# Back end Back end is the server-side portion of a web application that processes requests, accesses data, and returns responses. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Neon launches a production-ready backend suite with Postgres, storage, functions, auth, and AI Gateway DevFeed: [Neon launches a production-ready backend suite with Postgres, storage, functions, auth, and AI Gateway]() Original publisher: [Read original article]() Author: Bryan Clark Published: 2026-09-17T12:00:00Z Content type: release Language: en Sources: [Blog -- Neon Docs]() Topics: [Back end](), [Databases](), [Database](), [service](), [functions](), [gateway](), [deploy](), [autoscaling](), [Provisioning]() Tags: [ai-models](), [autoscaling](), [backend](), [backends](), [ci-cd](), [company](), [database](), [databases](), [deploy](), [functions](), [gateway](), [github](), [product]() ### AI overview Neon announces that its backend suite is generally available for production. The suite combines Lakebase Postgres, Object Storage, Functions, Managed Better Auth, and AI Gateway around a shared architecture designed for developer and coding-agent workflows. ### Source excerpt Neon is now a complete suite of backend primitives built around the database and rooted on the lakebase architecture: Lakebase Postgres, Object Storage, Functions, Managed Better Auth, and AI Gateway. All tools are GA and ready for production. Tell your agent to deploy them. ## 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. ## 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 ## On-Demand Masked Sessions with Twilio Proxy, Voice and Serverless DevFeed: [On-Demand Masked Sessions with Twilio Proxy, Voice and Serverless]() Original publisher: [Read original article]() Author: Leroy Chan Published: 2026-09-16T00:00:00Z Content type: tutorial Language: en Sources: [Twilio Blog]() Topics: [proxy](), [sessions](), [communications](), [Serverless](), [Back end](), [functions](), [callback]() Tags: [backend](), [callback](), [communications](), [developer-insights](), [functions](), [proxy](), [ridesharing](), [serverless](), [sessions](), [state](), [voice](), [workflow]() ### AI overview This tutorial explains how to build an on-demand masked calling system with Twilio Proxy, Twilio Voice, and Twilio Serverless Functions. It uses a reserved Twilio number, IVR-based tracking-code collection, a backend lookup, and a two-bounce workflow with Twilio Sync to create private sessions dynamically when phone numbers cannot be pre-associated. ### Source excerpt Manage marketplace communications without pre-associating numbers. Use Twilio Proxy, Voice, and Serverless to build dynamic, Just-in-Time masked session calls. ## 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 ## 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. ## 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). ## 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. ## 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... ## Supabase is now available in Gemini Enterprise DevFeed: [Supabase is now available in Gemini Enterprise]() Original publisher: [Read original article]() Author: Shaziya Bandukia; Elsa Heffernan Published: 2026-09-09T07:00:00Z Content type: release Language: en Sources: [Supabase Blog]() Topics: [Google](), [Model Context Protocol](), [Database](), [Back end]() Tags: [database](), [enterprise](), [gemini](), [google-cloud](), [mcp](), [mcp-server](), [partnership](), [real-time]() ### AI overview Supabase is available as a Gemini Enterprise connector, allowing teams to query and act on Supabase projects with natural language and live, real-time responses. ### Source excerpt Connect Supabase to Gemini Enterprise and query your Supabase projects with natural language, right inside the platform. ## What's new in Swift: August 2026 Edition DevFeed: [What's new in Swift: August 2026 Edition]() Original publisher: [Read original article]() Author: Dave Lester Published: 2026-09-04T16:30:00Z Content type: news Language: en Sources: [Swift.org]() Topics: [modern web development](), [Vapor](), [Back end]() Tags: [backend](), [cloudflare](), [demo](), [frontend](), [swift](), [wasm](), [webassembly]() ### AI overview A Swift community update highlights Elementary and ElementaryUI, projects for server-rendered and browser-based web UIs. It also describes Swift-to-WebAssembly progress and a Cloudflare full-stack demo using shared message types. ### Source excerpt Welcome to "What's new in Swift," a curated digest of releases, videos, and discussions in the Swift project and community. Here's an update from guest contributor Simon Leeb on Swift's progress as a language for web scenarios: Hi, Simon here! I am the creator of the elementary-swift project, a collection of packages born from a simple wish: I want to build web UIs in Swift and ultimately help Swift become a first-class choice for the web. This journey began after I started using Swift for backend services. The web frontend, however, still lived in a separate ecosystem, and I really wanted it to feel as ergonomic, safe, and efficient as the Swift I was writing everywhere else. That led to the creation of Elementary: a modern and efficient HTML rendering library with a familiar declarative API, built for the web. It integrates easily with frameworks like Vapor and Hummingbird, and has become a practical option for server-rendered web UIs. Around that same time, years of community work in the swift-wasm project made compiling Swift to WebAssembly increasingly viable, while Embedded Swift was taking its first experimental steps. This made me wonder: "How hard can it be to use Embedded Swift and build a state-driven web UI framework that produces tiny WebAssembly binaries?" Turns out: quite hard, actually! But it was too late. Despite my better judgment, I was in the middle of creating what is now known as ElementaryUI. Where Elementary renders HTML on the server, ElementaryUI runs in the browser itself. You can watch my talk at Swift@FOSDEM 2026 if you want to know more about the why, what, and how. To showcase where the project is heading, I recently posted a small Full-Stack Swift on Cloudflare demo. It features Swift in the browser communicating with a Swift backend on an edge worker through shared message types. I hope it gives people a concrete sense of how much the core technologies and the surrounding tooling have advanced. ElementaryUI is still young, with plen ## Build a real-time market data app with ClickHouse and Massive DevFeed: [Build a real-time market data app with ClickHouse and Massive]() Original publisher: [Read original article]() Author: Lionel Palacin Published: 2026-09-04T00:00:00Z Content type: tutorial Language: en Sources: [ClickHouse Blog]() Topics: [App](), [data](), [Node.js](), [Back end](), [API keys]() Tags: [analytics](), [api](), [app](), [backend](), [clickhouse](), [compression](), [data](), [demo](), [events](), [how-to](), [latency](), [low-latency](), [node](), [node-js](), [react](), [real-time](), [rest-api](), [storage](), [streams](), [visualization]() ### AI overview A tutorial for building a real-time market-data application that ingests, stores, and queries trade and quote ticks with Massive and ClickHouse, using Node.js for backend work and React for live visualization. ### Source excerpt Learn how to build a real-time financial analytics application with Massive and ClickHouse that scales to thousands of events per second. ## Catching Salesforce Custom Field Drift With the Postman AI Engineer DevFeed: [Catching Salesforce Custom Field Drift With the Postman AI Engineer]() Original publisher: [Read original article]() Author: Talia Kohan Published: 2026-09-03T16:00:00Z Content type: article Language: en Sources: [Postman Blog]() Topics: [Postman](), [Claude](), [Automation](), [API](), [Front end](), [OpenAPI Specification](), [GitHub](), [Back end](), [Pull Request](), [payload](), [Slack]() Tags: [ai](), [ai-automation](), [api](), [api-drift](), [backend](), [code](), [frontend](), [general](), [github](), [payload](), [postman-agent-mode](), [pull-request](), [salesforce](), [slack]() ### AI overview This Postman Blog article explains how a Salesforce custom field caused drift between an API collection, frontend code, and an OpenAPI specification. It describes using the Postman AI Engineer to diagnose the failing request and Postman AI Automation to update the affected artifacts and add tests through a GitHub pull request. ### Source excerpt A Salesforce custom field broke my app. See how Postman Agent Mode found the drift and how a Skill kept it from happening again. The post Catching Salesforce Custom Field Drift With the Postman AI Engineer appeared first on Postman Blog. ## How to Add Custom Providers to WorkOS Pipes DevFeed: [How to Add Custom Providers to WorkOS Pipes]() Original publisher: [Read original article]() Author: WorkOS Published: 2026-08-28T00:00:00Z Content type: tutorial Language: en Sources: [WorkOS Blog]() Topics: [OAuth](), [API keys](), [API](), [dashboards](), [Back end]() Tags: [api](), [api-keys](), [backend](), [integration](), [oauth](), [widget]() ### AI overview This tutorial explains how to add custom providers to WorkOS Pipes when a provider is not in the catalog. It covers custom credentials, organization-scoped providers, and custom provider configuration for OAuth or API-key-based integrations, while preserving the existing connection-consuming code. ### Source excerpt The path for adding a provider yourself, whether it speaks OAuth, odd OAuth, or only API keys. ## How to Use Svelte and Go to Build a Video Chat App DevFeed: [How to Use Svelte and Go to Build a Video Chat App]() Original publisher: [Read original article]() Author: Donal Toomey Published: 2026-08-28T00:00:00Z Content type: tutorial Language: en Sources: [Twilio Blog]() Topics: [Svelte](), [Go Language](), [Vite](), [REST API](), [Single-page application (SPA)](), [Back end](), [Front end]() Tags: [api](), [build-video-app](), [build-video-chat-app](), [cors](), [developer-insights](), [env-file-security](), [environment-variables](), [git](), [go](), [how-can-i-build-a-video-app](), [how-to](), [how-to-create-video-chat-app](), [how-to-make-video-chat-app](), [live-video-chat-app](), [node-js](), [npm](), [real-time](), [svelte](), [twilio-golang](), [twilio-programmable-video](), [twilio-video](), [twilio-video-tutorial](), [video-api](), [video-app](), [video-chat-app](), [vite]() ### AI overview This tutorial explains how to build a real-time video chat app using Svelte for the frontend, Go for the backend, and the Twilio Video API. It covers the application flow, project setup, dependencies, environment configuration, and credential handling. ### Source excerpt Build a real-time video chat app with Svelte 5, Vite, and Go using the Twilio Video API. ## 【etcd】写入路径深读：Txn、mod revision 与 quota/alarm DevFeed: [【etcd】写入路径深读：Txn、mod revision 与 quota/alarm]() Original publisher: [Read original article]() Author: Liao Tonglang Published: 2026-08-26T00:00:00Z Content type: tutorial Language: zh Sources: [土法炼钢 - 系统与基础设施]() Topics: [etcd](), [Back end](), [Raft](), [Remote Procedure Call (RPC)](), [maintenance]() Tags: [alarm](), [apiserver](), [applied-index](), [apply](), [batch](), [bbolt](), [committed-index](), [compaction](), [defrag](), [distributed](), [errcompacted](), [etcd](), [follower](), [k8s](), [kubernetes](), [mod-revision](), [nospace](), [quota](), [revision](), [txn](), [v3-5](), [v3-5-33]() ### AI overview This tutorial explains the etcd v3.5.33 write path from gRPC requests through Raft proposal, quota checks, MVCC apply, and ModRevision assignment. It also describes proposal backpressure, backend quota accounting, and how a NOSPACE alarm blocks writes while reads, deletes, and maintenance operations can continue. ### Source excerpt 走读 etcd v3.5.33 写入路径：raftRequest 背压、Txn compare/mod 与 ModRevision 分配、backend quota 与 NOSPACE alarm 如何把集群推入只读。 ## How Sprig Replaced Postgres, ClickHouse & Redis...with 4-8x Better Latency DevFeed: [How Sprig Replaced Postgres, ClickHouse & Redis...with 4-8x Better Latency]() Original publisher: [Read original article]() Author: Cynthia Dunlop Published: 2026-08-24T13:30:56Z Content type: article Language: en Sources: [ScyllaDB]() Topics: [Latency](), [Low Latency](), [Databases](), [PostgreSQL](), [clickhouse](), [Redis](), [real-time](), [Back end](), [recommendation systems](), [Artificial Intelligence]() Tags: [backend](), [clickhouse](), [data](), [databases](), [engineering](), [latency](), [low-latency](), [postgres](), [postgresql](), [real-time](), [recommendation-systems](), [redis](), [user-stories]() ### AI overview Sprig outgrew PostgreSQL as its AI-powered product research platform scaled to more than 1.3 trillion events, 75 billion attributes, and high-volume real-time processing. The article describes its database challenges and the path toward lower-latency data infrastructure involving PostgreSQL, ClickHouse, and Redis. ### Source excerpt With ScyllaDB, a small engineering team could focus on building their product instead of battling their databases. ## Measuring how coding-agent context delivery affects results per token DevFeed: [Measuring how coding-agent context delivery affects results per token]() Original publisher: [Read original article]() Author: Raymond Gitonga Published: 2026-08-21T13:36:56Z Content type: opinion Language: en Sources: [OLX]() Topics: [Artificial Intelligence](), [coding](), [Back end](), [Testing]() Tags: [agent](), [ai](), [ai-agent](), [ai-experiments](), [backend](), [coding](), [comparative-studies](), [context](), [cost](), [software-engineering](), [testing]() ### AI overview The article describes an experiment comparing four ways of delivering context to a coding agent. The same task, model, scoring method, and reference files were used across three runs of each setup, with the goal of measuring results relative to token cost. The task was adding a notification worker to a mature backend system spanning three codebases and approximately 18-20 files. ### Source excerpt I gave a coding agent the same job four different ways and measured what each cost. The packaging barely mattered, but something else did. At OLX, we've been pushing to get AI tools into everyday engineering work. This has enabled us to have faster delivery and better test coverage, with less time spent on the mechanical parts. Our biggest constraint, however, is tokens. Tokens being a finite resource, we keep running up against our allocated quota. So the real question is how to get the best results per token spent. There's no shortage of advice on how to feed context into agents: put everything into one prompt, split it into folders, or give one orchestrator the whole picture and let it delegate. But opinions vary on which way is better and which produces the best results relative to cost. So I decided to measure it. One job, four setups, three runs each: same model, same task, same scoring. The only thing that changed was how the agents got their context. The Setup The job was to add a notification worker to one of our fairly mature backend services. In plain terms, the worker watches for things happening in the system, decides who should get a notification, and logs the outcome. The worker lives in one service, but the change touched around 18-20 files across three codebases that talk to each other, tests included. All three have years of history and firm architectural conventions. In short: a realistic change in a mature system, not a toy task. Every setup got the same reference files, written once, and mostly derived from each codebase's own AGENTS.md file. They cover a repo map, one deep dive per codebase, the coding and testing conventions, and the contracts between the services. The CLAUDE.md files you'll see in the setups below are different: those are each setup's entry point, and part of what varied. The reference files were identical in every setup. Only the delivery changed. 1. Default: One manager owns everything. It keeps context in memory and spins ## How to add enterprise SSO to an Express.js API DevFeed: [How to add enterprise SSO to an Express.js API]() Original publisher: [Read original article]() Author: WorkOS Published: 2026-08-21T00:00:00Z Content type: tutorial Language: en Sources: [WorkOS Blog]() Topics: [Single sign-on (SSO)](), [Tutorial](), [Express](), [Node.js](), [API](), [JSON Web Tokens](), [OpenID connect (OIDC)](), [Authorization](), [Back end]() Tags: [api](), [authorization](), [how-to](), [js](), [jwt](), [middleware](), [node-js](), [oidc](), [security](), [sso]() ### AI overview A step-by-step tutorial for adding enterprise SSO to an Express.js API with WorkOS. It covers SAML and OIDC login redirects, callback handling, JWT verification on protected routes, and multi-tenant authorization using signed token claims. ### Source excerpt Add enterprise SSO (SAML and OIDC) to an Express.js API with WorkOS. Step-by-step Node.js code for the login redirect, the callback, and JWT verification on protected API routes. ## How Guideless uses Tinybird for real-time product analytics and training insights DevFeed: [How Guideless uses Tinybird for real-time product analytics and training insights]() Original publisher: [Read original article]() Author: Tinybird Published: 2026-08-20T16:00:00Z Content type: article Language: en Sources: [Tinybird]() Topics: [product analytics](), [real-time](), [Back end]() Tags: [analytics](), [backend](), [customer-stories](), [effective](), [insights](), [product-analytics](), [real-time]() ### AI overview The article describes how Guideless used Tinybird to launch real-time product analytics without building a separate analytics backend. It says the approach saved weeks of engineering work and produced engagement insights for improving training effectiveness. ### Source excerpt Learn how Guideless used Tinybird to launch real-time product analytics without building a separate analytics backend, save weeks of engineering work, and turn engagement data into insights that help users create more effective training. ## Query Neon backend logs DevFeed: [Query Neon backend logs]() Original publisher: [Read original article]() Author: Andre Landgraf Published: 2026-08-18T12:00:00Z Content type: article Language: en Sources: [Blog -- Neon Docs]() Topics: [Back end](), [observability](), [log management](), [Command-line interface](), [MCP Server](), [Serverless](), [Server](), [Node.js](), [Amazon S3](), [SDKs]() Tags: [api](), [backend](), [cli](), [logs](), [mcp-server](), [node](), [observability](), [product](), [s3](), [tool]() ### AI overview Neon adds branch-scoped access to backend logs from Neon Functions and Object Storage outside the Console. The logs can be queried through the CLI, MCP server, Logs API, SDK, or Loki endpoints, with filters and raw LogQL support. ### Source excerpt We keep expanding our backend observability - the most recent addition: you can now query backend logs outside the Console! neon logs reads what Neon Functions and Object Storage emit on a branch, with filters for source, severity, and message text. ## LLMs belong in your backend DevFeed: [LLMs belong in your backend]() Original publisher: [Read original article]() Author: Carlota Soto Published: 2026-08-17T12:00:00Z Content type: article Language: en Sources: [Blog -- Neon Docs]() Topics: [Large Language Model](), [Back end](), [API]() Tags: [ai-gateway](), [api](), [api-keys](), [backend](), [llms](), [product]() ### AI overview Neon presents its AI Gateway as part of a backend stack for calling LLMs through a unified credential, endpoint, billing flow, and model catalog. ### Source excerpt Neon AI Gateway is how we put LLM calls into the Neon backend the same way we put files, functions, and auth there. It allows you to call models right from Neon, instead of collecting lab accounts. [Next page]()