# validation Published articles for validation. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## New in Symfony 8.2: One Bundle per Component DevFeed: [New in Symfony 8.2: One Bundle per Component]() Original publisher: [Read original article]() Author: Javier Eguiluz Published: 2026-09-18T07:58:00Z Content type: article Language: en Sources: [Symfony Blog]() Topics: [Symfony](), [configuration]() Tags: [component](), [configuration](), [deprecated](), [forms](), [new-features](), [routing](), [services](), [symfony](), [validation]() ### AI overview Symfony 8.2 gives each component its own bundle, including its configuration, services, and related classes. Existing configuration keys continue to work through automatic registration and configuration aliasing, while several defaults and class locations change. ### Source excerpt Contributed by Nicolas Grekas in #65948 ,... ## Article: Your Next DSL Author Is a Language Model DevFeed: [Article: Your Next DSL Author Is a Language Model]() Original publisher: [Read original article]() Author: Irakli Betchvaia Published: 2026-09-16T11:00:00Z Content type: article Language: en Sources: [InfoQ]() Topics: [Large Language Model](), [Infrastructure as code](), [Retrieval Augmented Generation (RAG)](), [Code](), [Fable]() Tags: [ai](), [ai-ml-data-engineering](), [article](), [artificial-intelligence](), [benchmarks](), [development](), [domain-specific-languages](), [dsls](), [hallucinations](), [infrastructure-as-code](), [large-language-model](), [large-language-models](), [ml-data-engineering](), [next-dsl-author-language-model](), [validation]() ### AI overview The article introduces Typed Domain Grounding (TDG), which embeds a domain-specific language as a typed internal DSL in a mainstream host language. It argues that compiler type errors and generate-compile-repair loops can reduce syntactic hallucinations in language-model output. A benchmark reported higher Structural Fidelity and lower hallucination rates than two lenient external DSLs, although first-try compile rates were lower and results varied by model. ### Source excerpt In this article, the author introduces Typed Domain Grounding, an approach to reducing LLM hallucinations in domain-specific languages by embedding them in mainstream typed languages. Using kUML benchmarks and an infrastructure-as-code example, he explores how compiler validation and generate-compile-repair loops can make model-generated DSL output more reliable. By Irakli Betchvaia ## Stack Overflow for Agents adds a ChatGPT plugin, persistent knowledge sharing, and trust validation features DevFeed: [Stack Overflow for Agents adds a ChatGPT plugin, persistent knowledge sharing, and trust validation features]() Original publisher: [Read original article]() Author: Phoebe Sajor, David Gibson Published: 2026-09-15T17:00:00Z Content type: release Language: en Sources: [Stack Overflow Blog]() Topics: [Stack Overflow](), [Artificial Intelligence](), [API](), [Persistence](), [trust]() Tags: [agentic-ai](), [agents](), [ai](), [api](), [chatgpt](), [coding-agents](), [community](), [persistence](), [se-stackoverflow](), [se-tech](), [stack-overflow](), [trust](), [validation]() ### AI overview Stack Overflow describes updates to Stack Overflow for Agents, its API-first knowledge exchange for agents. The article discusses preserving solutions beyond individual sessions, adding trust scores and validation gateways, and introducing a ChatGPT plugin. ### Source excerpt We've learned a lot in the last three months since launching Stack Overflow for Agents, our API-first knowledge exchange for agents. Here's a few of our findings, what's new on the platform (including our new ChatGPT plugin), and how we're continuing to build Stack Overflow. ## The AI Hurricane Is Here DevFeed: [The AI Hurricane Is Here]() Original publisher: [Read original article]() Author: Manoj Nair Published: 2026-09-15T04:00:00Z Content type: opinion Language: en Sources: [Blog RSS Feed | Snyk]() Topics: [Artificial Intelligence](), [AI Development](), [Security](), [Vulnerabilities](), [Architecture & Design](), [audit trail]() Tags: [agentic-development](), [agents](), [ai](), [application-security](), [audit-trail](), [awareness](), [blog](), [code-security](), [developer](), [executive](), [security](), [security-labs](), [snyk-platform](), [snyk-security-intel](), [supply-chain-security](), [tech](), [validation](), [vulnerabilities](), [vulnerability-insights]() ### AI overview The article argues that AI is accelerating software creation and cyberattacks, widening the gap between machine-speed development and slower validation. It calls for securing agentic development, enforcing runtime controls, maintaining inventories and audit trails for production AI applications, and using independent validation. ### Source excerpt AI is accelerating software creation and cyberattacks alike. Leaders must secure agents and code at inception, enforce controls at runtime, and validate defenses independently. ## Percona and HexaCluster: Faster, Safer Oracle Migration DevFeed: [Percona and HexaCluster: Faster, Safer Oracle Migration]() Original publisher: [Read original article]() Author: Percona Team Published: 2026-09-14T22:19:16Z Content type: article Language: en Sources: [Blog - Percona]() Topics: [Oracle Database](), [migration](), [Open Source](), [PostgreSQL](), [Database Migration](), [Replication](), [Tooling]() Tags: [database](), [database-migration](), [database-trends](), [engineering](), [insight-for-dbas](), [insight-for-developers](), [migration](), [open-source](), [oracle](), [partners](), [percona](), [postgresql](), [replication](), [schema](), [sql](), [tooling](), [validation]() ### AI overview Percona and HexaCluster describe a partnership for moving organizations from Oracle, SQL Server, DB2, and Sybase ASE to supported open source databases, primarily PostgreSQL. HexaCluster provides assessment and migration engineering through DMAT and HexaRocket, while Percona provides distributions, operators, and long-term support. HexaRocket combines schema migration, data migration, validation, change data capture, live replication, and reverse replication in one platform. ### Source excerpt Percona and HexaCluster have partnered to remove the hardest part of an open source database migration: getting off Oracle, SQL Server, DB2 or Sybase ASE with confidence, on a predictable timeline, without a multi-year consulting program. Percona brings open source expertise, its own distributions, operators and enterprise support. HexaCluster brings the assessment and migration engineering ... Continued The post Percona and HexaCluster: Faster, Safer Oracle Migration appeared first on Percona. ## Benchmaxxing: When the Benchmark Becomes the Target DevFeed: [Benchmaxxing: When the Benchmark Becomes the Target]() Original publisher: [Read original article]() Author: Nathan Danneman Published: 2026-09-12T11:17:51.295154Z Content type: article Language: en Sources: [Blog]() Topics: [Benchmark](), [Artificial Intelligence](), [Cybersecurity](), [Ground truth / benchmark quality](), [Detection engineering]() Tags: [ai](), [ai-and-cybersecurity](), [benchmark](), [benchmarking](), [benchmarks](), [blog](), [cybersecurity](), [leaderboards](), [securing-ai](), [security](), [validation]() ### AI overview The article explains how public AI and cybersecurity benchmarks can become targets for optimization, a practice it calls "benchmaxxing." It argues that gaming, ceiling effects, data leakage, binary scoring, omitted costs, and aggregate scores can make benchmark results poor proxies for real-world defensive capability. The article proposes task-coupled internal benchmarks intended to evaluate end-to-end cyber agents and support rigorous science rather than visibility-driven score optimization. ### Source excerpt The more attention a benchmark receives, the stronger the incentive to optimize for it. In AI and cybersecurity, this can have significant consequences. ## 4 engineering patterns behind the strongest AI Agents Challenge submissions DevFeed: [4 engineering patterns behind the strongest AI Agents Challenge submissions]() Original publisher: [Read original article]() Author: Sergio Villani Published: 2026-09-12T11:04:33.891311Z Content type: article Language: en Sources: [Google Developers Blog]() Topics: [AI Bots](), [MSP MCP]() Tags: [agents](), [ai-agents](), [cost](), [engineering](), [event](), [inference](), [latency](), [mcp](), [mcp-server](), [routing](), [validation]() ### AI overview An article on engineering patterns found in leading AI Agents Challenge submissions. It highlights bidirectional MCP, asynchronous event buses, unified validation for model fallbacks, and tiered routing to make agentic workflows more resilient, faster, and less costly. ### Source excerpt The recent Google for Startups AI Agents Challenge revealed that the most successful multi-agent systems rely on foundational software engineering patterns rather than just raw model power. Winning architectures consistently implemented bidirectional MCP for seamless inter-agent communication, async event buses for parallel execution, strict unified validation for model fallbacks, and tiered routing to minimize expensive inference calls. By prioritizing these structural practices over simple linear prompt chains, developers can build more resilient, low-latency, and cost-effective agentic workflows. ## Introducing Credentio: Open Source C++ Library for C2PA Content Credentials from Google DevFeed: [Introducing Credentio: Open Source C++ Library for C2PA Content Credentials from Google]() Original publisher: [Read original article]() Author: Sherif Hanna Published: 2026-09-12T11:04:33.891311Z Content type: news Language: en Sources: [Google Developers Blog]() Topics: [C++](), [Google](), [Open Source](), [Local-First](), [API](), [data]() Tags: [api](), [c-plus-plus](), [data-privacy](), [google](), [local-first](), [open-source](), [performance](), [provenance](), [validation]() ### AI overview Google introduces Credentio, an open-source C++ library for validating C2PA Content Credentials locally within client applications, server pipelines, and edge software. It is designed for high-performance validation of large media assets with lower memory usage, configurable trust lists, and no need to transmit files to cloud or external validation services. Future plans include generating and embedding Content Credentials. ### Source excerpt Credentio is a newly released, open-source C++ library from Google that allows developers to integrate high-performance, local-first validation of C2PA Content Credentials into their client and server applications. By processing assets entirely locally with a highly optimized memory footprint, the library delivers instant validation verdicts for multi-gigabyte media files without incurring cloud latency, bandwidth costs, or data privacy risks. The library currently features deep manifest parsing alongside configurable trust list integration, and is available now on Google Source with future plans to support full credential generation and embedding. ## Harness Named a Leader in SecureIQLab's August 2026 Cloud WAAP v5.0 Validation Report DevFeed: [Harness Named a Leader in SecureIQLab's August 2026 Cloud WAAP v5.0 Validation Report]() Original publisher: [Read original article]() Author: Christine Ferrusi Ross Published: 2026-09-11T00:00:00Z Content type: news Language: en Sources: [Harness Blog]() Topics: [Security](), [Cloud](), [API](), [Web](), [Testing]() Tags: [2026](), [ai-security](), [api](), [cloud](), [harness](), [protection](), [report](), [security](), [testing](), [v5](), [validation](), [web]() ### AI overview Harness Web Application & API Protection was named a Leader in SecureIQLab's August 2026 Cloud WAAP v5.0 CyberRisk Validation Comparative Report. The article highlights reported scores for API protection, advanced-threat coverage, deployment and management, false-positive avoidance, and GenAI and LLM operational efficiency. ### Source excerpt In the August 2026 SecureIQLab Cloud WAAP v5.0 CyberRisk Validation Comparative Report, Harness Web Application & API Protection (WAAP) was named a Leader. | Blog ## Why don't machine learning research agents overfit? DevFeed: [Why don't machine learning research agents overfit?]() Original publisher: [Read original article]() Author: Martin Bertran Lopez; Aaron Roth Published: 2026-09-10T15:03:39Z Content type: article Language: en Sources: [Amazon Science homepage]() Topics: [machine learning overfitting](), [Training AI Models](), [AI research agents](), [Occam's razor machine learning]() Tags: [agentic-ai](), [agents](), [ai-research-agents](), [benchmark](), [benchmark-overfitting-machine-learning](), [compressibility-and-memorization](), [compression-and-generalization](), [generalization-in-machine-learning](), [generative-ai](), [information-bottleneck-overfitting](), [language-models](), [llm-compression-theory](), [machine-learning](), [machine-learning-overfitting](), [machine-learning-research](), [occam-s-razor-machine-learning](), [research](), [validation](), [why-don-t-ml-models-overfit-on-benchmarks]() ### AI overview The article explains why repeated evaluation on held-out benchmarks can cause overfitting, then frames the apparent contradiction in machine learning research, where benchmark-driven iteration is widespread. It also summarizes research suggesting that compressible models limit memorization. ### Source excerpt New research indicates that AI agents learn compressible models of data, which don't have enough space to enable memorization. ## 1.1.1.1 now supports post-quantum DNSSEC, all 2,420 bytes of it DevFeed: [1.1.1.1 now supports post-quantum DNSSEC, all 2,420 bytes of it]() Original publisher: [Read original article]() Author: Bas Westerbaan Published: 2026-09-10T13:00:00Z Content type: article Language: en Sources: [Cloudflare Blog]() Topics: [Cryptography]() Tags: [1-1-1-1](), [algorithms](), [cryptography](), [dns](), [dnssec](), [post-quantum](), [research](), [security](), [testing](), [tls](), [validation]() ### AI overview Cloudflare's 1.1.1.1 now validates DNSSEC signatures using the post-quantum ML-DSA-44 algorithm. The article discusses testing oversized DNS responses and preventing downgrade to conventional signatures while retaining compatibility with older resolvers. ### Source excerpt 1.1.1.1 now validates DNSSEC signatures using NIST's post-quantum ML-DSA-44 algorithm. Here is how we manage 2,420-byte signatures and downgrade risks at scale. ## Validating multi-Region DR for Terraform Enterprise with AWS FIS DevFeed: [Validating multi-Region DR for Terraform Enterprise with AWS FIS]() Original publisher: [Read original article]() Author: Frenil Randeria Published: 2026-09-09T21:05:02Z Content type: article Language: en Sources: [AWS Architecture Blog]() Topics: [Terraform](), [AWS Fault Injection Service (FIS)](), [Chaos Engineering](), [Amazon S3](), [Amazon EC2](), [Databases]() Tags: [advanced-300](), [amazon-aurora](), [amazon-ec2](), [amazon-s3](), [aws](), [aws-fault-injection-service-fis](), [disaster-recovery](), [postgresql](), [technical-how-to](), [terraform](), [validation]() ### AI overview This article explains how to validate customer-operated multi-Region disaster recovery for Terraform Enterprise on AWS using three-phase AWS FIS experiments. It covers failover and failback testing, hidden dependency discovery, and reported recovery times of 12-14 minutes. ### Source excerpt Learn how AWS, HashiCorp, and Athenahealth designed and chaos-tested a multi-Region disaster recovery strategy for Terraform Enterprise on AWS. This post walks through three-phase AWS Fault Injection Service experiments across Amazon EC2, Aurora, and Amazon S3, the 12-14 minute recovery times achieved, and the state file dependency pitfall to avoid. ## Spring Tools 5.4.0 released DevFeed: [Spring Tools 5.4.0 released]() Original publisher: [Read original article]() Author: martinlippert Published: 2026-09-09T00:00:00Z Content type: release Language: en Sources: [Spring]() Topics: [releases](), [Claude](), [Model Context Protocol](), [Visual Studio Code]() Tags: [batch](), [boot](), [claude](), [claude-code](), [cloud](), [event-driven](), [java](), [mcp](), [microservices](), [performance](), [reactive](), [release](), [release-notes](), [serverless](), [spring](), [tools](), [updates](), [validation](), [web-applications]() ### AI overview Spring Tools 5.4.0 has been released for several development environments, with new validations and quick fixes, Claude Code and MCP enhancements, and stability and performance improvements. ### Source excerpt On behalf of the team and everyone who has contributed, I am pleased to announce the 5.4.0 release of the Spring Tools for Visual Studio Code, Cursor, Eclipse, Theia - and Claude Code. Hightlights New Validations & Quick Fixes: Added validations and quick fixes to convert to @ApplicationModuleListener, @SpringJUnitConfig, @RestController, and specific @Scope annotations Claude Code / MCP Enhancements: Enabled the Claude Code plugin to render a project's logical structure Stability & Performance: Significant speedups around various quick fixes and repository-based version validation + improved reliability while indexing source code Updates to the Spring Tools for Eclipse distribution updated to the latest Eclipse 2026-09 release (new and noteworthy) Detailed changes can be found in the release notes: https://github.com/spring-projects/spring-tools/releases/tag/5.4.0.RELEASE Downloads To download the distribution for Eclipse and find links to the marketplace entries for Visual Studio Code, Cursor, and Theia, please go visit: Spring Tools: https://spring.io/tools/ Next up is the 5.5.0 release, currently scheduled for mid December 2026. ## Faster reviews and quality recognition for Microsoft Edge extensions DevFeed: [Faster reviews and quality recognition for Microsoft Edge extensions]() Original publisher: [Read original article]() Author: Microsoft Edge Team Published: 2026-09-08T16:02:17Z Content type: article Language: en Sources: [Microsoft Edge Blog]() Topics: [Edge](), [Extension](), [Microsoft](), [AI-assisted coding]() Tags: [add-ons](), [ai-assisted-coding](), [automation](), [edge](), [extension](), [extensions](), [review](), [security](), [uncategorized](), [validation]() ### AI overview Microsoft Edge describes automating repeatable extension-review validation checks and streamlining its review pipeline to shorten turnaround times while maintaining quality and security standards. ### Source excerpt Building an extension has never been easier. Rapid adoption of AI-assisted coding is enabling developers to build extensions faster than ever. We have seen that momentum on the Edge Add-ons site< The post Faster reviews and quality recognition for Microsoft Edge extensions appeared first on Microsoft Edge Blog. ## Red Hat Developer Hub software template authoring with rhdh-templates DevFeed: [Red Hat Developer Hub software template authoring with rhdh-templates]() Original publisher: [Read original article]() Author: Kashish Mittal Published: 2026-09-07T07:01:32Z Content type: tutorial Language: en Sources: [Red Hat Developer]() Topics: [Backstage](), [internal developer portal](), [Nunjucks](), [AI-assisted coding](), [YAML]() Tags: [ai-coding-agents](), [artificial-intelligence](), [backstage](), [developer-portal](), [developer-productivity](), [developer-tools](), [platform-engineering](), [quick-start](), [red-hat](), [software-templates](), [terminal](), [validation]() ### AI overview This tutorial introduces the rhdh-templates skill for building Red Hat Developer Hub and Backstage software templates with an AI coding assistant. It covers Nunjucks conventions, YAML schema checking, reference templates, local offline validation, repository templatization, and optional testing against a Red Hat Developer Hub instance. ### Source excerpt If you are a platform engineer or developer building Red Hat Developer Hub software templates, you know the friction of wrestling with Nunjucks syntax, guessing location.yaml placements, and discovering errors only after rendering in your developer portal. While AI coding agents handle generic YAML, they lack Red Hat Developer Hub and Backstage-specific conventions. The post Red Hat Developer Hub software template authoring with rhdh-templates appeared first on Red Hat Developer. ## Quiz: Build Your Own Face Recognition Tool With Python DevFeed: [Quiz: Build Your Own Face Recognition Tool With Python]() Original publisher: [Read original article]() Author: Real Python Published: 2026-09-05T12:00:00Z Content type: tutorial Language: en Sources: [Real Python]() Topics: [Python]() Tags: [command-line](), [face-recognition](), [how-to](), [python](), [recognition](), [tool](), [tutorial](), [validation]() ### AI overview An interactive 10-question quiz that tests understanding of a Python face recognition tool, including face detection versus recognition, face encodings, voting-based matching, model validation, bounding box coordinates, and argparse command-line options. ### Source excerpt Check your understanding of face detection, face recognition, face encodings, bounding boxes, and model validation in Python. ## ClickHouse as a streaming HTTP API DevFeed: [ClickHouse as a streaming HTTP API]() Original publisher: [Read original article]() Author: Mark Needham Published: 2026-09-05T00:00:00Z Content type: tutorial Language: en Sources: [ClickHouse Blog]() Topics: [web applications]() Tags: [api](), [architecture](), [clickhouse](), [data](), [features](), [how-to](), [http](), [streaming](), [validation]() ### AI overview A tutorial on using ClickHouse 26.8 to expose controlled queries as a streaming HTTP API with named HTTP handlers, access control, and result limits. ### Source excerpt Learn how to build a streaming HTTP API directly in ClickHouse 26.8 with named handlers, typed parameters, pagination, framing formats, and access control. ## Decoding the new AI lingo: Loops, harnesses, squads, hill climbing... oh my! DevFeed: [Decoding the new AI lingo: Loops, harnesses, squads, hill climbing... oh my!]() Original publisher: [Read original article]() Author: Cassidy Williams Published: 2026-09-02T21:00:00Z Content type: article Language: en Sources: [GitHub Engineering]() Topics: [AI Development](), [Software Engineering](), [Open Source Models & Datasets]() Tags: [agent](), [agentic-ai](), [agents](), [ai](), [ai-ml](), [engineering](), [github-podcast](), [loops](), [models](), [observability](), [open-source](), [routing](), [skills](), [validation](), [workflow]() ### AI overview A guide to emerging AI-development terminology, explaining loop engineering and Ralph loops, multi-agent squads and fleets, and related concepts such as open weights and open source models. ### Source excerpt From loop engineering to harnesses, squads, and open weights, the GitHub Podcast breaks down the AI terms showing up in developer conversations. The post Decoding the new AI lingo: Loops, harnesses, squads, hill climbing... oh my! appeared first on The GitHub Blog. ## Building Trust in AI DevOps: Validating the Harness Knowledge Graph DevFeed: [Building Trust in AI DevOps: Validating the Harness Knowledge Graph]() Original publisher: [Read original article]() Author: Vikram Sahu Published: 2026-08-31T18:37:00Z Content type: article Language: en Sources: [Harness Blog]() Topics: [DevOps](), [Testing](), [Graphs](), [Artificial Intelligence](), [Software]() Tags: [ai](), [ai-evals](), [api](), [automated](), [data](), [devops](), [evals](), [graph](), [knowledge-graph](), [lifecycle](), [operational](), [other](), [pipeline](), [schema](), [sdlc](), [security](), [services](), [software](), [software-delivery](), [validation]() ### AI overview This article explains how Harness validates answers from its SDLC Knowledge Graph. Its multi-layered approach combines AI evaluations, schema traversal, API checks, direct product verification, production data, and shift-left testing to improve reliability. ### Source excerpt Discover our multi-layered validation approach combining AI evals to ensure reliable AI-powered software delivery insights. | Blog ## GigaPath-Flash and GigaTIME-Flash: Toward population-scale discovery with efficient pathology foundation models DevFeed: [GigaPath-Flash and GigaTIME-Flash: Toward population-scale discovery with efficient pathology foundation models]() Original publisher: [Read original article]() Author: Naoto Usuyama, Jeya Maria Jose Valanarasu, Tristan Naumann Published: 2026-08-31T16:00:00Z Content type: article Language: en Sources: [Microsoft Research]() Topics: [foundation-models](), [datasets](), [data]() Tags: [analysis](), [data](), [datasets](), [foundation-models](), [models](), [performance](), [research](), [research-blog](), [scale](), [validation]() ### AI overview GigaPath-Flash and GigaTIME-Flash are efficient pathology foundation models designed to reduce computational demands while maintaining strong performance. They enable repeated analysis of larger cancer cohorts and support population-scale research into disease biology, biomarkers, and clinical outcomes. ### Source excerpt What if pathology foundation models could do more with less? GigaPath-Flash and GigaTIME-Flash cut computational demands while maintaining strong performance, opening the door to larger studies and broader exploration. The post GigaPath-Flash and GigaTIME-Flash: Toward population-scale discovery with efficient pathology foundation models appeared first on Microsoft Research. ## Introducing symfony lsp:check: Symfony-Aware Diagnostics in Your CI DevFeed: [Introducing symfony lsp:check: Symfony-Aware Diagnostics in Your CI]() Original publisher: [Read original article]() Author: Fabien Potencier Published: 2026-08-31T12:03:00Z Content type: article Language: en Sources: [Symfony Blog]() Topics: [Symfony](), [ci](), [Command-line interface](), [configuration](), [PHP](), [Stimulus]() Tags: [ci](), [cli](), [command-line](), [github](), [php](), [symfony](), [validation]() ### AI overview The Symfony Language Tools introduce a headless Symfony-aware diagnostics command for CI. It detects framework-specific issues that type checkers and code review may miss, including unknown routes, missing templates and translation keys, invalid configuration, unknown services, and other integration errors. ### Source excerpt A Symfony application is full of strings that mean something: route names, template paths, translation keys, service ids, bundle configuration keys. To PHP, they are strings like any others. Your CI pipeline verifies types with PHPStan or Psalm, style with... ## 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. ## 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 ## Extend Amazon Bedrock Guardrails to Tool Interactions Using the Strands Agents SDK DevFeed: [Extend Amazon Bedrock Guardrails to Tool Interactions Using the Strands Agents SDK]() Original publisher: [Read original article]() Author: Stephan Traub Published: 2026-08-27T16:20:05Z Content type: tutorial Language: en Sources: [AWS Security Blog]() Topics: [AI Bots](), [AWS IAM]() Tags: [ai-agents](), [amazon-bedrock](), [amazon-bedrock-guardrails](), [artificial-intelligence](), [aws-identity-and-access-management-iam](), [expert-400](), [generative-ai](), [model-context-protocol](), [security-blog](), [security-identity-compliance](), [strands-agents](), [tools](), [validation]() ### AI overview A tutorial on extending Amazon Bedrock Guardrails beyond model prompts and responses to validate AI-agent tool interactions with Strands Agents SDK lifecycle hooks. ### Source excerpt If you're running AI agents in production, Amazon Bedrock Guardrails protects the model boundary. But your agents also invoke tools, fetch external data, and communicate with other systems. That data flows outside the model boundary, where model-level guardrails can't reach. You can extend guardrail coverage to those interactions using three validation checkpoints built with the [...] [Next page]()