# structured Published articles for structured. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## How Marathon Incidents Expose Organizational Fragility and Require Structured Incident Response DevFeed: [How Marathon Incidents Expose Organizational Fragility and Require Structured Incident Response]() Original publisher: [Read original article]() Author: Vanessa Huerta Granda Published: 2026-09-17T09:30:00Z Content type: article Language: en Sources: [InfoQ]() Topics: [incident](), [Incident response]() Tags: [devops](), [incident](), [incident-response](), [infoq](), [limits](), [organizational](), [outages](), [presentation](), [qcon-san-francisco-2026](), [qcon-software-development-conference](), [real-world](), [stream-incidents](), [structured](), [system](), [transcripts]() ### AI overview Vanessa Huerta Granda discusses how prolonged incidents reveal gaps between planned work and work as performed. Drawing on real-world scenarios, the presentation examines organizational fragility, human limits, system interdependencies, and the need for structured, humane, cross-functional incident response. ### Source excerpt Vanessa Huerta Granda explains how marathon incidents expose the gap between work as imagined and work as done. Drawing from real-world scenarios, she shares how complex outages reveal organizational fragility, human limits, and system interdependencies--and why incident response requires structured endurance, humane rotations, and holistic cross-functional coordination. By Vanessa Huerta Granda ## A framework for sequential decisions in daily life and beyond DevFeed: [A framework for sequential decisions in daily life and beyond]() Original publisher: [Read original article]() Author: Nisarg Suthar Published: 2026-08-04T07:16:01Z Content type: article Language: en Sources: [Data Science at Microsoft]() Topics: [decision-making]() Tags: [data-modeling](), [data-science](), [decision-making](), [model](), [operations-research](), [sequential-decision](), [structured](), [supply-chain]() ### AI overview The article introduces a Universal Modeling Framework proposed by Warren Powell for reasoning about sequential decisions under uncertainty and incomplete information. It explains how mathematical modeling can clarify decision problems and support better choices across areas such as finance, healthcare, data-center infrastructure, and supply chains. ### Source excerpt Photo by Sophia Kunkel on Unsplash Decision-making occupies a significant portion of our mental uptime. Whether we work in finance, energy, transportation, healthcare, e-commerce, foreign policy, or global supply chains, we are constantly required to make choices in the presence of uncertainty and incomplete information. As new information arrives, decisions must be revised, refined, and sometimes completely reconsidered. Every decision carries consequences -- some rewarding, others costly. The ability to consistently make better choices is often a defining factor behind successful outcomes. Yet effective decision-making remains notoriously difficult. Describing a problem as "mind-bogglingly complex" is really just a by-product of a failure to think about the problem in a structured way.-- Dr. Warren Powell This article introduces a Universal Modeling Framework for reasoning about sequential decision problems proposed by Dr. Warren Powell, an operations researcher at Princeton University. The same framework can be applied across a remarkably diverse set of problems: buying or selling financial assets, evaluating a new user experience, selecting candidate drugs for clinical trials, investing in data-center infrastructure, or managing large-scale supply chains. Here we shall undertake an approach that focusses on identifying the core elements of a decision-making process. Central to our approach is the creation of a simple mathematical model that eliminates the ambiguity of describing problems in language. Modeling is an art, guided by a mathematical framework, and results in a well-defined problem that we can put on a computer to solve. Even when the ultimate goal is not to automate the decision, the act of modeling itself often leads to deeper understanding and better choices. A dynamic model for sequential ecisions A sequential decision process can be represented as follows: Where: Sₜ is the state variable capturing our state of knowledge at time t. For example, inve ## Modernizing sourcing at scale: How intelligent agents are changing procurement DevFeed: [Modernizing sourcing at scale: How intelligent agents are changing procurement]() Original publisher: [Read original article]() Author: Shreya Peddi Published: 2026-07-21T07:16:01Z Content type: article Language: en Sources: [Data Science at Microsoft]() Topics: [microsoft 365](), [Microsoft]() Tags: [agent](), [agentic-workflow](), [agents](), [generative-ai-finance](), [microsoft](), [microsoft-365](), [procurement](), [procurement-strategy](), [setup](), [structured](), [supply-chain](), [tool]() ### AI overview This article describes how a Microsoft procurement professional built the Supplier Price Interpreter with M365 Chat Agent Builder. The agent uses supplier price sheets stored in a structured Teams channel as a knowledge source and responds to plain-language prompts about the data, aiming to reduce manual comparison work in RFP and RFx processes. ### Source excerpt Image generated by AI Picture this: It's late in the week, an urgent sourcing decision is due, and sitting in your inbox are three separate supplier price sheets -- each formatted differently, each with its own tab structure, its own naming conventions, its own quirks. Your job is to compare them, find the gaps, flag the inconsistencies, and deliver a recommendation. The clock is ticking. This isn't a hypothetical scenario. It happens regularly in procurement teams running large scale RFP and RFx processes. For years, the solution was straightforward: Open each file and compare them line by line. It worked, but it was slow, manual, and far removed from the strategic work that sourcing professionals are hired to do. In a world where business decisions move fast and supplier negotiations are time-sensitive, delays in price review have real consequences. Slower cycles, missed windows, and sourcing teams spending their cognitive energy on extraction instead of analysis. So, we asked the question: What if the extraction part could just happen on its own? Starting small, at the desk The Supplier Price Interpreter didn't start as a grand initiative. It started as a practical frustration. Rupa Shastri, a procurement professional within Microsoft's broader procurement organization, built the agent right at her desk using M365 Chat Agent Builder. No dedicated engineering team, no lengthy development cycle, no new infrastructure. Just a clear problem, an available tool, and the willingness to try something different. Figure 1: M365 Copilot flow and the actionable insights it provides. Working within a large enterprise procurement environment, where supplier bids come in across multiple RFx engagements spanning vendors, scopes, and formats, the repetitive nature of price sheet review was hard to ignore. Every engagement required the same foundational steps. Open the files, read the data, compare the numbers, find what's missing. The methodology never changed. Only the suppliers ## Devavrat Shah's research and Ikigai Labs use tabular data for real-time forecasting and decision-making DevFeed: [Devavrat Shah's research and Ikigai Labs use tabular data for real-time forecasting and decision-making]() Original publisher: [Read original article]() Author: David Chandler | Laboratory for Information and Decision Systems Published: 2026-07-14T20:25:00Z Content type: article Language: en Sources: [MIT AI News]() Topics: [Artificial Intelligence](), [decision-making](), [Structured-data](), [AI Models](), [data](), [Time Series](), [Computer science](), [Electrical engineering and computer science (EECS)](), [Temporal data](), [systems]() Tags: [ai](), [ai-and-decision-making](), [ai-in-business-planning](), [ai-models](), [algorithms](), [artificial-intelligence](), [business-and-management](), [business-modeling](), [celonis-context-model](), [computer-science-and-technology](), [data](), [data-systems](), [decision-making](), [devavrat-shah](), [electrical-engineering-and-computer-science-eecs](), [faculty](), [forecasting](), [idss](), [ikigailabs](), [laboratory-for-information-and-decision-systems-lids](), [mit-eecs](), [mit-idss](), [mit-intellectual-property](), [mit-lids](), [mit-schwarzman-college-of-computing](), [predictions](), [profile](), [real-time](), [research](), [school-of-engineering](), [startups](), [structured](), [tabular-data](), [time-series-data]() ### AI overview MIT Professor Devavrat Shah's research led to a foundation model for tabular and time-series enterprise data. Developed with Ikigai Labs, the system continuously tests predictions against real outcomes to support large-scale forecasting and decision-making. ### Source excerpt Through research and entrepreneurship, Professor Devavrat Shah is helping to design methods that can handle constant decision-making using limited computational resources. ## Teaching the Agent Our Craft: Structured Agentic Development on a Real Codebase DevFeed: [Teaching the Agent Our Craft: Structured Agentic Development on a Real Codebase]() Original publisher: [Read original article]() Author: Alex Haldeman Published: 2026-07-06T16:00:00Z Content type: article Language: en Sources: [8th Light](), [8th Light Insights]() Topics: [Agentic development](), [Claude Code](), [Development](), [AI-assisted coding](), [Test-driven development]() Tags: [agentic](), [agentic-development](), [ai-and-emerging-tech](), [claude-code](), [clean-architecture](), [structured](), [test]() ### AI overview 8th Light describes a structured agentic development workflow built around Claude Code. The approach adapts Research-Plan-Implement by separating research, planning, and implementation, adding review cycles and involving product managers and designers at each transition. It also applies test-driven development, clean architecture, and explicit project conventions to address common agent failure modes. ### Source excerpt The Mission We recently partnered with a startup that had developed a clinically proven approach to alleviating neuroplastic chronic pain. Their program worked: a coach-led model that helped patients ease chronic pain at a lower cost than conventional treatment. The problem was reach. The in-person model could not scale to the demand they were seeing, and there were not enough coaches to close the gap. We were brought in to build a digital platform that could deliver the program to every patient who needed it. Our Development Philosophy and Inspiration At 8th Light, we approach agentic development the same way we approach any software engagement: with discipline around test-driven development, clean architecture, and code that is built to embrace change. But agentic development comes with its own failure modes. An agent produces code that covers the happy path and misses critical behaviors. A context window fills with stale reasoning from earlier attempts, and the agent starts working against itself. Without explicit conventions, the output works but looks like nobody on the team wrote it. Tyler Burleigh's Research-Plan-Implement gave us a useful frame for thinking about this. His core observation: the bottleneck is not code generation, it is ensuring the model understands what to build before it starts building. RPI addresses that by separating research, planning, and implementation into distinct phases, each with a review cycle before the next begins. We adapted that structure into our Claude Code workflow, with the additional goal of keeping product managers and designers genuinely in the loop at each transition, not just developers. What follows is a description of the harness we built from a Claude Code-specific perspective. The HarnessStructure Before walking through the pieces, it helps to see how they fit together. Everything that teaches the agent our craft lives in a handful of files at the project root and inside a single .claude/ directory. None of it is ## The Snapshot Machine: genie learn and diff as a Change-Validation Safety Net on CML2 DevFeed: [The Snapshot Machine: genie learn and diff as a Change-Validation Safety Net on CML2]() Original publisher: [Read original article]() Author: Brent Leekley Published: 2026-06-22T00:00:00Z Content type: tutorial Language: en Sources: [brent.leekley.me blog]() Topics: [networking](), [Cisco](), [Routing (disambiguation)](), [Structured-data](), [Command-line interface]() Tags: [change-validation](), [cisco](), [cisco-modeling-labs](), [cli](), [cml2](), [genie](), [genie-diff](), [genie-learn](), [maintenance-window](), [network-automation](), [network-testing](), [ospf](), [pyats](), [routing-table](), [snapshot](), [structured](), [validation]() ### AI overview This tutorial demonstrates using Cisco Modeling Labs 2 with Genie learn and diff to validate network changes. It snapshots structured operational state before and after a maintenance change, then compares the snapshots to reveal side effects that routine show commands may miss. The source summary reports that a changed OSPF cost reroutes three destinations while adjacencies remain FULL. ### Source excerpt Snapshot a CML2 fabric with genie learn before and after a maintenance change, then genie diff the structured state. One fat-fingered OSPF cost quietly reroutes three destinations while every adjacency stays FULL. Structured before-and-after diffs catch the side effects that show commands miss. ## From Intent to Reality: Driving pyATS Tests from NetBox Against a CML2 Fabric DevFeed: [From Intent to Reality: Driving pyATS Tests from NetBox Against a CML2 Fabric]() Original publisher: [Read original article]() Author: Brent Leekley Published: 2026-06-15T00:00:00Z Content type: tutorial Language: en Sources: [brent.leekley.me blog]() Topics: [NetBox](), [Network](), [Cisco](), [data](), [Structured-data]() Tags: [cisco-modeling-labs](), [cml](), [cml2](), [custom-fields](), [genie](), [ids](), [intent-based-networking](), [netbox](), [network](), [network-automation](), [network-testing](), [ospf](), [pyats](), [pynetbox](), [router-id](), [source-of-truth](), [spine-leaf](), [structured](), [testbed]() ### AI overview This tutorial shows how to store declared OSPF router-IDs in NetBox and use Cisco pyATS to compare them with the router-IDs on a live CML2 spine-leaf fabric. The comparison turns network intent validation into a pass-or-fail test. ### Source excerpt NetBox holds the intent (declared OSPF router-IDs), a CML2 spine-leaf is the network under test, and pyATS is the judge. Close the loop and prove the live network matches what you said it should be, with real pass and fail output. ## The Schema-Driven LLM Query Pattern DevFeed: [The Schema-Driven LLM Query Pattern]() Original publisher: [Read original article]() Author: Rick Donato Published: 2026-02-27T15:09:32Z Content type: tutorial Language: en Sources: [Packet Coders - Learn Network Automation]() Topics: [Large Language Model](), [Structured-data](), [Security](), [data](), [pandas](), [Query (disambiguation)]() Tags: [ai](), [blog](), [data](), [databases](), [graphql](), [llm](), [pandas](), [rest](), [schema](), [security](), [structured]() ### AI overview This tutorial presents the Schema-Driven LLM Query Pattern. Instead of sending raw data to an external LLM, it sends the data schema, receives query syntax, and executes the query locally to reduce data exposure and token usage. ### Source excerpt Typically, when working with an LLM, you send your data along with a prompt and receive a response. But this pattern often introduces issues, especially with public LLMs, due to security concerns and token bloat. There is a different pattern we can use: the Schema-Driven LLM Query Pattern. With ## Making Sense of Digital Transformation Initiatives: Seeing Past the Fuzziness DevFeed: [Making Sense of Digital Transformation Initiatives: Seeing Past the Fuzziness]() Original publisher: [Read original article]() Author: Josh Korr Published: 2026-02-13T23:41:00Z Content type: article Language: en Sources: [8th Light Insights]() Topics: [digital](), [product mindset](), [Optimization]() Tags: [digital-transformation](), [optimization](), [organizations](), [product](), [product-strategy-innovation](), [project](), [strategy](), [structured](), [transformation]() ### AI overview This article explains why digital transformation initiatives often become unclear in practice and argues that short, structured strategy and shaping phases can create shared clarity. It discusses how these initiatives may involve business-model shifts, cultural change, product thinking, and process optimization, illustrated by client examples. ### Source excerpt Digital transformation always sounds clear in an RFP or slide deck, but in practice things can get fuzzy fast. What exactly are we transforming? Is this about customers, or about ourselves? Are we reshaping workflows, or the entire business model? Without answers to these questions, projects stall, swirl, or never quite get started in the first place. As a Certified Sense-MakerTM and fuzziness connoisseur, I love helping clients make sense of their digital transformation initiatives.* This includes helping them understand the bigger picture, what their plans will mean for business operations, and what tools or solutions can help. By implementing short, structured strategy and shaping phases, organizations can gain shared clarity and overcome transformational fuzziness. * Just kidding: there isn't a Sense-Maker(tm) certification. (Sigh.) Fuzziness Factor #1: The Big Picture Digital transformation initiatives are typically triggered by something bigger: business model shifts or expansions, operational maturation, rebrands or relaunches. And these initiatives go far beyond technology upgrades. There is a very real shift in culture, a move towards a product mindset, and an optimization of processes that meet the demands of the market. When done well, these initiatives have a lasting impact on the organization and the benefits show up on the bottom line. But teams that are at the "ground level" of this transformative work often have a fuzzy understanding of the macro-strategy and how the immediate near-term effort fits in. Take a Fortune 500 media company I worked with. They had debated for a year whether to introduce self-serve sales alongside their high-touch direct sales model. Nothing with the project had moved because no one had named the real issue: This wasn't just a sales project -- it was a business model transformation. Misalignment across stakeholders was causing them to delay decisions. Once reframed as a fundamental cultural and business model shift, two thin ## Operation "Date Bait": AI-enabled scam targeting loveseekers DevFeed: [Operation "Date Bait": AI-enabled scam targeting loveseekers]() Original publisher: [Read original article]() Published: 2026-02-01T00:00:00Z Content type: news Language: en Sources: [OpenAI News]() Topics: [Artificial Intelligence](), [OpenAI](), [ChatGPT](), [Automation](), [API]() Tags: [ads](), [ai](), [api](), [automation](), [chatgpt](), [engagement](), [openai](), [safety](), [scam](), [social-media](), [structured](), [tasks](), [telegram](), [translation](), [trust]() ### AI overview OpenAI describes Operation "Date Bait," in which accounts likely originating in Cambodia used ChatGPT and API access for a semi-automated romance and task scam targeting Indonesian men. The operation used translated and emotionally manipulative outreach across advertisements, an AI chatbot, Telegram, and dating platforms. ### Source excerpt OpenAI banned accounts that very likely originated in Cambodia and used AI for scam outreach to Indonesian loveseekers, including translation and engagement. ## How to Get Structured Responses back from OpenAI DevFeed: [How to Get Structured Responses back from OpenAI]() Original publisher: [Read original article]() Author: Rick Donato Published: 2026-01-28T08:00:56Z Content type: tutorial Language: en Sources: [Packet Coders - Learn Network Automation]() Topics: [JSON Schema](), [OpenAI](), [Large Language Model]() Tags: [example](), [how-to](), [json](), [json-schema](), [llm](), [openai](), [responses](), [schema](), [structured](), [structured-output](), [tips]() ### AI overview This tutorial explains that structured responses from OpenAI can be obtained by defining the expected output format with JSON Schema. It includes a Python example using the OpenAI client. ### Source excerpt OpenAI Tip: If you need structured output from the LLM, use structured responses and define the expected format with JSON Schema. Here's an example: from openai import OpenAI import os client = OpenAI(api_key=os.getenv("OPENAI_API_KEY")) # Define the exact shape you want back. ## Gemini and Angular, Part II: Creating Generative UIs DevFeed: [Gemini and Angular, Part II: Creating Generative UIs]() Original publisher: [Read original article]() Author: Armen Vardanyan Published: 2026-01-22T13:48:08Z Content type: tutorial Language: en Sources: [Daniel Glejzner]() Topics: [Angular](), [Large Language Model](), [function calling](), [ui](), [text-generation](), [JSON](), [App]() Tags: [angular](), [article](), [code](), [function-calling](), [gemini](), [generative](), [generative-ui](), [json](), [llms](), [model](), [schemas](), [structured](), [text](), [text-generation]() ### AI overview This tutorial continues a series on LLMs and Gemini by exploring Generative UI in Angular. It describes dynamically rendering interactive Angular components from model responses, combining Gemini with Google's Nano Banana Pro image generation model, and using Angular signal forms. The article emphasizes minimizing boilerplate while balancing model capability, performance, and cost. ### Source excerpt Let's continue our journey into LLMs and Gemini! In the previous article, we moved beyond simple text generation and learned: how to force the model to speak our language using structured outputs (JSON schemas) how to connect the model to our actual code and logic using function calling ## Structured Tags in OpenAPI 3.2: Organising APIs the Way They Deserve DevFeed: [Structured Tags in OpenAPI 3.2: Organising APIs the Way They Deserve]() Original publisher: [Read original article]() Author: Zaid Daba'een Published: 2025-10-26T21:00:37Z Content type: article Language: en Sources: [DeveloperHub.io]() Topics: [OpenAPI Specification](), [Documentation](), [API](), [SDKs]() Tags: [api-design](), [api-documentation](), [engineering](), [modular](), [navigation](), [openapi](), [sdks](), [structured]() ### AI overview OpenAPI 3.2 adds structured, hierarchical tags that let APIs organize endpoints into nested categories. The article explains how this can improve documentation navigation, SDK organization, and maintainability for large or modular APIs. ### Source excerpt OpenAPI 3.2 introduces structured tags, allowing APIs to organize endpoints into nested categories for clearer, more maintainable documentation. ## Building a Python Package to Turn Unstructured Data into Financial Insights DevFeed: [Building a Python Package to Turn Unstructured Data into Financial Insights]() Original publisher: [Read original article]() Author: Pranshu Raj Published: 2025-06-23T08:50:45Z Content type: tutorial Language: en Sources: [Pranshu Raj - blog on backend systems, performance and sidequests]() Topics: [Python](), [Parsing](), [pdf](), [data](), [Structured-data]() Tags: [data](), [parsing](), [pdf](), [pipeline](), [python](), [structured]() ### AI overview This article explains how the authors built a Python package to extract structured information from messy earnings-call PDF transcripts. It covers PDF text extraction, cleaning and segmentation, speaker identification, categorization into sections such as management remarks and Q&A, and structured output. ### Source excerpt How we built concall-parser to extract structured insights from messy earnings call transcripts, dealing with PDFs, text processing, and speaker identification challenges. ## The path to structured content with Markdown DevFeed: [The path to structured content with Markdown]() Original publisher: [Read original article]() Author: Niklas Begley Published: 2024-06-06T07:00:00Z Content type: tutorial Language: en Sources: [Doctave - Build beautiful developer portals with docs-as-code]() Topics: [Markdown](), [Parser](), [Parsing](), [XML](), [Documentation]() Tags: [authoring](), [blog](), [documentation](), [markdown](), [structure](), [structured](), [syntax](), [technical](), [technical-documentation]() ### AI overview The article argues that Markdown has an underlying structure that is often hidden from authors, making it more similar to XML-based structured authoring than commonly assumed. It demonstrates how Markdown is transformed into an abstract syntax tree and represented as a tree-shaped JSON structure. ### Source excerpt This post was inspired by the Pros and Cons of Using Markdown for Technical Documentation Panel Discussion with Ed Marsh, Eric Holscher, and Fabrizio Ferri-Benedetti. Around the ~40 minute mark the discussion moves onto how to maintain a consistent style with Markdown documentation, especially in larger teams. The panel agrees that currently there are no clear ways to enforce a specific document structure when using Markdown. Fabrizzio then on goes on to say (54:15): Some day, someone is going to figure out a way to seamlessly grow Markdown into something that can mature into structured content I've been thinking about this a lot, and my claim is that Markdown and XML (which is traditionally used for structured authoring) are more similar than you would initially expect, and that the dream of adding validations and structure to Markdown is perhaps not too far away. The structure behind Markdown There's a commonly held belief that Markdown is not structured. There is some truth to this, but I'd argue it's more the case that the structure is hidden. While with most XML-based authoring tools you are manipulating the structure directly (or through a WYSIWYG editor), with Markdown we are one level removed from the actual underlying structure. To demonstrate, let's take a look at a quick example at how Markdown is actually almost equivalent to XML, if you squint a little. Let's take this Markdown content. 1 2 3 4 5 # Hello, world This is a paragraph **with some bold text** and this is an image When this Markdown is processed into HTML, it goes through a number of transformations. The first thing that happens is that the Markdown is converted into an Abstract Syntax Tree. This means taking the raw text of the Markdown, and converting into something more structured that a program can manipulate. Each Markdown parser does this a little bit differently, but we can see this in action with, for example, the AST Explorer tool. Let's take Markdown above, and paste it ## Cooking Effective Code Reviews. DevFeed: [Cooking Effective Code Reviews.]() Original publisher: [Read original article]() Author: Fernando Cejas (me@fernandocejas.com) Published: 2021-08-04T00:00:00Z Content type: tutorial Language: en Sources: [Fernando Cejas Blog]() Topics: [Code review](), [pull-requests](), [Development](), [engineering-culture](), [decision-making]() Tags: [android](), [code-quality](), [code-review](), [code-reviews](), [culture](), [development](), [engineering](), [infrastructure](), [ios](), [java](), [kotlin](), [legacy-code](), [linux](), [open-source](), [processes](), [programming](), [python](), [rust](), [scala](), [software](), [software-development](), [structured](), [team](), [tech-debt](), [web-development]() ### AI overview A tutorial on making pull request code reviews more effective through a structured process. It discusses their role in code quality, documentation, collaboration, engineering culture, and keeping reviews small enough to manage. ### Source excerpt **Reviewing code (PRs) is not an easy task**, so in this post I will share **tips and tricks** on how your code reviews can better contribute to **code quality**, **be more effective and increase team morale** by following a structured and organized process. **Let's jump in!** ## Using Craft CMS as a Headless CMS with the GraphQL API DevFeed: [Using Craft CMS as a Headless CMS with the GraphQL API]() Original publisher: [Read original article]() Author: andrew@nystudio107.com (Andrew Welch) Published: 2020-01-20T05:00:00Z Content type: tutorial Language: en Sources: [nystudio107 | Articles on modern web development.]() Topics: [Content Management System](), [GraphQL](), [API](), [Front end](), [Self-hosted]() Tags: [added](), [api](), [authoring](), [cms](), [craft](), [formalized](), [frontend](), [gives](), [graphql](), [headless](), [here-s](), [insights](), [layer](), [practical](), [structured](), [website]() ### AI overview This tutorial explains how to use Craft CMS as a headless CMS through its GraphQL API. It discusses the API's structured data access, its use with separate frontends, and the distinction between Craft CMS's first-party implementation and the CraftQL plugin. ### Source excerpt Craft CMS 3.3 added a GraphQL layer that gives your website a formalized, structured API out of the box. Here's how to use GraphQL + Craft CMS as a "headless" CMS ## Annotated JSON-LD Structured Data Examples DevFeed: [Annotated JSON-LD Structured Data Examples]() Original publisher: [Read original article]() Author: andrew@nystudio107.com (Andrew Welch) Published: 2019-05-29T12:00:00Z Content type: tutorial Language: en Sources: [nystudio107 | Articles on modern web development.]() Topics: [json-ld](), [Structured-data](), [JSON](), [Google Search](), [Content Management System]() Tags: [annotation](), [article](), [cms](), [data](), [examples](), [insights](), [json](), [json-ld](), [plus](), [real-world](), [structured](), [want](), [wild]() ### AI overview This article presents annotated, real-world JSON-LD structured data examples. It explains how schema.org vocabularies express context and connections, recommends selecting explicit best-fit schema types supported by Google, and introduces node identifiers for referencing schemas without unnecessary repetition. ### Source excerpt Want to see a some real-world examples of JSON-LD Structured Data "in the wild"? This article has that, plus annotation. ## Database Normalization and Primary Keys DevFeed: [Database Normalization and Primary Keys]() Original publisher: [Read original article]() Author: Dimitri Fontaine PostgreSQL Major Contributor; Author Published: 2018-03-09T17:41:33Z Content type: tutorial Language: en Sources: [Dimitri Fontaine]() Topics: [Databases](), [Database](), [Structured-data](), [SQL](), [data]() Tags: [article](), [database](), [reference](), [sql](), [structured](), [transitive-dependencies]() ### AI overview This article explains database normalization and the role of primary keys in relational database design. It reviews normal forms from 1NF through DKNF and discusses dependencies, candidate keys, and logically structured data used with SQL. ### Source excerpt In our previous article we saw three classic Database Modelization Anti-Patterns. The article also contains a reference to a Primary Key section of my book The Art of PostgresQL, so it's only fair that I would now publish said Primary Key section! So in this article, we dive into Primary Keys as being a cornerstone of database normalization. It's so important to get Primary Keys right that you would think everybody knows how to do it, and yet, most of the primary key constraints I've seen used in database design are actually not primary keys at all. ## JSON-LD and Structured Data for Describing Web Content DevFeed: [JSON-LD and Structured Data for Describing Web Content]() Original publisher: [Read original article]() Author: andrew@nystudio107.com (Andrew Welch) Published: 2017-02-11T17:07:00Z Content type: tutorial Language: en Sources: [nystudio107 | Articles on modern web development.]() Topics: [json-ld](), [Structured-data](), [Semantic Web](), [Google]() Tags: [apple](), [cares](), [data](), [deal](), [google](), [insights](), [json-ld](), [semantic](), [structured]() ### AI overview This tutorial explains JSON-LD and structured data as ways to communicate a web page's meaning, context, and relationships to computers. It describes Schema.org as a vocabulary for expressing that information and notes that Google and Apple care about JSON-LD. ### Source excerpt JSON-LD Structured Data is a big deal for the "Semantic Web." Google cares about it. Apple cares about it. You should, too.