# backends Server-side application components that provide data storage, business logic, request processing, and services for front-end clients. 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 ## Who am I ? DevFeed: [Who am I ?]() Original publisher: [Read original article]() Author: brice.dutheil@gmail.com (Brice Dutheil) Published: 2026-09-15T04:32:32.192351Z Content type: article Language: fr Sources: [The Coffee Workshop]() Topics: [Java](), [Kubernetes](), [Open Source](), [backends]() Tags: [java](), [jvm](), [kubernetes](), [open-source]() ### AI overview The author describes himself as primarily a Java and JVM developer, with experience using Kubernetes and contributing to open-source projects. He previously worked on Mockito, has spent ten years working on backends across various sectors, and currently works for BlaBlaCar. ### Source excerpt English πŸ‡¬πŸ‡§/πŸ‡ΊπŸ‡Έ I'm mostly a Java / JVM guy. Now I'm also a Kubernetes user in surface, so I now a few system tricks. ## 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. ## Concurrent Writes in Practice: Building High-Throughput Apps with Turso's MVCC DevFeed: [Concurrent Writes in Practice: Building High-Throughput Apps with Turso's MVCC]() Original publisher: [Read original article]() Author: Jeff Olson Published: 2026-08-27T00:00:00Z Content type: tutorial Language: en Sources: [Turso Blog]() Topics: [backends](), [cpu]() Tags: [benchmarks](), [cpu](), [database](), [performance](), [python](), [rust](), [sqlite](), [transactions](), [turso](), [typescript]() ### AI overview A practical guide to using Turso's concurrent-write capability for high-throughput SQLite applications. It explains contention from SQLite's single-writer model and discusses transaction conflicts, retries, and workloads that include computation. ### Source excerpt What it looks like to actually build with BEGIN CONCURRENT: when to reach for it, working code in TypeScript, Python, and Rust, and how to handle conflicts. ## How Ora benchmarks every major AI agent on Vercel DevFeed: [How Ora benchmarks every major AI agent on Vercel]() Original publisher: [Read original article]() Author: Kevin Sundstrom Published: 2026-08-21T21:00:00Z Content type: article Language: en Sources: [Vercel News]() Topics: [Artificial Intelligence](), [AI Agent](), [benchmarking](), [Vercel](), [agent observability](), [Traces](), [Claude Code](), [Latency](), [ChatGPT](), [OpenClaw](), [backends](), [Front end]() Tags: [ai](), [ai-agent](), [authentication](), [benchmarks](), [chatgpt](), [claude-code](), [deployment](), [latency](), [openclaw](), [software](), [tools](), [traces](), [vercel](), [web]() ### AI overview Ora benchmarks major AI agents by running them against live customer websites and recording the cost, latency, steps, and traces required to complete workflows. Built on Vercel, the platform provides the front end, back end, and agent runtime through one deployment path. ### Source excerpt Ora on Vercel Front end, back end, and agent runtime on one platform Every major agent tested side by side on live sites Hundreds of commits a day from a 16-person engineering team Ora sends agents onto live websites with instructions to sign up for a product, integrate with it, and pay for it. Agents often fail, and by Ora's estimate, 99% of the web isn't agent-ready. The platform shows customers where and why agents fail, and what to change. Assaf Elovic, co-founder of Ora, spent years helping agents discover the web. His previous company, Tavily, built a web search engine for AI agents and was acquired by Nebius earlier this year. Search solved half the problem, but an agent that finds a product still has to actually use it. He and co-founder Liad Yosef started Ora to measure how ready the web is for agents, and to fix the parts that aren't. Today that means spawning agents against live customer sites from journey.ora.ai, where Ora runs a journey and records the cost, latency, and steps an agent needs to finish a task. The platform runs on Vercel, including the agent runtime. Benchmarking every major agent side by sideEvery harness expects its own infrastructure Agents decompose into two parts: a model, which does the reasoning, and a harness, the software that gives the model its tools and drives it from step to step. Ora's lineup covers the agents customers use most: Claude Code, ChatGPT, Gemini, Hermes, OpenClaw, and eve, Vercel's agent framework. Ora runs each agent on a customer's website and watches how it handles common workflows. No two harnesses want the same infrastructure. Each expects its own environment and exposes its steps differently, so Ora runs a separate runtime for every harness and traces every step. Ido Finder, who leads engineering at Ora, calls that side-by-side coverage one of the most valuable things ora brings to its customers. When an agent stalls in a signup flow, the customer sees which step and what it tried. Without the trace, the ## Understanding Load Balancing for Beginners DevFeed: [Understanding Load Balancing for Beginners]() Original publisher: [Read original article]() Author: Pramodh Kumar M Published: 2026-08-17T17:00:34Z Content type: tutorial Language: en Sources: [Kubernetes - KodeKloud Blog | DevOps, Cloud, Kubernetes, AI Tutorials & More]() Topics: [backends](), [networking](), [Algorithms]() Tags: [backend](), [beginners](), [cloud](), [connection-draining](), [devops](), [failover](), [health-checks](), [high-availability](), [kubernetes](), [layer-4-vs-layer-7](), [least-connections](), [load-balancer](), [load-balancer-for-beginners](), [load-balancing](), [load-balancing-algorithms](), [networking](), [nginx](), [round-robin](), [session-affinity](), [sre](), [sticky-sessions](), [thundering-herd](), [traffic-management]() ### AI overview This beginner-oriented article explains load balancing as both traffic distribution and backend failure detection. It compares round robin and least-connections algorithms, distinguishes Layer 4 from Layer 7 routing, and discusses health checks, failover, draining, sticky sessions, and the risks of overly aggressive checks. ### Source excerpt Spreading traffic is the easy half of load balancing. The half that decides whether an outage lasts ten seconds or ten minutes is noticing that a backend has stopped working, and most health checks cannot. ## AI speeds up code writing, but coordination across distributed services remains a system-design challenge DevFeed: [AI speeds up code writing, but coordination across distributed services remains a system-design challenge]() Original publisher: [Read original article]() Author: hello@ankit-rana.com Published: 2026-08-02T00:00:00Z Content type: opinion Language: en Sources: [Ankit Rana | Mechanical Sympathy]() Topics: [Artificial Intelligence](), [distributed-systems](), [backends](), [Microservice](), [migration](), [OpenAPI Specification]() Tags: [ai](), [alignment](), [architecture](), [backend](), [distributed-systems](), [migration](), [openapi](), [services](), [system-design]() ### AI overview The article argues that AI assistants have accelerated implementation work while leaving coordination between teams and services largely unchanged. It presents ownership boundaries, contract stability, migration coordination, and failure semantics as central system-design concerns because failures often occur between otherwise correct components. ### Source excerpt AI made the write path fast and left the coordination path untouched, so failures now live in the gaps between services: two event schema versions across two regions, three teams, and one assumption that someone else handled the migration window. Alignment work, deciding ownership boundaries, contract stability, and failure semantics before anyone opens an IDE, is system design, and it produces concrete network cost when skipped. ## Making 768 servers look like 1 DevFeed: [Making 768 servers look like 1]() Original publisher: [Read original article]() Author: Ben Dicken Published: 2026-07-15T00:00:00Z Content type: article Language: en Sources: [Blog -- PlanetScale]() Topics: [Database](), [backends](), [MySQL]() Tags: [architecture](), [database](), [engineering](), [mysql](), [performance](), [postgres](), [scalability](), [servers]() ### AI overview An article about scaling Postgres databases through sharding, from a single node to hundreds of servers. It describes resource bottlenecks, read replicas, and the role of application architecture at high query volume. ### Source excerpt How to make 768 distinct Postgres servers look like 1 to your applications. ## Migrating Counter Service storage: Design choices and learnings DevFeed: [Migrating Counter Service storage: Design choices and learnings]() Original publisher: [Read original article]() Author: Jia Long Loh Published: 2026-07-03T00:00:00Z Content type: article Language: en Sources: [Grab Tech]() Topics: [Database](), [migration](), [Latency](), [real-time](), [backends]() Tags: [artificial-intelligence](), [backend](), [database](), [devsecops](), [engineering](), [evaluation](), [infrastructure](), [kubernetes](), [latency](), [migration](), [platform](), [real-time](), [security](), [storage]() ### AI overview Grab's Counter Service migrated its storage backend from a wide-column database to Aerospike to support a high-volume, low-latency fraud-detection workload. The article describes separating storage from business logic, revisiting data modeling and access patterns, and designing reader- and writer-side changes for a gradual, observable rollout. ### Source excerpt Introduction Counter Service is used across Grab's anti-fraud platform to answer time-windowed count questions, such as recent ride requests by a user or failed payment attempts on a card. The service handles tens of thousands of queries per second (QPS) with about a billion requests per day, while maintaining strict requirements around latency and reliability to support real-time fraud rule evaluation. For most of its life, Counter Service was backed by a wide-column database that served the workload reliably as the service scaled. As part of a broader infrastructure review mandated at an organizational level, our database team evaluated alternatives to this storage that many services relied on, including Counter Service. Based on their assessment, Aerospike emerged as a good fit for our use-case. We also used the migration as an opportunity to decouple storage concerns from business logic, a necessary first step for this migration, and one that would reduce the effort required for future storage changes. As part of the same effort, we revisited the data model and access patterns in detail, which helped us identify and apply several straightforward optimizations. This post walks through how we did it. What we built on the reader-side to make the migration safe, how we redesigned the writer-side data model around the new backend, and what we ran into during the gradual rollout. Setting the stage Counter data is stored in three time granularities: 15-minute, hourly, and daily buckets. A typical read would be along the lines of, "give me the count for key X over the last 90 minutes", which the service decomposes into the smallest possible set of buckets, one hourly in the middle, a few 15-minute buckets at the edges, fetches them, and sums. In the original setup, each granularity was stored in a separate table with a composite primary key: TABLE daily_count ( key TEXT, -- partition key day_ts TIMESTAMP, -- clustering key count BIGINT, PRIMARY KEY (key, day_ts) ); The ## A Practical CQRS Split for Read and Write Paths in a Node.js Service DevFeed: [A Practical CQRS Split for Read and Write Paths in a Node.js Service]() Original publisher: [Read original article]() Author: The T-Shaped Dev Published: 2026-06-21T12:19:52Z Content type: tutorial Language: en Sources: [The T-Shaped Dev]() Topics: [backends](), [Code]() Tags: [architecture](), [business-logic]() ### AI overview This tutorial presents a lightweight CQRS approach for a Node.js service: separate business-heavy write operations from read operations that serve screens, without requiring event sourcing or a second database. ### Source excerpt You don't need event sourcing or a second database. Just split reads from writes, and finally fix the 20-method service nobody wants to open. ## 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. ## Same-Day Model Integration: ESMC + ESMFold2 in Sheaf v0.11 DevFeed: [Same-Day Model Integration: ESMC + ESMFold2 in Sheaf v0.11]() Original publisher: [Read original article]() Published: 2026-05-27T00:00:00Z Content type: article Language: en Sources: [Alex Korbonits]() Topics: [Large Language Model](), [structure](), [Pydantic](), [backends](), [unit tests](), [Serverless]() Tags: [backend](), [claude-code](), [code](), [language](), [model](), [pypi](), [release](), [serverless](), [structure](), [tests]() ### AI overview The article explains how Sheaf v0.11 integrated ESMC and ESMFold2 on the same day they were released. It attributes the rapid integration to existing typed serving contracts and infrastructure, while describing verification, testing, H100 smoke testing, release work, and documentation. ### Source excerpt Chan Zuckerberg Biohub released a new protein language model and structure predictor this morning. Sheaf v0.11 shipped with both, same day. The story is less about hustle and more about what a typed serving contract buys you when a new model lands. ## 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. ## On-Demand Subscriptions: A Hybrid Billing Model that Works DevFeed: [On-Demand Subscriptions: A Hybrid Billing Model that Works]() Original publisher: [Read original article]() Author: Ayush Agarwal Published: 2026-05-14T00:00:00Z Content type: tutorial Language: en Sources: [Dodo Payments Blog]() Topics: [Software as a service](), [backends]() Tags: [add-ons](), [billing](), [how-to](), [payments](), [saas](), [subscription-billing](), [subscriptions](), [upgrades]() ### AI overview This article explains on-demand subscriptions, a hybrid billing model that combines recurring charges with optional one-off charges using the same saved payment mandate. It describes use cases including trials, upgrades, add-ons, overage, and credit top-ups, and outlines implementation on Dodo Payments. ### Source excerpt On-demand subscriptions combine recurring billing with on-demand charges so you can run trials, upsells, and tiered upgrades on a single saved payment method. ## ML based ranking using Nrtsearch DevFeed: [ML based ranking using Nrtsearch]() Original publisher: [Read original article]() Author: Mohammad Mohtasham (Software Engineer); Tao Yu (Software Engineer) Published: 2026-05-11T00:00:00Z Content type: tutorial Language: en Sources: [Yelp]() Topics: [Machine Learning & Artificial Intelligence](), [Inference](), [AI Inference](), [model-deployment](), [backends](), [Open Source]() Tags: [blog-post](), [inference](), [latency](), [machine-learning](), [ml](), [models](), [network](), [open-source](), [performance](), [pipeline](), [ranking](), [software]() ### AI overview Yelp extended its Lucene-based Nrtsearch engine with an Inference Plugin that embeds machine-learning ranking directly in the search layer. The article explains the ranking workflow, including model configuration and loading, and describes how co-locating feature storage and inference reduces network transfer, serialization overhead, and latency compared with a standalone inference service. ### Source excerpt We've extended Nrtsearch with the Inference Plugin, which embeds ML-based ranking directly in the search layer -- eliminating the need for a standalone scoring service. We use Nrtsearch (read more information on the blog post), a Lucene-based open-source search engine built by Yelp, to power a variety of applications such as business search, reviews search, ad delivery and photo search. In this blog post, we give a high-level overview of the Machine Learning (ML) based scoring workflow in Nrtsearch. We'll show how ML models are configured and loaded, and how different applications use custom business logic to develop, test, and... ## Vercel Flags is now generally available DevFeed: [Vercel Flags is now generally available]() Original publisher: [Read original article]() Author: Chris Widmaier Published: 2026-04-16T08:00:00Z Content type: release Language: en Sources: [Vercel News]() Topics: [feature flags](), [Vercel](), [SDKs](), [Next.js](), [OpenFeature](), [backends]() Tags: [feature-flags](), [frameworks](), [next-js](), [platform](), [sdk](), [vercel]() ### AI overview Vercel Flags is now generally available as a feature flag provider built into the Vercel platform. It supports targeting rules, user segments, environment controls, and framework-native use through the Flags SDK in Next.js and SvelteKit applications. An OpenFeature adapter supports other frameworks and custom backends. ### Source excerpt Vercel Flags is now generally available. Vercel Flags is a feature flag provider built into the Vercel platform. Create and manage feature flags with targeting rules, user segments, and environment controls directly in the Vercel Dashboard. The Flags SDK provides a framework-native way to define and use these flags within Next.js and SvelteKit applications, integrating directly with your existing codebase: Once you define a flag, you can use them within your application in a few lines of code: For teams using other frameworks or custom backends, the Vercel Flags adapter supports the OpenFeature standard, allowing you to plug Vercel Flags into their provider agnostic SDK. Try it out or learn more about Vercel Flags. Read more ## Moving from Junior to Mid-Level Backend Engineering Requires Shipping and System Design DevFeed: [Moving from Junior to Mid-Level Backend Engineering Requires Shipping and System Design]() Original publisher: [Read original article]() Author: Eric Roby Published: 2026-04-07T12:03:32Z Content type: opinion Language: en Sources: [Eric Roby]() Topics: [backends](), [ci](), [GitHub Actions](), [systems](), [coding]() Tags: [backend](), [ci-cd](), [github-actions](), [infrastructure](), [junior](), [junior-developer](), [skills](), [systems]() ### AI overview The article argues that progressing from junior to mid-level backend engineering involves more than writing code. It emphasizes building valuable products, contributing to business logic and features, understanding CI/CD and GitHub Actions, and making sound system-design and infrastructure decisions. ### Source excerpt The skills that aren't in job descriptions but are on everyone's mind ## 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. ## Type-Safe Caching in Encore.ts DevFeed: [Type-Safe Caching in Encore.ts]() Original publisher: [Read original article]() Author: Ivan Cernja Published: 2026-03-11T00:00:00Z Content type: article Language: en Sources: [Encore Updates]() Topics: [Caching](), [Cache](), [TypeScript](), [Redis](), [backends]() Tags: [backend](), [cache](), [caching](), [code](), [development](), [redis](), [typescript]() ### AI overview This article explains how string-based Redis caching in TypeScript can lose type safety and allow stale, colliding, or mismatched values. It presents Encore.ts v1.55's typed keyspaces, which preserve value and key constraints through to Redis and catch certain errors at compile time. ### Source excerpt What goes wrong with stringly-typed Redis, and what changes when the cache boundary is typed. ## Supabase Template is Now Available on DigitalOcean App Platform DevFeed: [Supabase Template is Now Available on DigitalOcean App Platform]() Original publisher: [Read original article]() Author: Bikram Gupta Published: 2026-02-26T16:43:31Z Content type: release Language: en Sources: [DigitalOcean]() Topics: [Supabase](), [Digital Ocean](), [Template](), [backends](), [PostgreSQL](), [Authentication](), [REST API](), [Security](), [Self-hosted](), [vendor lock-in]() Tags: [api](), [authentication](), [backend](), [digitalocean](), [open-source](), [platform](), [postgresql](), [product-updates](), [real-time](), [rest-api](), [sdks](), [security](), [self-hosted](), [storage](), [vendor-lock-in]() ### AI overview DigitalOcean App Platform now offers native Supabase template support. The template deploys a production-ready Supabase backend with managed PostgreSQL, authentication, REST APIs, file storage, and real-time capabilities. ### Source excerpt Modern applications need more than just a database. They need authentication, auto-generated APIs, file storage, and real-time subscriptions. Supabase is a powerful open-source Firebase alternative that transforms PostgreSQL into a complete backend platform. With its developer-friendly SDKs and instant API generation, Supabase is a top request from teams who want full control over their infrastructure without sacrificing speed. Today, we're excited to announce native Supabase template support on DigitalOcean App Platform. Deploy a production-ready Supabase backend with one click, complete with managed PostgreSQL, authentication, REST API, file storage, and real-time capabilities. Benefits Instant Backend: Get authentication, auto-generated REST APIs, file storage, and real-time subscriptions without writing backend code. Production-Ready Security: Every deployment includes JWT authentication, Row Level Security (RLS), and secure credential management out of the box. Self-Hosted Control: Keep your data, auth credentials, and file storage on infrastructure you control--no vendor lock-in. Full-Stack Ready: Deploy Supabase alongside your frontend application in a single App Platform project. Architecture Overview The Supabase template deploys a complete backend stack that auto-generates APIs from your PostgreSQL schema. Your frontend applications connect through four specialized services that handle data, authentication, files, and real-time updates. How Supabase Works Unlike traditional backends where you write API endpoints manually, Supabase auto-generates them from your database schema: Row Level Security (RLS) policies on your tables control who can access what - users only see their own data, automatically. How to Get Started The template repository contains everything you need to deploy Supabase on App Platform: github.com/AppPlatform-Templates/supabase-appplatform You have three deployment paths: One-Click: Use the Deploy to DigitalOcean button in the repository ## How Project Structure Helps AI Agents Write Consistent TypeScript Backends DevFeed: [How Project Structure Helps AI Agents Write Consistent TypeScript Backends]() Original publisher: [Read original article]() Author: Ivan Cernja Published: 2026-02-24T00:00:00Z Content type: article Language: en Sources: [Encore Updates]() Topics: [Artificial Intelligence](), [TypeScript](), [backends](), [Framework]() Tags: [ai](), [ai-agents](), [backend](), [fragmentation](), [framework](), [typescript]() ### AI overview The article argues that AI agents often create inconsistent TypeScript backends because projects offer many valid choices for frameworks, validation, databases, and infrastructure. It presents Encore.ts as a framework that uses typed APIs, declared infrastructure, and consistent service structure to guide agents toward existing project patterns and business logic. ### Source excerpt And what happens when you actually let them. ## How agentic AI could pressure databases and challenge eventual consistency DevFeed: [How agentic AI could pressure databases and challenge eventual consistency]() Original publisher: [Read original article]() Author: Becca Weng Published: 2026-02-13T00:00:00Z Content type: opinion Language: en Sources: [Cockroach Labs]() Topics: [Artificial Intelligence](), [Databases](), [data](), [consistency](), [backends](), [Concurrency]() Tags: [agentic-ai](), [agents](), [ai](), [back-end](), [concurrency](), [consistency](), [data](), [databases](), [systems]() ### AI overview The article summarizes a Cockroach Labs webinar about how agentic AI may increase pressure on databases and back-end systems. It argues that autonomous agents can generate unpredictable, high-volume traffic and suggests that operational databases may need strong serializable consistency, correct reads, and predictable behavior under extreme concurrency. ### Source excerpt AI agents are no longer a thought experiment. They're already writing code, calling APIs, retrying failed requests, and coordinating work at machine speed. And they're about to put unprecedented pressure on the systems we use to store and move data. ## BKND joins Supabase DevFeed: [BKND joins Supabase]() Original publisher: [Read original article]() Author: Paul Copplestone Published: 2026-02-03T07:00:00Z Content type: article Language: en Sources: [Supabase Blog]() Topics: [Supabase](), [backends](), [Databases](), [Open Source]() Tags: [agentic](), [announcement](), [building](), [databases](), [developer](), [ecosystem](), [open](), [open-source](), [product](), [sandboxes](), [systems](), [tooling]() ### AI overview Supabase welcomes Dennis Senn, creator of BKND, to develop a lightweight Supabase Lite offering for agentic workloads. The planned exploration includes simpler databases and a trimmed-down experience for sandboxes, while BKND remains open source. ### Source excerpt Dennis Senn, creator of BKND, is joining Supabase to build a Lite offering for agentic workloads. [Next page]()