# verification Published articles for verification. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## REVERSAL-BENCH: A Reversibility Axis and Reset Oracle for Measuring the Reset-Free RL Cliff DevFeed: [REVERSAL-BENCH: A Reversibility Axis and Reset Oracle for Measuring the Reset-Free RL Cliff]() Original publisher: [Read original article]() Published: 2026-09-17T00:00:00Z Content type: article Language: en Sources: [Apple Machine Learning Research]() Topics: [Benchmark](), [Reinforcement learning](), [Ground truth / benchmark quality](), [Simulation](), [dataset](), [Algorithms]() Tags: [agent](), [benchmark](), [measuring](), [physics](), [reinforcement-learning](), [rl](), [training](), [verification]() ### AI overview The paper introduces REVERSAL-BENCH, a benchmark that varies environmental reversibility and provides a reset oracle to test state recoverability in autonomous reinforcement learning. Across eight manipulation settings and five physics engines, reset-free agents tend to become trapped in irrecoverable states as irreversibility increases, while episodic agents continue learning more steadily. ### Source excerpt A central goal of autonomous reinforcement learning is continuous policy training without external resets. However, existing paradigms largely depend on underlying environmental reversibility, a property absent in real world manipulation, where events such as pushing objects off tables or spilling granular substances cannot be undone. We introduce REVERSAL-BENCH, a benchmark that controls reversibility via a continuous parameter ρ∈ [0, 1] and provides a reset oracle, a ground-truth verification mechanism to test state recoverability across eight manipulation settings in five physics engines... ## KYC Onboarding: From Signup to Accepting Payments DevFeed: [KYC Onboarding: From Signup to Accepting Payments]() Original publisher: [Read original article]() Author: Deepak Jangir Published: 2026-09-17T00:00:00Z Content type: tutorial Language: en Sources: [Dodo Payments Blog]() Topics: [Requirements](), [Security, Privacy and Abuse Prevention](), [account](), [Website]() Tags: [account](), [business](), [credentials](), [guide](), [onboarding](), [payments](), [saas](), [verification](), [website]() ### AI overview This guide explains KYC onboarding for online sellers, including the identity, eligibility, ownership, bank account, and website checks payment providers may perform before enabling payments and payouts. It outlines the onboarding sequence, common causes of delays, and differences between freelancers and companies. ### Source excerpt What KYC onboarding involves when you sign up to sell online: the stages, the documents, why applications stall, and how it differs for freelancers and companies. ## Beyond the sync: Argo CD needs an Enterprise Control Plane DevFeed: [Beyond the sync: Argo CD needs an Enterprise Control Plane]() Original publisher: [Read original article]() Author: Eric Minick Sudarshan Purohit Published: 2026-09-16T20:28:57.610955Z Content type: opinion Language: en Sources: [Harness Blog]() Topics: [argo-cd](), [GitOps](), [Orchestration](), [DevOps](), [Platform Engineering](), [Deployment](), [Artificial Intelligence]() Tags: [argo-cd](), [deployment](), [gitops](), [governance](), [orchestration](), [platform-engineering](), [verification]() ### AI overview This article argues that Argo CD's manifest synchronization is only one part of enterprise software delivery. It describes how scaling GitOps can create operational toil and Argo sprawl, and proposes an enterprise control plane with workflow orchestration, governance, and deployment verification. ### Source excerpt Scaling GitOps? Argo CD is great for syncing manifests, but enterprise delivery requires workflow orchestration, governance, and AI verification. | Blog ## Design in KiCad, Quote with AIVON: A Faster Path to PCB Prototyping (Sponsored) DevFeed: [Design in KiCad, Quote with AIVON: A Faster Path to PCB Prototyping (Sponsored)]() Original publisher: [Read original article]() Author: Sponsored Post Published: 2026-09-15T02:00:57Z Content type: article Language: en Sources: [CNX Software - Embedded Systems News]() Topics: [Hardware](), [systems]() Tags: [aivon](), [engineering](), [hardware](), [kicad](), [manufacturing](), [pcb](), [plugin](), [prototyping](), [sponsored](), [verification](), [workflow]() ### AI overview AIVON's official KiCad Plugin connects a completed PCB layout to an online quotation form. After design-rule checking, it prepares manufacturing data and reduces manual exporting, zipping, uploading, and form entry during PCB prototyping. ### Source excerpt Finished your PCB layout in KiCad? You don't necessarily need to leave the PCB Editor, export a separate set of manufacturing files, and manually upload them to a PCB manufacturer. AIVON's KiCad Plugin creates a direct path from your PCB design to an online PCB quote. AIVON has integrated an official KiCad plugin that connects a finished PCB layout to the company's online quotation page. After DRC, users can click the plugin in the PCB Editor, automatically prepare manufacturing data, and land on a pre-filled quote form instead of exporting, zipping, and uploading files by hand. Rather than introducing a new plugin framework, AIVON's integration leverages KiCad's existing Plugin and Content Manager ecosystem. For developers who already use KiCad for schematic capture and PCB layout, the integration provides another way to connect the design stage with PCB fabrication without manually repeating every manufacturing-file preparation step. Connecting KiCad PCB Design with [...] The post Design in KiCad, Quote with AIVON: A Faster Path to PCB Prototyping (Sponsored) appeared first on CNX Software - Embedded Systems News. ## iGaming Fraud Prevention: Key Strategies to Implement DevFeed: [iGaming Fraud Prevention: Key Strategies to Implement]() Original publisher: [Read original article]() Author: Ben Price Published: 2026-09-14T21:00:00Z Content type: article Language: en Sources: [Sift Science]() Topics: [Security, Privacy and Abuse Prevention](), [Exploit](), [math]() Tags: [2025](), [2026](), [acquisition](), [bonus-abuse](), [customer](), [fraud-prevention](), [gaming-fraud](), [igaming](), [igaming-fraud](), [igaming-fraud-prevention](), [industrial](), [marketing](), [multi-account-abuse](), [multi-accounting-detection](), [multi-accounting-fraud](), [network](), [prevent-fraud](), [revenue](), [synthetic](), [time](), [trust-and-safety](), [verification](), [volume]() ### AI overview This article explains how bonus abuse and multi-accounting have become major sources of iGaming fraud. It describes fraudsters exploiting promotional offers through repeated registrations, synthetic identities, stolen credentials, device farms, residential proxies, and synthetic documents, and argues that operators should justify prevention spending by measuring protected revenue. ### Source excerpt While every operator budgets for promotions as a customer acquisition cost, very few budget for the version of that cost that never converts into a real player. Bonus abuse and multi-accounting now account for the single largest fraud category in iGaming, making up 64% of fraud according to a recent study. But unlike chargebacks or [...] The post iGaming Fraud Prevention: Key Strategies to Implement appeared first on Sift. ## What Is SMS Pumping Fraud and How to Stop It DevFeed: [What Is SMS Pumping Fraud and How to Stop It]() Original publisher: [Read original article]() Author: Twilio Published: 2026-09-10T00:00:00Z Content type: tutorial Language: en Sources: [Twilio Blog]() Topics: [Security](), [Messaging](), [Bot](), [Authentication](), [Forms](), [Chat Bot]() Tags: [authentication](), [automated](), [bots](), [fraud](), [how-to](), [industry-insights](), [messaging](), [otp](), [security](), [verification]() ### AI overview This article explains SMS pumping fraud, also known as SMS toll fraud, in which attackers use bots and fake phone numbers to trigger large volumes of automated SMS messages. It describes impacts such as increased messaging costs and reduced conversion rates, and introduces detection and prevention measures. ### Source excerpt SMS pumping fraud is an urgent problem for businesses that use SMS. Learn more about SMS pumping and how to prevent it. ## Fragments: September 8 DevFeed: [Fragments: September 8]() Original publisher: [Read original article]() Author: Martin Fowler (martin@martinfowler.com) Published: 2026-09-08T15:22:00Z Content type: article Language: en Sources: [Martin Fowler]() Topics: [Artificial Intelligence](), [Automation](), [hugging face](), [OpenAI](), [dashboards](), [incident](), [Code]() Tags: [ai](), [ai-agents](), [ai-automation](), [article](), [automation](), [cost](), [dashboards](), [errors](), [history](), [how-to](), [hugging-face](), [incident](), [math](), [openai](), [verification]() ### AI overview The article discusses how AI reduces the cost of generating outputs more rapidly than the cost of verifying them. It argues that AI automation should be applied cautiously when effectiveness is difficult to measure, because incomplete metrics can produce short-term gains while creating hidden technical debt, correlated errors, and weakened human capability. It emphasizes preserving a history of decisions and judgment, and uses the OpenAI-Hugging Face incident to illustrate the consequences of optimizing agent capability without scoring relevant safety outcomes. ### Source excerpt Christian Catalini says we're in a situation where we are vastly reducing the cost of generating things, but not the cost of verifying them:. This explains why the first major AI products appeared in chat, image generation, and code assistance. Not because these were the hardest human problems, but because their outputs were relatively easy to inspect. A user can judge the tone of a message, look at an image, or run a test on a piece of code. [...] The old automation boundary was routine versus non-routine work. The new boundary is increasingly measurable versus non-measurable work. The issue is then over how well you can measure something. In our profession, we know there's a big difference between how many lines of code we write and how productive we are, and we've seen a regular failure to understand how to measure productivity. Too much of what makes work effective is subject to either slow feedback loops or assessments that require subtle judgment. The danger is that people use lots AI automation while using incomplete measurements of its effectiveness, leading to short-term dashboards going up, but disaster in longer time-scales. He refers to these illusory short-term gains as counterfeit utility. Scale this across companies and institutions and the result is a Hollow Economy: extraordinary measured activity sitting on top of weakening human capability, hidden technical debt, correlated errors, and outcomes that nobody can confidently stand behind. Another highlight in the article was his advice to "build a history of decisions, not a gallery of outputs". The point is that with AI we can all build really impressive things, but our value lies in the judgment that we've formed. It reminds me of how math problems were marked at school. We weren't just marked on getting the final answer, we were also marked based on our reasoning process. He uses the OpenAI-Hugging Face incident as an illustration of this gap between generation and verification. He criticizes those ## From AI Code to Trusted Software: Harness Engineering in Practice DevFeed: [From AI Code to Trusted Software: Harness Engineering in Practice]() Original publisher: [Read original article]() Author: Travis Frisinger Published: 2026-09-04T21:55:00Z Content type: opinion Language: en Sources: [8th Light](), [8th Light Insights]() Topics: [AI-assisted coding](), [Software](), [trust](), [observability](), [Authorization]() Tags: [agent](), [ai](), [ai-and-emerging-tech](), [code](), [engineering](), [engineering-and-devops](), [observability](), [permissions](), [trust](), [verification]() ### AI overview The article presents harness engineering as a repository-centered discipline for making AI-generated software more trustworthy. It argues that prompts and written standards are insufficient, and that permissions, quality gates, evidence, and observability should enforce organizational standards and support verification. ### Source excerpt If the same reasoning path writes the system change and defines the proof of success, you may be setting yourself up for an avoidable failure in the future. Harness engineering is meant to act as an extension of your own organizational guardrails, which were always meant to reduce risk and improve quality. Travis Frisinger, Head of Agentic AI Your team is shipping more AI-written code every quarter. How do you know it is any good? Good means it meets your standards, and you have probably already tried handing your agents the standards: a context file, a style guide, the wiki pasted into the prompt. The agent reads them, agrees, and still breaks them, because instructions to a model are suggestions. What the repository permits is what actually happens. Your people absorb standards through review comments and hallway corrections, and the lessons stick. An agent apologizes and forgets by the next session. The only place its lessons can accumulate is the repository itself. That gap used to be an annoyance. With AI doing real engineering work, the quality gap is the whole game. Harness engineering is the discipline that closes it. It is the process of imbuing a repository with your standards so that the repository itself enforces them: permissions and boundaries that say what any actor may touch, quality gates that fail closed, evidence attached to every change, and observability that spans runs rather than moments. Models supply software delivery capacity. The harness supplies observable accountability: every change carries what was done, which rule allowed it, and what happened as a result, no matter which model, agent, or person did the work. Why now The industry started using the term harness engineering back in February, 2026. Since then, Thoughtworks, LangChain, and others have built serious thought leadership around the same shape. When several firms independently converge on the same word, it usually means they are trying to name the same problem. The real proble ## How to Confirm Phone Number Ownership with Lookup Identity Match in PHP DevFeed: [How to Confirm Phone Number Ownership with Lookup Identity Match in PHP]() Original publisher: [Read original article]() Author: Kelley Robinson, Matthew Setter Published: 2026-08-31T00:00:00Z Content type: tutorial Language: en Sources: [Twilio Blog]() Topics: [PHP](), [API](), [.env]() Tags: [account](), [and-best-practices](), [api](), [code](), [developer-insights](), [fraud](), [how-to](), [lookup](), [php](), [tutorials](), [verification](), [verify](), [zero-knowledge]() ### AI overview This tutorial explains how to use Twilio Lookup Identity Match with PHP to confirm whether details such as a name, address, national ID, or date of birth match a phone number. It covers setup requirements, project configuration, and collecting the information needed for the match. ### Source excerpt Send details like name, address, or date of birth and the Identity Match API will tell you if they match a provided phone number. Reduce fraud and verify identities quickly in PHP. ## How to Approve Real Users and Block Fake Accounts at Sign Up with Lookup and Verify DevFeed: [How to Approve Real Users and Block Fake Accounts at Sign Up with Lookup and Verify]() Original publisher: [Read original article]() Author: Kelley Robinson Published: 2026-08-31T00:00:00Z Content type: tutorial Language: en Sources: [Twilio Blog]() Topics: [Node.js](), [Tutorial](), [API](), [Security](), [JavaScript](), [Bot]() Tags: [api](), [bots](), [developer-insights](), [fake-accounts](), [fraud](), [how-to](), [javascript](), [node-js](), [onboarding](), [tutorial](), [verification]() ### AI overview This tutorial shows JavaScript developers how to build a Node.js onboarding flow with Twilio Lookup and Verify. It combines line type checks, line status checks, name-to-phone identity matching, and SMS OTP verification to filter fake accounts and fraud while minimizing friction for legitimate users. ### Source excerpt Build a secure Node.js onboarding flow with Twilio. This tutorial covers 4 layers of defense: Line Type checks, Line Status, Identity Match (name-to-phone), and SMS OTP verification to stop bots and fraud. ## How to Confirm Phone Number Ownership with Lookup Identity Match in Python DevFeed: [How to Confirm Phone Number Ownership with Lookup Identity Match in Python]() Original publisher: [Read original article]() Author: Kelley Robinson, Dylan Frankcom Published: 2026-08-28T00:00:00Z Content type: tutorial Language: en Sources: [Twilio Blog]() Topics: [API](), [Python](), [Flask](), [Code]() Tags: [account](), [api](), [checkout](), [developer-insights](), [fake-accounts](), [flask](), [fraud](), [how-to](), [python](), [real-time](), [validation](), [verification](), [verify](), [zero-knowledge]() ### AI overview This tutorial explains how to use Twilio Lookup Identity Match with Python to confirm that a person is associated with a provided phone number. It builds a simple Flask application that collects a phone number and name, then uses the API to support fraud reduction and identity verification. ### Source excerpt Send details like name, address, or date of birth and the Identity Match API will tell you if they match a provided phone number. Reduce fraud and verify identities quickly. ## How to Approve Real Users and Block Fake Accounts at Sign Up with Lookup and Verify in C# DevFeed: [How to Approve Real Users and Block Fake Accounts at Sign Up with Lookup and Verify in C#]() Original publisher: [Read original article]() Author: Amanda Lange, Dhruv Patel, Kelley Robinson Published: 2026-08-28T00:00:00Z Content type: tutorial Language: en Sources: [Twilio Blog]() Topics: [C#](), [API](), [.NET 9]() Tags: [auth](), [c-sharp](), [developer-insights](), [fake-accounts](), [fraud](), [how-to](), [lookup](), [onboarding](), [sign-up](), [verification](), [verify]() ### AI overview This tutorial shows C# developers how to combine Twilio Lookup and Twilio Verify to screen phone numbers during sign-up, verify identity, and block fake accounts. It covers line-type, reachability, identity-match, and phone-possession checks. ### Source excerpt How to Approve Real Users and Block Fake Accounts at Sign Up with Lookup and Verify in C Sharp ## Designing the connect flow your users actually see DevFeed: [Designing the connect flow your users actually see]() Original publisher: [Read original article]() Author: WorkOS Published: 2026-08-27T00:00:00Z Content type: tutorial Language: en Sources: [WorkOS Blog]() Topics: [ui](), [Google](), [Microsoft]() Tags: [google](), [how-to](), [integrations](), [microsoft](), [permission](), [trust](), [ui](), [verification]() ### AI overview This article explains how to design the user-facing OAuth connection flow, including provider consent screens, branding verification, partial permission grants, administrative approval, and connection health after authorization. ### Source excerpt What the consent screen shows, why partial grants are normal, and how to model connection health. ## How to Approve Real Users and Block Fake Accounts at Sign Up with Lookup and Verify in Python DevFeed: [How to Approve Real Users and Block Fake Accounts at Sign Up with Lookup and Verify in Python]() Original publisher: [Read original article]() Author: Kelley Robinson, Dylan Frankcom Published: 2026-08-27T00:00:00Z Content type: tutorial Language: en Sources: [Twilio Blog]() Topics: [Python](), [API](), [Code](), [zero-knowledge]() Tags: [apis](), [code](), [developer-insights](), [fake-accounts](), [fraud](), [how-to](), [identity](), [python](), [verification](), [zero-knowledge]() ### AI overview This tutorial shows how to use Twilio Lookup and Verify APIs in Python to build a multi-step sign-up verification flow. It combines phone line-type, reachability, identity-match, and phone-possession checks to filter fake accounts while maintaining a smooth onboarding experience. ### Source excerpt Utilize Twilio Lookup and Verify APIs to build secure onboarding flow in Python, blocking fake accounts and approving real users. ## Using deterministic systems and provenance to make AI-generated financial analysis verifiable DevFeed: [Using deterministic systems and provenance to make AI-generated financial analysis verifiable]() Original publisher: [Read original article]() Published: 2026-08-25T00:00:00Z Content type: article Language: en Sources: [Chainguard: Unchained]() Topics: [Trustworthy AI](), [Artificial Intelligence](), [AI Platform](), [Large Language Model](), [Code](), [Finance]() Tags: [ai](), [ai-platform](), [financial](), [models](), [provenance](), [trustworthy-ai](), [verification]() ### AI overview Kepler describes a model-agnostic approach to trustworthy AI that combines language models with deterministic tools for retrieval, computation, provenance, and traceability. The article focuses on making financial analysis outputs verifiable by linking numbers to their sources, formulas, or computations. ### Source excerpt Trustworthy AI takes more than a powerful model. See how Kepler uses deterministic systems and provenance to make financial analysis verifiable. ## Why Code Verification Matters More Than Ever in the Age of AI DevFeed: [Why Code Verification Matters More Than Ever in the Age of AI]() Original publisher: [Read original article]() Author: ByteByteGo Published: 2026-08-24T15:31:01Z Content type: article Language: en Sources: [ByteByteGo]() Topics: [Code](), [AI-assisted coding](), [Artificial Intelligence]() Tags: [ai](), [ai-assisted-coding](), [code](), [cto](), [developers](), [verification]() ### AI overview The article explains why code verification is becoming more important as AI-assisted coding makes software production faster. It discusses the growing review burden, low confidence in AI-generated code, and research indicating that increased AI use can create pressure on delivery stability and developer productivity. ### Source excerpt In this article, we will look at how code verification works, why the rise of AI-generated code puts more pressure on it, along with the extremely useful insights from Andrea on what the future may look like. ## Human judgment doesn't leave the software factory. It relocates. DevFeed: [Human judgment doesn't leave the software factory. It relocates.]() Original publisher: [Read original article]() Author: Addy Osmani Published: 2026-08-21T00:00:00Z Content type: article Language: en Sources: [Addy Osmani]() Topics: [Software](), [coding](), [event driven](), [implementation](), [GitHub](), [Claude Code](), [codex]() Tags: [agent](), [architecture](), [automated](), [batch](), [build](), [claude-code](), [codex](), [event-driven](), [github](), [linear](), [maintainability](), [mutation-testing](), [pull-requests](), [quality](), [scanners](), [slack](), [software](), [testing](), [verification]() ### AI overview A field guide to building a repeatable software factory while keeping humans responsible for product intent, system design, quality standards, code review, and final merge decisions. It recommends early and continuous quality checks, deliberate constraints, and event-driven automation when ordinary coding workflows are no longer sufficient. ### Source excerpt A field guide to building a software factory that still has an owner. ## Raising machine-checked security benchmarks to advance hash-based SNARKs through agentic collaboration DevFeed: [Raising machine-checked security benchmarks to advance hash-based SNARKs through agentic collaboration]() Original publisher: [Read original article]() Author: Ethereum Foundation Formal Verification team Published: 2026-08-20T00:00:00Z Content type: article Language: en Sources: [Ethereum Foundation Blog]() Topics: [Formal verification](), [Lean](), [Ethereum](), [AI research agents](), [AI Models](), [Library](), [Security]() Tags: [agentic](), [ai-agents](), [collaboration](), [ethereum](), [formal-verification](), [leaderboard](), [paper](), [research](), [research-development](), [security](), [verification]() ### AI overview The Ethereum Foundation's Formal Verification team launched better.codes, an open autoresearch challenge focused on raising the machine-checked soundness bound of the Lean-formalized koalaIRS12 Reed-Solomon proximity problem toward a fixed 128-bit target. Submissions are checked by the Lean kernel and promoted proofs are shared publicly. ### Source excerpt better.codes, an open autoresearch challenge built by the Ethereum Foundation Formal Verification team in collaboration with Yukon and zkSecurity, is now live. better.codes takes a self-contained problem from the Proximity Prize research, formalized in Lean, and puts its soundness bound on a public leaderboard that anyone can push forward.... ## Securing the agentic era: Introducing formal verification for CEL DevFeed: [Securing the agentic era: Introducing formal verification for CEL]() Original publisher: [Read original article]() Author: Google Open Source (noreply@blogger.com) Published: 2026-08-18T18:30:00Z Content type: release Language: en Sources: [Google Open Source Blog]() Topics: [Formal verification](), [Automated reasoning](), [Testing](), [Artificial Intelligence]() Tags: [agent](), [ai](), [ai-agent](), [automated-reasoning](), [cel](), [common-expression-language](), [formal-verification](), [open-source](), [security](), [testing](), [verification](), [z3]() ### AI overview Google's Common Expression Language (CEL) Formal Verification Framework is available for proving the correctness of CEL expressions and policies. Powered by the Z3 theorem prover, it complements heuristic testing by using mathematical proofs to identify logic flaws, equivalence issues, and potentially unsafe policy behavior. ### Source excerpt by Sean Huh, Common Expression Language Team We are rapidly entering an era where AI agents can autonomously draft, refactor, and deploy policies that protect our users and our systems. But this velocity introduces a vital question: How do we trust AI-generated policies? Unit tests may fail to cover the infinite set of possible inputs that occur in production; thus, an AI agent that overfits its policy to existing tests may fail spectacularly in production. To secure automated policy authoring, we must combine heuristic testing with mathematical proofs. We are thrilled to announce the Common Expression Language (CEL) Formal Verification Framework is now available. Powered by the Z3 theorem prover, this framework allows you to prove the correctness of your CEL expressions and policies, serving as the ultimate safety net for the agentic policy. Automated reasoning definitively answers questions like: "Is there any combination of inputs that allows an unapproved request into production?" "Are we absolutely certain this AI-refactored policy matches the original behavior?" "Can a bad actor manipulate this rule to force an evaluation error?" Formal verification establishes mathematical certainty across the infinite spectrum of inputs. Proven policies protect your users and system while giving auditors clear proof of compliance. To see these capabilities in action, watch our video demonstrating how the CEL Verifier REPL catches subtle logic flaws in seconds: Proving rules from the ground up Getting started with formal verification doesn't require learning complex architectures right away. You can evaluate simple standalone CEL expressions to catch edge cases that tests easily miss. (Note: The examples below use our interactive REPL syntax--check out the REPL documentation to follow along!) 1. Catching logic bugs in simple expressions (Equivalence) How do you guarantee a refactored rule behaves identically to the original? Suppose we have a policy that allows ports 80 or 443 ## Add the dependency graph to a Kotlin K2 migration checklist DevFeed: [Add the dependency graph to a Kotlin K2 migration checklist]() Original publisher: [Read original article]() Published: 2026-08-18T00:00:00Z Content type: tutorial Language: en Sources: [Koin - Cloud-Inject.io -Kotzilla]() Topics: [Kotlin](), [migration](), [Dependency injection](), [Graphs](), [Compiler](), [toolchain](), [Android Gradle Plugin](), [Compose]() Tags: [android-gradle-plugin](), [compiler](), [compose](), [dependency-injection](), [graph](), [kotlin](), [migration](), [rollback](), [toolchain](), [verification]() ### AI overview A practical checklist for Kotlin K2 migrations in projects that generate or validate dependency wiring. It recommends mapping the compiler and build toolchain, upgrading from a known compatibility matrix, cleaning and inspecting generated output, verifying dependency graphs across flavors and targets, testing runtime slices, and keeping rollback changes coherent. ### Source excerpt A K2 migration is usually planned around source compatibility and compiler diagnostics. Projects that generate or validate dependency wiring need another checklist: compiler plugin versions, generated sources, metadata compatibility, and graph verification on every target. Map the toolchain first Record the Kotlin plugin, Compose compiler plugin, KSP or annotation tooling, dependency-injection compiler plugin, Android Gradle plugin, and target libraries. Upgrade from a known matrix instead of selecting each latest version independently. ## Keep Kotlin Multiplatform bindings at the platform boundary DevFeed: [Keep Kotlin Multiplatform bindings at the platform boundary]() Original publisher: [Read original article]() Published: 2026-08-18T00:00:00Z Content type: tutorial Language: en Sources: [Koin - Cloud-Inject.io -Kotzilla]() Topics: [Kotlin Multiplatform](), [Kotlin](), [implementation](), [modules](), [configuration](), [test]() Tags: [configuration](), [implementation](), [kotlin](), [kotlin-multiplatform](), [module](), [multiplatform](), [test](), [verification]() ### AI overview A guide to keeping Kotlin Multiplatform dependency bindings at platform boundaries. It recommends defining small capability interfaces in common code, implementing them in platform source sets, organizing modules around capabilities and product features, and testing both shared contracts and real platform bindings. ### Source excerpt Kotlin Multiplatform makes source-set boundaries visible, but a dependency container can blur them again. A shared module should state a capability such as secure storage, HTTP transport, or local time. The platform source set should supply the implementation. Put contracts where consumers can see them Define the smallest useful interface in common code. Avoid copying a platform API into that interface. A storage contract might expose read, write, and remove; it does not need to expose Android preferences or an Apple keychain type. ## Design and Planning Are the Key Skills for Building Software with AI Coding Agents DevFeed: [Design and Planning Are the Key Skills for Building Software with AI Coding Agents]() Original publisher: [Read original article]() Author: Owain Lewis Published: 2026-08-14T12:12:26Z Content type: article Language: en Sources: [The AI Engineer]() Topics: [AI-assisted coding](), [AI Development](), [Software Engineering](), [Development]() Tags: [agents](), [ai](), [ai-agents](), [ai-coding](), [coding-agents](), [design](), [development-process](), [requirements](), [verification]() ### AI overview The article argues that AI coding agents can handle much of software implementation, testing, and deployment. It says engineers should focus most on design and planning: defining requirements, making technical decisions, and breaking work into tasks agents can execute reliably. ### Source excerpt How to design better software and turn it into tasks AI agents can execute reliably. ## Elastic achieves Defence Cyber Certification Level 0 in the UK DevFeed: [Elastic achieves Defence Cyber Certification Level 0 in the UK]() Original publisher: [Read original article]() Author: Christine Bocci,Ryan Kam Published: 2026-08-13T00:00:00Z Content type: article Language: en Sources: [Elastic Blog - Elasticsearch, Kibana, and ELK Stack]() Topics: [Security](), [Resilience]() Tags: [2026](), [security](), [self-attestation](), [standards](), [supply-chain](), [uk](), [verification]() ### AI overview Elastic has achieved Defence Cyber Certification Level 0 in the UK, meeting the Ministry of Defence's baseline cyber assurance requirement for organisations in the defence supply chain ahead of the 31 December 2026 deadline. ### Source excerpt Elastic has achieved Defence Cyber Certification (DCC) Level 0 -- the UK Ministry of Defence's baseline cyber assurance standard for the defence supply chain. Here's what it means and why it matters. ## Nirmata's Cloud Agents Audited a 40-Cluster Kubernetes Fleet, and recovered 40% of the Cost DevFeed: [Nirmata's Cloud Agents Audited a 40-Cluster Kubernetes Fleet, and recovered 40% of the Cost]() Original publisher: [Read original article]() Author: Anubhav Sharma Published: 2026-08-12T20:52:35Z Content type: article Language: en Sources: [Nirmata]() Topics: [Kubernetes](), [Cloud](), [Automation](), [data]() Tags: [agents](), [ai](), [automation](), [cloud](), [cloud-native](), [clusters](), [cncf](), [compute](), [cost-savings](), [deployment](), [devsecops](), [kubernetes](), [kyverno](), [policy-management](), [resource](), [verification]() ### AI overview Nirmata describes applying its Cost Analyzer and Resource Hygiene Cloud Agents across an enterprise customer's 40-cluster production Kubernetes fleet. The scans identified a roughly $107,000 monthly compute baseline and about 50% recoverable through right-sizing before stale-resource cleanup, while revealing recurring sources of waste and governance gaps. ### Source excerpt Nirmata's Cloud Agents Audited a 40-Cluster Kubernetes Fleet, and recovered 40% of the Cost Most Kubernetes Cost overruns don't come from one singularly bad decision. They come from dozens of reasonable ones -- made independently, by different teams, at different times -- that... The post Nirmata's Cloud Agents Audited a 40-Cluster Kubernetes Fleet, and recovered 40% of the Cost first appeared on Nirmata. [Next page]()