# Product Development Published articles for Product Development. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Figma launches data residency in Japan DevFeed: [Figma launches data residency in Japan]() Original publisher: [Read original article]() Author: Figma Published: 2026-09-11T15:25:00Z Content type: release Language: en Sources: [Figma Blog]() Topics: [Figma](), [data]() Tags: [availability](), [collaboration](), [data](), [data-management](), [enterprise](), [figma](), [financial-services](), [healthcare](), [hosting](), [japan](), [launch](), [product-development](), [public-sector](), [security]() ### AI overview Figma launches data residency in Japan, allowing enterprise customers to host Figma file data domestically. The announcement highlights data protection, security, and data management needs across sectors including the public sector, healthcare, and financial services. ### Source excerpt Figma strengthens data protection and security for enterprise customers with local data hosting. ## Beyond Autonomous Teams in Software Product Development DevFeed: [Beyond Autonomous Teams in Software Product Development]() Original publisher: [Read original article]() Author: Ben Linders Published: 2026-09-10T10:29:00Z Content type: news Language: en Sources: [InfoQ]() Topics: [Self-organizing Team](), [Software Engineering]() Tags: [agile-conferences](), [autonomous](), [autonomous-software-teams](), [business-value](), [complex-systems](), [complexity](), [conference](), [culture-methods](), [delivering-value](), [management](), [networking](), [news](), [product-development](), [scrum](), [self-organizing-team](), [teamwork]() ### AI overview A conference talk argues for moving from product-focused autonomous teams to value centers, with organizational design shaped by value and its dependencies. It frames agency and coherence as a trade-off and uses the Viable Systems Model to suggest five questions for teams. ### Source excerpt Autonomous teams are an article of faith in modern software development. The shape of our value determines what we can do; it determines our trade-offs between agency and coherence. We all have a purpose, and we all have agency to do something, hence the suggestion is moving from product focus to value center thinking. By Ben Linders ## When to Reject Change in Software Development: Shannon's Demon and Volatility DevFeed: [When to Reject Change in Software Development: Shannon's Demon and Volatility]() Original publisher: [Read original article]() Author: Kent Beck Published: 2026-09-02T13:29:36Z Content type: opinion Language: en Sources: [Software Design: Tidy First?]() Topics: [Development](), [Software Engineering](), [Programming]() Tags: [change](), [product-development](), [software-development]() ### AI overview The article uses Shannon's Demon--a strategy that repeatedly rebalances between a stable asset and a volatile one--to explain how software development can create value by managing volatility. It discusses the exception to always embracing change and applies the metaphor to software product development. ### Source excerpt Volatility, Shannon's Demon, & Free Money ## How do you actually measure the impact of AI on design? DevFeed: [How do you actually measure the impact of AI on design?]() Original publisher: [Read original article]() Author: Shane Johnston Published: 2026-08-28T19:30:00Z Content type: article Language: en Sources: [Figma Blog]() Topics: [Artificial Intelligence](), [Figma]() Tags: [ai](), [ai-impact](), [ai-tools](), [collaboration](), [design](), [developers](), [product-development](), [productivity](), [research](), [workflows]() ### AI overview Figma describes a multi-year study measuring how AI affects design and product development. Its AI impact index compares expectations with reported experience across collaboration, personal productivity, projects, and products, using survey responses from designers, developers, and product managers. ### Source excerpt In 2024, we kicked off a study to understand how AI would reshape design and product development. Now, AI is helping us run it. ## From Concept to Restaurant Pilot in Under a Year DevFeed: [From Concept to Restaurant Pilot in Under a Year]() Original publisher: [Read original article]() Author: Global Technology Published: 2026-08-25T17:13:54Z Content type: article Language: en Sources: [McDonald's Technical Blog - Medium]() Topics: [User experience (UX)](), [User interface design](), [Usability](), [Front end](), [Testing]() Tags: [collaboration](), [digital-transformation](), [frontend](), [innovation](), [product-development](), [prototyping](), [testing](), [usability](), [user-research](), [ux](), [ux-design]() ### AI overview The article describes how McDonald's used user research, rapid prototyping, cross-functional collaboration, and real-world testing to develop Sesame UX and move it from concept to a live restaurant pilot in under a year. The work addressed crew needs, operational complexity, and business priorities for the company's global POS system. ### Source excerpt How McDonald's combined user research, rapid prototyping, and cross-functional collaboration to bring Sesame UX from idea to reality. by: Ryan Bush, Sr Manager, UX Design & Kartik Patel, Software Engineer II Quick Bytes Bringing a new Point-of-Sale (POS) experience to life at McDonald's scale required balancing crew needs, operational complexity, and business priorities Research, prototyping, and real-world testing showed a new approach could improve speed, accuracy, and usability for crew members Sesame UX moved from concept to a live restaurant pilot in under a year, demonstrating measurable improvements and a new approach to innovation at scale Creating meaningful change at the scale of McDonald's means navigating deeply entrenched operational practices, specialized technology, and market-specific requirements. Any new idea must not only solve a meaningful problem but also demonstrate value that resonates across a broad and diverse set of stakeholders. Driving transformative innovation in an organization at this scale requires building shared alignment around the root problem, the opportunity it presents, and the value a proposed solution can create. It also requires using cost-efficient methods to prove to stakeholders the undeniable value of the change, along with a team of determined individuals who can get creative and who aren't deterred by obstacles or setbacks. McDonald's global Point-of-Sale (POS) system, Sesame, is actively used by over one million crew members worldwide. Markets, Owner/Operators, and managers have invested significant time and resources in training crew members to use the system, and thousands of crew members have mastered the POS experience. With this level of scale and investment, how could an organization evolve the frontend POS experience while building on the strengths of an already proven platform? How could the benefits outweigh the cost of the change? Building on a foundation used by millions The Sesame platform has evolved over ## Sightlines issue no.2: What leaders need to know about building agents DevFeed: [Sightlines issue no.2: What leaders need to know about building agents]() Original publisher: [Read original article]() Author: Figma Published: 2026-08-20T19:36:00Z Content type: article Language: en Sources: [Figma Blog]() Topics: [Artificial Intelligence](), [Architecture & Design](), [Security](), [atlassian]() Tags: [agentic](), [agents](), [ai](), [autonomous](), [communication](), [design](), [leadership](), [product-development](), [security](), [testing](), [workflows]() ### AI overview This issue of Figma's Sightlines newsletter shares guidance from design, product, and engineering leaders on building agentic products. It focuses on trust, security, transparency, governance, testing, responsible autonomy, and keeping people accountable as agents handle more workflows. ### Source excerpt Agentic products pose a unique set of design challenges. How do you build a tool that's powerful and knowledgeable enough to be a true partner, while keeping trust and security at the core? Here's what we've heard from leaders. ## How RingCentral builds AI-native work from engineering to ops DevFeed: [How RingCentral builds AI-native work from engineering to ops]() Original publisher: [Read original article]() Published: 2026-08-12T00:00:00Z Content type: article Language: en Sources: [OpenAI News]() Topics: [Artificial Intelligence](), [ChatGPT](), [codex](), [coding](), [CI/CD]() Tags: [agentic](), [ai](), [chatgpt](), [ci-cd](), [codex](), [coding](), [engineering](), [operations](), [product-development](), [workflow]() ### AI overview RingCentral describes how it uses ChatGPT Work and Codex to promote AI-native development across engineering and operations. An AI-Native Challenge gave employees, including nontechnical staff and executives, the freedom to build end-to-end projects spanning planning, implementation, testing, documentation, CI/CD, and iteration. The company presents this approach as a way to accelerate development while keeping humans responsible for requirements, context, architecture, testing, and verification. ### Source excerpt See how RingCentral uses ChatGPT Work and Codex to accelerate AI product development and centralize operational intelligence across engineering and operations. ## +14% activated users for AppSignal: designing a new homepage in code DevFeed: [+14% activated users for AppSignal: designing a new homepage in code]() Original publisher: [Read original article]() Author: Travis Turner (richardturner@evilmartians.com) Published: 2026-08-04T00:00:00Z Content type: article Language: en Sources: [Evil Martians]() Topics: [Application Performance Management (APM)](), [error tracking](), [Tooling](), [Code](), [Figma](), [Artificial Intelligence]() Tags: [agentic-development](), [ai](), [apm](), [case-study](), [code](), [design](), [design-for-devtools](), [developer-marketing](), [developer-products](), [engineering](), [error-tracking](), [figma](), [product-development](), [tooling]() ### AI overview Evil Martians designed and shipped a new AppSignal homepage primarily in code, combining code, Figma, and AI during exploration and prototyping. The selected direction was validated through an A/B test that showed a 14% increase in activated users over the previous site. ### Source excerpt We designed and shipped a fresh AppSignal homepage in code, then validated it in an A/B test with a 14% lift in activated users. ## Code, craft, and the making of nested folders DevFeed: [Code, craft, and the making of nested folders]() Original publisher: [Read original article]() Author: Ethan Adams; Cai Charniga; Sachi Shah Published: 2026-08-03T12:00:00Z Content type: article Language: en Sources: [Figma Blog]() Topics: [Figma](), [Code](), [pull-requests](), [Artificial Intelligence]() Tags: [code](), [collaboration](), [design](), [development](), [feature](), [figma](), [product-development](), [pull-requests]() ### AI overview Figma describes how its teams changed their product development process while building nested folders. The approach emphasized using code and pull requests to test ideas earlier, flexible responsibilities, and closer collaboration across product, design, and engineering as the project's requirements and context evolved. ### Source excerpt From treating code as a proposal to turning handoffs into conversations, here's how we shaped a new way of working to ship nested folders. ## How TRM Built a Custom Internal Harness for Product and Engineering Work DevFeed: [How TRM Built a Custom Internal Harness for Product and Engineering Work]() Original publisher: [Read original article]() Author: Luca Rossi Published: 2026-07-29T07:31:16Z Content type: article Language: en Sources: [Refactoring]() Topics: [Development](), [Artificial Intelligence]() Tags: [case-study](), [development](), [engineering](), [harness](), [product-development]() ### AI overview This case study describes how TRM built a custom internal harness for product and engineering work. ### Source excerpt How TRM built a custom internal harness for product and engineering work. ## What is the Double Diamond design process? DevFeed: [What is the Double Diamond design process?]() Original publisher: [Read original article]() Author: Victory Brown Published: 2026-07-28T17:30:37Z Content type: article Language: en Sources: [LogRocket Blog]() Topics: [User experience (UX)](), [data]() Tags: [analytics](), [article](), [blog](), [data](), [design](), [design-trends](), [framework](), [product-development](), [research](), [uncategorized](), [ux](), [ux-design]() ### AI overview The article explains the Double Diamond, a UX framework that guides teams from problem discovery to solution delivery through alternating divergent and convergent phases. It covers the Discover and Define stages, user research, data analysis, prototyping, testing, and the framework's relationship to design thinking and modern product development. ### Source excerpt The Double Diamond design process helps UX teams balance exploration with decision-making, guiding projects from problem discovery to solution delivery. Learn how each phase works, which tools to use, when the reverse Double Diamond makes sense, and why the framework remains relevant for modern product development. The post What is the Double Diamond design process? appeared first on LogRocket Blog. ## Product Quality: A Shared Commitment to Craft in the wake of AI DevFeed: [Product Quality: A Shared Commitment to Craft in the wake of AI]() Original publisher: [Read original article]() Author: kmena Published: 2026-07-17T19:45:23Z Content type: opinion Language: en Sources: [Slack Design]() Topics: [Slack](), [Usability](), [User interface design](), [Software]() Tags: [design](), [engineering](), [product-development](), [slack](), [software](), [uncategorized](), [usability]() ### AI overview Slack argues that product quality is a shared commitment to craft: making software polished, responsive, attractive, simple, powerful, and useful. The article frames utility, usability, and feel as connected qualities, and explains why organizational incentives, subjective standards, and competing craft languages make consistent quality difficult to achieve. ### Source excerpt Slack is a loved product, and that's not an accident In 2013, Stewart Butterfield wrote what amounted to a promise: "You're buying a commitment to the craft of making high quality software: polished, responsive, attractive, simple, powerful, and, above all, useful. You're buying attention to detail that saves you time and makes your life just [...] The post Product Quality: A Shared Commitment to Craft in the wake of AI appeared first on Slack Design. ## Sightlines issue no.1: Insights from Config DevFeed: [Sightlines issue no.1: Insights from Config]() Original publisher: [Read original article]() Author: Figma Published: 2026-07-16T16:37:00Z Content type: article Language: en Sources: [Figma Blog]() Topics: [Artificial Intelligence](), [OpenAI]() Tags: [ai](), [beginner](), [collaboration](), [design](), [development](), [engineering](), [experiment](), [insights](), [leadership](), [product-development](), [prototyping]() ### AI overview Sightlines issue no.1 shares lessons from Config's Leadership Collective about leading product, design, and engineering teams in the age of AI. It emphasizes enduring leadership fundamentals, experimentation, collaboration, human judgment, and maintaining quality as teams adopt AI. ### Source excerpt The conversations we're still replaying from Config's Leadership Collective. ## Why Product Development Bottlenecks Are Distributed and Context-Dependent DevFeed: [Why Product Development Bottlenecks Are Distributed and Context-Dependent]() Original publisher: [Read original article]() Author: John Cutler Published: 2026-06-26T06:13:28Z Content type: opinion Language: en Sources: [The Beautiful Mess]() Topics: [Development](), [systems](), [Loop Engineering](), [Large Language Model]() Tags: [ai](), [complexity](), [development](), [engineering](), [llm](), [product-development]() ### AI overview The article argues that product development is not a simple linear system with one bottleneck. It describes the work as a combination of sensing, deciding, learning, aligning, making, changing, supporting, and adapting, and says apparent bottlenecks are often temporary symptoms of interacting constraints. It acknowledges that LLMs can accelerate work in situations with abundant data, repetition, and situational awareness, but says this does not represent most cases. ### Source excerpt My goodness. ## 2 Martians, greenfield to MVP in 4 weeks: agentic coding on Rails DevFeed: [2 Martians, greenfield to MVP in 4 weeks: agentic coding on Rails]() Original publisher: [Read original article]() Author: Travis Turner (richardturner@evilmartians.com) Published: 2026-06-09T00:00:00Z Content type: article Language: en Sources: [Evil Martians]() Topics: [agentic-coding](), [Rails](), [coding](), [React](), [Storybook](), [Open Source](), [stripe]() Tags: [agentic](), [agentic-coding](), [ai](), [app](), [authentication](), [building](), [case-study](), [coding](), [designer](), [developer-marketing](), [developer-products](), [google-analytics](), [onboarding](), [open-source](), [payments](), [platform](), [product](), [product-development](), [production](), [prototype](), [rails](), [react](), [skills](), [storybook]() ### AI overview A designer and an engineer describe how they shipped Thicket, a production MVP, in four weeks using agentic coding with Rails, Inertia, React, and Storybook. They discuss their workflow, project-specific skills, user validation, and the resulting open-source practices. ### Source excerpt A designer and an engineer shipped a production MVP in four weeks on Rails + Inertia. In this post, we share our agentic coding stack, the skills we built, and why it clicked. ## Why Agile Designers Fear Shipping Imperfect Work DevFeed: [Why Agile Designers Fear Shipping Imperfect Work]() Original publisher: [Read original article]() Author: Paivi Salminen Published: 2026-05-28T03:39:21Z Content type: opinion Language: en Sources: [UX Magazine]() Topics: [Agile](), [User experience (UX)](), [Development]() Tags: [agile](), [development](), [product-development](), [teams](), [ux]() ### AI overview This commentary examines why designers may resist releasing early versions of features in agile development. It argues that minimum viable products must be usable, because confusing or broken releases produce unreliable feedback, and that iteration is essential for early releases to provide value. ### Source excerpt One insight from Laura Klein that stuck with me is that in a minimum viable product, teams often forget the word "viable." A thing that surprised me while studying agile product development is how psychological it is. We often talk about agile as if it were purely a process. There are sprints, backlogs, standups, and The post The Part of Agile Designers Fear the Most: Imperfect Work appeared first on UX Magazine. ## AI is becoming a first hire for small businesses DevFeed: [AI is becoming a first hire for small businesses]() Original publisher: [Read original article]() Published: 2026-05-25T00:00:00Z Content type: article Language: en Sources: [OpenAI News]() Topics: [Artificial Intelligence](), [ChatGPT]() Tags: [ai](), [business](), [chatgpt](), [customer-service](), [global-affairs](), [product-development](), [research]() ### AI overview OpenAI reports that at least four million people in the United States used ChatGPT during March 2026 to plan, start, run, or grow small businesses. The article describes how entrepreneurs use it for tasks such as branding, product development, business validation, marketing, pricing, customer communication, and administrative work. ### Source excerpt New research shows how 4 million Americans use ChatGPT to start, run, and grow small businesses, lowering the cost of entrepreneurship with AI. ## Inside the New ESP-Brookesia v0.7 Architecture DevFeed: [Inside the New ESP-Brookesia v0.7 Architecture]() Original publisher: [Read original article]() Author: John Lee Published: 2026-05-04T00:00:00Z Content type: article Language: en Sources: [Blog on Developer Portal]() Topics: [Framework](), [AI Agent](), [Development](), [MCP](), [Large Language Model]() Tags: [ai-agent](), [aiot](), [architecture](), [blog](), [components](), [esp-brookesia](), [framework](), [hmi](), [llms](), [mcp](), [modules](), [orchestration](), [product-development]() ### AI overview This article introduces the redesigned ESP-Brookesia v0.7 architecture for building AIoT interaction products. It describes the framework's layered structure, including hardware abstraction, device services, AI Agent and AI Expression modules, and application layers. It also explains how Function Calling and MCP connect LLMs with device-side functionality. ### Source excerpt This article introduces the redesigned ESP-Brookesia v0.7 architecture, from Utils, HAL, and General Service to AI Agent and AI Expression. It explains how these modules fit together and highlights two examples that help you quickly understand how to build AIoT interaction products on top of the framework. ## How to Monetize Figma Plugins in 2026 DevFeed: [How to Monetize Figma Plugins in 2026]() Original publisher: [Read original article]() Author: Ayush Agarwal Published: 2026-03-29T00:00:00Z Content type: tutorial Language: en Sources: [Dodo Payments Blog]() Topics: [Figma plugins](), [Figma plugin](), [Figma Community](), [Development]() Tags: [2026](), [developers](), [figma](), [figma-plugins](), [how-to](), [license-keys](), [merchant-of-record](), [monetization](), [payment](), [payments](), [plugins](), [product-development](), [revenue](), [scale](), [validation](), [visibility]() ### AI overview A guide to monetizing Figma plugins through a freemium model, external payment infrastructure, Merchant of Record services, and secure license key management. ### Source excerpt Learn how to build a sustainable revenue stream from your Figma plugins using a freemium model and secure license key management. ## Scaling Experimentation Quality at Booking.com DevFeed: [Scaling Experimentation Quality at Booking.com]() Original publisher: [Read original article]() Author: Edgar Cano Published: 2026-03-24T11:44:21Z Content type: article Language: en Sources: [Booking.com Data Science]() Topics: [experiments](), [Development](), [decision-making](), [human review]() Tags: [best-practices](), [data-science](), [decision-making](), [experimentation](), [featured](), [human-review](), [metrics](), [product-development](), [quality]() ### AI overview Booking.com describes how it addressed declining experimentation quality as experiment volume grew. The article discusses arbitrary test durations, significance-seeking, and the trade-offs between enforcing standards and educating product teams. ### Source excerpt Authors: Edgar Cano, Daisy Duursma, Nils Skotara, Melanie Mueller Figure 1. Three-pillar components of Booking.com's Experimentation Quality Experimentation is at the core of product development in Booking.com, powered by our in-house platform, "ET" (Experiment Tool). At any given moment, we run approximately 1,000 parallel experiments to evaluate product changes. These experiments or A/B tests allow teams to directly compare a new version of the website against the existing one, validating hypotheses about how specific changes impact important metrics. In our organization, these pitfalls became more evident as our experiment volume grew. We observed that experimenters might set an arbitrary "two-week" duration without thinking about sufficient power, or extend a test until results "became significant" or "trended positive." Knowing that this lack of consistency leads to flawed decision-making we dedicated significant effort to increasing the quality of our experimentation process, making Experimentation Quality a key KPI for our program. However, identifying the problem was only the start; the greater challenge is how to implement these standards across a large organization. Enforcement vs. Education When deciding how to scale quality, we faced a fundamental choice: Do we enforce strict controls or we rely on education. Ultimately, we left it to product teams to decide how to conduct their experiments. This choice entailed several trade-offs: Enforcement: Ensures comparability, consistency, and reliability. However, it comes at the cost of flexibility. There is a risk that people follow "rules" blindly without understanding the rationale. Education: Aims for a culture where experimenters understand the why behind best practices. This leads to better buy-in, allows teams to challenge methods, and highlights individual responsibility. It also prevents bottlenecking. If enforcement requires human review, it slows down development. However, education requires a massive ## Issue no.15: The state of design DevFeed: [Issue no.15: The state of design]() Original publisher: [Read original article]() Author: Figma Published: 2026-03-16T21:35:00Z Content type: article Language: en Sources: [Figma Blog]() Topics: [Artificial Intelligence](), [User experience (UX)](), [Figma](), [Canvas](), [Claude](), [Claude Code](), [Model Context Protocol]() Tags: [ai](), [claude](), [claude-code](), [figma](), [mcp](), [product-development](), [prompting](), [quality](), [research](), [workflows]() ### AI overview Figma's State of the Designer research examines how AI is affecting design work, hiring, and the skills designers need. The article reports that 91% of respondents say AI tools help improve their work, while 82% of hiring managers say demand for designers has stayed stable or increased. ### Source excerpt AI-powered tools and workflows are fundamentally changing the way we design. ## 5 design skills to sharpen in the AI era DevFeed: [5 design skills to sharpen in the AI era]() Original publisher: [Read original article]() Author: Madeline Stafford Published: 2026-03-10T19:15:37.885000Z Content type: article Language: en Sources: [Figma Blog]() Topics: [Artificial Intelligence](), [Figma]() Tags: [2026](), [ai](), [collaboration](), [design](), [figma](), [product-development](), [prompt](), [prototype]() ### AI overview Figma discusses five design skills that are becoming more important in the AI era, including AI fluency, prototyping, structured prompting, and cross-functional collaboration. The article draws on findings from the State of the Designer 2026 report. ### Source excerpt Our latest research highlights the skills designers need to meet the moment. ## OpenAI Codex and Figma launch seamless code-to-design experience DevFeed: [OpenAI Codex and Figma launch seamless code-to-design experience]() Original publisher: [Read original article]() Published: 2026-02-26T06:00:00Z Content type: news Language: en Sources: [OpenAI News]() Topics: [Figma](), [codex](), [MCP Server](), [OpenAI](), [MCP](), [Code](), [ChatGPT]() Tags: [agents](), [ai](), [ai-agents](), [codex](), [collaboration](), [creativity](), [figma](), [global-affairs](), [integration](), [mcp](), [mcp-server](), [openai](), [product-development](), [workflow]() ### AI overview OpenAI and Figma are expanding their partnership with a Codex-to-Figma integration that enables teams to move between code and editable Figma designs. The Figma MCP Server connects Codex with Figma Design, Figma Make, and FigJam, supporting a roundtrip workflow for implementing designs, exploring ideas, and iterating on digital products. ### Source excerpt OpenAI and Figma launch a new Codex integration that connects code and design, enabling teams to move between implementation and the Figma canvas to iterate and ship faster. ## Atoms is Out, a Multi-Agent AI Team that Builds Full-Stack Apps for You DevFeed: [Atoms is Out, a Multi-Agent AI Team that Builds Full-Stack Apps for You]() Original publisher: [Read original article]() Author: Carlota Soto Published: 2026-01-13T17:48:26Z Content type: article Language: en Sources: [Blog -- Neon Docs]() Topics: [Artificial Intelligence](), [App](), [Multitenancy](), [Per-user Database](), [Databases](), [Back end](), [Front end](), [Automation](), [Deployment](), [Software as a service](), [Iris](), [AI research agents]() Tags: [agent](), [ai](), [ai-agents](), [apps](), [architecture](), [automation](), [backend](), [case-studies](), [code](), [data](), [database](), [deployment](), [infrastructure](), [product](), [product-development](), [saas](), [scale](), [software]() ### AI overview Atoms is a multi-agent AI platform that builds full-stack software products from a user prompt. Specialized agents handle research, product requirements, architecture, engineering, data analysis, testing, and deployment. Its multi-tenant architecture gives each user app a dedicated Postgres database provisioned dynamically through Neon. ### Source excerpt "We chose Neon as our backend because of scale, cost-efficiency, and superior developer and user experience, all critical for Atoms' multi-tenant architecture. Each user app needs its own dedicated database, and Neon lets us spin up Postgres instances dynamically while only payin... [Next page]()