The Model Context Protocol Registry is the official, community-driven registry service for Model Context Protocol (MCP)) servers, essentially an app store for MCP servers. It gives MCP clients a curated, queryable list of available servers instead of everyone managing connections by hand. The refere Read more ›
Extending the OpenAPI specification is a widely used, but seldom talked about superpower of the specification. People who aren’t in the know hit the wall with what the specification can’t do, and they move on and create a new specification — where those in the know understand the specification has become the lingua franca of API operations over the last 16 years, and craft their own extensions for the specification to make it do what they need it to do. Read more ›
Large language models have moved out of the research lab and into engineers’ daily workflow. LLMs serve as reasoning engines that can orchestrate complex tasks including identifying vulnerabilities in source code and transforming fragmented project discussions into rigorous technical specifications.While the general public uses AI tools to write email and plan vacations, technical professionals use LLMs as core architectural elements that are fundamentally changing how digital infrastructures... Read more ›
The Open Source Technology Improvement Fund is proud to share the results of our security audit of Kubeflow. Kubeflow functions for building and deploying customizable machine learning workflows in Kubernetes, and has many subprojects able to be implemented individually or in combination. Thanks to ADA Logics and the Cloud Native Computing Foundation, Kubeflow underwent a custom security engagement that audited 6 projects in the Kubeflow ecosystem. Read more ›
Over the last year, I've lost count of how many conversations I've seen about prompt engineering. Every week there seems to be another article explaining how a different prompt structure, a new framework, or a carefully chosen set of words can dramatically improve the quality of AI-generated responses. It's an interesting topic, and there is certainly some truth to it. A well-written prompt usually produces a better answer than a vague one. But after spending more time using AI in real DevOps... Read more ›
NocoDB is an open-source no-code platform that puts a spreadsheet-style UI on top of a relational database, with grid, form, Kanban, and gallery views plus a REST API. This guide deploys NocoDB using Docker Compose with a PostgreSQL backend and Traefik handling automatic HTTPS, then exercises the API with a sample base. By the end, you'll have NocoDB serving a base over HTTPS with API access at your domain. Set Up the Directory Structure 1. Create the project directories: $ mkdir -p ~/nocodb/... Read more ›
A vector database stores data as vectors (embeddings) and finds items by meaning, not exact match. What it is, how similarity search works, how it differs from a normal database, and why RAG and AI search depend on it. Read more ›
At Dynatrace, we believe the future of observability and cloud-native operations is open. Not “open” as a slide-deck buzzword, but open as in showing up every day to write code, review PRs, chair working groups, and build tools the community can use, extend, and make their own. We’re proud to be an active contributor to […] The post appeared first on . Read more ›
The near-term value of AI agents is partial automation. Buyers should ask what is automated now, what systems agents touch and who owns the workflow. Read more ›
The Azure Functions MCP extension provides simple abstractions to help you build and host MCP servers without having to learn the protocol details yourself. You can use the triggers and bindings model to expose any function as an MCP tool, resource, or prompt. Since its initial preview, the extension has grown from supporting a single trigger Read more ›
Let’s Talk About Where Your APIs Actually Run Quick answer: On-premises API security keeps API discovery, detection, and enforcement inside your own perimeter instead of a third-party cloud—the model regulated industries need. Deploying it natively on Kubernetes (sidecar sensors → a discovery controller → an inline WAF gateway) makes it practical: continuous API discovery surfaces […] The post appeared first on <a href=" Read more ›
The Knowledge Augmentation Spectrum: CAG vs RAG vs CRAG For the past year, the industry has been obsessed with RAG \(Retrieval-Augmented Generation\) \. It was the “gold standard” for giving LLMs access to enterprise data\. But as our production requirements shift toward lower latency, higher accuracy, and better reliability, we are seeing the emergence of new paradigms\. If you are building AI applications today, you need to understand the architectural trade-offs between RAG , CAG \(Cache-A... Read more ›
DAI Studio — Computer-Aided Context Engineering (CACE) for large language models. Structure your LLM inputs with precision using .dai files. Read more ›
AI is becoming smarter every month. Does that mean prompt engineering is becoming less important — or more valuable than ever? Read more ›
Elasticsearch DiskBBQ achieves up to 7x higher vector search throughput than Qdrant at comparable recall on network-attached storage. Explore the benchmark methodology and full results. Read more ›
In this post, you learn how Stripe built a production-grade AI agent system for financial compliance. We cover the technical architecture of Stripe’s ReAct agent framework and the infrastructure decisions behind a dedicated agent service. We also discuss the role of human oversight in maintaining accountability, and key lessons about task decomposition, orchestration patterns, and cost optimization through prompt caching. By the end, you will understand how to design agentic systems that scal... Read more ›