov 2025)
In web development, responsive and user-friendly components have never been more important. One of these is a filter component that enables web users to quickly filter the user interface (UI) and data elements and display only relevant fields. The challenge is creating a filter component that can fit any screen size.  This article will demonstrate how to implement a responsive filter component using React. The tutorial explains how developers can build flexible filters into their web applications.
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Building Gateway Analytics: My Journey to Making API Traffic Data Useful (Tue, 18 Nov 2025)
APIs are everywhere today. Whether it's buying something online, logging into a mobile app, or streaming a movie, an API is always working behind the scenes. Over the last decade, APIs have become the backbone of modern software systems. As an application scales, the volume of API calls increases rapidly, and managing them becomes more complex. This is where API gateways come into action. An API gateway acts as an entry point for all internal or external API traffic. It sits in front of the backend services and handles responsibilities such as authentications, routing, rate limiting, logging, performance monitoring, and more.
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Embedding Ethics Into Multi-AI Agentic Self-Healing Data Pipelines (Tue, 18 Nov 2025)
The race to design a fully autonomous system is fostering innovations in the development of modern data systems. Developers are striving to create data ecosystems that are self-correcting and have minimal downtime so as to manage data movement effectively within their organizations. Due to such a drive for automation, the use of self-healing data pipelines has increased rapidly.  A conventional data pipeline consists of data processing elements connected in a relevant manner to move data between two different data systems. For example, extracting data from IoT devices, such as temperature sensors, and loading it into an analytical database for monitoring forms a simple ELT data pipeline. Such traditional pipelines are prone to limitations, including downtime, crashes, low scalability, and excessive monitoring overhead.
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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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