# Insights Published articles for Insights. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Atomic macOS (AMOS) Stealer Activity DevFeed: [Atomic macOS (AMOS) Stealer Activity]() Original publisher: [Read original article]() Author: Bradley Duncan Published: 2026-09-16T10:00:06Z Content type: article Language: en Sources: [Unit 42]() Topics: [Malware](), [Aeternum](), [macOS](), [sensitive data](), [Zsh](), [ClickFix](), [cURL]() Tags: [ads](), [clickfix](), [credentials](), [cryptocurrency](), [curl](), [general](), [insights](), [macos](), [malware](), [sensitive-data](), [threat-intelligence](), [unit-42](), [zsh]() ### AI overview This article analyzes a laboratory-generated Atomic macOS (AMOS) stealer infection observed on Aug. 5, 2026. It describes a deceptive macOS toolkit installation page that led users to paste a command into Terminal, retrieving a Zsh script containing an encoded compressed payload and a follow-up script designed to run a Mach-O binary. AMOS targets macOS and can exfiltrate system information, login credentials, and sensitive data from applications including browsers and cryptocurrency wallets. ### Source excerpt Modern macOS malware uses deceptive setup guides to steal credentials and sensitive user data. Learn how to identify and block these threats. The post Atomic macOS (AMOS) Stealer Activity appeared first on Unit 42. ## Honoring #IconsOfQuality: Keith Klain DevFeed: [Honoring #IconsOfQuality: Keith Klain]() Original publisher: [Read original article]() Author: Rajrupa Roychowdhury Published: 2026-09-10T11:56:43Z Content type: article Language: en Sources: [BrowserStack Blog]() Topics: [Testing](), [Artificial Intelligence](), [systems](), [Software]() Tags: [ai](), [community](), [enterprise](), [financial-services](), [icons-of-quality](), [infrastructure](), [insights](), [integration](), [keynote](), [leadership](), [operational](), [quality-engineering](), [systems](), [technology](), [testing](), [thought-leadership](), [uk]() ### AI overview BrowserStack profiles Keith Klain as an influential figure in software quality and testing. The article highlights his work in AI assurance, quality engineering, operational risk, systems thinking, and strategic enterprise transformation, along with his contributions to the testing community and thought leadership. ### Source excerpt To celebrate the relentless passion and invaluable contributions of leaders in software quality, BrowserStack is proud to honour Icons of Quality. ## Accounts & Self-Service UX Research Update and Expansion: 3 Key Takeaways DevFeed: [Accounts & Self-Service UX Research Update and Expansion: 3 Key Takeaways]() Original publisher: [Read original article]() Author: Sally Collins Published: 2026-09-09T08:05:00Z Content type: article Language: en Sources: [Baymard Institute]() Topics: [User interface design](), [Authentication](), [passwords](), [MFA](), [web applications](), [password reset]() Tags: [article](), [authentication](), [insights](), [mobile](), [passwords](), [research](), [security](), [usability](), [web]() ### AI overview Baymard's updated Accounts & Self-Service UX research examines how ecommerce account experiences have changed since 2018. Based on more than 4,000 hours of research, 1,400 usability issues, and a survey of over 1,000 US adults, the article highlights account security and sign-in changes, including passkeys, one-time passcodes, and two-factor authentication. ### Source excerpt (Note: Unfortunately, e-mail and RSS don't support advanced layouts and features. If the graphics in this article look strange, you may want to read the article in your web browser.) Key Takeaways Baymard has new research on Accounts & Self-Service UX The research uncovered UX issues and identified UX solutions specific to the Accounts area of ecommerce sites Much has changed in account security and sign in, order management and tracking, and order returns since our last large-scale research in 2018 Key Stats 4,000+ new hours of Accounts & Self-Service research 1,400+ usability issues observed in testing 1,000+ US adults surveyed as part of our complementary quantitative research Today at Baymard, we're announcing the launch of our new updated and expanded Accounts & Self-Service UX research. We first investigated UX issues in Accounts & Self-Service in 2018. In this update, we've retested all the UX issues observed in 2018, verified all our UX solutions, wrote new guidelines based on newly observed Accounts & Self-Service UX issues, and rewritten the bulk of our verified guidelines for improved usefulness and clarity. This work has resulted in 57 new and updated Accounts & Self-Service guidelines based on 1,400+ usability issues observed during testing. The 57 guidelines are a key foundation for ensuring a high-performing Accounts and Self-Service UX. Additionally, we've included mobile test observations for the first time. As a result, this study represents our most comprehensive findings on Accounts & Self Service ecommerce UX. In this article, we'll highlight 3 high-level insights from our new Accounts & Self-Service UX research findings. New Insights for Accounts & Self-Service UX Compared to 2018, the landscape of Accounts & Self-Service has changed dramatically, particularly in 3 areas. 1) Account Security and Sign In At J.Crew, participants updating their passwords were provided no guidance regarding the site's password requirements, and they were unable to ## A first look at Campaigns DevFeed: [A first look at Campaigns]() Original publisher: [Read original article]() Author: Webflow Team Published: 2026-09-03T00:00:00Z Content type: article Language: en Sources: [Webflow Blog]() Topics: [webflow](), [Web](), [Forms](), [Artificial Intelligence](), [Google]() Tags: [agents](), [ai](), [conversion](), [forms](), [google](), [inside-webflow](), [insights](), [launch](), [marketing](), [product](), [scale](), [tool](), [webflow](), [workflow]() ### AI overview Webflow previews Campaigns, a standalone tool for demand gen and performance marketers that brings ideation, campaign building, launch, and iteration into one workflow. It supports on-brand landing pages and ad creative, form connections, Google Ads publishing, traffic and conversion tracking, and AI-driven improvement recommendations. ### Source excerpt Webflow's new standalone tool that helps demand gen and performance marketers create, launch, and measure on-brand campaigns with fewer handoffs. ## An AI-Assisted Cyber Attack: Inside a Unit 42 Investigation DevFeed: [An AI-Assisted Cyber Attack: Inside a Unit 42 Investigation]() Original publisher: [Read original article]() Author: Renzon Cruz, Nicolas Bareil, Eric Semaan and Omar Jbari Published: 2026-09-02T10:00:46Z Content type: article Language: en Sources: [Unit 42]() Topics: [Security Attacks](), [Large Language Model](), [CI/CD]() Tags: [agentic](), [agentic-ai](), [ai](), [ai-agents](), [attacks](), [autonomous](), [breach](), [ci-cd](), [enterprise](), [frontier-ai](), [general](), [insights](), [llm](), [secrets](), [security](), [threat-research]() ### AI overview An investigation of a ransom attack in which a human attacker used AI agents and frontier models to automate intrusion, reconnaissance, credential theft, and CI/CD pipeline abuse against an enterprise network. ### Source excerpt Using autonomous AI agents, an attacker breached an enterprise network in a matter of hours. Understand how to address and defend against agentic attacks. The post An AI-Assisted Cyber Attack: Inside a Unit 42 Investigation appeared first on Unit 42. ## Perturbation Probing: A New Diagnostic for the Fragility of LLM Safety DevFeed: [Perturbation Probing: A New Diagnostic for the Fragility of LLM Safety]() Original publisher: [Read original article]() Author: Tony Li, Hongliang Liu and Yuhao Wu Published: 2026-08-28T22:00:07Z Content type: article Language: en Sources: [Unit 42]() Topics: [Large Language Model](), [ai safety](), [Machine Learning, Security Attacks](), [Security](), [Artificial Intelligence](), [Reinforcement learning](), [human feedback]() Tags: [ai](), [ai-safety](), [benchmark](), [benchmarks](), [external](), [general](), [insights](), [internals](), [jailbreak](), [llm](), [llms](), [model](), [optimization](), [research](), [safety](), [security]() ### AI overview The article presents perturbation probing, a low-cost method for identifying neurons causally responsible for targeted behaviors in aligned large language models. It reports that very small neuron subsets control refusal or false-agreement behaviors, suggesting that LLM safety can be fragile and concentrated rather than broadly distributed. ### Source excerpt New research reveals that AI safety refusal lives in a thin neural layer, highlighting the critical need for external, multi-layered security. The post Perturbation Probing: A New Diagnostic for the Fragility of LLM Safety appeared first on Unit 42. ## Planetary prediction engine: Automating global models via Earth AI DevFeed: [Planetary prediction engine: Automating global models via Earth AI]() Original publisher: [Read original article]() Published: 2026-08-27T17:37:00Z Content type: article Language: en Sources: [The latest research from Google]() Topics: [Earth AI](), [Artificial Intelligence](), [data-engineering](), [Google](), [Machine learning](), [AI Development](), [Feature Engineering](), [Model Development]() Tags: [agents](), [ai](), [analytics](), [data-engineering](), [earth-ai](), [evaluation](), [feature-engineering](), [generative-ai](), [google](), [insights](), [machine-intelligence](), [mapping](), [research](), [training](), [validation](), [workflow](), [workflows]() ### AI overview Google Research introduces the Planetary Prediction Engine, an experimental Earth AI capability that autonomously performs geospatial data discovery, cleanup, feature engineering, model training, evaluation, and report generation from natural-language queries. The system targets applications including public health, food security, environmental risk, and socioeconomic analysis, reducing the stated workflow from weeks of manual data engineering to minutes. ### Source excerpt Earth AI ## How Redis Builds AI-Native Engineering Teams DevFeed: [How Redis Builds AI-Native Engineering Teams]() Original publisher: [Read original article]() Author: Gregor Ojstersek Published: 2026-08-27T12:35:46Z Content type: opinion Language: en Sources: [Engineering Leadership]() Topics: [Redis](), [Artificial Intelligence]() Tags: [ai](), [engineering](), [insights](), [redis](), [teams]() ### AI overview The article presents insights from a conversation with Redis Distinguished Engineer Eric Sammer about how Redis builds AI-native engineering teams. The supplied evidence does not provide specific practices or findings beyond that subject. ### Source excerpt Insights from my conversation with Eric Sammer, Distinguished Engineer at Redis. ## EC Chair's Preview: September EC Meeting and APNIC 62 DevFeed: [EC Chair's Preview: September EC Meeting and APNIC 62]() Original publisher: [Read original article]() Author: Kenny Huang Published: 2026-08-27T00:52:41Z Content type: article Language: en Sources: [APNIC Blog]() Topics: [Internet](), [FIRST]() Tags: [apnic-62](), [apnic-ec](), [apnic-survey](), [blog](), [community](), [conference](), [events](), [governance](), [insights](), [internet](), [nro](), [open](), [research](), [series](), [survey]() ### AI overview This preview outlines the APNIC Executive Council's priorities ahead of its September meeting at APNIC 62 in Mumbai. It covers APNIC's future activity plan and budget, long-range direction informed by the 2026 APNIC Survey, and work to strengthen governance and accountability across the regional Internet registry system. ### Source excerpt Get an overview of the topics the EC are considering ahead of the September meeting at APNIC 62. ## Rethinking Data Visualisation: A UX Approach To Dashboards That Actually Drives Decisions DevFeed: [Rethinking Data Visualisation: A UX Approach To Dashboards That Actually Drives Decisions]() Original publisher: [Read original article]() Author: hello@smashingmagazine.com (Meriem Benhabiles) Published: 2026-08-26T13:00:00Z Content type: article Language: en Sources: [Articles on Smashing Magazine -- For Web Designers And Developers]() Topics: [dashboards](), [User experience (UX)](), [data](), [datasets]() Tags: [dashboards](), [data](), [data-visualization](), [design](), [insights](), [ux]() ### AI overview This article argues that effective dashboards require UX thinking, not just available data. It explains how designing for a specific audience, question, and intended decision can turn charts into functional tools for insight and action. Examples including Anscombe's datasets show how visualisation can reveal patterns and communicate meaning that raw numbers conceal. ### Source excerpt Data visualisation sits at the intersection of two disciplines that rarely talk to each other: data and design. Meriem Benhabiles explores what changes when you bring structured UX thinking to dashboards and data presentations, from the questions you ask before opening any tool to the decisions that determine whether an insight actually lands. ## Harness RT Agents Detect Resilience Risks and Generate Tests for CD Pipelines and Kubernetes Workloads DevFeed: [Harness RT Agents Detect Resilience Risks and Generate Tests for CD Pipelines and Kubernetes Workloads]() Original publisher: [Read original article]() Author: Uma Mukkara Published: 2026-08-24T00:00:00Z Content type: release Language: en Sources: [Harness Blog]() Topics: [Resilience](), [Testing](), [Continuous Delivery (CD)](), [Kubernetes](), [dashboards]() Tags: [agentic](), [agents](), [blog](), [chaos](), [configuration](), [continuous-delivery](), [dashboards](), [deployment](), [experiments](), [infrastructure](), [insights](), [kubernetes](), [load](), [pipelines](), [product](), [resilience](), [services](), [teams](), [testing](), [tests]() ### AI overview Harness announces an update to Resilience Testing called RT Agents. The agents analyze CD pipelines and Kubernetes workloads for resilience risks, recommend the testing needed to confirm those risks, and can generate and run chaos experiments or load tests and interpret the results. ### Source excerpt RT Agents detect resilience risk in your CD pipelines and Kubernetes workloads, then generate and run chaos experiments or load tests to confirm it. | Blog ## Connecting the Dots: Securing the Overlooked Corners of the Software Development Lifecycle (SDLC) Supply Chain DevFeed: [Connecting the Dots: Securing the Overlooked Corners of the Software Development Lifecycle (SDLC) Supply Chain]() Original publisher: [Read original article]() Author: Yaron Avital Published: 2026-08-21T23:00:21Z Content type: article Language: en Sources: [Unit 42]() Topics: [sdlc](), [Security](), [Application Security](), [CI/CD](), [GitHub Actions](), [npm](), [Bun](), [Python](), [Claude Code](), [vs-code](), [Claude](), [Ethereum]() Tags: [blockchain](), [c2](), [chaindrop](), [ci-cd](), [claude-code](), [general](), [github-actions](), [insights](), [malware](), [npm-packages](), [persistence](), [python](), [sdlc](), [security](), [software-supply-chain-attack](), [supply-chain](), [vs-code]() ### AI overview Unit 42 describes how software supply-chain attackers are targeting developer tools, CI/CD pipelines, accounts, packages, setup scripts and developer environments before software reaches production. It examines the ChainDrop npm worm, which used malicious preinstall hooks, a Bun runtime, an obfuscated payload, Python-based memory theft, stolen tokens and secrets, self-propagation, persistence in VS Code and Claude Code, and Ethereum-managed command-and-control infrastructure. ### Source excerpt Attackers are targeting CI/CD pipelines and developer tools instead of application code, requiring total SDLC visibility and strict security controls The post Connecting the Dots: Securing the Overlooked Corners of the Software Development Lifecycle (SDLC) Supply Chain appeared first on Unit 42. ## How Guideless uses Tinybird for real-time product analytics and training insights DevFeed: [How Guideless uses Tinybird for real-time product analytics and training insights]() Original publisher: [Read original article]() Author: Tinybird Published: 2026-08-20T16:00:00Z Content type: article Language: en Sources: [Tinybird]() Topics: [product analytics](), [real-time](), [Back end]() Tags: [analytics](), [backend](), [customer-stories](), [effective](), [insights](), [product-analytics](), [real-time]() ### AI overview The article describes how Guideless used Tinybird to launch real-time product analytics without building a separate analytics backend. It says the approach saved weeks of engineering work and produced engagement insights for improving training effectiveness. ### Source excerpt Learn how Guideless used Tinybird to launch real-time product analytics without building a separate analytics backend, save weeks of engineering work, and turn engagement data into insights that help users create more effective training. ## How Shutterstock Builds AI-Native Engineering Teams DevFeed: [How Shutterstock Builds AI-Native Engineering Teams]() Original publisher: [Read original article]() Author: Gregor Ojstersek Published: 2026-08-20T15:08:01Z Content type: opinion Language: en Sources: [Engineering Leadership]() Topics: [Artificial Intelligence]() Tags: [ai](), [engineering](), [insights](), [teams]() ### AI overview The article presents insights from a conversation with Jefferson Frazer, Shutterstock's Director of AI Metadata and Delivery, about how Shutterstock builds AI-native engineering teams. ### Source excerpt Insights from my conversation with Jefferson Frazer, Director, AI Metadata and Delivery, Shutterstock. ## The Hidden Cost of Bad UX: How Much Revenue Are You Leaving on the Table? DevFeed: [The Hidden Cost of Bad UX: How Much Revenue Are You Leaving on the Table?]() Original publisher: [Read original article]() Author: Christian Holst Published: 2026-08-19T08:12:00Z Content type: article Language: en Sources: [Baymard Institute]() Topics: [User experience (UX)](), [Usability](), [web applications](), [coding]() Tags: [article](), [best-practices](), [business](), [checkout](), [cost](), [design](), [ecommerce](), [insights](), [leadership](), [metrics](), [product](), [research](), [revenue](), [usability](), [ux]() ### AI overview Baymard presents an Opportunity Sizing Calculator that estimates the revenue recoverable from reducing ecommerce UX friction. It translates user research and usability improvements across areas such as checkout, navigation, and product pages into projections that can support roadmap prioritization, budget decisions, and executive buy-in. ### Source excerpt (Note: Unfortunately, e-mail and RSS don't support advanced layouts and features. If the graphics in this article look strange, you may want to read the article in your web browser.) How much revenue is your site losing each month because of unnecessary UX friction? If you are like most ecommerce leaders or UX managers, you know that friction points across checkout, product pages, and navigation cause shoppers to abandon. Yet, when competing for roadmap resources or budget approvals, UX research is often categorized as an internal software or subscription expense rather than a top-line revenue driver. Without concrete financial estimates to back up your design priorities, critical usability improvements often get pushed aside. To help you translate UX friction into clear commercial impact, Baymard has developed an Opportunity Sizing Calculator. This tool converts Baymard's actionable user research into quantifiable projections, showing you the exact revenue you can unlock by systematically applying proven UX best practices to your site. Moving from Opinions to Research-Backed Forecasting Prioritizing a product roadmap often turns into a debate of subjective opinions, where the loudest voice in the room or the most senior person's viewpoint takes priority. UX designers and product managers must frequently advocate for critical usability fixes against competing internal requests, relying on qualitative arguments that struggle to secure executive support. Baymard's Opportunity Sizing Calculator bridges the gap between design priorities and executive decision-making, helping you secure buy-in. This means you're better able to: Connect specific usability insights directly to top-line revenue Build an unshakeable business case for leadership Give directors and VPs clear line-of-sight into the ROI of resolving site friction Get your personalized opportunity metrics: Book a short call with our team and find out how much revenue you could recover with better UX. Book a demo ## Amazon Aurora DSQL observability concepts and usage with Amazon CloudWatch DevFeed: [Amazon Aurora DSQL observability concepts and usage with Amazon CloudWatch]() Original publisher: [Read original article]() Author: James Morle Published: 2026-08-17T23:37:48Z Content type: article Language: en Sources: [AWS Database Blog]() Topics: [DSQL](), [telemetry]() Tags: [amazon](), [amazon-aurora](), [amazon-cloudwatch](), [cost](), [dash](), [database](), [diagnostics](), [dsql](), [insights](), [intermediate-200](), [metrics](), [monitoring](), [mysql](), [observability](), [performance](), [postgresql](), [sql](), [technical-how-to]() ### AI overview This article explains Amazon Aurora DSQL's time-based observability model and its link to performance and DPU-based cost. It introduces DASH session sampling and using Amazon CloudWatch Database Insights, PromQL, and system diagnostics to identify bottlenecks. ### Source excerpt Amazon Aurora DSQL offers time-based observability through Amazon CloudWatch Database Insights. Learn how the DSQL observability model, DASH, Database Insights, PromQL, and the system diagnostics AI skill help you find performance bottlenecks and connect session time directly to cost. ## Food Delivery & Takeout UX Benchmark: 3,100+ Performance Scores and 1,900+ Best Practice Examples DevFeed: [Food Delivery & Takeout UX Benchmark: 3,100+ Performance Scores and 1,900+ Best Practice Examples]() Original publisher: [Read original article]() Author: Anders Nielsen Published: 2026-08-13T08:01:00Z Content type: article Language: en Sources: [Baymard Institute]() Topics: [Benchmark](), [User experience (UX)](), [App](), [web applications]() Tags: [apps](), [article](), [benchmark](), [case-studies](), [customer](), [ecommerce](), [insights](), [mobile](), [performance](), [research](), [retail](), [user-testing](), [ux]() ### AI overview Baymard presents a Food Delivery & Takeout UX benchmark based on eight mobile sites and apps assessed across more than 390 research-based UX parameters. The benchmark reports over 3,100 weighted performance scores and 1,900 best-practice examples, with overall experiences ranging from poor to mediocre. Key weaknesses include repeat ordering, dietary and customization support, category filters, navigation, fulfillment timing, and tipping details. ### Source excerpt (Note: Unfortunately, e-mail and RSS don't support advanced layouts and features. If the graphics in this article look strange, you may want to read the article in your web browser.) At Baymard, we've just released a new UX benchmark with 8 "Food Delivery & Takeout" UX case studies. This follows our large-scale user testing on Food Delivery & Takeout UX and adds to our existing ecommerce UX benchmark. In this article, we give you a snapshot of the overall UX performance. 8 Food Delivery & Takeout UX Case Studies and the Overall Performance Just Eat mediocre Food Delivery & Takeout 49 page designs: mobile, app KFC mediocre Food Delivery & Takeout 48 page designs: mobile, app Uber Eats mediocre Food Delivery & Takeout 54 page designs: mobile, app Domino's Pizza poor Food Delivery & Takeout 34 page designs: mobile, app DoorDash poor Food Delivery & Takeout 48 page designs: mobile, app McDonald's poor Food Delivery & Takeout 39 page designs: mobile, app Deliveroo poor Food Delivery & Takeout 53 page designs: mobile, app Burger King broken Food Delivery & Takeout 41 page designs: mobile, app YourSite.com? Want to know how your site performs? Get Premium access to review your own site or have it audited by Baymard researchers. These are the 8 in-depth Food Delivery & Takeout UX case studies. The 8 mobile sites and apps have been manually assessed across 390+ research-based UX parameters relevant to Food Delivery & Takeout, resulting in 3,100+ weighted UX performance scores and 1,900+ best practice examples from these mobile sites and apps. Each of the 3,100+ UX performance scores from the 8 case studies is summarized in the interactive scatterplot below -- showing you how they perform collectively and individually: {{ scatterplot-graph: size=big + habitat=public + base-sites=collection:online-food-delivery + view-structure-id=gemini-st_l9jw5tjp }} The overall UX performance of the Food Delivery & Takeout sites and apps ranges from "poor" to "mediocre". What is missing is a ## Virgin Atlantic sharpens customer journeys with ChatGPT Work DevFeed: [Virgin Atlantic sharpens customer journeys with ChatGPT Work]() Original publisher: [Read original article]() Published: 2026-08-10T00:00:00Z Content type: article Language: en Sources: [OpenAI News]() Topics: [ChatGPT](), [data](), [dataset]() Tags: [airline](), [analysis](), [analytics](), [chatgpt](), [data](), [explore](), [insights](), [product](), [strategy](), [tools](), [work]() ### AI overview Virgin Atlantic uses ChatGPT Work to connect customer-journey information, accelerate competitive research, support product planning, and turn insights into strategy and digital-experience decisions. ### Source excerpt Virgin Atlantic is accelerating research, product planning, and decision-making with ChatGPT Work, helping teams connect signals across the customer journey. ## Inside the Modern SOC: The Identity Front Door DevFeed: [Inside the Modern SOC: The Identity Front Door]() Original publisher: [Read original article]() Author: Sharon Maydar Published: 2026-08-07T23:00:01Z Content type: article Language: en Sources: [Unit 42]() Topics: [Incident response](), [MFA](), [Vulnerabilities]() Tags: [ai](), [attacks](), [authentication](), [credential-theft](), [identity](), [incident-response](), [inside-the-modern-soc](), [insights](), [mfa](), [phishing](), [soc](), [social-engineering](), [unit-42-incident-response-report](), [vulnerabilities]() ### AI overview The article examines identity-based initial access, including credential theft, MFA manipulation, session hijacking, phishing, and social engineering. It describes how attackers expand access through persistence, privilege escalation, and lateral movement, challenging SOC teams to correlate signals across environments. ### Source excerpt Identity-based attacks drive 90% of incidents. Learn how modern attackers exploit identities and what SOC leaders can do to respond. The post Inside the Modern SOC: The Identity Front Door appeared first on Unit 42. ## Discovered Stacks: One Place for All Your Infrastructure DevFeed: [Discovered Stacks: One Place for All Your Infrastructure]() Original publisher: [Read original article]() Author: Alejandro Cotroneo Published: 2026-07-30T00:00:00Z Content type: release Language: en Sources: [Pulumi]() Topics: [Infrastructure as code](), [migration](), [AWS CloudFormation](), [Azure](), [Terraform](), [Cloud]() Tags: [aws-cloudformation](), [azure](), [cloud](), [deployment](), [features](), [fragmentation](), [governance](), [infrastructure](), [insights](), [migration](), [product](), [pulumi-cloud](), [terraform]() ### AI overview Pulumi launches Discovered Stacks, a Pulumi Cloud capability that models AWS CloudFormation stacks and Azure Resource Manager deployments alongside Pulumi stacks. It tracks resource migration status from live state and supports migration into Pulumi management with verification before changes are made. ### Source excerpt Today we're launching Discovered Stacks: Pulumi Cloud now models your AWS CloudFormation stacks and Azure Resource Manager deployments as stacks, right alongside your Pulumi IaC stacks. And when you're ready to bring them under Pulumi management, migration is built in, with every resource tracked until the code provably matches the cloud. Why: your infrastructure doesn't live in one tool Almost nobody's cloud estate is a single technology. There's the CloudFormation that came with the AWS account, the ARM templates from the Azure team, the Terraform from an acquisition, and the Pulumi you're standardizing on. Each tool has its own console, its own grouping, its own idea of state, and no single place shows you everything you run. That fragmentation is also why migrations stall. Moving a stack to Pulumi has never been the hard part; knowing where you stand is. The tracking lives in a spreadsheet, the spreadsheet goes stale the day it's written, and six months later nobody can say which of the 800 resources made it across and which were quietly forgotten. Nothing gets lost Discovered Stacks gives you confidence that your migration plan or governance efforts will include all resources. This catches a common failure mode where resources are missed by your existing migration scripts or automations. When Pulumi Insights scans your accounts, every CloudFormation stack and ARM deployment becomes a discovered stack, and every resource in it appears as a row with an explicit migration status: ready to migrate, requiring review before migration, or already migrated. Every status is computed from live state on both ends -- what Pulumi manages and what the source tool reports -- so it's never a stale annotation someone forgot to update. Each resource shows its origin type (AWS::S3::Bucket) next to its Pulumi type (aws:s3/bucket:Bucket), with the origin properties side by side with Pulumi's view, so you can verify that Pulumi sees exactly what your source tool sees before you change ## Migrate CloudFormation to Pulumi with Discovered Stacks DevFeed: [Migrate CloudFormation to Pulumi with Discovered Stacks]() Original publisher: [Read original article]() Author: Alejandro Cotroneo Published: 2026-07-30T00:00:00Z Content type: tutorial Language: en Sources: [Pulumi]() Topics: [Infrastructure as code](), [AWS CloudFormation](), [migration](), [Amazon Web Services](), [VPC](), [Amazon S3](), [DynamoDB](), [AWS IAM]() Tags: [aws](), [cloudformation](), [dynamodb](), [git](), [iac](), [iam](), [import](), [insights](), [kms](), [migration](), [pulumi](), [pulumi-cloud](), [pulumi-neo](), [s3](), [tutorials](), [vpc]() ### AI overview A tutorial shows how to migrate a 61-resource AWS CloudFormation stack to Pulumi using Discovered Stacks in Pulumi Cloud. It explains how to find the stack, review migration status, start the migration, resolve unmatched resources, and verify the result with a zero-diff pulumi preview. ### Source excerpt With Discovered Stacks, Pulumi Cloud does the bookkeeping for a CloudFormation migration: every resource in the stack gets an explicit migration status, and the migration is done when the code provably matches the cloud. In this tutorial, we take one real CloudFormation stack from discovered to migrated and managed by Pulumi IaC, end to end. What we're migrating Our example is payments-api, a CloudFormation stack with 61 resources: a VPC, an Aurora ledger database behind an RDS Proxy, an assets S3 bucket, a DynamoDB ledger table, a Kinesis payment-events pipeline, and the IAM roles, KMS keys, and secrets that wire them together. The plan has five steps: Find the stack in Pulumi Cloud. Review the migration breakdown at a glance. Start the migration. Resolve the stragglers, so every resource is accounted for. Confirm the quality gate: a zero-diff pulumi preview. We'll use Pulumi Neo to do the heavy lifting, but nothing here depends on it. The same flow works with your own coding agent or entirely by hand, because migration status is derived from actual stack state -- however the work gets done, the console shows the same progress. Step 1: Find your stack in Pulumi Cloud Discovered Stacks builds on Discovery, so the only prerequisite is a scanned cloud account -- the AWS account holding your CloudFormation stacks. Once a scan has run, open the Stacks page and turn on Show Discovered Stacks. Your CloudFormation stacks appear alongside your Pulumi stacks. The project name comes from the CloudFormation stack (payments-api), and the stack name encodes the account and region it came from, so the same template deployed to two regions shows up as two distinct discovered stacks. Step 2: Plan the migration at a glance Open the discovered stack's Migration tab. It lays out all 61 resources of payments-api by status, so you can visualize the migration before touching anything: 54 Ready: mapped to a Pulumi type and confirmed to exist -- importable right now. 2 Not found: mapped, but ## The CI/CD moment for data analytics DevFeed: [The CI/CD moment for data analytics]() Original publisher: [Read original article]() Author: Gaurav Nanda Published: 2026-07-23T05:40:01Z Content type: article Language: en Sources: [Platform Engineering Blog]() Topics: [data analytics](), [DataOps](), [real-time](), [data-engineering](), [CI/CD](), [Kafka](), [apache-flink]() Tags: [ai](), [analytics](), [apache-flink](), [architectures](), [bridging](), [cost](), [data-analytics](), [data-engineering](), [engineering](), [event](), [fraud](), [google](), [insights](), [kafka](), [latency](), [real-time](), [scale](), [streaming](), [systems](), [tools]() ### AI overview The article argues that data analytics is approaching a CI/CD-like transition toward continuous analytics. It describes how multi-step pipelines introduce latency, operational risk, and maintenance burden, and explains why real-time insight is becoming a baseline platform capability for applications such as personalization, fraud detection, reliability, and AI-driven features. ### Source excerpt How converging OLTP and OLAP architectures are driving 'Continuous Analytics', the CI/CD moment for data to deliver real-time, unified insights and operational simplicity. ## 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. ## Connect Your Cloud Accounts to Pulumi in Minutes DevFeed: [Connect Your Cloud Accounts to Pulumi in Minutes]() Original publisher: [Read original article]() Author: Levi Blackstone Published: 2026-07-15T00:00:00Z Content type: release Language: en Sources: [Pulumi]() Topics: [Cloud](), [Amazon Web Services](), [Azure](), [OpenID connect (OIDC)](), [Google Cloud Platform (GCP)](), [configuration](), [Authentication]() Tags: [aws](), [azure](), [cloud](), [configuration](), [esc](), [google-cloud](), [governance](), [identity](), [insights](), [openid-connect](), [policy](), [product](), [pulumi-cloud]() ### AI overview Pulumi introduces a Connect cloud accounts wizard for discovering and onboarding AWS accounts, Azure subscriptions, and Google Cloud projects in a single guided flow. It automates OIDC setup, creates Pulumi ESC environments, and applies discovery scans and policy packs. ### Source excerpt Pulumi Insights gives you visibility and governance across your entire cloud footprint, but that visibility is only as complete as the set of accounts you've connected. Until now, connecting an account meant repeating a manual setup for each one: OIDC configuration, hand-written Pulumi ESC environments, and per-account scan and policy setup. For an organization with dozens or hundreds of AWS accounts, Azure subscriptions, or Google Cloud projects, that per-account friction was the biggest obstacle to getting a complete picture. Today, the new Connect cloud accounts wizard removes it: discover every account in your cloud organization, select the ones you want, and onboard them all in a single guided flow. From hours of setup to a single flow The time savings are real: connecting a batch of accounts across AWS, Azure, and Google Cloud takes about three minutes end to end. Setting up those same accounts with the manual per-account workflow would take hours to days. The wizard handles the entire onboarding lifecycle for AWS, Microsoft Azure, and Google Cloud: Bulk discovery: Authenticate once with your cloud organization and the wizard lists every account, subscription, or project you can access. Accounts that are already connected are recognized and skipped. Automatic OIDC setup: The recommended flows configure each account with short-lived credentials based on OpenID Connect (OIDC) and workload identity federation. No long-lived cloud secrets are stored in Pulumi Cloud. ESC environments, created for you: The wizard generates Pulumi ESC environments that follow the best practices from the manual OIDC guides -- environments that previously had to be authored one by one. Scans and policies from day one: By default, scheduled discovery scans and a pre-built policy pack are applied to every account as part of setup: the Pulumi Best Practices pack on the Team and Enterprise editions, or a compliance pack (CIS, or NIST 800-53 for AWS and Google Cloud) on Business Critical. Ho [Next page]()