# backend Published articles for backend. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Worker Backpressure (Part 1) DevFeed: [Worker Backpressure (Part 1)]() Original publisher: [Read original article]() Author: Mikalai Barysau Published: 2026-09-17T00:00:00Z Content type: article Language: en Sources: [Canva Engineering]() Topics: [backpressure](), [Back end](), [reliability]() Tags: [backend](), [backpressure](), [dependencies](), [outage](), [queue](), [reliability](), [worker]() ### AI overview Canva describes Worker Backpressure, a reliability mechanism for queue workers that slows processing when dependencies fail and speeds it back up after recovery. During a cloud-provider outage, the mechanism helped prevent failed messages from accumulating on the Dead Letter Queue and avoided an on-call page. ### Source excerpt How we taught Canva's queue workers to slow down when dependencies fail, then speed back up on their own. ## Dropbox Evolves Riviera Content Processing Platform to Support AI Workloads DevFeed: [Dropbox Evolves Riviera Content Processing Platform to Support AI Workloads]() Original publisher: [Read original article]() Author: Leela Kumili Published: 2026-09-16T14:42:00Z Content type: news Language: en Sources: [InfoQ]() Topics: [dropbox](), [Artificial Intelligence](), [API](), [Orchestration](), [Retrieval Augmented Generation (RAG)]() Tags: [ai](), [ai-ml-data-engineering](), [apache](), [apis](), [architecture](), [architecture-design](), [asynchronous-architecture](), [backend](), [caching](), [data-pipelines](), [development](), [distributed-systems](), [dropbox](), [dropbox-riviera-ai-platform](), [embeddings](), [enterprise-content-management](), [large-language-models](), [ml-data-engineering](), [model-context-protocol-mcp](), [news](), [orchestration](), [platform-engineering](), [plugins](), [rag](), [tika]() ### AI overview Dropbox has expanded Riviera from an internal file-preview service into a content-processing platform supporting more than 300 file formats and over 100 transformation capabilities. The platform supports Dropbox products including Search, Replay, Sign, and Dash, and provides APIs for asynchronous document conversion, media transcription, and structured metadata extraction for AI and RAG workflows. ### Source excerpt Dropbox has evolved Riviera from a file preview service into a universal content processing platform supporting more than 300 file formats and over 100 transformation capabilities. Processing hundreds of thousands of transformations per second, Riviera now supports Search, Replay, Sign, and Dash, while its APIs enable asynchronous content extraction for AI and RAG workflows. By Leela Kumili ## Our Transition to React Native DevFeed: [Our Transition to React Native]() Original publisher: [Read original article]() Author: Khan Academy Published: 2020-07-09T22:00:00Z Content type: opinion Language: en Sources: [Khan Academy]() Topics: [React Native](), [Android](), [iOS](), [Mobile](), [React]() Tags: [android](), [android-apps](), [architectures](), [backend](), [bridge](), [business-logic](), [change](), [code](), [engineering](), [ios](), [mobile](), [news](), [react-native]() ### AI overview Khan Academy describes completing its transition to React Native for its iOS and Android apps. The article explains why the team moved away from separate platform codebases and outlines the transition's exploration, straddling, and extinction phases. ### Source excerpt In 2017, Khan Academy started using React Native in our iOS and Android apps. As of this year, we've reached a big milestone: our transition to React Native is complete! Every screen in the app is rendered in React Native. ## App Engine Flex Language Shootout DevFeed: [App Engine Flex Language Shootout]() Original publisher: [Read original article]() Author: Khan Academy Published: 2017-04-17T22:00:00Z Content type: article Language: en Sources: [Khan Academy]() Topics: [Hackathon](), [Back end](), [Elixir](), [distributed-systems](), [Open Source]() Tags: [backend](), [distributed-systems](), [elixir](), [engineering](), [hackathon](), [infrastructure](), [news](), [open-source]() ### AI overview A first-person account of a Khan Academy hackathon project exploring Google App Engine Flex and comparing programming-language options. The author describes Khan Academy's existing Python backend, interest in gaining experience with Flex, and personal interest in Elixir, while noting that a language switch was not imminent. ### Source excerpt By Amos Latteier This is the second time I've been to Silicon Valley. Some years ago - never ... Read more ## Memcached-Backed Content Infrastructure DevFeed: [Memcached-Backed Content Infrastructure]() Original publisher: [Read original article]() Author: Khan Academy Published: 2017-05-15T22:00:00Z Content type: article Language: en Sources: [Khan Academy]() Topics: [Caching](), [Back end](), [data](), [Front end]() Tags: [backend](), [cache](), [engineering](), [infrastructure](), [memcached](), [news](), [profiling]() ### AI overview Khan Academy describes testing and rolling out a Memcached-backed content-serving backend. The article explains the existing content versioning and storage model, the performance limitations of fetching many individual items, and the prior approach of loading a compressed site-wide content bundle at server startup. ### Source excerpt By Ben Kraft Last post, I wrote about how we did profiling on App Engine's Memcached service to ... Read more ## Go + Services = One Goliath Project DevFeed: [Go + Services = One Goliath Project]() Original publisher: [Read original article]() Author: Khan Academy Published: 2019-12-20T23:00:00Z Content type: opinion Language: en Sources: [Khan Academy]() Topics: [Go Language](), [Python](), [Back end](), [Kotlin](), [Server]() Tags: [backend](), [end-of-life](), [engineering](), [go](), [infrastructure](), [kotlin](), [news](), [performance](), [services]() ### AI overview Khan Academy describes its effort to rebuild server software on Go's modern stack. The article explains why moving from Python 2 to Python 3 offered limited benefits, considers Kotlin and other options, and presents faster languages as a way to improve responsiveness and reduce server costs. ### Source excerpt By Kevin Dangoor Go + Services = One Goliath Project Khan Academy is embarking on a huge effort ... Read more ## New in Symfony 8.2: KeyManagement Component DevFeed: [New in Symfony 8.2: KeyManagement Component]() Original publisher: [Read original article]() Author: Javier Eguiluz Published: 2026-09-15T07:11:00Z Content type: release Language: en Sources: [Symfony Blog]() Topics: [Symfony](), [Encryption](), [Amazon Web Services](), [Azure](), [Google]() Tags: [2026](), [api](), [article](), [aws](), [azure](), [backend](), [encryption](), [features](), [google](), [hashicorp-vault](), [kms](), [new-features](), [openssl](), [sdk](), [symfony]() ### AI overview Symfony 8.2 introduces an experimental KeyManagement component that provides one API for AWS KMS, Azure Key Vault, Google Cloud KMS, and HashiCorp Vault. It supports direct and envelope encryption, local development backends, provider bridges, console commands, debug-toolbar integration, and Doctrine support. ### Source excerpt This is the first article in a series showcasing the most important new features introduced by Symfony 8.2, which will be released at the end of November 2026. Contributed... ## How Delphi ships 100 times a day with its Python backend on Vercel DevFeed: [How Delphi ships 100 times a day with its Python backend on Vercel]() Original publisher: [Read original article]() Author: Kevin Sundstrom Published: 2026-09-15T04:00:00Z Content type: article Language: en Sources: [Vercel News]() Topics: [Python](), [Vercel](), [Back end](), [feature flags](), [Amazon Web Services](), [Docker](), [airflow]() Tags: [aws](), [backend](), [docker](), [feature-flags](), [python](), [vercel](), [workflow](), [workflows]() ### AI overview A Vercel case study describes how Delphi rebuilt its Python backend on Vercel after previously using AWS, ECS, Docker Desktop, and local databases. The move gave the team a shared deployment workflow, faster onboarding, and more than 100 production deploys per day, while Vercel Workflows and Queues support long-running work and background jobs. ### Source excerpt Delphi on Vercel 10 engineers with no dedicated infrastructure role Everyone ships code, including product and design 100+ production deploys a day behind feature flags Delphi builds digital minds. They capture what someone has written, recorded, and taught, so anyone can tap that expertise on demand. Delphi is "a destination where you can find and interact with interesting minds," says founding engineer Spencer Schoeben. Nobody has built a digital mind network before, so Delphi figures out what to build by shipping features and watching how people use them. That only works if the product is fast to change. "If we are going to win, we need to have an excellent developer and agentic experience," Spencer says. "Otherwise, we could not adapt to how fast our user's demands change." Delphi's frontend had run on Vercel since day one. "The frontend has always been easy," Spencer says, "but the backend was a nightmare." Six months ago, the team rebuilt its Python backend on Vercel as well. Today the whole team ships to production 100+ times a day. Building a Python backend that's easy to work onThe backend slowed every change Delphi's backend previously ran on AWS, with ECS, Docker Desktop, and local databases. In the earliest days, the setup worked because the team was small and much of the context lived in people's heads. As Delphi grew, that made onboarding slower than the team wanted: getting a new engineer to their first deploy took a full day of environment setup. Delphi could have fixed that without leaving AWS. But fixing it properly would have meant designing and owning the infrastructure themselves, and the team didn't want to own infrastructure at all. "That is not our current goal as a company," Spencer says. After moving the backend to Vercel, new engineers could get to production much faster, with the same deploy workflow the rest of the team used every day. Workflows and Queues made the move possible When Delphi was founded, Vercel Workflows and Vercel Queues ## Agent session chains that can't escalate DevFeed: [Agent session chains that can't escalate]() Original publisher: [Read original article]() Author: WorkOS Published: 2026-09-15T00:00:00Z Content type: article Language: en Sources: [WorkOS Blog]() Topics: [Authorization](), [API](), [API keys](), [systems]() Tags: [agent](), [api](), [auth](), [authorization](), [backend](), [multi-agent](), [multi-agent-systems](), [permission](), [server](), [systems]() ### AI overview The article explains how Agent Auth session chaining lets an agent create separately scoped child sessions for subtasks. Each child records its parent, uses its own access and single-use refresh tokens, can be revoked independently, and cannot outlive the root session. The supplied text also states that the backend creates the chain server-side using an API key. ### Source excerpt An agent can open a child session of itself for a sub-task. The chain re-derives authority at every hop and can never outlive its root. ## Introducing Encore x E2B DevFeed: [Introducing Encore x E2B]() Original publisher: [Read original article]() Author: Ivan Cernja Published: 2026-09-15T00:00:00Z Content type: release Language: en Sources: [Encore Updates]() Topics: [AI-assisted coding](), [Cloud](), [backends](), [SDKs](), [Databases](), [Deployment](), [API](), [Code]() Tags: [agents](), [api](), [backend](), [cloud](), [code](), [coding](), [coding-agents](), [database](), [deployment](), [pull-request](), [sandbox](), [sdk]() ### AI overview Encore and E2B can provide coding agents with isolated sandboxes containing complete local backends. The integration lets agents build and verify end-to-end changes against infrastructure such as databases, queues, and APIs, while supporting deployment in the user's AWS or GCP account. ### Source excerpt Give coding agents an isolated sandbox with a complete backend inside it -- and keep both in your own cloud. ## Calling the WorkOS API from server-side Swift DevFeed: [Calling the WorkOS API from server-side Swift]() Original publisher: [Read original article]() Author: WorkOS Published: 2026-09-15T00:00:00Z Content type: tutorial Language: en Sources: [WorkOS Blog]() Topics: [Swift](), [API](), [Back end](), [SDK](), [Vapor]() Tags: [api](), [async](), [backend](), [sdk](), [swift]() ### AI overview This article explains how to call the WorkOS API from trusted server-side Swift applications. It covers typed resource clients, structured errors, automatic retries for transient failures, and AsyncSequence-based pagination. ### Source excerpt Typed resources, structured errors, automatic retries, and AsyncSequence pagination for server-side Swift apps. ## AI Agents in 2026 DevFeed: [AI Agents in 2026]() Original publisher: [Read original article]() Author: iHateReading Published: 2026-09-13T09:23:56Z Content type: opinion Language: en Sources: [iHateReading]() Topics: [Artificial Intelligence]() Tags: [agents](), [ai](), [ai-agents](), [ai-agents-ai-business-agents-ideas](), [ai-agents-in-2026-why-every-founder-s-stack-just-changed-for-good](), [backend](), [blockchain](), [business-agents](), [coding](), [development](), [frontend](), [ideas](), [ihatereading](), [javascript](), [node-js](), [product](), [programming](), [react](), [typescript](), [web-development]() ### AI overview The author revisits an earlier post that called AI agents "the next big thing," arguing that by September 2026 this no longer needs to be argued. ### Source excerpt Hey there!A while back, I wrote a short post calling AI agents "the next big thing." It's September 2026 now, and I don't need to convince anyone of that anymo... ## Company's Building AI Agent Sandbox SDKs & APIs DevFeed: [Company's Building AI Agent Sandbox SDKs & APIs]() Original publisher: [Read original article]() Author: iHateReading Published: 2026-09-12T13:15:15Z Content type: opinion Language: en Sources: [iHateReading]() Topics: [AI Agent](), [SDKs](), [Artificial Intelligence](), [Development]() Tags: [ai](), [ai-agent](), [ai-sandboxes](), [ai-sandboxes-vercel-aws-google-daytona](), [apis](), [aws](), [backend](), [blockchain](), [coding](), [company-s-building-ai-agent-sandbox-sdks-apis](), [daytona](), [developer](), [development](), [frontend](), [google](), [ihatereading](), [javascript](), [node-js](), [product](), [programming](), [react](), [sdks](), [typescript](), [vercel](), [web-development]() ### AI overview An opinion article discusses companies building AI agent sandboxes, SDKs, and APIs, in the context of an ongoing competition around AI sandboxes. ### Source excerpt Company's Building AI Agent Sandbox SDKs & APIs I wrote about the fight for AI sandboxes back in March -- the short version was that every AI agent that wri... ## The Grafana AI SDK for Go: a shared foundation for building AI applications DevFeed: [The Grafana AI SDK for Go: a shared foundation for building AI applications]() Original publisher: [Read original article]() Author: Luccas Quadros Published: 2026-09-12T11:22:06.456390Z Content type: article Language: en Sources: [Grafana Labs blog on Grafana Labs]() Topics: [vercel ai sdk](), [SDKs](), [Go Language](), [AI Development](), [Large Language Model](), [agent observability](), [Model Context Protocol](), [React]() Tags: [agent-observability](), [ai](), [analytics](), [backend](), [building](), [go](), [grafana](), [logs](), [metrics](), [monitoring](), [observability](), [sdk](), [streaming](), [tools](), [traces]() ### AI overview Grafana Labs introduces the Grafana AI SDK for Go, an open-source shared foundation for building AI applications. The SDK standardizes model calls, streaming, tool execution, structured output, multi-step agents, workflow controls, and operational features such as retries, logging, metrics, and Agent Observability. It also supports streaming Go backends to Vercel AI SDK frontend hooks. ### Source excerpt Starting an experiment with an LLM has never been easier. Keeping a growing collection of those experiments consistent is another matter. Earlier this year, as more teams began exploring AI features here at Grafana Labs, we repeatedly encountered the same pattern: a new experiment would start, move quickly, and build its own client for whichever model provider it needed. The next experiment would do the same, with a slightly different abstraction for streaming, tools, errors, or provider configuration. This was understandable, given the circumstances. Model providers were changing quickly, our teams were learning quickly, and coding agents made it possible to turn an idea into a working integration faster than ever. But that speed also made it easier for every integration to develop its own architecture. Eventually, we were maintaining a collection of solutions to what was essentially the same problem. And since most of our backend is written in Go, we built the Grafana AI SDK for Go to give our teams a shared foundation to work from. It provides common interfaces for calling models, streaming responses, executing tools, producing structured output, and running multi-step agents. It also speaks the protocol used by Vercel AI SDK frontend hooks, so a Go backend can stream directly to useChat, useCompletion, and useObject. We built it because we needed it inside Grafana Labs, but we open sourced it last month (alongside a broader collection of tools we released for building, operating, and understanding AI systems during our first Grafana Labs AI Week) because we think other teams building AI applications in Go are likely to encounter many of the same problems. We would like to build the next part together, so in this blog I'll tell you a bit more about the project, including how you can put it to use today, as well as how you can help us improve it. What teams can build with it today The SDK supports both simple model calls and larger application workflows: Generate ## How to call external APIs from Webflow without leaking API keys DevFeed: [How to call external APIs from Webflow without leaking API keys]() Original publisher: [Read original article]() Author: Ismail Ajagbe Published: 2026-09-12T00:00:00Z Content type: article Language: en Sources: [Webflow Blog]() Topics: [API](), [API keys](), [Authentication](), [Back end](), [Automation](), [Caching](), [Next.js](), [Node.js](), [GitHub]() Tags: [api-keys](), [apis](), [authentication](), [automation](), [backend](), [cache](), [github](), [guides](), [how-to](), [next-js](), [node-js]() ### AI overview A practical guide to integrating external APIs with Webflow through a Webflow Cloud server-side proxy, client-side fetch for public APIs, or no-code automation. It emphasizes keeping API credentials out of the browser and explains why the proxy is preferred for authenticated production integrations. ### Source excerpt Learn three ways to connect external APIs to Webflow (server-side proxy, client-side fetch, and no-code automation). ## How to connect Neon Postgres as a database backend for Webflow Cloud DevFeed: [How to connect Neon Postgres as a database backend for Webflow Cloud]() Original publisher: [Read original article]() Author: Ismail Ajagbe Published: 2026-09-12T00:00:00Z Content type: tutorial Language: en Sources: [Webflow Blog]() Topics: [Database](), [Workers](), [Next.js](), [CRUD](), [Node.js](), [API]() Tags: [api](), [backend](), [cloudflare-workers](), [database](), [guides](), [how-to](), [next-js](), [node-js](), [postgres](), [webflow]() ### AI overview A practical guide to connecting Neon Postgres to a Webflow Cloud project running Next.js on Cloudflare Workers. It explains why Neon's HTTP driver fits the Workers runtime and covers project creation, connection configuration, querying, and deployment. ### Source excerpt Learn how to set up Neon Postgres as a database backend for Webflow Cloud. ## How to Prevent Race Conditions in Django DevFeed: [How to Prevent Race Conditions in Django]() Original publisher: [Read original article]() Author: Mari Published: 2026-09-11T21:50:46Z Content type: tutorial Language: en Sources: [freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More]() Topics: [Concurrency](), [Database](), [SQLite](), [Docker]() Tags: [backend](), [concurrency](), [database](), [django](), [how-to](), [postgresql](), [python](), [race-condition](), [sqlite](), [transactions]() ### AI overview A Django tutorial that demonstrates a credit-spending race condition and explains how database transactions and row locks prevent concurrent requests from using the same credit. ### Source excerpt Let's say you have enough credit left to generate one more image in an AI app. You submit a request in one browser tab, then submit another in a second tab before the first finishes. The app accepts b ## A Developer's Guide to API Access Policies in Auth0 DevFeed: [A Developer's Guide to API Access Policies in Auth0]() Original publisher: [Read original article]() Author: Madhuri Ravindra Mohan Published: 2026-09-11T00:00:00Z Content type: tutorial Language: en Sources: [Auth0 Blog]() Topics: [Auth0](), [API](), [Authorization](), [Security](), [Back end](), [Web app]() Tags: [api](), [auth0](), [authorization](), [backend](), [fintech](), [security](), [web-app]() ### AI overview A developer guide to Auth0 API Access Policies for Applications. It explains how to configure per-API policies through the Auth0 Dashboard or Management API, and how to distinguish machine-to-machine client access from user-delegated access. ### Source excerpt Explore how to properly configure Auth0 API Access Policies and client grants to draw a hard line between strict M2M security and user-delegated access. ## OpenAI split a voice model's brain. Then one team deleted 23,000 lines of code. DevFeed: [OpenAI split a voice model's brain. Then one team deleted 23,000 lines of code.]() Original publisher: [Read original article]() Author: Amanda Caswell Published: 2026-09-10T20:08:18Z Content type: news Language: en Sources: [The New Stack]() Topics: [Artificial Intelligence]() Tags: [agent](), [agents](), [ai-agents](), [ai-engineering](), [api](), [architecture](), [backend](), [benchmark](), [code](), [event-driven](), [gpt](), [large-language-models](), [openai]() ### AI overview OpenAI launched GPT-Live-1 in its API to give outside developers a full-duplex voice model that can manage live conversation while delegating heavier processing to other backend models. The article describes its interruption handling, conversational filler during handoffs, event-driven delegation, and reported benchmark results. ### Source excerpt Building an AI voice agent has always been clunkier than it seems. Most voice agents are really a chain of The post OpenAI split a voice model's brain. Then one team deleted 23,000 lines of code. appeared first on The New Stack. ## How to Get Free Traffic to Your Website in 2026: The Complete Free-to-Paid Growth Playbook DevFeed: [How to Get Free Traffic to Your Website in 2026: The Complete Free-to-Paid Growth Playbook]() Original publisher: [Read original article]() Author: iHateReading Published: 2026-09-10T12:46:02Z Content type: tutorial Language: en Sources: [iHateReading]() Topics: [Website](), [Software as a service]() Tags: [2026](), [backend](), [blockchain](), [coding](), [development](), [founders](), [frontend](), [growth](), [guide](), [how-to](), [how-to-get-free-traffic-to-your-website-in-2026-the-complete-free-to-paid-growth-playbook](), [ihatereading](), [javascript](), [marketing](), [node-js](), [product](), [programming](), [react](), [seo](), [seo-traffic-growth-founders-marketing](), [traffic](), [typescript](), [web-development](), [website]() ### AI overview A guide to getting free website traffic in 2026 and moving from free acquisition methods toward paid growth. ### Source excerpt ree SaaS directories to submit for free 2026, Reddit marketing for startups without ads, Hacker News Show HN launch guide, Dev.to article distribution for deve... ## Claimable Neon: Provisioned by agents, claimed by humans DevFeed: [Claimable Neon: Provisioned by agents, claimed by humans]() Original publisher: [Read original article]() Author: Andre Landgraf Published: 2026-09-10T12:00:00Z Content type: release Language: en Sources: [Blog -- Neon Docs]() Topics: [AI-assisted coding](), [AI Bots]() Tags: [agents](), [api](), [auth](), [backend](), [community](), [database](), [oauth](), [postgres](), [product](), [sql]() ### AI overview Claimable Neon lets agents anonymously provision a temporary Neon project, continue building with scoped credentials, and generate a link for a human to claim the project later. ### Source excerpt Claimable Neon implements the anonymous registration method in auth.md, the open agent registration protocol authored by WorkOS, to give agents a way to provision a temporary Neon project without creating an account or collecting payment details. ## How to Build a Voice AI Agent with OpenAI's GPT-Live, Twilio Agent Connect, and Python DevFeed: [How to Build a Voice AI Agent with OpenAI's GPT-Live, Twilio Agent Connect, and Python]() Original publisher: [Read original article]() Author: Xinghao Huang Published: 2026-09-10T00:00:00Z Content type: tutorial Language: en Sources: [Twilio Blog]() Topics: [voice ai](), [AI Agent](), [Python](), [OpenAI](), [SDKs](), [API]() Tags: [ai](), [ai-agent](), [and-best-practices](), [api](), [backend](), [code](), [developer-insights](), [how-to](), [openai](), [python](), [sdk](), [tutorials](), [voice-ai]() ### AI overview A tutorial for building inbound and outbound phone voice AI agents with Python, Twilio Agent Connect, and OpenAI's GPT-Live. It covers tool calls, proactive greetings, per-call customization, and configuration for Twilio Media Streams. ### Source excerpt Build inbound and outbound voice AI assistants that can handle tool calls using Python, Twilio Agent Connect, and OpenAI's GPT-Live. ## Run AI in the Browser: A Practical Guide to Transformers.js DevFeed: [Run AI in the Browser: A Practical Guide to Transformers.js]() Original publisher: [Read original article]() Author: Freek Van der Herten (freek@spatie.be) Published: 2026-09-09T14:50:26Z Content type: tutorial Language: en Sources: [freek.dev - all blogposts]() Topics: [transformers.js](), [browser](), [Artificial Intelligence](), [AI Models](), [Back end](), [API keys]() Tags: [ai](), [ai-models](), [api-keys](), [article](), [backend](), [browser](), [guide](), [internet](), [javascript](), [js](), [machine-learning](), [practical](), [run](), [transformers](), [transformers-js]() ### AI overview A practical guide to Transformers.js, covering how it runs AI models directly in the browser, available models, and the trade-offs of client-side AI compared with traditional AI providers. ### Source excerpt Transformers.js lets you run AI models directly in the browser without a backend, API keys, or an internet connection after the model is cached. The article explores how it works, which models are available, and the trade-offs of client-side AI compared to traditional AI providers. Read more ## Beyond embedding: How to secure AI/BI Dashboards for every viewer DevFeed: [Beyond embedding: How to secure AI/BI Dashboards for every viewer]() Original publisher: [Read original article]() Author: Sonakshi Pandey Published: 2026-09-09T14:04:44Z Content type: tutorial Language: en Sources: [Databricks]() Topics: [databricks](), [Authorization](), [Grafana](), [SDKs](), [Back end](), [data]() Tags: [authorization](), [backend](), [dashboards](), [databricks](), [embedded](), [guide](), [idp](), [mapping](), [platform](), [product](), [sdk](), [sql]() ### AI overview This guide presents a Databricks design pattern for securing embedded AI/BI Dashboards for different viewers. It uses scoped embed tokens, __aibi_external_value, Unity Catalog row filters and column masks, and identity-provider-synchronized groups so one dashboard can show each viewer only the authorized regions and fields. The same entitlement table governs embedded dashboard access and direct Databricks SQL queries. ### Source excerpt The challengeEmbedding a Databricks AI/BI Dashboard in a customer-facing application is relatively straightforward... [Next page]()