# patterns Published articles for patterns. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Profiling App Engine Memcached DevFeed: [Profiling App Engine Memcached]() Original publisher: [Read original article]() Author: Khan Academy Published: 2017-05-01T22:00:00Z Content type: article Language: en Sources: [Khan Academy]() Topics: [Latency](), [Logging](), [Data structures]() Tags: [engineering](), [infrastructure](), [latency](), [logging](), [memcached](), [news](), [patterns](), [profiling](), [tests]() ### AI overview Khan Academy engineers profile App Engine Memcached to estimate access latency and evaluate whether on-demand content fetching could support separate content trees for each language. Most requests showed 1-4 ms latency, usually below 10 ms, while a single multi-get performed similarly to a single get. ### Source excerpt By Ben Kraft Last year, Willow wrote about how we optimized our in-memory content data to take up ... Read more ## An experiment with Gemma 4 on a Raspberry Pi explores what an LLM would do with its own computer DevFeed: [An experiment with Gemma 4 on a Raspberry Pi explores what an LLM would do with its own computer]() Original publisher: [Read original article]() Author: Alex Ewerlöf Published: 2026-09-14T09:09:42Z Content type: opinion Language: en Sources: [Alex Ewerlof Notes]() Topics: [gemma4](), [Large Language Model](), [Raspberry Pi](), [Go Language](), [Simulation]() Tags: [computer](), [data](), [experiment](), [gemma-4](), [go](), [llm](), [patterns](), [raspberry-pi]() ### AI overview The author describes an experiment in which Gemma 4 12B was asked what it would do with its own computer. Running on a Raspberry Pi 1 through a Go harness, the model imagined ingesting large amounts of data, finding patterns, and running large-scale simulations. ### Source excerpt LLM's response ## Domain-Driven Design Clearly Explained DevFeed: [Domain-Driven Design Clearly Explained]() Original publisher: [Read original article]() Author: Nikki Siapno Published: 2026-09-12T12:52:28Z Content type: tutorial Language: en Sources: [Level Up Coding System Design Newsletter]() Topics: [Domain-driven design (DDD)](), [Code](), [systems]() Tags: [concepts](), [developers](), [domain](), [domain-driven-design](), [engineering](), [model](), [patterns]() ### AI overview An introduction to Domain-Driven Design (DDD), explaining how shared business language and bounded contexts help teams model complex products and keep code aligned with the domain. ### Source excerpt The mental model that makes DDD click. ## Exception Handling in Practice DevFeed: [Exception Handling in Practice]() Original publisher: [Read original article]() Author: Franco Fernando Published: 2026-08-28T05:50:28Z Content type: tutorial Language: en Sources: [The Polymathic Engineer]() Topics: [Exception](), [Code]() Tags: [code](), [exception](), [exception-handling](), [patterns]() ### AI overview An article about patterns and anti-patterns for signaling, catching, and surviving failures in code. ### Source excerpt Patterns and anti-patterns for signaling, catching, and surviving failures in your code. ## Three ways to let an AI agent call third-party APIs on behalf of a user DevFeed: [Three ways to let an AI agent call third-party APIs on behalf of a user]() Original publisher: [Read original article]() Author: WorkOS Published: 2026-08-27T00:00:00Z Content type: tutorial Language: en Sources: [WorkOS Blog]() Topics: [AI Agent](), [OAuth](), [Security]() Tags: [agent](), [ai-agent](), [apis](), [hubspot](), [linear](), [oauth](), [patterns](), [proxy](), [security](), [slack](), [third-party](), [tokens]() ### AI overview This article explains three patterns for letting an AI agent access third-party APIs on a user's behalf: storing OAuth credentials yourself, fetching tokens at runtime, or using a proxy that keeps tokens out of the agent-controlled runtime. It compares their operational and security implications and recommends choosing based on where the code runs and the system's requirements. ### Source excerpt Store the token yourself, fetch it at runtime, or never hold it at all. Where the credential ends up in each pattern, what each one costs, and how to pick without guessing. ## The New Default Nix Flake Template Explained DevFeed: [The New Default Nix Flake Template Explained]() Original publisher: [Read original article]() Author: Jacek Galowicz Published: 2026-08-17T00:00:00Z Content type: tutorial Language: en Sources: [Nixcademy Blog]() Topics: [Nix](), [Template](), [Development](), [Tooling]() Tags: [architecture](), [how-to](), [learn](), [packages](), [patterns](), [shell](), [standard](), [template]() ### AI overview An explanation of the new default Nix flake template, which supports multiple platforms without flake-utils. The article describes the underlying pattern, how to extend it for real projects, and its limitations and advanced cases. ### Source excerpt The default Nix flake template is now multi-platform without flake-utils. Learn how the new pattern works and how to extend it for real projects. ## A field guide to splitting systems, keeping data correct, and surviving failure in production DevFeed: [A field guide to splitting systems, keeping data correct, and surviving failure in production]() Original publisher: [Read original article]() Author: Neo Kim Published: 2026-07-23T10:24:07Z Content type: tutorial Language: en Sources: [System Design Newsletter]() Topics: [Microservice](), [Microservices](), [Architecture & Design](), [systems]() Tags: [data](), [microservices](), [patterns](), [production](), [systems]() ### AI overview This field guide covers microservices design patterns for splitting systems, maintaining data correctness, and handling failures in production. ### Source excerpt #164: A field guide to splitting systems, keeping data correct, and surviving failure in production ## GitOps architecture, patterns and anti-patterns DevFeed: [GitOps architecture, patterns and anti-patterns]() Original publisher: [Read original article]() Author: Artem Lajko Published: 2026-07-23T05:40:01Z Content type: tutorial Language: en Sources: [Platform Engineering Blog]() Topics: [GitOps](), [Architecture & Design](), [Platform Engineering](), [CI/CD](), [Security]() Tags: [gitops](), [patterns](), [reconciliation](), [security]() ### AI overview A guide to designing GitOps for production, covering declarative and versioned configuration, pull-based deployment, continuous reconciliation, multi-cluster operation, and common anti-patterns. ### Source excerpt A platform engineer's guide to production GitOps architecture. Master advanced patterns, multi-cluster strategies, security, and avoid critical anti-patterns for scale ## Read Replica Migration: Lessons and Query Routing Patterns DevFeed: [Read Replica Migration: Lessons and Query Routing Patterns]() Original publisher: [Read original article]() Author: Johanna Larsson Published: 2026-07-21T11:00:45Z Content type: tutorial Language: en Sources: [The incident.io Blog]() Topics: [Databases](), [Database]() Tags: [incident](), [incident-channel](), [incident-management](), [incident-response](), [learnings](), [migrations](), [outage](), [patterns](), [post-mortem](), [read-replica](), [slack-incident]() ### AI overview The article shares incident.io's experience migrating workload to a read replica, including the benefits, operational complexity, and patterns for routing queries between the replica and primary database. ### Source excerpt Our learnings from implementing a product-wide read replica migrations, including some useful patterns for routing queries to replica and primary ## Zero Downtime Database Migrations: Safe Schema Changes DevFeed: [Zero Downtime Database Migrations: Safe Schema Changes]() Original publisher: [Read original article]() Author: Animesh Pathak Published: 2026-07-16T00:00:00Z Content type: tutorial Language: en Sources: [Harness Blog]() Topics: [Database](), [Database Migration](), [migration](), [Availability](), [Code]() Tags: [availability](), [blog](), [compatibility](), [consistency](), [database](), [database-migration](), [learn](), [migrations](), [patterns](), [production](), [schema](), [strategy](), [uptime]() ### AI overview This tutorial explains zero-downtime database migrations through backward-compatible schema changes, the expand-and-contract pattern, dual writes, and phased rollouts. It emphasizes preserving application availability, data consistency, and compatibility while old and new application versions run together. ### Source excerpt Learn zero downtime database migration strategies using backward-compatible schema changes, dual writes, and safe rollout patterns. | Blog ## How to Achieve Pruning When Querying by Non-Partitioned Columns in PostgreSQL DevFeed: [How to Achieve Pruning When Querying by Non-Partitioned Columns in PostgreSQL]() Original publisher: [Read original article]() Author: Haki Benita Published: 2026-07-08T21:00:00Z Content type: tutorial Language: en Sources: [Haki Benita]() Topics: [PostgreSQL](), [Databases]() Tags: [articles](), [how-to](), [index](), [partition](), [patterns](), [performance](), [postgresql](), [query]() ### AI overview This tutorial explains PostgreSQL partition pruning and shows how certain data patterns can enable pruning when queries filter on columns other than the partition key. It uses event data partitioned by year and examines queries by date range and session. ### Source excerpt Under conventional wisdom, pruning can only be achieved when querying by the partition key. However, if your data follows certain patterns, using some clever tricks you can achieve pruning even when filtering by non-partition key columns. ## You Don't Need to Build It to Design It DevFeed: [You Don't Need to Build It to Design It]() Original publisher: [Read original article]() Author: Humayun Tanwar Published: 2026-07-06T11:05:34Z Content type: tutorial Language: en Sources: [Mindorks - Medium]() Topics: [systems](), [data-architecture](), [Latency](), [Requirements]() Tags: [business](), [code](), [compliance](), [data-architecture](), [design](), [latency](), [machine-learning](), [patterns](), [requirements](), [software-engineering](), [systems](), [tech-stack](), [technology]() ### AI overview This article explains why solutions engineers can contribute meaningfully to system design without writing the implementation. It connects integration scoping, data ownership, real-time communication choices, and failure handling to core design concepts, then presents five questions about scale, constraints, dependencies, failure tolerance, and success criteria. ### Source excerpt System design is one of those topics that feels like it belongs exclusively to engineers who write the code. The diagrams, the architecture reviews, the database choices , it all looks like backend territory from the outside. You're Already Doing This Solutions engineers sit at an interesting intersection. You're deep enough in the technical conversation to understand how systems connect, and close enough to the customer to understand what they actually need from those systems. That combination gives you a view that's genuinely useful in design conversations, and one that's worth developing deliberately. When you scope an integration and map which system owns which data, that's data architecture. When you recommend a webhook approach over polling because the customer needs real-time updates, that's a latency and reliability trade-off. When you ask a customer how they handle failures before a build starts, that's systems thinking. The formal vocabulary of system design is mostly a structured way of describing what you're already doing in the field. Building that vocabulary, and understanding the patterns underneath it, makes it easier to contribute in technical conversations, ask the right questions earlier, and connect your field experience to the broader design decisions being made around you. The Five Questions That Structure Any Design Good design conversations don't start with a diagram. They start with these. Scale. What volume are you designing for, not today but at the ceiling that changes the architecture? A thousand requests a day is a different system from a thousand per second. That number changes your database choice, whether you need a queue, and how retries need to behave. Constraints. What's already fixed? Existing tech stack, compliance requirements, vendor contracts. Every real system gets built inside constraints. Name them before drawing anything. Dependencies. What does this system talk to, and what talks to it? Systems that look simple in a conv ## 3 Resilience Patterns That Keep a Backend Service Up When Its Dependencies Aren't DevFeed: [3 Resilience Patterns That Keep a Backend Service Up When Its Dependencies Aren't]() Original publisher: [Read original article]() Author: The T-Shaped Dev Published: 2026-06-27T10:19:01Z Content type: tutorial Language: en Sources: [The T-Shaped Dev]() Topics: [Resilience](), [Back end](), [API]() Tags: [backend](), [circuit](), [dependencies](), [patterns](), [resilience](), [retry](), [timeout]() ### AI overview A tutorial on using timeouts, retries with backoff, and circuit breakers to limit cascading failures in backend services when dependencies become slow or unreliable. It explains how unbounded waiting can exhaust server resources and describes how to layer these resilience patterns. ### Source excerpt Timeout, retry with backoff, and circuit breaker -- what each one actually does, and the order to stack them. ## What Is Software, and Will LLMs Replace It? DevFeed: [What Is Software, and Will LLMs Replace It?]() Original publisher: [Read original article]() Author: Federico Tomassetti Published: 2026-06-23T08:56:06Z Content type: opinion Language: en Sources: [Federico Tomassetti]() Topics: [Large Language Model](), [Software](), [data](), [integrity](), [Software as a service]() Tags: [ai](), [business](), [chatbots](), [consistency](), [data](), [future-of-ai](), [future-of-software](), [integrity](), [interfaces](), [language-engineering](), [llms](), [mcp](), [opinion](), [patterns](), [reflections](), [saas](), [software](), [software-development](), [sql](), [structure](), [visualization]() ### AI overview The article argues that large language models are unlikely to replace software. Instead, they may provide more flexible interfaces while software's underlying structures--organized data, schemas, constraints, consistency rules, visualizations, and guided processes--remain essential. ### Source excerpt Software isn't being replaced by LLMs, it's being fronted by them, with the deterministic core (schemas, constraints, processes) staying as essential as ever. The post What Is Software, and Will LLMs Replace It? appeared first on Federico Tomassetti. ## Service Mesh vs API Gateway (Part III) DevFeed: [Service Mesh vs API Gateway (Part III)]() Original publisher: [Read original article]() Author: Franco Fernando Published: 2026-06-20T09:02:00Z Content type: comparison Language: en Sources: [The Polymathic Engineer]() Topics: [Architecture & Design](), [API]() Tags: [api](), [api-gateway](), [mesh](), [patterns](), [vs]() ### AI overview This article compares service mesh and API Gateway patterns, explaining that they address different problems and can work best together. ### Source excerpt Two patterns that look similar, solve different problems, and work best together. ## AI SaaS Monetization in 2026: What Actually Works DevFeed: [AI SaaS Monetization in 2026: What Actually Works]() Original publisher: [Read original article]() Author: Ayush Agarwal Published: 2026-05-15T00:00:00Z Content type: tutorial Language: en Sources: [Dodo Payments Blog]() Topics: [Artificial Intelligence](), [Software as a service](), [Software]() Tags: [2026](), [ai](), [article](), [billing](), [churn](), [cost](), [margin](), [monetization](), [packaging](), [patterns](), [pricing](), [revenue](), [saas](), [subscription](), [unit-economics](), [variable]() ### AI overview The article explains three pricing eras for AI SaaS: flat subscriptions, raw consumption pricing, and a hybrid model combining subscriptions, included usage, overage pricing, and optional larger commitments. It argues that the hybrid approach offers buyers predictability while protecting seller margins. ### Source excerpt How AI SaaS companies are pricing, billing, and packaging in 2026. Learn the real monetization patterns for AI products and what to avoid. ## Incremental Maintenance DevFeed: [Incremental Maintenance]() Original publisher: [Read original article]() Published: 2026-05-14T01:53:09Z Content type: opinion Language: en Sources: [Blogs on Ryan Harter]() Topics: [maintenance](), [Dependency injection](), [implementation](), [screen](), [App]() Tags: [core](), [dependencies](), [dependency-injection](), [incremental](), [maintenance](), [patterns](), [product](), [screen]() ### AI overview An opinion article about incremental maintenance in a large, aging application. It argues that small refactoring choices can reduce entrenched patterns, using dependency injection to keep feature-gating details out of a monolithic tab-host screen. ### Source excerpt I recently reviewed some code at work that made me pause. The change affected a part of the codebase that we affectionately call "The Monolith", which is some of the oldest code in our 16 year old app. While there are reasonable arguments that it's hard to tease apart this older hairball of code, there are often small choices that can either move you closer to the decoupled promised land, or further entrench you in bad patterns. ## 20 Mistakes That Quietly Destroy JavaScript/TypeScript Codebases (Part 2) DevFeed: [20 Mistakes That Quietly Destroy JavaScript/TypeScript Codebases (Part 2)]() Original publisher: [Read original article]() Author: The T-Shaped Dev Published: 2026-05-09T02:19:51Z Content type: tutorial Language: en Sources: [The T-Shaped Dev]() Topics: [JavaScript](), [TypeScript](), [Code](), [Node.js](), [Testing]() Tags: [async](), [code](), [javascript](), [memory-leaks](), [mistakes](), [node-js](), [patterns](), [test](), [typescript]() ### AI overview Part 2 of a JavaScript and TypeScript code-quality series presents common runtime and code-quality mistakes, including mutating function parameters, memory leaks from uncleared listeners, timers and subscriptions, and async cancellation issues. It includes fixes and testing or debugging guidance. ### Source excerpt Common JS/TS patterns that feel fine until they don't. 11 mistakes, before/after code for each. (11 min) ## 20 Mistakes That Quietly Destroy JavaScript/TypeScript Codebases (Part 1) DevFeed: [20 Mistakes That Quietly Destroy JavaScript/TypeScript Codebases (Part 1)]() Original publisher: [Read original article]() Author: The T-Shaped Dev Published: 2026-05-02T02:19:25Z Content type: tutorial Language: en Sources: [The T-Shaped Dev]() Topics: [JavaScript](), [TypeScript](), [Code](), [modern web development]() Tags: [bugs](), [code](), [javascript](), [mistakes](), [patterns](), [typescript]() ### AI overview This tutorial examines common JavaScript and TypeScript codebase mistakes that increase maintenance costs without necessarily breaking builds or tests. Part 1 focuses on type modeling, error handling, and module design, including TypeScript strict mode and unsafe type escapes. ### Source excerpt Common JS/TS patterns that feel fine until they don't. 11 mistakes, before/after code for each. (11 min) ## Embedded Payments for SaaS Platforms: A 2026 Implementation Guide DevFeed: [Embedded Payments for SaaS Platforms: A 2026 Implementation Guide]() Original publisher: [Read original article]() Author: Ayush Agarwal Published: 2026-04-29T00:00:00Z Content type: tutorial Language: en Sources: [Dodo Payments Blog]() Topics: [Software as a service](), [API](), [tokenization](), [ui](), [Mobile]() Tags: [2026](), [api](), [app](), [architectures](), [checkout](), [compliance](), [conversion](), [guide](), [integration](), [mobile](), [patterns](), [payment](), [payments](), [pci-dss](), [platform](), [regulatory](), [revenue](), [saas](), [subscription]() ### AI overview A 2026 guide to implementing embedded payments in SaaS platforms. It compares inline checkout, modal overlays, and raw card APIs, with attention to PCI compliance, regulatory obligations, fraud, settlement risk, support, and implementation patterns. ### Source excerpt How modern SaaS platforms add embedded payments without becoming a payments company. Architectures, compliance, revenue share, and implementation patterns. ## Hybrid Billing Models: 7 Patterns Every SaaS Should Consider in 2026 DevFeed: [Hybrid Billing Models: 7 Patterns Every SaaS Should Consider in 2026]() Original publisher: [Read original article]() Author: Ayush Agarwal Published: 2026-04-29T00:00:00Z Content type: tutorial Language: en Sources: [Dodo Payments Blog]() Topics: [Software as a service](), [Software](), [Binance]() Tags: [2026](), [billing](), [examples](), [guide](), [patterns](), [pricing](), [saas](), [subscriptions](), [usage-based-billing](), [variable]() ### AI overview A guide to seven hybrid billing models for SaaS businesses, combining subscriptions with usage, seats, allowances, optional features, or one-off charges. It also covers the math behind the models and how to choose one based on a company's stage. ### Source excerpt Explore 7 hybrid billing models that combine subscriptions, usage, and seats. Real-world examples, proration math, and when each pattern fits your SaaS stage. ## Agent Memory Patterns DevFeed: [Agent Memory Patterns]() Original publisher: [Read original article]() Published: 2026-04-27T00:00:00Z Content type: tutorial Language: en Sources: [Tim Kellogg]() Topics: [systems](), [file](), [data](), [structure](), [Caching]() Tags: [agents](), [blocks](), [files](), [guide](), [how-to](), [memory](), [patterns](), [skills]() ### AI overview This guide explains three common forms of mutable memory for agents: files, memory blocks, and skills. It describes how files can store data and knowledge, while memory blocks provide learnable prompt content for behavior, preferences, identity, and character. ### Source excerpt A short HOW TO guide for agent memory systems. Especially the difference between blocks, files and skills. ## An Interactive Study of Publish/Subscribe Messaging for Event-Driven Backend Systems DevFeed: [An Interactive Study of Publish/Subscribe Messaging for Event-Driven Backend Systems]() Original publisher: [Read original article]() Author: Ivan Cernja Published: 2026-04-24T00:00:00Z Content type: tutorial Language: en Sources: [Encore Updates]() Topics: [Publish-subscribe pattern](), [Messaging](), [Back end](), [distributed-systems](), [event driven]() Tags: [decoupling](), [developers](), [distributed-systems](), [event-driven](), [events](), [flow](), [messaging](), [patterns](), [publisher](), [subscriber]() ### AI overview An interactive tutorial explains publish/subscribe messaging by contrasting it with direct service calls. It shows how topics decouple publishers from subscribers, support fan-out, and let subscribers process messages independently, including when downstream services are unavailable. ### Source excerpt An interactive study of publish/subscribe messaging ## Uncovering the Shape of Fraud with Cosmos Explorer: Visual Metaphors Behind Millions of Transactions DevFeed: [Uncovering the Shape of Fraud with Cosmos Explorer: Visual Metaphors Behind Millions of Transactions]() Original publisher: [Read original article]() Author: João Bernardo Narciso Published: 2026-04-07T17:24:51Z Content type: article Language: en Sources: [Feedzai]() Topics: [Data visualization](), [data]() Tags: [data](), [data-visualization](), [dataviz](), [decision-making](), [design](), [feedzai](), [fraud](), [fraud-detection](), [patterns](), [research]() ### AI overview Feedzai's Data Visualization Research team is developing Cosmos Explorer, an interface that uses universe-inspired visual metaphors to help analysts examine patterns, trends, outliers, and possible fraud across hundreds of millions or billions of transactions. The project explores how to preserve meaningful details at very large scale while supporting data analysts and data scientists. ### Source excerpt Uncovering the Shape of Fraud with Cosmos Explorer: Visual Metaphors Behind Millions of Transactions The Data Visualization Research team is developing Cosmos Explorer, an interface that leverages universe-related visual metaphors to convey information about the billions of transactions processed by Feedzai. Pedro Cruz, professor at Northeastern University, partnered with Feedzai to bring this idea to life by contributing with his creativity and expertise to solve this challenging visualization problem. https://medium.com/media/1b2ecfadd91d204640462f89fa6ff67f/href When we look out into the universe, we don't just see emptiness. We see an unimaginable scale: billions of galaxies, each containing billions of stars, each a point of light carrying its own story. No single observer can take it all in at once. Yet with the right instruments, patterns emerge: the structure of the cosmos itself becomes visible. In the digital realm, there is another universe just as vast and intricate. Every day, hundreds of millions of events flow through Feedzai's system which assesses them to protect consumers all over the world. Each one is a unique data point (e.g., a purchase, a login, a transfer). Individually, they don't tell us much. Together they form a living universe of behavior that represents the diversity in people's lives. But fraud lurks in everyday transactions, with criminals trying to hide their activities within the sheer volume of transactions. The question is: how can we represent those patterns meaningfully, the normal behaviors and the fraudulent behaviors, the trends and the outliers, to empower data analysts and data scientists in their decision-making processes? The biggest challenge is scale. No one can look at billions of events one by one. Aggregation helps, but it smooths over the details, which often encode the most interesting signals like the faint outlines of fraud or unusual clusters of activity. But what if we could see it all at once? Not just a summa [Next page]()