# GraphQL Published articles for GraphQL. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Scheduling gRPC and GraphQL Requests with Postman Monitors (Beta) DevFeed: [Scheduling gRPC and GraphQL Requests with Postman Monitors (Beta)]() Original publisher: [Read original article]() Author: Harsh Vardhan Published: 2026-09-16T16:00:00Z Content type: tutorial Language: en Sources: [Postman Blog]() Topics: [gRPC](), [Postman](), [Monitoring](), [Authentication]() Tags: [beta](), [general](), [graphql](), [graphql-monitoring](), [how-to](), [monitor-grpc-streaming](), [monitoring](), [monitors](), [postman](), [postman-monitors-grpc-graphql](), [scheduling](), [test]() ### AI overview This tutorial explains how to schedule gRPC and GraphQL requests with Postman Monitors in beta. It covers supported request types, streaming caveats, response testing, authentication, and current feature limitations. ### Source excerpt Postman Monitors now run gRPC and GraphQL requests in beta. Learn how to schedule them, test responses, and send feedback. Start monitoring today. The post Scheduling gRPC and GraphQL Requests with Postman Monitors (Beta) appeared first on Postman Blog. ## Creating Query Components with Apollo DevFeed: [Creating Query Components with Apollo]() Original publisher: [Read original article]() Author: Khan Academy Published: 2017-06-12T22:00:00Z Content type: tutorial Language: en Sources: [Khan Academy]() Topics: [GraphQL](), [React](), [React Component](), [API]() Tags: [apollo](), [engineering](), [examples](), [flow](), [graphql](), [how-to](), [news](), [react](), [react-component](), [web-frontend]() ### AI overview This tutorial explains a Query Components pattern for React applications using Apollo and GraphQL. The pattern separates data definitions from presentational components, allowing the presentational layer to remain independent of Apollo and GraphQL. Examples cover filtering by postal code, loading and error states, empty results, and Flow types. ### Source excerpt By Brian Genisio Here at Khan Academy, we've been using GraphQL in several projects with great success. GraphQL ... Read more ## What's New in Apollo Client 4.3 DevFeed: [What's New in Apollo Client 4.3]() Original publisher: [Read original article]() Author: Jerel Miller Published: 2026-09-15T12:01:05Z Content type: release Language: en Sources: [Apollo Blog]() Topics: [apollo-client](), [GraphQL](), [TypeScript](), [Cache](), [Code generation]() Tags: [apollo-client](), [cache](), [codegen](), [graphql](), [native](), [typescript]() ### AI overview Apollo Client 4.3 adds native custom scalar support, type-safe cache access, and improved TypeScript handling for incremental GraphQL data. The release also updates generated types and data-state narrowing for deferred fields. ### Source excerpt Apollo Client 4.3 is here: native custom scalar support, type-safe cache access, and more consistent @defer and @stream behavior. See what's new and upgrade today. ## Apollo Client 4.3 adds native custom scalar support and safer TypeScript types DevFeed: [Apollo Client 4.3 adds native custom scalar support and safer TypeScript types]() Original publisher: [Read original article]() Author: Jerel Miller Published: 2026-09-15T12:01:05Z Content type: release Language: en Sources: [Apollo Blog]() Topics: [GraphQL](), [TypeScript](), [Cache](), [Caching](), [JSON]() Tags: [cache](), [graphql](), [json](), [typescript](), [upgrade]() ### AI overview Apollo Client 4.3 adds native custom scalar integration, type-safe cache access, and improved TypeScript handling for incremental GraphQL data. ### Source excerpt Apollo Client 4.3 is here: native custom scalar support, type-safe cache access, and more consistent @defer and @stream behavior. See what's new and upgrade today. ## APIs & Service Communication: The Essential Guide DevFeed: [APIs & Service Communication: The Essential Guide]() Original publisher: [Read original article]() Author: Nikki Siapno Published: 2026-09-08T13:03:41Z Content type: tutorial Language: en Sources: [Level Up Coding System Design Newsletter]() Topics: [API](), [Microservice](), [GraphQL](), [gRPC](), [Kafka](), [WebSocket]() Tags: [api](), [apis](), [graphql](), [http](), [kafka](), [rest]() ### AI overview A tutorial comparing communication patterns for distributed systems. It explains how request-response APIs, event streams, and bidirectional connections affect responsiveness, service coupling, failure propagation, operational complexity, reliability, and scalability, then introduces REST and GraphQL with their main strengths, tradeoffs, and use cases. ### Source excerpt REST, gRPC, Kafka, WebSockets, Pub/Sub, and more. How do they all fit together? ## Migrating evilmartians.com from Gatsby to Astro in two weeks with Claude DevFeed: [Migrating evilmartians.com from Gatsby to Astro in two weeks with Claude]() Original publisher: [Read original article]() Author: Travis Turner (richardturner@evilmartians.com) Published: 2026-09-08T00:00:00Z Content type: tutorial Language: en Sources: [Evil Martians]() Topics: [Astro](), [gatsby](), [migration](), [Claude](), [Large Language Model](), [GraphQL](), [React](), [Security](), [build performance]() Tags: [ai](), [astro](), [build](), [claude](), [dx](), [graphql](), [javascript](), [migration](), [react](), [security](), [typescript]() ### AI overview Evil Martians describes migrating evilmartians.com from Gatsby to Astro in two weeks with Claude. The article outlines the site's Gatsby maintenance, security, and build-time costs, and discusses four bugs that could have shipped if the agent had operated without oversight. ### Source excerpt 367 posts, four years on Gatsby, and no dedicated team: here's how we moved evilmartians.com to Astro in two weeks with an LLM. ## GHarchive data has become unreliable for measuring GitHub activity DevFeed: [GHarchive data has become unreliable for measuring GitHub activity]() Original publisher: [Read original article]() Author: KD (noreply@blogger.com) Published: 2026-09-03T16:00:00Z Content type: opinion Language: en Sources: [Google Open Source Blog]() Topics: [data](), [datasets](), [GitHub](), [Crawler](), [GitHub API](), [Open Source](), [GraphQL]() Tags: [collect](), [data](), [dataset](), [datasets](), [github](), [google](), [google-open-source](), [graphql](), [open-data-sets](), [open-source](), [oss](), [pipeline](), [pull-requests](), [retention](), [stream](), [volume]() ### AI overview This article examines the reliability of open source data, focusing on GHarchive's coverage of GitHub events. It argues that GHarchive should not be used for real-time or volume-based metrics because event retention has declined and some activity is omitted by the GitHub Event stream and API limitations. ### Source excerpt by Sophia Vargas, Google Open Source & Andrew Nesbitt, Ecosyste.ms Every second, open source contribution quietly shapes the software we rely on, and yet our view of this open ecosystem is surprisingly opaque. Open source development is performed in public spaces -- we can see the commits, issues and comments, the APIs and endpoints are free to use -- the logs are just sitting there, so why can't we just collect all of the data? ...Said every researcher, everywhere. However in most cases of open source related data, we are only looking at part of the whole. Why am I writing this post? Because many of us (including many business decision-makers) are too comfortable with unsubstantiated data. We've gotten used to it. Our models assume that it's smelly and we adjust the logic and weights to compromise. When it comes to open source, our confidence is even lower, even though our resulting decisions can directly impact individuals whom we collectively depend on. Let's consider one of my favorite datasets: GHarchive. Started as a hobby project in 2011, this crawler has amassed more than 15 years of event data from GitHub. While this source provides a historical record of open source development on GitHub, as a real-time or comprehensive source of metrics, it's unreliable and should not be a source for volume-based metrics. In 2025, GHarchive captured 14% fewer events than in 2024, despite steady growth in platform adoption. Since 2025, we estimate that data retention in GHarchive has fallen to ~50% and in 2026 it may be as low as 20% for some event types (see figure below). Prior to 2025, you could make the general assumption that the majority of events would be represented in this pipeline. Since 2025, we must now assume we may be missing at least half of events and possibly more -- not to mention all of the additional activity that's left out of the event API (see GitHub's GraphQL API.) The crawler logic behind this dataset is simple: give me all the events from the GitHub Ev ## Apollo GraphOS and Apollo MCP Server Named 2026 API Award Winners DevFeed: [Apollo GraphOS and Apollo MCP Server Named 2026 API Award Winners]() Original publisher: [Read original article]() Author: Jennifer Tyrseck Published: 2026-09-02T16:44:59Z Content type: release Language: en Sources: [Apollo Blog]() Topics: [API](), [MCP Server](), [GraphQL](), [Model Context Protocol](), [Orchestration]() Tags: [2026](), [agents](), [announcement](), [api](), [api-ecosystem](), [apollo-mcp-server](), [graphos](), [graphql](), [mcp](), [mcp-server]() ### AI overview Apollo GraphOS and Apollo MCP Server won 2026 API Awards in the Best Innovation in API Orchestration and Best Innovation in AI API categories, respectively. The article describes GraphOS as a platform for federating and governing APIs and Apollo MCP Server as a way to expose GraphQL APIs for direct use by AI agents through the Model Context Protocol. ### Source excerpt Exposing GraphQL APIs as tools AI agents can call directly earned Apollo MCP Server a 2026 API Award for Best Innovation in AI API. See why. ## Classify Domain and System Errors to Set HTTP Status, Logging, and Paging DevFeed: [Classify Domain and System Errors to Set HTTP Status, Logging, and Paging]() Original publisher: [Read original article]() Author: The T-Shaped Dev Published: 2026-08-29T04:19:38Z Content type: tutorial Language: en Sources: [The T-Shaped Dev]() Topics: [Code](), [HTTP](), [API]() Tags: [api](), [code](), [errors](), [graphql](), [http](), [logs](), [middleware](), [on-call]() ### AI overview This tutorial distinguishes expected domain errors, such as invalid input or forbidden actions, from unexpected system errors, such as database failures, upstream timeouts, and memory exhaustion. It proposes encoding the distinction in error types so middleware can determine the HTTP status, user message, log level, and paging behavior. ### Source excerpt One distinction that sets your HTTP status, your log level, and who gets paged. (6 min) ## Where Apollo MCP Server Stands on the OWASP MCP Top 10 DevFeed: [Where Apollo MCP Server Stands on the OWASP MCP Top 10]() Original publisher: [Read original article]() Author: Camille Lawrence Published: 2026-08-14T17:11:10Z Content type: article Language: en Sources: [Apollo Blog]() Topics: [Model Context Protocol](), [MCP Server](), [audit](), [Shared Responsibility Model](), [configuration](), [GraphQL](), [Rust](), [Self-hosted]() Tags: [announcement](), [apollo](), [apollo-mcp-server](), [audit](), [configuration](), [graphql](), [hardening](), [mcp](), [mcp-server](), [rust](), [self-hosted](), [shared-responsibility]() ### AI overview This article evaluates Apollo MCP Server against all ten risks in the latest reviewed OWASP MCP Top 10 revision. It finds that the server's architecture directly closes one risk because typed GraphQL operations are parsed and type-checked without shell execution. The remaining risks require configuration, monitoring, and team-owned security decisions. ### Source excerpt Architecture alone closes 1 of the ten risks in OWASP's MCP Top 10. See how Apollo MCP Server scores on the rest, and which configuration choices remain yours. ## Apollo Summit 2026: Turn Your API Platform Into Your AI Platform DevFeed: [Apollo Summit 2026: Turn Your API Platform Into Your AI Platform]() Original publisher: [Read original article]() Author: Eric Holzhauer Published: 2026-08-04T12:00:00Z Content type: article Language: en Sources: [Apollo Blog]() Topics: [GraphQL](), [GraphOS](), [AI Platform](), [API Platform]() Tags: [2026](), [agent](), [agents](), [ai-agents](), [ai-platform](), [announcement](), [api-platform](), [apollo](), [graphos](), [graphql](), [mcp](), [summit]() ### AI overview Apollo Summit 2026 will take place in San Francisco from October 6 to 8. The article previews sessions and workshops on using governed GraphQL graphs as an access and context layer for applications and AI agents, including case studies from Block, Brex, Coolblue, and Starbucks. ### Source excerpt Turning an API platform into an AI platform starts with a governed graph. See real patterns from Block, Brex, and Starbucks at Apollo Summit 2026, Oct 6-8. ## Focus on the Feature, Not the Fixture: GenAI powered GraphQL mocks DevFeed: [Focus on the Feature, Not the Fixture: GenAI powered GraphQL mocks]() Original publisher: [Read original article]() Author: Samuel Vazquez Published: 2026-07-31T11:01:02Z Content type: article Language: en Sources: [Expedia]() Topics: [GraphQL](), [genai](), [Large Language Model](), [API](), [Command-line interface]() Tags: [ai](), [api](), [artificial-intelligence](), [cli](), [developer](), [genai](), [graphql](), [llm](), [rust](), [schema](), [software-engineering](), [tools]() ### AI overview The article presents mockql-rs, a CLI that combines a GraphQL schema, query annotations, hints, and an LLM to generate contextual mock responses. It argues that GraphQL constrains the response shape while the LLM fills only annotated fields, reducing handwritten fixtures and allowing real and mocked fields to coexist. ### Source excerpt Expedia Group Technology -- EngineeringCombine a GraphQL schema, hints and a LLM to generate contextual GraphQL mock responsesPhoto by Samuel Vazquez somewhere in New Zealand A product developer on our navigation header team spent an afternoon hand-typing a 200-line GraphQL JSON mock response so they could keep building the UI while a resolver was pending to be implemented. The schema changed the next morning. We threw the mock away. That is the boring tax on every GraphQL prototype: mocks that drift, fixtures that rot, frontends blocked on backends, and demos slipping because nobody wanted to update the same mock data again. Mock data is just data. It should not be the most expensive part of trying an idea. GenAI + GraphQL: a match made in heaven Most tools that promise "AI generates an API" share the same flaw: the model invents a shape from scratch, and you spend the afternoon reshaping its output to match your actual types. GraphQL flips that. The selection set is the spec. The schema is the type system. Hand an LLM a query and it already knows the exact JSON it must return, field by field, type by type. That's the unlock: LLMs are bad at inventing shapes and great at filling them in, and GraphQL hands them a bounded shape for free. mockql-rs is what happens when you take that pairing seriously: mark what you want mocked directly in the query with a "@mock" directive: query LoyaltyRewards @mock { loyaltyRewards { heading @mock(hint: "Platinum member") { text } subtitles @mock(hint: "At least 4 items") { text theme } } } Annotate the field, add a hint, keep building. mockql-rs parses the operation, validates it against the schema, and prompts an LLM to fill in only the fields you annotated with "@mock". { "data": { "loyaltyRewards": { "heading": { "text": "Welcome back, Platinum Member" }, "subtitles": [ { "text": "2,450 points until your next reward", "theme": "HIGHLIGHT" }, { "text": "3 nights earned this quarter", "theme": "STANDARD" }, { "text": "Breakfast inc ## GraphQL's Maturity and Readiness for Production and Agentic Workflows DevFeed: [GraphQL's Maturity and Readiness for Production and Agentic Workflows]() Original publisher: [Read original article]() Author: Jeff Auriemma Published: 2026-07-29T14:02:52Z Content type: opinion Language: en Sources: [Apollo Blog]() Topics: [GraphQL](), [Orchestration](), [agentic workflows](), [AI Agent]() Tags: [2026](), [agentic-workflows](), [ai-agents](), [announcement](), [enterprise](), [gartner](), [graphql](), [linux-foundation](), [orchestration](), [standards]() ### AI overview The article argues that GraphQL's placement in Gartner's Trough of Disillusionment reflects a useful shift from hype toward evaluating production readiness. It says GraphQL's existing enterprise adoption may better prepare companies to deploy AI agents, while forecasting that GraphQL will reach the Plateau of Productivity in less than two years. ### Source excerpt American Airlines built its API orchestration layer before AI agents existed, then used it as the agent layer. See why using GraphQL is the real head start. ## What's New in Rover: Docker Image, Schema Search, Client Checks, and More DevFeed: [What's New in Rover: Docker Image, Schema Search, Client Checks, and More]() Original publisher: [Read original article]() Author: Kaitlyn Barnard Published: 2026-07-17T11:24:07Z Content type: release Language: en Sources: [Apollo Blog]() Topics: [GraphQL](), [GraphOS](), [Docker](), [Docker Image](), [Command-line interface](), [GitHub Container Registry](), [CI/CD](), [AI research agents]() Tags: [agents](), [announcement](), [ci-cd](), [cli](), [container-image](), [feature](), [github-container-registry](), [graphos](), [graphql]() ### AI overview Apollo's latest Rover release adds an official immutable Docker image, atomic schema check-and-publish, native client operation checks, and schema search commands for teams building AI agents. ### Source excerpt Rover's latest release adds an official Docker image, atomic check-and-publish, native client checks, and schema search for agents. ## Migrating from Apollo Tooling to GraphQL Codegen at Yelp DevFeed: [Migrating from Apollo Tooling to GraphQL Codegen at Yelp]() Original publisher: [Read original article]() Author: Igor Kusakov, Software Engineer Published: 2026-07-16T00:00:00Z Content type: article Language: en Sources: [Yelp]() Topics: [GraphQL](), [Code generation](), [migration](), [Tooling](), [React](), [TypeScript](), [monorepo]() Tags: [code-generation](), [deprecated](), [graphql](), [migrate](), [monorepo](), [react](), [tooling](), [typescript]() ### AI overview Yelp describes migrating its frontend React monorepo from deprecated Apollo Tooling to GraphQL Codegen for generating TypeScript definitions from GraphQL queries and the server schema. ### Source excerpt Introduction At Yelp, we rely heavily on GraphQL and Apollo for data loading in our frontend React monorepo. When a developer writes a GraphQL query or mutation inside a React component, the shape of the response is defined by the query itself -- but TypeScript has no way to know what that shape looks like at compile time. That's where code generation (codegen) comes in. We rely on codegen to read our queries and server schema, then produce TypeScript type definitions for every operation's input variables and output data. In our codebase, this looks like: src/components/UserProfile/ ├── UserProfile.tsx <- contains... ## Scale Faster with Managed Weaviate: Now in Public Preview on DigitalOcean DevFeed: [Scale Faster with Managed Weaviate: Now in Public Preview on DigitalOcean]() Original publisher: [Read original article]() Author: Waverly Swinton Published: 2026-07-09T19:08:52Z Content type: release Language: en Sources: [DigitalOcean]() Topics: [Digital Ocean](), [Cloud](), [Databases](), [Retrieval Augmented Generation (RAG)](), [autoscaling](), [Self-hosted](), [GraphQL](), [gRPC]() Tags: [autoscaling](), [configuration](), [database](), [databases](), [digitalocean](), [graphql](), [high-availability](), [hosting](), [infrastructure](), [preview](), [product-updates](), [real-time](), [rest](), [retrieval](), [run](), [security](), [storage](), [upgrades](), [workflows]() ### AI overview DigitalOcean has placed Managed Weaviate into public preview. The service provides fully managed Weaviate clusters with automated backups, security patching, version upgrades, high availability, and storage autoscaling, with pricing starting at $20 per month. ### Source excerpt Production Weaviate in minutes, managed by DigitalOcean. Starting at $20/month. Vector databases have become a core piece of the AI application stack. Whether you're building retrieval-augmented generation (RAG), semantic search, agentic workflows and memory, or similarity-based recommendations, you need a vector store that's reliable, fast, and doesn't require a dedicated ops engineer to keep running. Weaviate has become a critical part of that stack -- its open-source AI-native vector database powers semantic search, RAG, and agentic workflows for thousands of companies. Self-hosting Weaviate is doable but it comes at a cost. You're on the hook for backups, version upgrades, security patches, high availability configuration, and storage scaling. That's real time and real engineering capacity that isn't going toward your product. Managed alternatives from larger cloud vendors exist, but they often come with per-query fees, per-dimension surcharges, and pricing models that are difficult to predict as usage grows. Today, we're announcing that Managed Weaviate is now in public preview on DigitalOcean, offering an easy way for you to run Weaviate in production, at a price that makes sense from day one. The easiest way to run Weaviate in production Managed Weaviate on DigitalOcean handles the operational work so you don't have to. Provision a fully managed Weaviate cluster directly from the DigitalOcean control panel. From there, automated backups, security patching, version upgrades, high availability, and storage autoscaling are handled for you. Full Weaviate client compatibility via GraphQL, REST, and gRPC on port 443 means your existing code works without modification. This means you get Weaviate's full capabilities -- semantic and hybrid search, RAG pipelines, and support for agent-driven workflows -- without spending engineering time on the infrastructure beneath them. Predictable pricing, starting at $20/month We built Managed Weaviate with flat, predictable monthly ## WebSockets, gRPC, And GraphQL In The Core DevFeed: [WebSockets, gRPC, And GraphQL In The Core]() Original publisher: [Read original article]() Author: Shai Almog Published: 2026-06-07T00:00:00Z Content type: tutorial Language: en Sources: [CodeName One]() Topics: [WebSocket](), [GraphQL](), [gRPC](), [real-time](), [API](), [Code generation]() Tags: [api](), [graphql](), [pipeline](), [real-time](), [tutorial](), [websocket]() ### AI overview A hands-on tutorial covering WebSockets, typed GraphQL clients, and typed gRPC clients in the core framework. It explains how to build a real-time chat, generate typed clients from GraphQL schemas and proto files, and use WebSocket subscriptions. ### Source excerpt A hands-on walk through the three connectivity features that landed together this week, building a live chat over the new core WebSocket API and typed clients from a GraphQL schema and a proto file. ## API Key Management now in GraphOS Studio DevFeed: [API Key Management now in GraphOS Studio]() Original publisher: [Read original article]() Author: Meryl Charleston Published: 2026-06-02T09:00:00Z Content type: release Language: en Sources: [Apollo Blog]() Topics: [API keys](), [GraphQL](), [Security](), [Authorization]() Tags: [announcement](), [api](), [api-keys](), [audit-trail](), [graphos](), [graphql](), [incident-response](), [management](), [security]() ### AI overview Apollo launched an API keys page in GraphOS Studio for viewing and managing SCIM, subgraph, and operator keys. The page supports searching, filtering, creation, expiration updates, rotation, revocation, and deletion while preserving records for investigation and audits. ### Source excerpt Unified API key management for GraphQL teams. Create, rotate, and revoke SCIM, subgraph, and operator keys in GraphOS Studio without disrupting workflows. ## How to Design APIs DevFeed: [How to Design APIs]() Original publisher: [Read original article]() Author: Saurabh Dashora Published: 2026-06-02T06:41:25Z Content type: tutorial Language: en Sources: [System Design Codex]() Topics: [API](), [Architecture & Design](), [interface](), [CRUD](), [GraphQL](), [gRPC](), [HTTP](), [JSON](), [XML]() Tags: [api](), [apis](), [crud](), [graphql](), [how-to](), [http](), [interface](), [json](), [rest](), [rpc]() ### AI overview A tutorial on API design covering interface contracts, resource and endpoint naming, request and response formats, API paradigms, and modeling relationships between entities. ### Source excerpt Key considerations to keep in mind ## REST vs GraphQL vs gRPC DevFeed: [REST vs GraphQL vs gRPC]() Original publisher: [Read original article]() Author: Nikki Siapno Published: 2026-05-28T10:32:52Z Content type: comparison Language: en Sources: [Level Up Coding System Design Newsletter]() Topics: [API](), [GraphQL](), [gRPC](), [HTTP](), [JSON]() Tags: [apis](), [graphql](), [http](), [json](), [rest]() ### AI overview A comparison of REST, GraphQL, and gRPC explains how each API style organizes communication and the tradeoffs each makes between flexibility, simplicity, efficiency, and control. ### Source excerpt The tradeoffs behind resource-driven, query-driven, and method-driven APIs. ## Adding embeddings/RAG support to the Koog-based AI agent in Confetti DevFeed: [Adding embeddings/RAG support to the Koog-based AI agent in Confetti]() Original publisher: [Read original article]() Published: 2026-05-25T23:00:00Z Content type: tutorial Language: en Sources: [John O'Reilly]() Topics: [AI Agent](), [Embeddings](), [Retrieval-Augmented Generation](), [multiplatform](), [Kotlin](), [GraphQL]() Tags: [agent](), [ai-agent](), [compose-multiplatform](), [embeddings](), [graphql](), [kotlin](), [rag]() ### AI overview This tutorial explains how to add semantic search to the Koog-based AI agent in the Confetti Compose Multiplatform sample. It uses embeddings and retrieval-augmented generation to find related conference sessions even when query wording differs from session titles or descriptions. ### Source excerpt Recently I started looking at reworking the recommendation feature in the Confetti Compose Multiplatform sample (which uses Apollo Kotlin to consume a GraphQL API serving data for various conferences). The new implementation uses a Koog AIAgent with a ToolRegistry that includes session and speaker tools, giving the LLM on-demand access to the conference data it needs. ## MongoDB and GraphQL: A Perfect Match DevFeed: [MongoDB and GraphQL: A Perfect Match]() Original publisher: [Read original article]() Author: Nic Raboy Published: 2026-05-15T12:00:00Z Content type: tutorial Language: en Sources: [Nic Raboy]() Topics: [GraphQL](), [MongoDB](), [Tutorial](), [API](), [TypeScript](), [Node.js](), [Express]() Tags: [api](), [data](), [database](), [express](), [graphql](), [mongodb](), [node-js](), [tutorial](), [typescript]() ### AI overview This tutorial explains how to build a GraphQL API with MongoDB using TypeScript. It covers the consistent JSON-like data flow between GraphQL queries, the application, and MongoDB, and lists prerequisites including MongoDB Atlas, Node.js 22+, and the MongoDB Node.js Driver 6.x. ### Source excerpt GraphQL is a powerful and efficient way to build APIs. The client queries the API, similarly to how they would a database, and that API returns only the data that they've requested, often reducing the... The post MongoDB and GraphQL: A Perfect Match appeared first on DataCamp. ## Apollo Kotlin 5 Is Now Available DevFeed: [Apollo Kotlin 5 Is Now Available]() Original publisher: [Read original article]() Author: Martin Bonnin Published: 2026-05-13T12:00:25Z Content type: release Language: en Sources: [Apollo Blog]() Topics: [Kotlin](), [GraphQL](), [Cache](), [Caching](), [Gradle](), [Maven Central](), [Agent Skills]() Tags: [agent-skills](), [cache](), [changelog](), [community](), [gradle](), [gradle-plugin](), [graphql](), [kotlin](), [kotlin-multiplatform](), [maven-central]() ### AI overview Apollo Kotlin 5 is available on Maven Central with GraphQL Golden Path support, a new normalized cache, a modernized Gradle plugin, compiler plugin APIs, and an Apollo Kotlin agent skill. The release includes experimental GraphQL RFC support and provides a migration path from version 4. ### Source excerpt Apollo Kotlin 5 is now available on Maven Central. GraphQL Golden Path ready, with a new normalized cache, agent skills support, and a modernized Gradle plugin. ## Realtime PostgreSQL: From Data Connect to SQL Connect DevFeed: [Realtime PostgreSQL: From Data Connect to SQL Connect]() Original publisher: [Read original article]() Author: Cynthia Wang Published: 2026-04-29T00:00:00Z Content type: news Language: en Sources: [Firebase Blog]() Topics: [PostgreSQL](), [SQL](), [Firebase](), [GraphQL](), [Caching](), [reactive](), [SDKs]() Tags: [admin-sdk](), [caching](), [cloud-functions](), [firebase](), [full-text-search](), [graphql](), [postgresql](), [real-time](), [realtime](), [sdks](), [sql](), [sql-connect](), [updates]() ### AI overview Firebase announces that Firebase Data Connect is becoming Firebase SQL Connect, with a renewed focus on PostgreSQL and expanded SQL capabilities. The update includes Realtime Query Updates, Offline Caching, raw SQL alongside GraphQL, and lower-friction access for prototypes and testing. ### Source excerpt News, tutorials, and updates from the Firebase team. [Next page]()