# validating Published articles for validating. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Reparo: Synthesizing and Validating Access Control Policies DevFeed: [Reparo: Synthesizing and Validating Access Control Policies]() Original publisher: [Read original article]() Author: Ayesha Binte Mostofa, Gehao Zhang, Sam Stern, Juan Zhai, Pubali Datta Published: 2026-11-02T05:00:00Z Content type: article Language: en Sources: [arXiv: Cryptography and Security](), [arXiv: Software Engineering]() Topics: [Access Control](), [Authorization](), [policy-as-code](), [AI Chat](), [Test automation]() Tags: [access-control](), [authorization](), [code](), [costs](), [cs-cr](), [cs-se](), [documentation](), [github](), [llm](), [manual-review](), [minutes](), [oracle](), [pipeline](), [policies](), [rego](), [repair](), [repositories](), [sample](), [synthesis](), [test](), [validating](), [validation]() ### AI overview Reparo uses repository evidence from code, comments, and documentation to synthesize access control policies, repair language errors with official validators, and check behavior against allow/deny requests generated by a separate LLM. Across 228 GitHub repositories using Cedar, OpenFGA, or Rego, 40.6-55.7% of existing policies failed language validation; after repair, Reparo produced 99.0-100% valid policies. Agreement with its behavioral oracle varied by language: 98.2-99.6% for OpenFGA, 82.0-86.5% for Cedar, and 76.3-80.2% for Rego. Manual review found 91.5-96.9% of 390 sampled oracle cases correct. Processing one repository took about five minutes and cost $0.55-$1.57. ### Source excerpt arXiv:2610.41800v1 Announce Type: cross Abstract: Writing correct access control policies for software applications is difficult because it requires expertise in the authorization language and a thorough understanding of the application's authorization requirements. Existing synthesis and mining approaches assume explicit authorization inputs and a trusted reference policy. However, authorization logic is often distributed across code, comments, and documentation rather than captured in a single artifact, and developer-defined policies in real-world application repositories are often incomplete, outdated, or incorrect. We present Reparo, an LLM-aided pipeline for access control policy synthesis, repair, and validation. Reparo collects repository evidence for authorization intent and uses it to guide an LLM in generating an initial policy. It then uses official policy-language validation tools to identify and automatically repair errors. For semantic validation, a different LLM derives a behavioral oracle from the same repository evidence, consisting of concrete authorization requests with expected allow or deny decisions, which Reparo executes against the generated policy. We manually assess the oracle on a statistically sized sample of 390 test cases. We evaluate Reparo on 228 real-world GitHub repositories that use Cedar, OpenFGA, or Rego. In these repositories, 40.6-55.7% of the repository policies fail their language's validation tool, whereas Reparo produces 99.0-100% valid policies after repair. Agreement with the oracle is 98.2-99.6% for OpenFGA, 82.0-86.5% for Cedar, and 76.3-80.2% for Rego. Manual review confirms 91.5-96.9% of the sampled test cases as correct. Synthesizing and validating the policy for one repository takes about five minutes and costs $0.55-$1.57. These results show that repository evidence can support both access policy synthesis and independent validation, even when no reliable reference policy exists. ## Part 3: From CI to Gating -- Validating Downstream OpenStack with Zuul DevFeed: [Part 3: From CI to Gating -- Validating Downstream OpenStack with Zuul]() Original publisher: [Read original article]() Author: Geonhee Kim Published: 2026-10-28T07:00:04Z Content type: article Language: en Sources: [Superuser]() Topics: [CI/CD](), [openstack](), [Continuous integration](), [digital sovereignty](), [Pull Request](), [Code review]() Tags: [ci](), [code](), [digital-sovereignty](), [openstack](), [operator](), [ovn](), [sovereign-cloud](), [technical-deep-dive](), [validating](), [validation](), [zuul]() ### AI overview kt cloud's KTC Gating System uses Zuul to require validation before downstream OpenStack changes merge, inheriting upstream jobs as the quality baseline while specifying downstream repositories and release targets. Its Kubernetes-hosted control plane coordinates pipelines, and an OpenStack resource provider supplies isolated, ephemeral VMs for job execution. Validation spans Python unit and Tox checks, DevStack environment setup, Tempest functional tests, Rally performance and regression checks, and Helm/GeneStack deployment validation. This approach reuses evolving upstream procedures instead of maintaining copied test logic, while allowing targeted KCP-specific checks. The system is still early and focused on OpenStack code; kt cloud says it has not yet demonstrated blocking production regressions. Planned work includes integrated testing closer to KCP architecture. ### Source excerpt If Part 1 explored network sovereignty--the ability to explain traffic paths and security policy--Part 2 turns to validation sovereignty: the ability to explain code changes and the quality criteria behind them. For an operator running its own cloud platform, sovereignty depends not only on which changes enter the operational baseline, but also on how those [...] ## Validating FKG.in: Soundness Assessment in LLM-Augmented Indian Food Knowledge DevFeed: [Validating FKG.in: Soundness Assessment in LLM-Augmented Indian Food Knowledge]() Original publisher: [Read original article]() Author: Saransh Kumar Gupta, Armaan Shah, Lipika Dey, Partha Pratim Das, Ramesh Jain Published: 2026-10-28T04:00:00Z Content type: article Language: en Sources: [arXiv: Artificial Intelligence](), [arXiv: Machine Learning]() Topics: [Large Language Model](), [ai evaluation](), [база знаний](), [Reliable AI outputs](), [generative-ai-tools]() Tags: [abstract](), [application](), [applications](), [approach](), [arxiv](), [assessment](), [automated](), [based](), [coherence](), [contain](), [content](), [cs-ai](), [cs-cl](), [cs-ir](), [cs-lg](), [data](), [domains](), [ecosystem](), [era](), [food](), [indian](), [knowledge](), [llm](), [validating]() ### AI overview The paper presents a semi-automated workflow for checking structured recipe data extracted from informal sources and augmented by LLMs, developed for FKG(.in), a knowledge graph of Indian food. It targets structural inconsistencies, semantic or logical incoherence, and departures from source text using formal grammars, vocabulary checks, statistical heuristics, Set Transformer-based coherence modeling, and retrieval-based verification. The abstract says the methods were evaluated on Indian recipes and may apply to broader multilingual and multicultural culinary domains; it gives no detailed results or performance measures. ### Source excerpt arXiv:2608.29249v3 Announce Type: replace Abstract: The online culinary ecosystem is increasingly populated by recipe content generated, modified, or summarized by LLMs. Although often convincing, such outputs may contain hallucinated ingredients, misrepresented quantities, or culturally implausible combinations, limiting their suitability for downstream applications and knowledge graph construction. In this paper, we present a semi-automated soundness assessment workflow for validating structured recipe data extracted from informal culinary sources and augmented by LLMs. Developed as part of FKG(.in), a knowledge graph of Indian food, the pipeline identifies and addresses common failure modes, including structural inconsistencies, semantic and logical incoherence, and deviations from the source text, through a multi-stage process combining formal grammars, vocabulary-based checks, statistical heuristics, Set Transformer-based coherence modeling, and retrieval-based verification. Although evaluated on Indian recipes, the proposed methods are applicable to broader multilingual and multicultural culinary domains. We provide a practical, auditable, and application-agnostic approach to improving the reliability of machine-readable food knowledge infrastructures in the era of LLM-generated content. ## Validating the Comparison, Not Just the Scorer: Why Overall Accuracy Can Mislead in Interactive LLM Evaluation DevFeed: [Validating the Comparison, Not Just the Scorer: Why Overall Accuracy Can Mislead in Interactive LLM Evaluation]() Original publisher: [Read original article]() Author: Ancil Crayton Published: 2026-10-26T04:00:00Z Content type: article Language: en Sources: [arXiv: Artificial Intelligence]() Topics: [LLM evaluation / benchmarking](), [Large Language Model](), [AI Chat]() Tags: [audit](), [automatic](), [compare](), [comparison](), [comparisons](), [cs-ai](), [cs-cl](), [cs-hc](), [errors](), [evaluation](), [interactive](), [llm](), [matching](), [mistakes](), [model](), [models](), [performance](), [prompt](), [questions](), [responses](), [uncertainty](), [validating]() ### AI overview Overall scorer accuracy can conceal prompt-specific errors that distort measured differences between LLM follow-up prompts. In this study, a balanced audit of 288 responses had an overall F₁ of .916, yet the scorer flagged 2.6%-33.3% of responses the primary LLM rater judged not to contain unsupported claims, with uncertain prompt-specific estimates. For one comparison, an apparent 9.3-point increase fell to 2.3 points under two separate raters; the primary-rater interval included zero. The authors recommend auditing both prompts on matching cases and accounting for scorer and rater uncertainty. Their attempted prompt-specific correction remained too uncertain for a firm conclusion. ### Source excerpt arXiv:2610.31678v1 Announce Type: cross Abstract: Many LLM studies compare how models respond to different follow-up prompts. For example, one prompt may neutrally ask a model to reconsider, while another insists that an invented film really exists. The difference is used to measure the effect of conversational pressure. An automatic scorer then labels whether each response makes an unsupported factual claim. Researchers commonly validate the scorer with one overall accuracy or $F_1$ score. However, high overall performance can hide prompt-specific mistakes: if the scorer makes more errors after one prompt, the measured difference can be too large, too small, or point in the wrong direction. An audit containing mostly difficult responses can also misrepresent performance in the full study. We derive how such errors alter a prompt comparison and examine the problem using six models, 180 invented-entity questions, and six prompt conditions. In a balanced audit of 288 responses, overall $F_1$ was $.916$. Yet, depending on the prompt, the scorer flagged 2.6%-33.3% of responses that the primary LLM rater judged not to contain an unsupported claim; these prompt-specific estimates were uncertain. In a second audit, the scorer and two separate LLM raters evaluated the same 300 model-question cases under each prompt. An apparent 9.3-point increase from asserting certainty rather than neutrally asking the model to reconsider fell to 2.3 points under both raters. The primary-rater interval included zero, so it did not establish an increase. Two other prompt comparisons kept their direction under both raters, while an attempted prompt-specific correction remained too uncertain for a firm conclusion. Researchers should therefore validate the exact difference they plan to report, not just the scorer as a whole: audit both prompts on matching cases and include scorer and rater uncertainty in the final result. ## DNSSEC Validation and Deployment Planning for ISPs DevFeed: [DNSSEC Validation and Deployment Planning for ISPs]() Original publisher: [Read original article]() Author: j2sw Published: 2026-10-25T12:38:43Z Content type: tutorial Language: en Sources: [Justin Wilson (j2sw)]() Topics: [DNSSEC](), [Domain Name System (DNS)]() Tags: [data](), [deployment](), [dns](), [dnssec](), [internet-architecture](), [isp](), [network-operations](), [planning](), [recursive-resolver](), [resolver](), [trust](), [validating](), [validation]() ### AI overview A DNSSEC validating resolver should return SERVFAIL for a signed domain with a broken chain of trust, while unsigned domains and valid signed domains resolve normally. Test this with dnssec-failed.org, inspect resolver logs to distinguish signature or chain errors from timeouts, and check each node's clock. Verify UDP and TCP port 53 from the resolver VLAN, and identify which resolver actually performs validation when queries are forwarded. For a controlled pilot, use a separate test resolver and identifiable subscribers; on anycast networks, confirm which node receives their queries. When a domain fails validation, investigate its DS, DNSKEY, and RRSIG records rather than disabling validation globally. If an exception is necessary, use a negative trust anchor with an end date and ticket reference. Signing ISP authoritative zones is a separate project: check parent DS records, key rollovers, signature refresh, and reverse zones independently. ### Source excerpt When a signed domain has a broken chain of trust, a validating resolver returns SERVFAIL. ISP operators need enough resolver data to tell that failure from an ordinary DNS outage. The post DNSSEC Validation and Deployment Planning for ISPs appeared first on Justin Wilson (j2sw). ## Getting started with the CircleCI CLI DevFeed: [Getting started with the CircleCI CLI]() Original publisher: [Read original article]() Author: Roger Winter Published: 2026-10-22T16:00:00Z Content type: tutorial Language: en Sources: [CircleCI]() Topics: [circleci cli](), [CircleCI](), [CI/CD](), [Tutorial](), [Agent Skills](), [Visual Studio Code](), [cURL]() Tags: [2](), [3](), [account](), [agents](), [ai-coding-agents](), [ai-development](), [changes](), [ci-cd-tutorial](), [ci-for-coding-agents](), [circleci](), [circleci-cli](), [circleci-cli-for-coding-agents](), [circleci-cli-tutorial](), [circleci-run-get-failure-report](), [cli](), [coding](), [coding-agents](), [commands](), [connect](), [debugging-failed-builds](), [developer-tools](), [engineering](), [getting](), [getting-started](), [how-to](), [install-circleci-cli](), [monitoring](), [pipelines](), [tutorials](), [using](), [validating](), [with]() ### AI overview CircleCI's CLI lets coding agents and developers monitor pipelines, diagnose failures, retrieve test results, logs and artifacts, and validate configuration from a terminal. Agents can use shell commands with structured JSON output and exit codes, or the CLI's local MCP server through supported editors; both use existing CLI authentication. The tutorial covers installation, sign-in and project onboarding, then demonstrates tracing a deliberate version-comparison bug through a consolidated failure report and targeted test and artifact commands. `circleci config validate` checks configuration before a push, while `run watch` reports pass or failure and `--failfast` exits when a job fails. Workflow reruns can help investigate flaky failures, though the article says developers should establish when agents need approval for reruns or cancellations. CLI v1 supports CircleCI Cloud; CircleCI Server users should continue with v0. ### Source excerpt Learn how to connect coding agents to CircleCI using the CLI, with commands for monitoring pipelines, diagnosing failures, and validating changes. ## Validating Autoformalized Specifications via Contrapuntal Languages DevFeed: [Validating Autoformalized Specifications via Contrapuntal Languages]() Original publisher: [Read original article]() Author: Yihan Dai, Xuanlin Liu, Haoxiang Jia, Tianchi Li, Xin Zhang, Sergey Mechtaev Published: 2026-10-22T04:00:00Z Content type: article Language: en Sources: [arXiv: Software Engineering]() Topics: [Formal verification](), [Spec Driven Development]() Tags: [ai](), [ai-generated-code](), [algorithm](), [cs-se](), [diversity](), [errors](), [formal](), [human-review](), [implement](), [key](), [language](), [livecodebench](), [llm](), [logic](), [natural-language](), [oracle](), [pairs](), [specifications](), [user](), [user-intent](), [validating]() ### AI overview The paper validates LLM-generated formal specifications without human review by having an LLM independently formalize intent in two task-specific "contrapuntal" languages, then accepting the specification only when a solver proves the formalizations equivalent. The languages are designed without a structure-preserving translation between them, aiming to reduce correlated errors; a synthesis algorithm builds them by reusing fragments of sampled seed specifications as syntactic sugar over a base language. In first-order logic experiments on HumanEval+ and LiveCodeBench, using GPT-5.4 mini and Qwen3.6-Flash with and without thinking, the method raises the share of accepted specifications that are complete and sound by 12.0% and 42.9%, respectively, over the strongest baseline. Complete accepted specifications reject 98.6% and 96.2% of buggy variants, versus 92.8% and 78.3% for that baseline. ### Source excerpt arXiv:2610.30059v1 Announce Type: new Abstract: Formal specifications can make AI-generated code mechanically checkable. Because user intent is typically expressed in natural language rather than formal logic, specifications must first be derived from it, e.g., via LLM-based autoformalization. Yet autoformalized specifications are themselves untrusted, so a key challenge is validating whether they capture user intent without relying on a costly oracle such as human review. In this oracle-free setting, a natural strategy is to vote among model outputs, but because model errors are often correlated, agreement among outputs may reflect shared mistakes rather than correctness. Our key insight is that these errors can be decorrelated through representational diversity. We achieve this diversity with contrapuntal languages: pairs of task-specific formal languages, each designed to express the intended specification, yet admitting no structure-preserving translation between them. An LLM formalizes the intent independently in each language, and we accept a specification only if a solver proves the two formalizations equivalent. To construct such languages, we present a synthesis algorithm that reuses fragments of sampled seed specifications as syntactic sugar over a base specification language. We implement the approach for first-order logic specifications and evaluate it on HumanEval+ and LiveCodeBench using GPT-5.4 mini and Qwen3.6-Flash with and without thinking. Over the strongest baseline, our method increases the proportion of accepted specifications that are correct, i.e., complete and sound, by 12.0% on HumanEval+ and 42.9% on LiveCodeBench. The accepted specifications judged complete reject 98.6% of buggy program variants on HumanEval+ and 96.2% on LiveCodeBench, compared with 92.8% and 78.3% for the strongest baseline. ## Binarization Flattens the Score Space DevFeed: [Binarization Flattens the Score Space]() Original publisher: [Read original article]() Author: Jacob Cole Published: 2026-10-21T04:00:00Z Content type: article Language: en Sources: [arXiv: Machine Learning]() Topics: [llm-reasoning](), [Large Language Model]() Tags: [0-1](), [1-5](), [covariance](), [cs-ai](), [cs-lg](), [gaussian](), [improvement](), [language-model](), [large-language-model](), [llm](), [loading](), [machine-learning](), [model](), [policy](), [power](), [scale](), [score](), [space](), [stretch](), [test](), [validating]() ### AI overview Collapsing LLM judge scores to pass/fail hides proportional changes in the underlying criterion scores: under the paper's model, those changes can leave every verdict unchanged. A third grade adds a threshold that reveals this affine-stretch ambiguity. In tests using MATH and SciBench outputs from one seven-criterion judge, all 14 constructed criterionwise stretches were invisible with binary verdicts but visible with three grades; a test given both population laws had at least 96.5% power at a 1.5x stress with n=1,024. The authors recommend retaining at least three grades, such as 0, 0.5, and 1, and externally validating gains. Grades do not resolve arbitrary within-grade changes or fixed-covariance, loading-aligned mean shifts that may make sycophancy appear as competence. The shared-factor reference approximation fit MATH and SciBench but not HealthBench. ### Source excerpt arXiv:2609.35797v2 Announce Type: replace Abstract: Large language model (LLM) judges are often used as rewards to train policies on objectives that deterministic verifiers cannot capture. However, these rewards are often collapsed to pass/fail ({0, 1}), which reports the verdict but not how well a response met each criterion. We model each pass/fail verdict as a score on an unreported scale, compared with one cutoff. A stretch of that scale moves every score proportionally toward or away from the cutoff, but never across it, so no verdict changes. A policy is therefore free to apply any stretch without changing anything the panel reports. Under a joint-Gaussian model, a third grade adds a second threshold and removes this affine stretch ambiguity. On MATH and SciBench outputs from one seven-criterion judge, all 14 constructed criterionwise stretches were invisible after binarization but visible with three grades. At $n=1{,}024$, a test given both population laws had at least 96.5% power at a $1.5\times$ stress. Retaining grades closes one blind spot created by binarization, but verdicts alone remain insufficient as some changes are still indistinguishable from genuine improvement. These include arbitrary within-grade changes and fixed-covariance, loading-aligned mean shifts -- the signature of a sycophancy-shaped lift the panel reads as competence. The shared-factor reference approximation fit MATH and SciBench but not HealthBench, delineating its empirical scope. We recommend keeping at least three grades (for example, asking the judge whether each criterion is fully, partially, or not met and rewarding {0, 0.5, 1}), and externally validating gains along the remaining direction, which no finer scale removes. ## Agentic Development: Time to Shift the Focus DevFeed: [Agentic Development: Time to Shift the Focus]() Original publisher: [Read original article]() Author: Riccardo Cocetta Published: 2026-10-16T00:00:00Z Content type: opinion Language: en Sources: [Auth0 Blog]() Topics: [agentic-coding](), [AI-assisted coding](), [development-process](), [DevSecOps]() Tags: [agentic](), [agentic-development](), [coding](), [risk](), [time](), [validating]() ### AI overview The article argues that generating pull requests with AI agents is becoming a well-understood part of software development. It says teams should focus on choosing features that serve customers and fit the product, then validating generated code before shipping. The proposed approach varies validation depth according to the risk of each software component. ### Source excerpt Agents can turn ideas into pull requests in almost no time. The hard part now is deciding what to build and validating that generated code is safe to ship. Here is a risk-based approach to both. ## Cypress Test For Optional Data DevFeed: [Cypress Test For Optional Data]() Original publisher: [Read original article]() Author: Gleb Bahmutov Published: 2026-10-14T04:00:00Z Content type: tutorial Language: en Sources: [Gleb Bahmutov]() Topics: [Test automation](), [Test-driven development]() Tags: [cypress](), [products](), [test](), [validating]() ### AI overview The tutorial demonstrates Cypress end-to-end tests for an online shopping site's shipping-dimension predictions. It covers showing a loading state, handling a missing dimensions value, and checking predicted dimensions while validating the response shape against the server and a simple schema. ### Source excerpt Imagine you are building an online shopping site. Your goal is to build the best customer-to-customer site, similar to ## Rolling the root key DevFeed: [Rolling the root key]() Original publisher: [Read original article]() Author: Geoff Huston Published: 2026-10-09T02:15:56Z Content type: article Language: en Sources: [APNIC Blog]() Topics: [DNSSEC](), [Domain Name System (DNS)](), [Encryption](), [TTL in DNS]() Tags: [cloudflare](), [dns](), [dnssec](), [infrastructure](), [isp](), [ksk](), [ksk-roll](), [resolvers](), [tech-matters](), [validating]() ### AI overview The DNS root key is scheduled to roll on 11 October 2026. The article explains how DNSSEC resolvers learn and trust the incoming KSK-2024, then examines adoption measurements: about 90% of reporting resolvers and about 70% of tested systems that appear to support RFC 8509 indicated that the new key had been loaded. The measurements do not directly establish how many end users may be affected. ### Source excerpt The root key of the DNS is scheduled to roll on 11 October. How many DNSSEC-validating recursive resolvers have learned of KSK-2024 and incorporated it into their TA sets? ## The feature works. But does it belong? DevFeed: [The feature works. But does it belong?]() Original publisher: [Read original article]() Author: Hendrik Krack Published: 2026-10-08T00:00:00Z Content type: news Language: en Sources: [CodeRabbit]() Topics: [AI-enhanced product development](), [AI-assisted coding](), [Product Management](), [Test automation](), [human review](), [feature bloat](), [User interface design](), [CI/CD]() Tags: [about](), [accelerated](), [added](), [advice](), [agent](), [agents](), [ai](), [ai-assisted](), [ai-code-review](), [alerts](), [allowed](), [but](), [development](), [english](), [feature](), [labs](), [shipping](), [take](), [trm-labs](), [validating](), [work](), [works]() ### AI overview Both founders describe agents making implementation faster, which raises the stakes of deciding what to build and how to review it. TRM Labs uses a "product cortex" to turn customer feedback into prioritized ideas, requirements, and prototypes; adding its design system and strategic priorities improved agent suggestions, and Rahul estimated about 10% of signals became work the team would eventually build. TRM required human approval for customer-facing pull requests and deployments while developing automated test evidence and controls to route security-sensitive changes for human review. CodeRabbit is exploring an approval step before background agents open pull requests and senior review of prompts; developers remain responsible for validating changes. Faster output also pushed CodeRabbit to improve build and delivery infrastructure. Both teams let product and design contribute directly to code while engineers retain responsibility for decisions such as scale and API design. ### Source excerpt TRM Labs' Rahul Raina and CodeRabbit's Harjot Gill on choosing, validating, and shipping work as agents take on more development. ## Rejecting Invalid Ingress Routes at Apply Time DevFeed: [Rejecting Invalid Ingress Routes at Apply Time]() Original publisher: [Read original article]() Author: Veronika Volokitina Published: 2026-04-08T22:00:00Z Content type: article Language: en Sources: [Zalando]() Topics: [Kubernetes](), [control-plane](), [proxy](), [Routing (disambiguation)](), [api server](), [configuration](), [Parsing]() Tags: [api](), [api-server](), [backend](), [backends](), [configuration](), [control-plane](), [kubernetes](), [open-source](), [parsing](), [platform-engineering](), [reverse-proxy](), [risk](), [routing](), [scale](), [server](), [skipper](), [sre](), [validating](), [webhook](), [zalando]() ### AI overview Zalando describes using Skipper as a Kubernetes validating admission webhook to reject invalid ingress routes before they are applied. The approach reuses Skipper's runtime validation for filters and predicates, addressing configuration errors that Kubernetes can accept as structurally valid. ### Source excerpt How Zalando used Skipper as a validating admission webhook to reject invalid filters and predicates at apply time, and what it took to make that safe on the Kubernetes control-plane path. ## Optionally skip approval for Copilot coding agent Actions workflows DevFeed: [Optionally skip approval for Copilot coding agent Actions workflows]() Original publisher: [Read original article]() Author: Allison Published: 2026-03-13T18:14:48Z Content type: release Language: en Sources: [GitHub Changelog]() Topics: [GitHub Actions](), [GitHub Copilot]() Tags: [github-actions](), [github-copilot-coding-agent](), [risks](), [validating]() ### AI overview GitHub now offers a repository setting that lets administrators skip human approval before GitHub Actions workflows triggered by Copilot coding agent changes run. Approval remains required by default. The option can speed up validation, but workflows may have access to tokens, secrets, or repository permissions depending on configuration. ### Source excerpt When Copilot coding agent opens a pull request or pushes changes, Copilot is treated like an outside contributor in an open source project. GitHub Actions workflows do not run until... ## What product teams build on Lovable DevFeed: [What product teams build on Lovable]() Original publisher: [Read original article]() Author: Talia Moyal Published: 2026-03-05T14:59:00Z Content type: article Language: en Sources: [Lovable]() Topics: [Product Management](), [AI-enhanced product development](), [experiments](), [internal developer platform](), [alert triage]() Tags: [aggregates](), [changelog](), [discovery](), [engineering](), [inside-lovable](), [problems](), [product](), [product-management](), [validating]() ### AI overview The article argues that product teams need to validate ideas before development and assess outcomes after release. It describes PM-led prototypes for feature testing, scoping, demos, and onboarding, along with internal dashboards and apps for tracking adoption, releases, and feedback. It presents faster uncertainty reduction as an emerging product management skill. ### Source excerpt Product management hasn't changed at its core. PMs are still responsible for identifying the right problems, prioritizing what matters, validating before committing, and making decisions with evidence, not instinct. What has changed is the environment they operate in. AI-native companies ship faster than ever. Feasibility has expanded. The surface area of what you could build is dramatically larger. And when possibility expands, prioritization becomes harder. ## GitHub Actions now supports a digest for validating your artifacts at runtime DevFeed: [GitHub Actions now supports a digest for validating your artifacts at runtime]() Original publisher: [Read original article]() Author: Allison Published: 2025-03-18T15:38:31Z Content type: release Language: en Sources: [GitHub Changelog]() Topics: [GitHub Actions](), [hashing](), [CI/CD]() Tags: [actions](), [artifacts](), [automatically](), [developers](), [github](), [github-actions](), [integrity](), [runtime](), [security](), [sha256](), [validating]() ### AI overview With artifacts v4 or newer, `upload-artifact` computes and stores a SHA256 digest, and `download-artifact` recomputes it and compares the values to check that the downloaded artifact matches the uploaded one. A mismatch produces a warning in the workflow UI and job logs; it does not fail the run. The digest is shown in the upload step logs and artifact output. The feature is not currently available on GitHub Enterprise Server. ### Source excerpt Developers using upload-artifact and download-artifact in their Actions workflows can now ensure the integrity of their artifacts with the new SHA256 digest. This feature automatically verifies that the artifact uploaded... ## Rethinking the startup MVP: Building a competitive product DevFeed: [Rethinking the startup MVP: Building a competitive product]() Original publisher: [Read original article]() Published: 2024-02-28T20:26:00Z Content type: article Language: en Sources: [Linear]() Topics: [product-experimentation](), [product analytics](), [API as a Product]() Tags: [alternatives](), [building](), [feedback](), [mvp](), [product-market-fit](), [validating]() ### AI overview The article argues that a startup MVP in a crowded market should be a compelling early version of a product that improves on existing alternatives, rather than only a quick test of a novel idea. It recommends narrowing the initial audience, gathering feedback through a targeted waitlist, iterating on the product, and assessing user sentiment and willingness to pay before a public launch. ### Source excerpt Today's startup MVP is about iterating early to build the highest quality competitor to an existing idea, not validating a novel one. This journey involves refining early ideas to compete in an existing market, narrowing the target audience, strategically using a waitlist for feedback, and recognizing indicators of early product-market fit. ## Ledger: Stripe's system for tracking and validating money movement DevFeed: [Ledger: Stripe's system for tracking and validating money movement]() Original publisher: [Read original article]() Author: Ilya Ganelin Published: 2024-02-16T00:00:00Z Content type: article Language: en Sources: [Stripe Dot Dev Blog]() Topics: [Data Quality](), [data-architecture]() Tags: [ledger](), [metrics](), [payment](), [payment-processing](), [processing](), [state](), [stripe](), [tracking](), [validating]() ### AI overview Stripe describes how it built Ledger, a system for tracking money movement, and how teams use data quality metrics across its payment processing network. ### Source excerpt Technical details on how Stripe built Ledger, a state-of-the-art money movement tracking system, including how teams at Stripe interact with the data quality metrics that underlie our payment processing network. ## Stupid Docker Tricks - Validating Outbound Connectivity from an Image DevFeed: [Stupid Docker Tricks - Validating Outbound Connectivity from an Image]() Original publisher: [Read original article]() Author: Fuzzygroup Published: 2020-07-25T00:00:00Z Content type: tutorial Language: en Sources: [Scott Johnson]() Topics: [Docker](), [network troubleshooting commands](), [Software Testing](), [Tool]() Tags: [connectivity](), [docker](), [languages](), [python](), [ruby](), [validating]() ### AI overview The excerpt shows how to check outbound connectivity from Ruby and Python Docker images with `docker run -it ping google.com`; both `ruby:2.7.1` and `python:3.7.2` return ping replies, disproving the apparent Ruby-specific connectivity problem in this case. It explains that `-it` opens an interactive terminal and demonstrates starting `/bin/bash` in each image to check the language version and run a Hello World command. These examples illustrate using Docker images to test language versions; the excerpt does not establish broader connectivity behavior. ### Source excerpt I recently had the odd situation where it seemed like I had outbound Internet connectivity issues under Ruby powered by Docker but not with Python powered by Docker. Here's how I disproved this: docker run -it ruby:2.7.1 ping google.com 64 bytes from ord30s26-in-f238.1e100.net (216.58.192.238): icmp_seq=1 ttl=37 time=15.2 ms 64 bytes from ord30s26-in-f238.1e100.net (216.58.192.238): icmp_seq=2 ttl=37 time=15.5 ms and docker run -it python:3.7.2 ping google.com 64 bytes from ord30s26-in-f238.1e100.net (216.58.192.238): icmp_seq=1 ttl=37 time=21.9 ms 64 bytes from ord30s26-in-f238.1e100.net (216.58.192.238): icmp_seq=2 ttl=37 time=31.3 ms The docker run command starts up an image. The -it option opens an interactive terminal underneath it and giving it a command like 'ping google.com' runs that in the terminal. One of the things that this Docker trick makes exceptionally easy is testing different versions of languages. Here's an example for Ruby: docker run -it ruby:2.7.1 /bin/bash root@4460a9daddff:/# ruby --version ruby 2.7.1p83 (2020-03-31 revision a0c7c23c9c) [x86_64-linux] root@4460a9daddff:/# irb irb(main):001:0> puts "Hello World" Hello World => nil irb(main):002:0> And for Python: docker run -it python:3.7.2 /bin/bash root@3fe135f566bb:/# python --version Python 3.7.2 root@3fe135f566bb:/# python Python 3.7.2 (default, Mar 27 2019, 08:41:08) [GCC 6.3.0 20170516] on linux Type "help", "copyright", "credits" or "license" for more information. >>> print("Hello World") Hello World >>> Credit for this goes to my friend and Docker Guru / Docker Captain, Nick Janetakis. Nick teaches Docker online and I highly recommend his stuff. ## A Regular Expression for Validating An Internet Domain DevFeed: [A Regular Expression for Validating An Internet Domain]() Original publisher: [Read original article]() Author: Fuzzygroup Published: 2020-02-03T00:00:00Z Content type: article Language: en Sources: [Scott Johnson]() Topics: [Regular expression](), [domain]() Tags: [allowed](), [domain](), [expression](), [internet](), [regex](), [regular](), [validating]() ### AI overview The author corrects an overly broad domain-validation regex: `^\S+\.\S+$` accepts non-whitespace characters that are not valid domain characters, including `_` in the Rubular example. The proposed pattern, `^[A-Za-z0-9\-]+\.[A-Za-z0-9\-]+$`, restricts each side of a single dot to letters, digits, and hyphens. The excerpt presents this as a correction to the author's validation attempt; it does not establish broader domain-validation requirements. ### Source excerpt Despite the power and truth of Jamie Zawinski's law: Regular Expressions: Now You Have Two Problems Jeff Atwood's Perspective Like Jeff, I too really, really love regular expressions or regexes. I use this one a lot and I finally learned to use \S (Any non-whitespace character) so here's a regex ^\S+\.\S+$ that I wrote yesterday to "validate" the permitted characters in an Internet domain. I was all proud of this and wrote this blog post only to realize that pride really does goeth before a fall - this will NOT correctly validate an Internet domain. As I write this post, I realize that the number of allowed characters in an Internet domain are actually NOT any non-whitespace characters and here's the proof that I actually got that wrong yesterday when I put something online using it: Note: The fact that Rubular allows through an _ which is NOT a valid character in domains is problematic. So the right way to do this, DAMN IT, is something like this: ^[A-Za-z0-9\-]+\.[A-Za-z0-9\-]+$ And this actually works: The [A-Za-z0-9-]+ is a "character class" which says "Any uppercase or lowercase letter plus 0-9 plus a -" are allowed (any order, any quantity)". Regular Expressions - Now you have two problems. ## Clay.io - A Reflection DevFeed: [Clay.io - A Reflection]() Original publisher: [Read original article]() Author: Zoli Kahan Published: 2016-05-04T05:00:00Z Content type: opinion Language: en Sources: [Zolmeister]() Topics: [Users](), [test]() Tags: [early-stage](), [focus](), [founders](), [growth](), [metric](), [retention](), [validating]() ### AI overview A former Clay team member reflects on lessons from running the company, emphasizing focus, measurable goals, retention metrics, validating assumptions quickly, and using evidence-based discussion to guide decisions. ### Source excerpt Clay.io - A Reflection Moving On As of January I am no longer with Clay. I've learned so much over the past year and a half, more so than I could have ever imagined. Every minute working on Clay was a joy, and without a doubt one of the most fulfilling experiences of my life. As for my next adventure, stay tuned. Lesson 1: Focus Focus proved to be the most important lesson I've learned about running a company. It's so easy to get distracted and off track, without even realizing it.