# backends Published articles for backends. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Prototyping with Framer DevFeed: [Prototyping with Framer]() Original publisher: [Read original article]() Author: Khan Academy Published: 2016-10-03T22:00:00Z Content type: tutorial Language: en Sources: [Khan Academy]() Topics: [Testing](), [coding](), [backends]() Tags: [backends](), [engineering](), [framer](), [news](), [prototype](), [prototyping](), [user-testing](), [web-frontend]() ### AI overview Khan Academy describes how it uses prototypes in user testing to gather feedback on new designs. The article explains the role of low- and high-fidelity prototypes, their limited lifespan, and the choice of Framer for creating and revising prototypes quickly. ### Source excerpt By Nick Breen We strive to make all of Khan Academy's new features & experiences easy to understand ... Read more ## More than 100,000 fake stores are out to steal your card details DevFeed: [More than 100,000 fake stores are out to steal your card details]() Original publisher: [Read original article]() Author: Pieter Arntz Published: 2026-09-09T15:02:53Z Content type: news Language: en Sources: [Malwarebytes]() Topics: [Cybersecurity](), [Security & Privacy](), [WebSocket]() Tags: [authentication](), [backends](), [cybersecurity](), [doppelcart](), [fake-webshops](), [news](), [scams]() ### AI overview Researchers identified DoppelCart, a large network of cloned online stores that copies real retailers and steals card data and bank confirmation codes during checkout. ### Source excerpt DoppelCart's fake stores copy real retailers and steal shoppers' card details and one-time bank confirmation codes. ## Run multiple frameworks in one project with Vercel Services DevFeed: [Run multiple frameworks in one project with Vercel Services]() Original publisher: [Read original article]() Author: Yury Selivanov Published: 2026-06-30T00:00:00Z Content type: release Language: en Sources: [Vercel News]() Topics: [Vercel](), [Frameworks](), [Back end](), [Front end](), [Express](), [FastAPI](), [Flask](), [Hono](), [Command-line interface](), [Go](), [Routing (disambiguation)](), [Rust]() Tags: [backend](), [backends](), [cli](), [frameworks](), [frontend](), [go](), [routing](), [rust](), [ui](), [vercel]() ### AI overview Vercel Services lets developers deploy multiple frontend and backend services using different frameworks within one Vercel project. Services share a domain, communicate privately through service bindings, and build, preview, and roll back together. ### Source excerpt You can now deploy multiple frontends and backends together within a single Vercel project. Vercel Services is now available, allowing you to deploy full stack apps with multiple frameworks on a shared domain, where services talk to each other privately and deployments build, preview, and roll back together. Services are defined in vercel.json: From there, your services show up across the dashboard and CLI: The Deployments panel visualizes the services graph The Logs UI filters by individual service vercel dev runs every service locally for a production-like environment Service bindings Services talk to each other internally with the new bindings key, without routing through the public internet: The frontend reaches the backend privately through the URL in BACKEND_INTERNAL_URL: Framework-defined infrastructure Most frameworks run with zero configuration. Framework-defined infrastructure means each service's framework is auto-detected and auto-provisioned, from FastAPI and Flask to Express and Hono, with first-class support for Go and Rust. Services run on Fluid compute with Active CPU pricing, so you only pay for the time your code is actually running. Read the documentation to get started. Read more ## Deploy Node servers with zero configuration DevFeed: [Deploy Node servers with zero configuration]() Original publisher: [Read original article]() Author: Jeff See Published: 2026-06-23T07:00:00Z Content type: release Language: en Sources: [Vercel News]() Topics: [Node.js]() Tags: [app](), [backends](), [cli](), [compute](), [cpu](), [deployment](), [development](), [local](), [node](), [node-js](), [server](), [servers](), [vercel]() ### AI overview Vercel now automatically detects a server.ts file at the project root or in src and deploys it as a Node.js application without configuration. The Vercel CLI also supports local development and deployment for these servers. ### Source excerpt You can now deploy a Node.js server to Vercel with zero configuration. Vercel detects a server.ts file at the project root or at src/server.ts and deploys it as a Node.js application, in addition to existing zero-configuration backends like Express, Koa, and NestJS: Vercel CLI can handle local development and deployment: Backends on Vercel are powered by Fluid compute with Active CPU pricing. Learn more about the Node.js runtime on Vercel. Read more ## Player sessions that survive anything DevFeed: [Player sessions that survive anything]() Original publisher: [Read original article]() Author: Cecil Phillip Published: 2026-05-28T00:00:00Z Content type: tutorial Language: en Sources: [Temporal Blog]() Topics: [sessions](), [Actor](), [backends](), [Processes](), [Server](), [Python]() Tags: [actor](), [backends](), [build](), [crashes](), [database](), [distributed](), [how-to](), [python](), [server](), [sessions](), [temporal-concepts](), [workflow]() ### AI overview This article presents a Temporal Python pattern for modeling each multiplayer player session as a durable, uniquely addressable Actor Workflow. The workflow keeps player state, resumes after server failures, coordinates game actions, and uses Continue-As-New to prevent unbounded history. ### Source excerpt Build multiplayer player sessions that survive server crashes. See how to model each player as a durable Actor Workflow in Temporal Python. ## Are TypeScript backend frameworks ready for AI agents? DevFeed: [Are TypeScript backend frameworks ready for AI agents?]() Original publisher: [Read original article]() Author: Ivan Cernja Published: 2026-05-20T00:00:00Z Content type: article Language: en Sources: [Encore Updates]() Topics: [Benchmark](), [TypeScript](), [Claude Code](), [backends](), [Express](), [Fastify](), [Nest]() Tags: [ai-coding](), [backends](), [benchmark](), [claude-code](), [express](), [other](), [typescript]() ### AI overview The article reports a three-run benchmark of Claude Code building TypeScript backends with Encore, Express, Fastify, Hono, and NestJS. Although all frameworks passed the first-run tests, only Encore's output met the authors' production-readiness rubric at that stage. ### Source excerpt We set out to run one benchmark across five TypeScript backend frameworks. Reading the diffs sent us into two more runs, and the picture changed each time. ## A new programming model for durable execution DevFeed: [A new programming model for durable execution]() Original publisher: [Read original article]() Author: Pranay Prakash Published: 2026-04-16T04:00:00Z Content type: release Language: en Sources: [Vercel News]() Topics: [Orchestration](), [vercel ai sdk](), [SDKs](), [AI Bots]() Tags: [agents](), [ai](), [backends](), [orchestration](), [python](), [typescript](), [vercel](), [workflows]() ### AI overview Vercel announces the general availability of Workflows, a durable-execution model for long-running agents, backends, and multi-step workloads. It keeps orchestration in application code, records execution state in an event log, integrates with the AI SDK, and adds a beta Python SDK. ### Source excerpt The gap between prototypes and production-ready systems is huge. Code that's trivial to run locally falls apart the moment it needs to handle failures, restarts, and real traffic. Framework defined infrastructure solved this for web applications. When you deploy, Vercel infers the right configuration from the app itself. Workflows extends that model to long-running systems. Instead of managing a separate codebase for orchestration, durable workflows are an extension of your application code. Since launching in beta in October 2025, Workflows has processed over 100 million runs and over 500 million steps across more than 1,500 customers, with more than 200K npm downloads every week. Today, Vercel Workflows is generally available. Built for agents, backends, and long-running workloads Workflows is built for any workload that doesn't fit in a single request. Agents: Deep integration with the AI SDK enables infinitely long running durable agents that can maintain state, tools, and handle external events or interruptions. AI SDK v7 is taking this further with WorkflowAgent. Backends: Workflow SDK proved this programming model in TypeScript codebases, and we're bringing it to a new language. The Workflow Python SDK is now in beta. Long-running workloads: Workflows can be used for any function that needs to execute reliably, including multi-step onboarding flows, payment processing, ETL pipelines, or any backend work that would otherwise require you to wire up your own queues and retry logic. How it works Shipping a reliable long-running process to production typically means splitting your code across queues, workers, status tables, retry logic, and monitoring. Dedicated orchestration services add yet another layer with long-lived background processes you run in Kubernetes, scale horizontally, and dedicate engineering time to keep healthy. They are distributed systems you pay for on top of your core application compute. Workflows eliminates the orchestrator entirely. All c ## What is the BFF Pattern? DevFeed: [What is the BFF Pattern?]() Original publisher: [Read original article]() Author: Saurabh Dashora Published: 2026-04-07T08:41:56Z Content type: tutorial Language: en Sources: [System Design Codex]() Topics: [BFF](), [Amazon API Gateway](), [backends](), [client](), [interface](), [resiliency](), [Orchestration](), [rate-limiting]() Tags: [api-gateway](), [authentication](), [backends](), [bff](), [cache-control](), [client](), [decoupling](), [mobile](), [rate-limiting](), [resiliency]() ### AI overview The article explains the Backends-for-Frontends (BFF) pattern, in which dedicated API gateways serve different client types such as web browsers, mobile apps, and public or partner APIs. It describes how BFFs tailor APIs, isolate client-specific logic, and support functions including rate limiting, authentication, header sanitization, and cache control. It also discusses benefits such as resiliency, client-specific optimization, and faster development, along with drawbacks including code duplication and increasing complexity. ### Source excerpt And when do you need it? ## From prompt to production: Build full-stack apps faster with Google AI Studio and Firebase DevFeed: [From prompt to production: Build full-stack apps faster with Google AI Studio and Firebase]() Original publisher: [Read original article]() Author: Kara Yu; Sam Phillips Published: 2026-03-19T00:00:00Z Content type: release Language: en Sources: [Firebase Blog]() Topics: [Firebase](), [Google AI](), [Firestore](), [Google](), [Authentication](), [backends](), [Agent Harness]() Tags: [agent](), [agent-harness](), [ai](), [ai-studio](), [antigravity](), [authentication](), [backends](), [build](), [firebase](), [firebase-studio](), [firestore](), [google](), [security]() ### AI overview Firebase announces an integration with Google AI Studio that can provision Firebase projects, Firestore, Authentication, sign-in pages, application code, and draft Security Rules with user approval. The announcement also describes a new Google AI Studio coding agent for multi-step code edits and connections to services such as payment processors and Google Maps. ### Source excerpt Today, we're announcing that Firebase is now integrated with Google AI Studio, accelerating your path from prompt to production so you can turn your vibe-coded ideas into fully functional apps with robust backends. Read on to learn how this works and what this means for Firebase Studio. ## Temporal raises $300M Series D at a $5B valuation as AI drives demand for Durable Execution DevFeed: [Temporal raises $300M Series D at a $5B valuation as AI drives demand for Durable Execution]() Original publisher: [Read original article]() Author: Allanah Hughes Published: 2026-02-17T00:00:00Z Content type: release Language: en Sources: [Temporal Blog]() Topics: [distributed-systems](), [Artificial Intelligence](), [Orchestration](), [Cloud](), [Disaster Recovery](), [backends]() Tags: [agentic-ai](), [ai](), [ai-agents](), [announcements](), [backends](), [cloud](), [disaster-recovery](), [distributed-systems](), [fault-tolerance](), [orchestration]() ### AI overview Temporal announces a $300 million Series D at a $5 billion post-money valuation, led by Andreessen Horowitz. The company says demand for durable execution is increasing as AI and other long-running production workflows require state preservation and recoverable failures. ### Source excerpt Temporal raises $300M Series D at a $5B valuation as AI drives demand for Durable Execution. ## Cursor can now build backends that deploy directly to AWS DevFeed: [Cursor can now build backends that deploy directly to AWS]() Original publisher: [Read original article]() Author: Ivan Cernja Published: 2025-12-09T00:00:00Z Content type: article Language: en Sources: [Encore Updates]() Topics: [cursor](), [Amazon Web Services](), [backends](), [Infrastructure as code](), [AWS IAM](), [Databases](), [Cloud]() Tags: [aws](), [backends](), [cloud](), [code](), [cursor](), [iac](), [iam](), [infrastructure](), [postgresql](), [terraform]() ### AI overview The article explains how Cursor can generate backend application code with Encore while Encore derives and provisions the corresponding infrastructure. It describes local PostgreSQL development and deployment to AWS resources such as RDS, SNS, SQS, S3, VPC networking, ECS, IAM roles, and security groups, contrasting this approach with AI-generated Terraform or Pulumi. ### Source excerpt When Cursor writes application code with Encore, it's automatically writing the infrastructure too ## How Delivery Hero Accelerates UX Experiments with Server-Driven UI and Apollo DevFeed: [How Delivery Hero Accelerates UX Experiments with Server-Driven UI and Apollo]() Original publisher: [Read original article]() Author: Valeria Gomez Published: 2025-12-01T11:19:22Z Content type: article Language: en Sources: [Apollo Blog]() Topics: [experiments](), [GraphQL](), [ui](), [User experience (UX)](), [cross-platform](), [Mobile](), [configuration](), [data]() Tags: [apollo](), [backend](), [backends](), [configuration](), [cross-platform](), [engineering](), [events](), [experimentation](), [experiments](), [graphql](), [layout](), [mobile](), [mobile-apps](), [ux]() ### AI overview Delivery Hero described at GraphQL Summit 2025 how a two-layer Apollo supergraph separated business logic from UI presentation. This approach replaced months of coordinated releases with about a week of configuration work and supported UX experimentation across brands, markets, and devices. ### Source excerpt At GraphQL Summit 2025, Delivery Hero shared how a two-layer Apollo supergraph enabled faster iteration and better experiences across brands, markets, and devices. ## Why we built app.build DevFeed: [Why we built app.build]() Original publisher: [Read original article]() Author: Arseni Kravchenko Published: 2025-08-25T15:55:20Z Content type: opinion Language: en Sources: [Blog -- Neon Docs]() Topics: [AI-assisted coding](), [Large Language Model](), [app builder](), [backends](), [Databases](), [CI/CD](), [Authentication]() Tags: [ai-coding](), [app-builder](), [authentication](), [backends](), [ci-cd](), [code-generation](), [databases](), [llms](), [open-source](), [product]() ### AI overview Neon explains why it built app.build as an open-source reference architecture for AI-native systems. The project demonstrates how agents can generate complete applications with backends, databases, testing, authentication, CI/CD, and deployments, addressing the limitations of code-generation tools that focus mainly on compiling code or rendering components. ### Source excerpt LLMs are fantastic at code generation. They can produce hundreds of lines of code, entire functions and components, that compile perfectly, implement complex algorithms elegantly, and even follow best practices for style and structure. They suck at full-blown software generation,... ## HTTP/1.1 Must Die: What This Means for In-House Pentesters DevFeed: [HTTP/1.1 Must Die: What This Means for In-House Pentesters]() Original publisher: [Read original article]() Author: Andrzej Matykiewicz Published: 2025-08-06T22:23:41Z Content type: opinion Language: en Sources: [PortSwigger Blog]() Topics: [Vulnerabilities](), [Security](), [Web](), [Architecture & Design](), [backends]() Tags: [2025](), [architectures](), [attacks](), [backends](), [black-hat](), [defcon](), [exploits](), [http](), [research](), [security](), [techniques](), [vulnerabilities](), [web]() ### AI overview The article presents PortSwigger research arguing that HTTP request smuggling and desynchronization attacks remain widespread and increasingly difficult to detect. It attributes the risk to inconsistent request-boundary parsing across proxies, CDNs, and distributed backends, and argues that moving upstream traffic to HTTP/2 is a stronger long-term response than patching individual implementations. ### Source excerpt At Black Hat USA and DEFCON 2025, PortSwigger's Director of Research, James Kettle, issued a stark warning: request smuggling isn't dying out, it's evolving and thriving. Despite years of defensive ef ## HTTP/1.1 Must Die: What This Means for Contract Pentesters and MSSPs DevFeed: [HTTP/1.1 Must Die: What This Means for Contract Pentesters and MSSPs]() Original publisher: [Read original article]() Author: Andrzej Matykiewicz Published: 2025-08-06T22:23:07Z Content type: article Language: en Sources: [PortSwigger Blog]() Topics: [Vulnerabilities](), [web applications]() Tags: [attacks](), [backends](), [bug](), [complexity](), [exploits](), [http](), [research](), [security](), [vulnerabilities](), [web]() ### AI overview PortSwigger argues that HTTP/1.1 request-smuggling vulnerabilities remain widespread because interconnected systems can disagree on request boundaries. The article advocates upstream HTTP/2 and frames the issue as a service opportunity for contract pentesters and MSSPs. ### Source excerpt At Black Hat USA and DEFCON 2025, PortSwigger's Director of Research, James Kettle, issued a stark warning: request smuggling isn't dying out, it's evolving and thriving. Despite years of defensive ef ## The Invisible Database: Running Postgres at Runtime DevFeed: [The Invisible Database: Running Postgres at Runtime]() Original publisher: [Read original article]() Author: Carlota Soto Published: 2025-08-04T15:52:32Z Content type: article Language: en Sources: [Blog -- Neon Docs]() Topics: [Databases](), [Database](), [backends](), [Persistence](), [developer tooling](), [systems](), [Software]() Tags: [agentic](), [agents](), [apps](), [auth](), [backends](), [database](), [infrastructure](), [latency](), [persistence](), [platform](), [postgres](), [product](), [scale](), [software](), [state](), [systems]() ### AI overview The article describes how agent-built applications are changing database infrastructure. Instead of long-lived, manually provisioned assets, Postgres databases may be created per application, session, or exploration path, used immediately, branched to preserve application states, and later discarded or idled. It outlines requirements for databases that operate as part of an application runtime, including rapid creation, immediate availability, high-scale provisioning, temporary lifetimes, and branching for previews, comparisons, and rollbacks. ### Source excerpt More and more software will be created by agents, not just code snippets but full-stack apps with interfaces, backends, persistence, auth. This means a big change in the shape of the devtools and software infrastructure supporting these apps. Many systems are now created at runti... ## Databutton Just Made Their Agent Smarter, with Postgres and Auth Built In DevFeed: [Databutton Just Made Their Agent Smarter, with Postgres and Auth Built In]() Original publisher: [Read original article]() Author: Carlota Soto Published: 2025-06-19T15:21:55Z Content type: release Language: en Sources: [Blog -- Neon Docs]() Topics: [AI Agent](), [Supabase](), [Authentication](), [Persistence](), [Vibe coding](), [FastAPI](), [React](), [web applications]() Tags: [agent](), [ai-agent](), [app](), [auth](), [authentication](), [backends](), [case-studies](), [code](), [coding](), [configuration](), [data](), [database](), [monitor](), [persistence](), [postgres](), [real-time](), [vibe-coding](), [web-applications]() ### AI overview Databutton, a vibe coding platform, now provisions a production-grade Postgres database and built-in user authentication for every new app. Its AI Agent can create and manage React frontends, FastAPI backends, database schemas, migrations, secrets, tests, and common debugging tasks. ### Source excerpt "Integrating Neon was a no-brainer. It gives every Databutton app a production-grade Postgres database in seconds, with zero overhead. Our AI agent can now create, manage, and debug the entire stack, not just code" (Martin Skow Røed, CTO and co-founder of Databutton) Databutton i... ## Exploring Quantization Backends in Diffusers DevFeed: [Exploring Quantization Backends in Diffusers]() Original publisher: [Read original article]() Author: Derek Liu; Marc Sun; Sayak Paul Published: 2025-05-21T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [AI, ML & Data Engineering]() Tags: [artificial-intelligence](), [backends](), [benchmarks](), [black-forest-labs](), [diffusers](), [diffusion](), [diffusion-transformers](), [embeddings](), [flux](), [gpu](), [guide](), [hugging-face](), [images](), [memory](), [quantization]() ### AI overview The article examines quantization backends in Hugging Face Diffusers using the FLUX.1-dev image-generation pipeline. It compares high-precision and 4-bit or 8-bit variants, explains pipeline components and memory use, and discusses backends including bitsandbytes, GGUF, torchao, Quanto, and FP8. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## Building Your First MCP Server with Genkit DevFeed: [Building Your First MCP Server with Genkit]() Original publisher: [Read original article]() Author: Pavel J Published: 2025-04-25T14:09:50Z Content type: tutorial Language: en Sources: [Firebase Developers - Medium]() Topics: [Genkit](), [Model Context Protocol](), [MCP Server](), [backends](), [Firestore](), [Structured-data](), [AI Development](), [Testing](), [developer tooling]() Tags: [ai](), [backends](), [firebase](), [firestore](), [generative-ai](), [generative-ai-tools](), [genkit](), [guides](), [mcp](), [mcp-server](), [schema](), [testing]() ### AI overview This tutorial explains how to build a custom MCP server with Genkit. It covers defining order data schemas with zod, generating optional test data and storing it in Firestore, creating flows to retrieve orders, and exposing tools that can be called by AI models or external clients such as Cline. ### Source excerpt Generative AI Building robust backends for AI applications often involves integrating various components. The Model Context Protocol (MCP) offers a standardized communication layer, and Genkit provides a powerful framework to leverage it. This article guides you through creating a custom MCP server using Genkit, from defining data schemas to interacting with the server using development tools like Cline. This article assumes some familiarity with Genkit concepts like flows and tools. Learn more about Genkit: https://firebase.google.com/docs/genkit If you prefer this tutorial in video format, check out: https://medium.com/media/908bbf0df5ad1306b125a4c7411c203c/href1. Defining Your Data Schema Before building any application, you need to define the structure of your data. In this example, we're building a system to manage website orders for a consulting company. We use zod, a popular schema declaration library, integrated with Genkit. The OrderSchema defines the necessary fields for each order: const OrderSchema = z.object({ id: z.string().describe("Order Id. Format: A-XXX-XXX, ex. A-123-456"), name: z.string().describe("Name of the business"), colorScheme: z.object({ mainColor: z.string().describe("RGB color prefixed with #, ex. #FFAA66"), highlight: z.string().describe("RGB color prefixed with #, ex. #AA6611"), textColor: z.string().describe("RGB color prefixed with #, ex. #224455"), }), email: z.string(), jobDescription: z .string() .describe( "Detailed description of the website they need. Ex. if they are a construction " + "company describe what they want on the home, contact age, testimonials, etc. " + "pages. Not too crazy, usually static website." ), address: z.string(), }); This schema clearly outlines each piece of information associated with an order, including nested details like the colorScheme. 2. Generating Realistic Test Data (optional) During development, you need ways to both populate your system with test data and inspect that data. Manually creatin ## REST API Orchestration With GraphQL DevFeed: [REST API Orchestration With GraphQL]() Original publisher: [Read original article]() Author: Matt DeBergalis Published: 2025-02-19T08:45:00Z Content type: release Language: en Sources: [Apollo Blog]() Topics: [API](), [Orchestration](), [GraphQL](), [REST API](), [SDKs](), [Microservice]() Tags: [announcement](), [api](), [apollo-connectors](), [authorization](), [backends](), [declarative](), [error-handling](), [graph](), [graphql](), [graphql-federation](), [orchestration](), [performance](), [rest](), [rest-apis](), [router](), [sdks](), [teams]() ### AI overview Apollo announces Apollo Connectors, a declarative, graph-based technology for orchestrating REST API calls. The article explains how API calls can be chained, parallelized, transformed, and handled for errors, timeouts, authentication, and authorization. ### Source excerpt Today we are releasing Apollo Connectors, a major update that brings our declarative, graph-based orchestration technology to teams using REST APIs. Modern applications integrate dozens or even hundreds of different APIs. The task is significant. These APIs must be called in a precise manner and sequence. API calls need to be chained together, feeding the output of one into the next. In some cases, it's desirable to parallelize them for better performance. ## Introducing multi-backends (TRT-LLM, vLLM) support for Text Generation Inference DevFeed: [Introducing multi-backends (TRT-LLM, vLLM) support for Text Generation Inference]() Original publisher: [Read original article]() Author: Morgan Funtowicz; Hugo Larcher Published: 2025-01-16T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [tgi](), [model-deployment](), [Rust](), [vllm](), [TensorRT-LLM](), [Concurrency](), [Memory Safety](), [servers](), [llama.cpp](), [sglang](), [Python](), [Code]() Tags: [backend](), [backends](), [community](), [concurrency](), [http](), [inference](), [jetstream](), [llama-cpp](), [memory-safety](), [neuron](), [open-source](), [performance](), [production](), [python](), [rust](), [sglang](), [tensorrt-llm](), [tgi](), [vllm]() ### AI overview Hugging Face introduces TGI Backends, a unified frontend architecture for integrating inference solutions such as vLLM, SGLang, llama.cpp, and TensorRT-LLM. The approach lets users switch backends based on model, hardware, and performance requirements while supporting production deployment across diverse accelerators. The article also describes TGI's Rust and Python components, including Rust-based HTTP and scheduling layers designed for memory safety and concurrency. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## Introducing Neon RLS: Simplifying Row-Level Security For Postgres DevFeed: [Introducing Neon RLS: Simplifying Row-Level Security For Postgres]() Original publisher: [Read original article]() Author: David Gomes Published: 2024-10-30T13:58:41Z Content type: release Language: en Sources: [Blog -- Neon Docs]() Topics: [Authorization](), [Database](), [backends](), [Deployment]() Tags: [auth](), [authentication](), [backends](), [company](), [engineering](), [integration](), [json](), [open-source](), [postgres](), [security]() ### AI overview Neon announces Neon RLS, a Postgres row-level security tool that integrates JWT-based authentication with declarative database access policies. ### Source excerpt Today we're launching Neon RLS, a tool that aims to simplify the usage of Postgres row-level security policies while enabling new deployment models for app developers. With Neon RLS, you can manage RLS directly in your codebase and integrate with any authentication provider, maki... ## Essential Node.js backend examples for developers in 2024 DevFeed: [Essential Node.js backend examples for developers in 2024]() Original publisher: [Read original article]() Author: Liran Tal Published: 2024-06-12T05:00:00Z Content type: tutorial Language: en Sources: [Blog RSS Feed | Snyk]() Topics: [Node.js](), [backend-development](), [JavaScript](), [Streaming](), [Streams](), [Code](), [SDKs](), [OpenAI](), [GitHub]() Tags: [acquisition](), [api](), [application-security](), [async](), [asynchronous](), [backend](), [backends](), [blog](), [building](), [code](), [code-security](), [developer](), [developers](), [devrel](), [examples](), [github](), [javascript](), [js](), [node](), [node-js](), [openai](), [snyk-code](), [streaming](), [streams]() ### AI overview A practical introduction to Node.js backend examples for 2024, covering streams, async iterables, cryptography, buffers, Symbols, and template literals for HTML and SQL generation. ### Source excerpt Boost your Node.js skills with these backend code snippets for 2024. Copy and paste them into your own projects to save time when building backends. ## Keeping it 100(x) with real-time data at scale DevFeed: [Keeping it 100(x) with real-time data at scale]() Original publisher: [Read original article]() Author: Arushi Bandi Published: 2024-05-17T00:00:00Z Content type: article Language: en Sources: [Figma Blog]() Topics: [Figma](), [real-time](), [GraphQL](), [PostgreSQL](), [Replication](), [React](), [backends](), [JSON](), [Front end]() Tags: [architecture](), [backends](), [figma](), [graphql](), [json](), [performance](), [postgresql](), [react](), [real-time](), [replication](), [scale](), [web]() ### AI overview Figma describes LiveGraph, its real-time data-fetching service for multiplayer collaboration. LiveGraph exposes a web API for GraphQL-like queries, returns results as JSON trees, and uses a custom React Hook to trigger front-end updates. The article explains the LiveGraph 100x initiative to redesign the architecture for greater read and database-update scale, including support for more PostgreSQL shards and replication-stream processing. ### Source excerpt After years of playing catch-up with Figma's growth, it was time to fundamentally rethink LiveGraph's architecture. [Next page]()