# Adoption Published articles for Adoption. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Presentation: Teaching Engineers, Trusting AI: How Education Enabled Autonomous Code Review DevFeed: [Presentation: Teaching Engineers, Trusting AI: How Education Enabled Autonomous Code Review]() Original publisher: [Read original article]() Author: Sarah Deitke Published: 2026-09-16T11:00:00Z Content type: article Language: en Sources: [InfoQ]() Topics: [Artificial Intelligence](), [Code review](), [Pull Request](), [Tooling](), [engineering-culture](), [observability]() Tags: [adoption](), [agile](), [ai](), [ai-ml-data-engineering](), [architecture-design](), [artificial-intelligence](), [automated](), [automation](), [autonomous](), [case-study](), [code-review](), [code-reviews](), [culture](), [culture-methods](), [developer](), [developer-experience](), [development](), [duolingo-ai-literacy-code-review](), [infoq](), [metrics](), [ml-data-engineering](), [observability](), [pairing](), [presentation](), [qcon-london-2026](), [qcon-software-development-conference](), [transcripts]() ### AI overview Sarah Deitke presents Duolingo's approach to cultural AI adoption through internal AI literacy workshops, observability dashboards, and safe AI guardrails. The presentation includes a case study on redesigning code review with an automated PR risk-assessment bot and reports faster delivery without increased defect rates. ### Source excerpt Sarah Deitke discusses how Duolingo drives cultural AI adoption beyond tooling access. She explains their internal AI literacy workshops and observability dashboards, then shares a case study on redesigning code review using an automated PR risk-assessment bot. Deitke demonstrates how pairing targeted developer education with safe AI guardrails speeds up delivery without increasing defect rates. By Sarah Deitke ## Beyond the data: what a Business Analyst does at Nubank DevFeed: [Beyond the data: what a Business Analyst does at Nubank]() Original publisher: [Read original article]() Author: Nubank Editorial Published: 2026-09-15T12:14:41Z Content type: opinion Language: en Sources: [Nubank]() Topics: [Data analysis](), [decision-making](), [context](), [Tech Lead]() Tags: [adoption](), [business-analyst](), [data](), [data-analysis](), [data-analytics](), [data-science-machine-learning](), [decision-making](), [manager](), [tech-lead](), [user-experience]() ### AI overview A first-person account of the Business Analyst role at Nubank, describing how BAs connect data analysis, context, and experimentation to product and business decisions. The article emphasizes clarifying trade-offs, investigating metrics, and collaborating with product and technical roles. ### Source excerpt Understand how Business Analysts at Nubank use data, context, and experimentation to guide product decisions. The post Beyond the data: what a Business Analyst does at Nubank appeared first on Building Nubank. ## How to accelerate agentic adoption DevFeed: [How to accelerate agentic adoption]() Original publisher: [Read original article]() Author: Simon Sear Published: 2026-08-06T08:37:00Z Content type: article Language: en Sources: [Scott Logic]() Topics: [Artificial Intelligence](), [Software Engineering](), [Automation]() Tags: [adoption](), [agentic-ai](), [agents](), [ai](), [ai-adoption-spectrum](), [apps](), [artificial-intelligence](), [automation](), [business-process](), [claude](), [delivery](), [engineering](), [safely](), [software-engineering]() ### AI overview This article presents a framework for accelerating agentic AI adoption by grouping initiatives into four categories: employee productivity tools, business transformation and workflow automation, AI features embedded in existing applications, and AI-enabled software engineering. It emphasizes clear direction, safe deployment, secure data access, education, and organizational change. ### Source excerpt Agentic AI is impacting every part of an organisation, from customer journeys to back office, supply chains and software engineering. Everything is changing at an accelerating speed, all at the same time, everywhere, all at once. In response, organisations don't want drawn-out analysis engagements with consultants or long strategy projects; they want action. They want to quickly and safely deploy agents to production and deliver value fast. ## From COBOL to Copilot: 30 Years of Data, BI, and AI with David Langer DevFeed: [From COBOL to Copilot: 30 Years of Data, BI, and AI with David Langer]() Original publisher: [Read original article]() Author: Daniel Beach Published: 2026-07-01T13:43:11Z Content type: article Language: en Sources: [Data Engineering Central]() Topics: [Artificial Intelligence](), [cobol](), [Data Science](), [mainframes](), [Software Engineering](), [AI Strategy](), [self-service](), [dashboards](), [jupyter notebooks]() Tags: [adoption](), [ai](), [business-intelligence](), [cobol](), [data-science](), [enterprise](), [jupyter-notebooks](), [mainframes](), [programming](), [self-service](), [semantic]() ### AI overview A podcast conversation with Dave Langer about nearly three decades spanning COBOL, enterprise architecture, business intelligence, analytics, data science, machine learning, and AI. It discusses persistent data-industry problems, self-service analytics, dimensional modeling, AI adoption, semantic layers, governance, and career advice for data professionals. ### Source excerpt What happens when someone who started programming on a Commodore 64 watches AI reshape the entire data industry? ## Five sessions and a hackathon: How we turned skeptics into agent builders DevFeed: [Five sessions and a hackathon: How we turned skeptics into agent builders]() Original publisher: [Read original article]() Author: Jay Garg Published: 2026-06-23T07:16:01Z Content type: opinion Language: en Sources: [Data Science at Microsoft]() Topics: [Artificial Intelligence](), [Learning]() Tags: [adoption](), [agents](), [ai](), [ai-agents](), [artificial-intelligence](), [building](), [engineering](), [engineering-management](), [hackathon](), [leadership](), [productivity](), [software-engineering]() ### AI overview This article presents lessons from a five-week AI enablement series and a two-hour hackathon for an infrastructure engineering team. It argues that low AI adoption was caused less by access or time than by a lack of practical mental models and opportunities to re-engage with AI, culminating in participants building working agents. ### Source excerpt A field-tested playbook for teaching an engineering team to actually work with AI -- not just have access to itIllustration of team members collaborating around a table with laptops and notebooks, discussing holographic charts of network nodes and hexagonal shapes representing AI agents being assembled. Image generated with Microsoft Designer. Three sessions in on teaching a class whose curriculum I'd designed around building with AI, I asked the room a question I'd been avoiding. What's the thing about AI you don't say out loud? The answers came slowly at first. "I don't know where to start." "I'm worried it'll replace me." "I tried it once and it gave me garbage, so I stopped." That last one came up the most. People had given AI a shot a couple of years back, when the first wave hit. The output didn't meet the bar they hold their own work to, so they quietly filed it under "fun toy, not a serious tool" and moved on. That was the moment the curriculum I'd written stopped being a curriculum and became a conversation. This is what we learned running a five-week, thirty-minute-a-week AI enablement series for a high-performing infrastructure engineering team -- and the two-hour hackathon at the end where everyone, including managers and PMs, shipped a working agent. The paradox we started with Picture a senior engineering team. Already shipping. Already busy. Tools available, leadership encouraging use, no policy blockers. And yet -- adoption was low and shallow. A handful of folks were using AI daily. The rest were using it for autocomplete and stopping there or not touching it at all. The same people who would happily spend a weekend learning a new distributed systems primitive weren't spending 15 minutes learning how to make an agent do their service support. That's a paradox worth sitting with for a moment. The barrier wasn't access. It wasn't even time, not really. It was that everybody had tried AI in its bad season, and nobody had been given a reason to try it agai ## Why Decision-Makers Adopt Kotlin Multiplatform DevFeed: [Why Decision-Makers Adopt Kotlin Multiplatform]() Original publisher: [Read original article]() Published: 2026-04-29T18:00:00Z Content type: article Language: en Sources: [Touchlab | Enterprise Mobile Innovation & Development]() Topics: [Kotlin Multiplatform](), [multiplatform](), [Kotlin](), [Maintainability](), [Developer experience](), [jetbrains]() Tags: [adoption](), [business-value](), [developer](), [jetbrains](), [kmp](), [kotlin](), [kotlin-multiplatform](), [maintainability]() ### AI overview The article explains that Kotlin Multiplatform adoption depends on decision-makers seeing measurable business value, not only on developer enthusiasm. It highlights reduced duplicated work across iOS and Android, native UI and workflows, potentially less code, faster release cycles, and improved maintainability. ### Source excerpt KMP adoption is not driven by developer enthusiasm alone. It moves forward when leaders see a clear case for measurable business value. ## Keycloak Community Survey Finds Demand for Official Helm Charts and More Robust GitOps Flows DevFeed: [Keycloak Community Survey Finds Demand for Official Helm Charts and More Robust GitOps Flows]() Original publisher: [Read original article]() Author: Keycloak Cloud Native team Published: 2026-03-18T00:00:00Z Content type: article Language: en Sources: [Keycloak Blog]() Topics: [Keycloak](), [Helm charts](), [argocd](), [GitOps](), [configuration]() Tags: [adoption](), [argocd](), [challenges](), [community](), [configuration](), [containerized](), [deployments](), [gitops](), [helm-charts](), [idm](), [kerberos](), [keycloak](), [ldap](), [openid-connect](), [results](), [saml](), [sso](), [survey]() ### AI overview Keycloak's survey of more than 360 respondents examined how the community deploys and manages Keycloak. Respondents expressed demand for official Helm Charts and more robust GitOps workflows, while adoption of the Keycloak Operator was reported as low because its setup is difficult to understand and its functionality is perceived as insufficient. ### Source excerpt Earlier this year, we conducted a survey to better understand how the community deploys and manages Keycloak in the real world. With over 360 respondents (!!), the results provide a better understanding of the community's needs and wishes. What we learned The survey results highlight a community that is deeply invested in the containerized ecosystem but is facing specific challenges in day-to-day operations. Key Findings: The Helm Charts: A significant portion of the community explicitly requested official Helm Charts, particularly for managing Keycloak installation and managing deployments via tools like ArgoCD. Operator: Adoption of the Keycloak Operator is not very high in the community, which is mainly caused by the fact that the Operator setup is hard to grasp, and do not provide the required functionality. GitOps & Configuration: There is a strong desire for more robust GitOps flows, especially when it comes to managing Keycloak resources like Realms and Clients. The whole Keycloak team is aware of these results and is discussing the next steps. Thank you to everyone who shared their feedback in this survey! More details: You can browse the detailed breakdown of the survey results in the slides below: ## AI Infrastructure Needs an Agentic Identity Framework -- We're Building It DevFeed: [AI Infrastructure Needs an Agentic Identity Framework -- We're Building It]() Original publisher: [Read original article]() Author: ev@goteleport.com (Ev Kontsevoy) Published: 2026-01-27T00:00:00Z Content type: opinion Language: en Sources: [Teleport]() Topics: [AI Infrastructure](), [Artificial Intelligence](), [AI Strategy]() Tags: [adoption](), [agentic-ai](), [agents](), [ai](), [ai-infrastructure](), [api-keys](), [cloud](), [cryptographic](), [databases](), [mcp](), [on-prem](), [orchestration](), [passwords](), [secrets](), [security](), [servers](), [services](), [standards](), [systems]() ### AI overview The article argues that autonomous AI agents operating across production cloud and on-premises infrastructure do not fit legacy identity models built for humans and simple service accounts. It presents the Teleport Agentic Identity Framework as a standards-driven approach to unified cryptographic identity for securing agentic AI at scale. ### Source excerpt Deploy AI agents securely with the Teleport Agentic Identity Framework, a standards driven approach to cryptographic, unified identity for agentic AI at scale. ## Why documentation is one of the most important surfaces for marketers DevFeed: [Why documentation is one of the most important surfaces for marketers]() Original publisher: [Read original article]() Author: Peri Langlois Published: 2026-01-14T00:00:00Z Content type: article Language: en Sources: [Mintlify Blog]() Topics: [Documentation](), [trust](), [implementation]() Tags: [adoption](), [best-practices](), [developer](), [developers](), [documentation](), [marketing]() ### AI overview This article argues that documentation is a core marketing surface for technical products. It explains how clear documentation can shape product perception, support high-intent evaluation, reduce adoption friction, and influence conversion and retention. ### Source excerpt A look at why documentation is one of the most influential surfaces in a technical product's funnel, how it shapes evaluation and adoption, and why marketers should treat it as a core part of their narrative. ## Context is Key: How HubSpot Scaled AI Adoption DevFeed: [Context is Key: How HubSpot Scaled AI Adoption]() Original publisher: [Read original article]() Author: blamattina@hubspot.com (Brian LaMattina) Published: 2025-09-24T14:41:07Z Content type: article Language: en Sources: [HubSpot]() Topics: [AI Strategy](), [AI-assisted coding](), [GitHub Copilot](), [hubspot](), [User experience (UX)](), [Architecture & Design]() Tags: [adoption](), [ai](), [ai-adoption](), [ai-coding](), [engineering](), [github-copilot](), [measurement](), [organizational](), [teams](), [training]() ### AI overview HubSpot describes how it scaled AI coding-tool adoption across its engineering organization, moving from cautious experimentation to broad adoption. The article focuses on organizational commitment, enablement, measurement, and lessons for teams scaling beyond proof-of-concept projects. ### Source excerpt This post is intended to be the first in a series about empowering product, UX, and engineering teams with AI. We're going to focus on how we've approached and scaled the use of AI in the context of writing code. ## Go's adoption and relevance for developers DevFeed: [Go's adoption and relevance for developers]() Original publisher: [Read original article]() Published: 2025-06-25T00:00:00Z Content type: opinion Language: en Sources: [Gaurav Sarma's Blog]() Topics: [Go Language](), [Docker](), [Kubernetes](), [Terraform]() Tags: [adoption](), [career](), [docker](), [future](), [go](), [kubernetes](), [language](), [tech](), [terraform]() ### AI overview The article argues that Go is a strong language choice for experienced developers because its adoption by companies and use in projects such as Kubernetes, Docker, and Terraform indicate continuing relevance. ### Source excerpt Original Tweet - https://x. com/sarmag77/status/1948641731966369923 Back in 2018-2019, Go experienced a spike massive adoption with popular projects like Kubernetes, Docker, Terraform... ## Temporal raises $146M Series C to power the future of durable applications DevFeed: [Temporal raises $146M Series C to power the future of durable applications]() Original publisher: [Read original article]() Author: Allanah Hughes Published: 2025-03-31T00:00:00Z Content type: release Language: en Sources: [Temporal Blog]() Topics: [Open Source](), [Cloud](), [Back end](), [Scalability](), [Artificial Intelligence]() Tags: [adoption](), [ai](), [announce](), [announcements](), [backend](), [community](), [developer](), [funding](), [open-source](), [series](), [valuation]() ### AI overview Temporal announces a $146 million Series C funding round led by Tiger Global at a $1.72 billion post-money valuation. The company cites growth in open-source developer adoption and Temporal Cloud customers, and describes plans to support durable execution and AI-focused applications. ### Source excerpt Temporal announces a $146M Series C funding round led by Tiger Global, reaching a $1.72B valuation. Learn how our thriving open-source community, rapid growth, and AI-focused vision are shaping the future of Durable Execution. ## Estimating Incremental Lift in Customer Value (Delta CV) using Synthetic Control DevFeed: [Estimating Incremental Lift in Customer Value (Delta CV) using Synthetic Control]() Original publisher: [Read original article]() Author: Mahshid Moha Published: 2025-02-14T20:41:49Z Content type: article Language: en Sources: [PayPal Technology]() Topics: [Data Science](), [Transactions](), [digital wallet]() Tags: [adoption](), [causal-inference](), [customer-value](), [customers](), [data-science](), [digital-wallet](), [margin](), [monetization](), [paypal](), [products](), [revenue](), [synthetic-control](), [transactions]() ### AI overview This article explains PayPal's Delta CV metric, which estimates the incremental profit margin generated during the first year after a customer adopts a product or completes an action. It distinguishes Delta CV from customer lifetime value and describes direct and halo effects on engagement, revenue, and margin. ### Source excerpt How we measure the impact of user actions and product adoptions at PayPal In today's competitive digital landscape, understanding user interactions with your products is essential for driving revenue and building lasting customer relationships. At PayPal, our Data Science teams use causal inference to evaluate the impact of key customer actions, such as adopting a new product or adding a credit card to their wallet, on engagement (measured by Transactions per Account, or TPA), revenue, and margin to help make data-driven strategic decisions. The direct profit from a product adoption or a user action on the app could be $0 if viewed in isolation. However, that does not necessarily mean that these events are not driving engagement and monetization across the PayPal ecosystem; they can change the user engagement with other PayPal products in such a way that the user starts generating more profit. To measure the overall impact of user actions or product adoption, we introduced Delta CV (delta in Customer Value), and we defined it as a customer's incremental profit margin in the first year after adoption of a new product or completing an action. For example, if the average Delta CV for adoption of Crypto is $20, we expect customers who adopt Crypto for the first time to bring an additional $20 in margin on average in the next 12 months after the adoption. We define Delta Revenue (or TPA) in the same manner except we calculate the incremental lift in revenue (or TPA) instead of profit margin. The concept of Delta CV is very different from customer life-time value (CLV) which estimates the total profit generated by a customer over the course of their relationship with PayPal. Delta CV gives us a wholistic view on how new adoptions affect the engagement and value of an existing PayPal user. Adoption of a new product or completing an action can increase the customer's value in a few ways: The product itself may be a profit generating (Direct effect). For example, paying with ## Tech predictions for 2025 DevFeed: [Tech predictions for 2025]() Original publisher: [Read original article]() Author: Mads Brodt Published: 2025-01-03T13:43:36Z Content type: opinion Language: en Sources: [Front-End Focus]() Topics: [Front end](), [Frameworks](), [React](), [Astro](), [Angular](), [Vite](), [Svelte](), [Vue.js](), [Artificial Intelligence](), [Job](), [Qwik]() Tags: [2025](), [adoption](), [ai](), [angular](), [astro](), [build-tool](), [community](), [components](), [developers](), [job](), [predictions](), [qwik](), [react](), [release](), [svelte](), [tailwind](), [tech](), [ui](), [vite](), [vue]() ### AI overview An opinion piece forecasting front-end development trends for 2025. It discusses the continued prominence of React-based production stacks, the potential standardization of React, NextJS, Tailwind, and ShadCN UI, the effects of framework competition and AI on developers, and expected growth for Astro and Vite/Rolldown. ### Source excerpt Happy New Year! ## AI Adoption in Software Engineering: Practical Limits and the Need for Human Review DevFeed: [AI Adoption in Software Engineering: Practical Limits and the Need for Human Review]() Original publisher: [Read original article]() Author: Peter Lawrey (noreply@blogger.com) Published: 2024-12-16T12:03:00Z Content type: article Language: en Sources: [Vanilla Java]() Topics: [Artificial Intelligence](), [AI Strategy](), [Software Engineering](), [AI-assisted coding]() Tags: [adoption](), [ai](), [ai-adoption](), [human-review](), [info](), [opinion](), [security](), [testing]() ### AI overview The article examines the practical challenges of adopting AI in software engineering, including inaccurate or outdated suggestions, weak contextual understanding, inconsistent style, and security or compliance risks. It argues that human review, domain expertise, testing, and validation remain necessary. ### Source excerpt Artificial Intelligence (AI) has long promised to transform software development. Yet, as many experienced engineers discover, initial enthusiasm often settles into a more subdued reality. This is the "Trough of Disillusionment" within the Gartner Hype Cycle--where inflated expectations give way to measured assessments. In this phase, teams confront the practical limitations of AI-driven tools, refine their strategies, and seek a balance between what AI can deliver and what human expertise must still provide. This article continues from AI on the Hype Cycle. We do these things not because they are easy, but because we thought they were going to be easy. -- Programmer's Credo Challenges of AI Adoption When integrating AI into software engineering workflows--be it code completion, architectural documentation, or performance tuning hints--teams quickly encounter stumbling blocks: Accuracy and Reliability: AI-generated content may contain inaccuracies, out-of-date references, or misunderstandings of domain-specific terms. Ensuring factual correctness requires careful human review and validation. AI outputs often present plausible suggestions that fail strict validation. For instance, an AI tool may confidently return a code snippet referencing APIs deprecated in Java 11 or misapply concurrency constructs from Java 21 libraries. Ensuring correctness demands human review, domain expertise, and rigorous testing Contextual Understanding: AI suggestions may misalign your codebase's patterns or standards without proper context. For example, given a legacy codebase optimised around ConcurrentSkipListMap, an AI may suggest using HashMap for "simplicity." Senior developers must provide guardrails, review outputs, and ensure that each recommendation aligns with existing architectural guidelines and performance expectations. Maintaining Consistency and Style: Projects often follow strict coding conventions and documentation formats. AI outputs might vary in style, indentation, or nami ## AI and Innovation Take Center Stage at trivago Tech Get Together 2024 DevFeed: [AI and Innovation Take Center Stage at trivago Tech Get Together 2024]() Original publisher: [Read original article]() Author: Ololade Ogeye Published: 2024-07-26T00:00:00Z Content type: article Language: en Sources: [Trivago]() Topics: [Artificial Intelligence](), [AI Strategy](), [Google](), [site-reliability-engineering]() Tags: [adoption](), [ai](), [artificial-intelligence](), [conference](), [engineering-culture](), [event](), [google](), [keynote](), [knowledge-sharing](), [talks](), [tech](), [trends](), [workshops]() ### AI overview trivago's 2024 Tech Get Together brought together more than 300 mostly tech employees for talks, keynotes, workshops, and discussions focused on artificial intelligence, innovation, and technology trends. The event covered AI adoption, ethical considerations, productivity claims, and other technical topics including Graph QL, Site Reliability Engineering, and Quality Assurance. ### Source excerpt In May this year, more than 300 of (mostly tech) trivago talents gathered for a full-day conference focused on knowledge sharing and valuable exchanges on the latest tech trends. Known as the tr... ## Supporter spotlight: Simon Butler on business adoption of Reproducible Builds DevFeed: [Supporter spotlight: Simon Butler on business adoption of Reproducible Builds]() Original publisher: [Read original article]() Published: 2023-08-01T11:00:00Z Content type: article Language: en Sources: [reproducible-builds.org]() Topics: [reproducible builds](), [Open Source](), [Software Engineering](), [data](), [Internet of things]() Tags: [adoption](), [builds](), [open-source](), [org](), [reproducible-builds](), [research](), [software-engineering](), [spotlight]() ### AI overview This supporter spotlight interviews Simon Butler, a software engineering researcher at the University of Skövde, about a collaborative project with six Swedish businesses that use open source software. The project's paper examines the businesses' knowledge of, experience with, and perceived value of Reproducible Builds. ### Source excerpt The Reproducible Builds project relies on several projects, supporters and sponsors for financial support, but they are also valued as ambassadors who spread the word about our project and the work that we do. This is the seventh instalment in a series featuring the projects, companies and individuals who support the Reproducible Builds project. We started this series by featuring the Civil Infrastructure Platform project, and followed this up with a post about the Ford Foundation as well as recent ones about ARDC, the Google Open Source Security Team (GOSST), Bootstrappable Builds, the F-Droid project and David A. Wheeler. Today, however, we will be talking with Simon Butler, an associate senior lecturer in the School of Informatics at the University of Skövde, where he undertakes research in software engineering that focuses on IoT and open source software, and contributes to the teaching of computer science to undergraduates. Chris: For those who have not heard of it before, can you tell us more about the School of Informatics at Skövde University? Simon: Certainly, but I may be a little long-winded. Skövde is a city in the area between the two large lakes in southern Sweden. The city is a busy place. Skövde is home to the regional hospital, some of Volvo's manufacturing facilities, two regiments of the Swedish defence force, a lot of businesses in the Swedish computer games industry, other tech companies and more. The University of Skövde is relatively small. Sweden's large land area and low population density mean that regional centres such as Skövde are important and local universities support businesses by training new staff and supporting innovation. The School of Informatics has two divisions. One focuses on teaching and researching computer games. The other division encompasses a wider range of teaching and research, including computer science, web development, computer security, network administration, data science and so on. Chris: You recently had a ope ## Overcoming KMM's Early Adopter Blues - Samuel Hill DevFeed: [Overcoming KMM's Early Adopter Blues - Samuel Hill]() Original publisher: [Read original article]() Published: 2022-10-04T20:12:22Z Content type: opinion Language: en Sources: [Touchlab | Enterprise Mobile Innovation & Development]() Topics: [kotlin multiplatform mobile](), [Kotlin Multiplatform](), [Mobile](), [configuration](), [Development](), [jetbrains](), [legacy](), [migration]() Tags: [adoption](), [challenges](), [configuration](), [development](), [featured](), [jetbrains](), [kmm](), [kotlin](), [kotlin-multiplatform](), [legacy](), [memory-management](), [migration](), [production]() ### AI overview The article reviews challenges faced by early Kotlin Multiplatform Mobile adopters, including difficult project setup, library integration, and the legacy memory model. It describes Touchlab's KaMPKit and Stately efforts and notes JetBrains' subsequent project and memory-model improvements, before introducing challenges for larger production teams. ### Source excerpt As we approach a Kotlin Multiplatform Mobile Beta, we take a look back at some of the challenges and missteps early KMM adoption had to overcome. ## The process of creating SDK tutorials for new users DevFeed: [The process of creating SDK tutorials for new users]() Original publisher: [Read original article]() Author: Cully Wakelin Published: 2020-11-12T04:00:00Z Content type: article Language: en Sources: [Temporal Blog]() Topics: [SDKs](), [Documentation](), [Users](), [Development](), [Docker](), [Go Language](), [Java]() Tags: [adoption](), [docker](), [documentation](), [go](), [java](), [sdk](), [temporal-concepts](), [tutorials](), [users]() ### AI overview The article explains how Temporal created SDK tutorials for new users before its V1 launch. It describes documentation gaps and inaccuracies inherited from a predecessor, core changes to Temporal's system and SDKs, and the challenge of explaining Temporal's new application-development model. It identifies the Go and Java SDKs as the main source of friction for new users and contrasts local Docker-based setup with live backend deployment. ### Source excerpt In early September it was clear that V1 of Temporal would be ready by the end of that month and we would be announcing our funding shortly thereafter. ## Why No-Code Software Is Growing Now DevFeed: [Why No-Code Software Is Growing Now]() Original publisher: [Read original article]() Published: 2019-09-04T09:12:28Z Content type: opinion Language: en Sources: [Blog by Michael Dubakov]() Topics: [No-code](), [Software](), [Programming]() Tags: [adoption](), [no-code](), [software](), [valuation]() ### AI overview The article considers why no-code software is gaining momentum now rather than 20 years earlier or later. It places the discussion in a historical account of commercial software and describes overlapping waves of software influence from 1960 to 2030, focusing mainly on productivity tools. ### Source excerpt It seems no-code software is booming. Fast adoption, huge rounds, insane valuation 🦄, happy customers. Why now? Why not 20 years ago? Why... ## Incentives and Multifactor Authentication DevFeed: [Incentives and Multifactor Authentication]() Original publisher: [Read original article]() Author: Adam Published: 2019-02-01T00:00:00Z Content type: opinion Language: en Sources: [Shostack & Friends Blog]() Topics: [MFA](), [Security](), [Users]() Tags: [adoption](), [authentication](), [security](), [users]() ### AI overview The article explores whether gamification and in-game rewards could improve adoption of multifactor authentication, noting that adoption rates remain poor and asking how effective such incentives might be. ### Source excerpt What if we gamified security? ## PostgreSQL Conference US, 2016 DevFeed: [PostgreSQL Conference US, 2016]() Original publisher: [Read original article]() Published: 2016-04-17T23:00:00Z Content type: article Language: en Sources: [Dimitri Fontaine]() Topics: [PostgreSQL](), [backups](), [data](), [Script]() Tags: [adoption](), [association](), [automated](), [awareness](), [backup](), [backups](), [community](), [conference](), [data](), [postgresql](), [recovery](), [resources](), [scripts](), [training]() ### AI overview This PostgreSQL Conference US 2016 presentation explains why backups should be tested and why automated recovery is more useful than relying only on backup scripts. Its focus is the ability to recover data when a serious failure occurs. ### Source excerpt You'd better have tested backups explains why you must implement an automated recovery solution rather than merely backup scripts. Noboday cares about backups, as such. What everybody cares about is the actual ability to recover data in case when the worst happens... ## PGConf NYC 2014 DevFeed: [PGConf NYC 2014]() Original publisher: [Read original article]() Published: 2014-04-02T22:00:00Z Content type: article Language: en Sources: [Dimitri Fontaine]() Topics: [PostgreSQL](), [SQL](), [Databases](), [Structured-data]() Tags: [adoption](), [awareness](), [community](), [conference](), [developers](), [env-file-security](), [postgresql](), [resources](), [sql](), [training](), [window-functions]() ### AI overview This conference page describes a PostgreSQL for Developers talk covering a SQL use case, SQL window functions, and advanced PostgreSQL features such as ranges. ### Source excerpt The PostgreSQL for Developers talk begins with a simple use case for developers to solve entirely in SQL. Discover SQL window functions if you don't know them already, and then see about advanced PostgreSQL stuff such as ranges.