# Database Published articles for Database. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Perplexity's AI agents helped build a database. They weren't allowed to run it. DevFeed: [Perplexity's AI agents helped build a database. They weren't allowed to run it.]() Original publisher: [Read original article]() Author: Amanda Caswell Published: 2026-09-16T21:51:15Z Content type: article Language: en Sources: [The New Stack]() Topics: [Database](), [DynamoDB](), [Rust](), [Artificial Intelligence](), [rocksdb](), [Amazon S3]() Tags: [agents](), [ai](), [ai-agents](), [ai-infrastructure](), [api](), [coding](), [coding-agents](), [database](), [databases](), [dynamodb](), [open-source](), [performance](), [perplexity](), [rocksdb](), [rust](), [s3]() ### AI overview Perplexity built CobbleDB, a Rust key-value store, after finding DynamoDB too costly and insufficiently controllable for its search workload. Coding agents helped develop it, but were not allowed to run it in production. Perplexity measured lower read latency and expects lower costs, with plans to open-source the database. ### Source excerpt Perplexity decided it was paying too much for DynamoDB and wasn't getting the control it wanted over read performance. So The post Perplexity's AI agents helped build a database. They weren't allowed to run it. appeared first on The New Stack. ## Ensuring transaction-safety in Google App Engine DevFeed: [Ensuring transaction-safety in Google App Engine]() Original publisher: [Read original article]() Author: Khan Academy Published: 2016-06-27T22:00:00Z Content type: article Language: en Sources: [Khan Academy]() Topics: [Transactions](), [Databases](), [Code](), [Google]() Tags: [cache](), [code](), [database](), [engineering](), [google](), [hooks](), [infrastructure](), [news](), [tracing](), [transactions]() ### AI overview The article explains how Khan Academy uses transactions and user-write locks with Google App Engine to prevent database corruption caused by concurrent requests overwriting one another. It describes hooks that detect transaction-safety violations and highlights caching and code-path mistakes that can cause stale data to be used inside transactions. ### Source excerpt By Craig Silverstein In last week's exciting post, I described an alternative to transactions that we use at ... Read more ## Build a WhatsApp AI agent with Appwrite Functions and TablesDB DevFeed: [Build a WhatsApp AI agent with Appwrite Functions and TablesDB]() Original publisher: [Read original article]() Author: Atharva Deosthale Published: 2026-09-16T00:00:00Z Content type: tutorial Language: en Sources: [Appwrite Blog]() Topics: [Appwrite](), [AI Agent](), [Messaging](), [Serverless](), [API](), [Database]() Tags: [ai](), [ai-agent](), [api](), [app](), [build](), [data](), [database](), [messages](), [messaging](), [meta](), [serverless](), [support](), [tutorial](), [tutorials](), [whatsapp]() ### AI overview This tutorial shows how to build a WhatsApp AI support agent with Appwrite Functions and TablesDB. One function receives and stores incoming messages, while a second function retrieves conversation history, uses an order-lookup tool, sends replies through Meta's WhatsApp Cloud API, and stores the responses. Conversation summaries help keep long threads within the model's token budget. ### Source excerpt Turn a WhatsApp number into an AI support agent. Two Appwrite Functions receive messages and reply, TablesDB keeps the conversation history, and a compaction step keeps the context small. ## Turso Accident Protection Enables Database Restoration for Up to 5 Days DevFeed: [Turso Accident Protection Enables Database Restoration for Up to 5 Days]() Original publisher: [Read original article]() Author: Pedro Muniz Published: 2026-09-16T00:00:00Z Content type: release Language: en Sources: [Turso Blog]() Topics: [Turso](), [Databases](), [MCP Server]() Tags: [ai-agents](), [database](), [databases](), [feature](), [mcp](), [mcp-server](), [turso]() ### AI overview Turso has released Accident Protection, allowing databases deleted on a paid plan to be restored for up to five days at no additional cost. The feature is available through the UI and Turso MCP server. ### Source excerpt Agents delete things. Now you can undelete them. Restore databases for up to 5 days after deletion, on any paid plan, at no additional cost. ## How to Build an Endpoint Data Loss Prevention Strategy for Your Development Team DevFeed: [How to Build an Endpoint Data Loss Prevention Strategy for Your Development Team]() Original publisher: [Read original article]() Author: Alex Tray Published: 2026-09-15T21:03:09Z Content type: tutorial Language: en Sources: [freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More]() Topics: [data loss prevention](), [Development](), [Access Control](), [data]() Tags: [access-control](), [cybersecurity](), [cybersecurityawareness](), [data-loss-prevention](), [data-protection](), [database](), [development](), [encryption](), [endpoint-security](), [hardcoded-credentials](), [it-security](), [net-conf](), [remote-access](), [secrets](), [sensitive-data](), [web-development]() ### AI overview A tutorial for development teams on building endpoint data loss prevention strategies. It covers auditing laptops for secrets and sensitive data, removing unnecessary copies, rotating exposed credentials, and establishing access controls. ### Source excerpt A developer's laptop holds more sensitive data than most people realize: API keys, database credentials, staging environment secrets, and sometimes entire copies of production data pulled down "just f ## How automated testing improves development of complex features DevFeed: [How automated testing improves development of complex features]() Original publisher: [Read original article]() Author: Dan Lew Published: 2026-09-15T13:16:49Z Content type: opinion Language: en Sources: [Dan Lew Blog]() Topics: [Testing](), [coding](), [Concurrency](), [Database]() Tags: [automated](), [concurrency](), [database](), [regression](), [testing]() ### AI overview The author contrasts manual testing with automated testing while developing job cancellation in a work queue. Automated tests help expose concurrency and regression risks, cover corner cases, and simulate mid-execution cancellation more reliably. ### Source excerpt It's hard for me to overstate how much my coding has improved since embracing automated testing. And yet, I used to think of it as a waste of time! This post is for my former self, demonstrating why I embrace the practice now. Allow me to demonstrate with ## Kubernetes 1.36 restores a lost guarantee for database backups DevFeed: [Kubernetes 1.36 restores a lost guarantee for database backups]() Original publisher: [Read original article]() Author: Shubham Pampattiwar Published: 2026-09-15T13:00:00Z Content type: news Language: en Sources: [Kubernetes Overview, News and Trends | The New Stack](), [The New Stack]() Topics: [Kubernetes](), [PostgreSQL](), [Databases]() Tags: [backups](), [cncf](), [consistency](), [database](), [databases](), [kubernetes](), [post-contributed](), [postgresql](), [snapshots](), [sponsor-cncf](), [sponsored-post-contributed](), [stateful](), [storage]() ### AI overview The article explains how Kubernetes 1.36 restores coordinated snapshots for applications whose state spans multiple volumes, addressing inconsistent database backups caused by taking individual PersistentVolumeClaim snapshots at different times. ### Source excerpt It's 2 a.m., and you're restoring a PostgreSQL cluster from last night's backup. Its data directory lives on one PersistentVolumeClaim The post Kubernetes 1.36 restores a lost guarantee for database backups appeared first on The New Stack. ## What's new in CYPEX v2.0.0: tenancy moves from the query to the catalog DevFeed: [What's new in CYPEX v2.0.0: tenancy moves from the query to the catalog]() Original publisher: [Read original article]() Author: Svitlana Lytvynenko Published: 2026-09-15T06:06:05Z Content type: article Language: en Sources: [CYBERTEC PostgreSQL | Services & Support]() Topics: [PostgreSQL](), [tenant data protection](), [Security](), [Database]() Tags: [cypex](), [data-protection](), [database](), [how-to](), [news](), [postgresql](), [product](), [schema](), [security]() ### AI overview CYPEX 2.0.0 moves tenant isolation from application-level query filters into PostgreSQL row-level security policies stored in the database catalog. The policies use JWT claims to apply organization-specific access rules to every connection and query. ### Source excerpt This blog highlights the details of CYPEX 2.0, with each feature being explained in detail. Read to know more. The post What's new in CYPEX v2.0.0: tenancy moves from the query to the catalog appeared first on CYBERTEC PostgreSQL | Services & Support. ## 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. ## What Managed Postgres Services Handle for Teams DevFeed: [What Managed Postgres Services Handle for Teams]() Original publisher: [Read original article]() Author: Databricks Staff Published: 2026-09-14T23:36:59Z Content type: article Language: en Sources: [Databricks]() Topics: [PostgreSQL](), [Database](), [Serverless](), [databricks](), [AI Development]() Tags: [data-plus-ai-foundations](), [database](), [databricks](), [developer-tooling](), [migration](), [postgres](), [postgresql](), [recovery](), [security](), [serverless]() ### AI overview The article defines managed Postgres by the operational responsibilities a provider assumes, including patching, scaling, failover, and backups. It discusses how these responsibilities can vary between providers and describes Lakebase Postgres as a serverless offering with automatic scaling, PostgreSQL compatibility, recovery, and Databricks integrations. ### Source excerpt Every Postgres vendor calls itself "managed." Few of them agree on what that word... ## Percona and HexaCluster: Faster, Safer Oracle Migration DevFeed: [Percona and HexaCluster: Faster, Safer Oracle Migration]() Original publisher: [Read original article]() Author: Percona Team Published: 2026-09-14T22:19:16Z Content type: article Language: en Sources: [Blog - Percona]() Topics: [Oracle Database](), [migration](), [Open Source](), [PostgreSQL](), [Database Migration](), [Replication](), [Tooling]() Tags: [database](), [database-migration](), [database-trends](), [engineering](), [insight-for-dbas](), [insight-for-developers](), [migration](), [open-source](), [oracle](), [partners](), [percona](), [postgresql](), [replication](), [schema](), [sql](), [tooling](), [validation]() ### AI overview Percona and HexaCluster describe a partnership for moving organizations from Oracle, SQL Server, DB2, and Sybase ASE to supported open source databases, primarily PostgreSQL. HexaCluster provides assessment and migration engineering through DMAT and HexaRocket, while Percona provides distributions, operators, and long-term support. HexaRocket combines schema migration, data migration, validation, change data capture, live replication, and reverse replication in one platform. ### Source excerpt Percona and HexaCluster have partnered to remove the hardest part of an open source database migration: getting off Oracle, SQL Server, DB2 or Sybase ASE with confidence, on a predictable timeline, without a multi-year consulting program. Percona brings open source expertise, its own distributions, operators and enterprise support. HexaCluster brings the assessment and migration engineering ... Continued The post Percona and HexaCluster: Faster, Safer Oracle Migration appeared first on Percona. ## Troubleshooting row lock contention in Amazon Aurora PostgreSQL: Part 1 - Understanding row lock contention in PostgreSQL DevFeed: [Troubleshooting row lock contention in Amazon Aurora PostgreSQL: Part 1 - Understanding row lock contention in PostgreSQL]() Original publisher: [Read original article]() Author: Sameer Kumar Published: 2026-09-14T16:02:08Z Content type: tutorial Language: en Sources: [AWS Database Blog]() Topics: [Amazon Aurora](), [PostgreSQL](), [Concurrency](), [Monitoring](), [Transactions](), [Amazon CloudWatch](), [Amazon RDS](), [Architecture & Design](), [Extension]() Tags: [advanced-300](), [amazon-aurora](), [amazon-cloudwatch](), [amazon-rds](), [architecture](), [availability](), [concurrency](), [database](), [database-performance](), [extension](), [locks](), [monitoring](), [postgresql](), [production](), [rds-for-postgresql](), [technical-how-to](), [transactions](), [troubleshooting]() ### AI overview This first part of a two-part series explains row lock contention in PostgreSQL and Amazon Aurora PostgreSQL. It covers how concurrent transactions competing for the same rows can reduce throughput and cause timeouts despite healthy CPU and I/O, then introduces PostgreSQL locking internals and monitoring techniques using system views, functions, the pgrowlocks extension, and log_lock_waits. The article notes that the same behavior and investigation approach apply to Amazon RDS for PostgreSQL. ### Source excerpt Row lock contention can collapse database throughput during a flash sale even when CPU and I/O look healthy. In Part 1 of this series, learn how PostgreSQL row locking works and how to monitor lock contention in Amazon Aurora PostgreSQL and Amazon RDS for PostgreSQL using system views, the pgrowlocks extension, and the log_lock_waits parameter. ## I Added Break-Glass Accounts Before My Home Lab Locked Me Out DevFeed: [I Added Break-Glass Accounts Before My Home Lab Locked Me Out]() Original publisher: [Read original article]() Author: Brandon Lee Published: 2026-09-14T12:16:15Z Content type: article Language: en Sources: [Virtualization Howto]() Topics: [Homelab](), [Authentication](), [Single sign-on (SSO)](), [Docker](), [Database](), [networking](), [Server](), [Amazon Route 53]() Tags: [authentication](), [container](), [database](), [dns](), [docker](), [home-lab](), [home-server](), [infrastructure](), [networking](), [server](), [sso]() ### AI overview The article explains why a home lab that centralizes authentication through single sign-on can become inaccessible during an outage. It introduces "break glass" accounts as a recovery measure and emphasizes treating the authentication service as infrastructure with dependencies such as its application, database, certificates, networking, reverse proxy, and DNS. ### Source excerpt One of the coolest things that you can do is centralize your authentication in the home lab. So, instead of having all kinds of separate usernames and passwords that are... The post I Added Break-Glass Accounts Before My Home Lab Locked Me Out appeared first on Virtualization Howto. ## Inside LuBot's database-per-tenant architecture DevFeed: [Inside LuBot's database-per-tenant architecture]() Original publisher: [Read original article]() Author: Carlota Soto Published: 2026-09-14T12:00:00Z Content type: article Language: en Sources: [Blog -- Neon Docs]() Topics: [Database](), [Software as a service](), [Supabase](), [Provisioning](), [stripe](), [API]() Tags: [api](), [architecture](), [billing](), [case-studies](), [checkout](), [database](), [infrastructure](), [product](), [provisioning](), [saas](), [scale](), [stripe]() ### AI overview This article explains LuBot's database-per-tenant architecture. LuBot uses Supabase for shared administrative data such as organizations, users, and billing state, while each paying customer receives an isolated Postgres database. Stripe checkout triggers a webhook that provisions a Neon branch through the Neon API, with deterministic naming to prevent duplicate tenants. Neon branches provide independent compute and can scale idle tenants to zero. ### Source excerpt It took six days to go from zero to isolated Postgres per tenant, provisioned the moment someone pays. A traditional Postgres setup would have been a full quarter of work. ## Article: Implementing Durable Workflows on Postgres Without an External Orchestrator DevFeed: [Article: Implementing Durable Workflows on Postgres Without an External Orchestrator]() Original publisher: [Read original article]() Author: Raman Varma Published: 2026-09-14T11:00:00Z Content type: article Language: en Sources: [InfoQ]() Topics: [Databases](), [SQL](), [Provisioning](), [incident](), [CI/CD](), [Kubernetes](), [AWS Step Functions](), [Artificial Intelligence]() Tags: [ai](), [ai-ml-data-engineering](), [architecture-design](), [article](), [automation](), [aws-step-functions](), [database](), [durable-workflows-postgres](), [incident-response](), [infrastructure](), [kubernetes](), [ml-data-engineering](), [orchestration](), [postgres](), [queue](), [relational-databases](), [sql](), [workflows]() ### AI overview This article explains how to implement durable workflows on Postgres without an external orchestrator. It describes using row-level locking as a concurrent work queue, primary-key checkpoints for idempotency, and leases with a sweeper for crash recovery. Workflow state, sleeps, and human approvals can persist in the database and survive process restarts. ### Source excerpt Postgres can serve as the durable state store and coordination layer for workflows, eliminating the need for an external orchestrator. SKIP LOCKED enables concurrent work processing, primary-key checkpoints enforce idempotency, and leases support crash recovery. Workflow sleeps and human approvals can also be persisted as database state and survive restarts. By Raman Varma ## Announcing ADK for Kotlin 1.0: Building Production-Ready AI Agents in Kotlin, Android, and Beyond DevFeed: [Announcing ADK for Kotlin 1.0: Building Production-Ready AI Agents in Kotlin, Android, and Beyond]() Original publisher: [Read original article]() Author: Guillaume Laforge Published: 2026-09-12T11:04:33.891311Z Content type: release Language: en Sources: [Google Developers Blog]() Topics: [Artificial Intelligence](), [Kotlin](), [AI Development](), [Android](), [Kotlin Multiplatform](), [multiplatform](), [Orchestration](), [Google](), [Persistence](), [Agent Skill](), [LiteRT](), [Cloud]() Tags: [agent-skill](), [agents](), [ai](), [ai-agent](), [ai-agents](), [android](), [building](), [database](), [development-kit](), [firebase](), [incident](), [kotlin](), [multiplatform](), [orchestration](), [persistence](), [production]() ### AI overview Google announces the 1.0 general availability release of the Agent Development Kit (ADK) for Kotlin, a production-ready toolkit for building multi-agent applications with Kotlin, Java, and Android. Built on Kotlin Multiplatform, it provides feature parity with the ADK 1.0 Core and adds Android-first extensions for on-device agents with LiteRT-LM and ML Kit, hybrid cloud workflows through Firebase AI Logic, and state persistence with Room and AppSearch. The release also includes type-safe, compile-time function calling through KSP and declarative agent skills. ### Source excerpt Google has officially released version 1.0 of the Agent Development Kit (ADK) for Kotlin, achieving full feature parity with the Python and Java ADK cores to enable idiomatic, multi-agent AI development. Built on Kotlin Multiplatform (KMP), the framework leverages Kotlin Symbol Processing (KSP) for zero-reflection, type-safe function calling, alongside advanced orchestration capabilities like human-in-the-loop workflows and context compaction. Additionally, the release introduces a robust suite of Android-first extensions, allowing mobile developers to integrate local models via LiteRT-LM, cloud reasoning through Firebase AI, session persistence using Room, and semantic memory powered by AppSearch. ## How we shipped 15 Tbps for OpenAI in 90 days (Session 2 of 3) DevFeed: [How we shipped 15 Tbps for OpenAI in 90 days (Session 2 of 3)]() Original publisher: [Read original article]() Author: Sridhar Rajarao Published: 2026-09-12T00:00:00Z Content type: article Language: en Sources: [Sridhar Rajarao]() Topics: [OpenAI](), [Network](), [GPU](), [Server](), [Cache](), [API](), [Oracle Database]() Tags: [api](), [architecture](), [build](), [cache](), [capacity](), [database](), [debugging](), [execution](), [gateway](), [gpu](), [network](), [object](), [openai](), [performance](), [platform-engineering](), [server](), [servers](), [sre](), [storage](), [testing](), [warp]() ### AI overview The second session describes turning an architecture for OpenAI's 15 Tbps system into a delivery plan. It covers coordinated capacity planning across network, gateway, server, storage, and database teams; caching object names through the Inventory API; delivery tracking; and performance validation. Early WARP testing found packet drops caused by an unsuitable MTU of 1500, which was changed to 9100. ### Source excerpt Architecture was only the first week. Session 2 is about the build: capacity, execution discipline, and the first signs that performance would be the real test. ## 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 build a member portal with login and a dashboard in Webflow Cloud DevFeed: [How to build a member portal with login and a dashboard in 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: [dashboards](), [Authentication](), [Database](), [SQLite](), [Nextra](), [Node.js](), [App]() Tags: [auth](), [authentication](), [cloud](), [data](), [database](), [guide](), [guides](), [how-to](), [next-js](), [node-js](), [platform](), [sqlite](), [webflow]() ### AI overview This tutorial explains how to build a Webflow Cloud member portal with login, a SQLite-backed data model, session-scoped queries, a member dashboard, and profile editing. It emphasizes deriving the member ID from the session rather than from the request so users can access only their own data. ### Source excerpt Learn how to build a member portal on Webflow Cloud where every query is scoped to the signed-in member. ## How to build a service marketplace with provider profiles in Webflow Cloud DevFeed: [How to build a service marketplace with provider profiles in 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: [data-architecture](), [Database](), [SQLite](), [Next.js](), [Node.js](), [Authentication](), [Content Management System]() Tags: [data-modeling](), [database](), [guide](), [guides](), [how-to](), [next-js](), [node-js](), [sqlite](), [webflow]() ### AI overview A guide to building the discovery side of a service marketplace on Webflow Cloud. It explains why provider profiles should be modeled as database rows rather than CMS content when they require owner editing, flexible filtering, and approval before publication. ### Source excerpt Learn how to build the discovery half of a service marketplace on 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 ## Bringing custom knowledge to agents with AutoRAG DevFeed: [Bringing custom knowledge to agents with AutoRAG]() Original publisher: [Read original article]() Author: Diego Alvarez Ponce, Diego Garcia Perez Published: 2026-09-11T13:01:50Z Content type: article Language: en Sources: [Red Hat](), [Red Hat Developer]() Topics: [Retrieval Augmented Generation (RAG)](), [Model Context Protocol (MCP)](), [Databases](), [Large Language Model](), [AI Development](), [LLM evaluation / benchmarking]() Tags: [ai](), [ai-inference](), [artificial-intelligence](), [database](), [developer](), [generative-ai](), [gpu](), [guide](), [how-to](), [large-language-models-llms](), [model-context-protocol-mcp](), [rag](), [red-hat](), [terminal]() ### AI overview This guide explains how to use AutoRAG in Red Hat OpenShift AI to optimize retrieval-augmented generation for enterprise, domain-specific data. It also shows how to integrate a Model Context Protocol server that retrieves customer information from an internal database, using a fictional banking scenario. ### Source excerpt The large language models (LLMs) that power generative AI work by drawing upon the patterns and information present in their training data. Without access to the right data, LLMs struggle to comprehend context--like our internal corporate vocabulary--and as a result, start to hallucinate. The post Bringing custom knowledge to agents with AutoRAG appeared first on Red Hat Developer. ## Eloquent Performance and Database Design: Evidence Before Eager Loading DevFeed: [Eloquent Performance and Database Design: Evidence Before Eager Loading]() Original publisher: [Read original article]() Author: Freek Van der Herten (freek@spatie.be) Published: 2026-09-11T12:30:27Z Content type: tutorial Language: en Sources: [freek.dev - all blogposts]() Topics: [Eloquent ORM](), [Database](), [Laravel](), [PHP]() Tags: [database](), [eager-loading](), [eloquent](), [indexes](), [laravel](), [optimization](), [performance](), [php](), [query]() ### AI overview A practical deep dive into improving Eloquent performance and database design for a growing team dashboard. It covers detecting N+1 queries, choosing aggregates and indexes, inspecting query plans, pagination, chunking, and transaction boundaries. ### Source excerpt A deep dive into Eloquent performance, from detecting N+1 queries to choosing aggregates, indexes, query plans, pagination, chunking, and transaction boundaries for a growing team dashboard. Read more ## Rapidly scaling online storage to serve over 1 billion ChatGPT users DevFeed: [Rapidly scaling online storage to serve over 1 billion ChatGPT users]() Original publisher: [Read original article]() Published: 2026-09-11T10:00:00Z Content type: article Language: en Sources: [OpenAI News]() Topics: [cloud-infrastructure](), [Database](), [data]() Tags: [chatgpt](), [data](), [database](), [engineering](), [global](), [infrastructure](), [openai](), [platform](), [python](), [scale](), [storage]() ### AI overview OpenAI describes scaling Habitat from a Python client-side library into a distributed online storage platform for global product traffic. The article focuses on reliability, performance, and capacity decisions made during rapid growth. ### Source excerpt Learn how OpenAI evolved Habitat from a Python library into a globally distributed storage platform serving 1 billion ChatGPT users and 22M requests per second. [Next page]()