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AI coding tools are incredible. But I noticed something — they ship code fast, skip the rules, and nobody catches it until it's already in production. That's exactly what GuardDuo is built to fix. The Problem We're in the age of vibe-coding. You describe what you want, the AI builds it, it works — and you ship it. But "works" and "correct" are two very different things. Imagine asking an AI to build a login form. It works perfectly. But under the hood it has hardcoded API keys, no input validation, missing aria-labels , and it's using fetch directly instead of your project's apiClient wrapper. Your Issue said none of that was allowed. Nobody caught it. That's the vibe-coding trap — and it's happening on every team using AI-assisted development right now. What is GuardDuo GuardDuo is a GitLab Duo Agent skill that acts as your AI guardian. Instead of just reviewing code in isolation, it cross-references your code changes against the actual intent of the linked GitLab Issue — using the Orbit Knowledge Graph , which is essentially the brain that knows your project's rules, requirements, and success criteria. In plain terms: GuardDuo reads what the Issue asked for , reads what the code actually does , and tells you exactly where they don't match. It audits across three dimensions: 🔐 Security — hardcoded secrets, SQL injection, missing input validation ♿ Accessibility — missing alt text, aria-labels , poor color contrast 📐 Standards — deviations from your project's established patterns and conventions And when it finds a problem, it doesn't just flag it — it fixes it. How It Works Just open GitLab Duo Chat or GitLab Agent Platform(on your choice of IDE) -> choose the agent as GuardDuo and type: Audit issue #[issue no.] — GuardDuo pulls the Issue context from Orbit, analyzes the code, and returns a structured report Fix issue #[issue no.] — GuardDuo generates a corrected implementation that satisfies all requirements Or paste any code snippet directly and ask it to audit o

Every serious AI workflow eventually runs into the same failure. The agent does useful work in one session. It learns the shape of the project. It figures out which assumptions were wrong. It follows a correction, makes a decision, and gets closer to the real work. Then the session changes. The next run starts too cold. Old context comes back without the correction that changed it. The agent asks for the same setup again. It repeats an assumption that was already fixed yesterday. You end up managing the memory of the work instead of moving the work forward. That is the problem Pith is built for. Pith gives AI agents durable project memory they can trust when facts change. It is not trying to make an agent remember everything. That would be the wrong goal. Real projects are messy. Facts change. Decisions get reversed. A note that was useful last week can become stale after a release, a migration, a new customer constraint, or one correction from the human operator. The harder problem is not recall. The harder problem is knowing which memory is still useful. Why Longer Context Is Not Enough Longer context helps, but it does not solve continuity by itself. A long prompt can carry more text into a single run. It cannot automatically decide which prior facts survived a correction, which decision is now superseded, or which evidence should come back when the project resumes three days later. Developers working with agents already feel this. The friction shows up as small taxes: repeating project background that the agent should already know; re-explaining decisions that were already made; correcting stale assumptions that were already corrected; losing the reason behind a prior choice; restarting from a cold state after every meaningful break. Those taxes compound. The more serious the workflow, the more expensive the memory gap becomes. If an agent is helping with a toy task, forgetting is annoying. If an agent is helping with a codebase, a release, a customer workflow,

devtools #infrastructureascode #cdk #aws Index TL;DR Major Features Bedrock AgentCore — From Alpha to Stable Fn::GetStackOutput & Weak Cross-Stack References Validations Framework Performance Improvements CloudWatch PromQL Alarms CLI Improvements New L2 Constructs Service Enhancements Community Highlights Community Content & Resources How Can You Be Involved Hey CDK community! Here's an update covering everything that shipped in April and May 2026. TL;DR Bedrock AgentCore graduated to stable — production-ready AI agent infrastructure with semver guarantees. Cross-region references got a major upgrade with native Fn::GetStackOutput support and weak cross-stack references. The new Validations framework replaces policyValidationBeta1 with a richer plugin system. And file fingerprinting is ~33% faster with persistent asset caching. These features are available in aws-cdk-lib v2.247.0 through v2.257.0 and aws-cdk CLI v2.1116.0 through v2.1125.0. Full changelogs on GitHub Releases ( Library | CLI ). Major Features Bedrock AgentCore — From Alpha to Stable The @aws-cdk/aws-bedrock-agentcore-alpha module has graduated to aws-cdk-lib/aws-bedrockagentcore — stable APIs, semver guarantees, production-ready. If you've been building AI agents with Bedrock but held off on CDK because of the alpha label, it's time to upgrade. ( #37876 ) AgentCore provides the core infrastructure for building AI agents: runtimes, gateways, identity management, observability, and online evaluation. The Policy submodule remains in alpha as it continues to evolve rapidly. ┌─────────────────────────────────────────────────────┐ │ Bedrock AgentCore (Stable) │ ├─────────────────────────────────────────────────────┤ │ │ │ ┌──────────┐ ┌──────────┐ ┌──────────────────┐ │ │ │ Runtime │ │ Gateway │ │ Identity │ │ │ │ (L2) │ │ (L2) │ │ (L2) │ │ │ └────┬─────┘ └────┬─────┘ └────────┬─────────┘ │ │ │ │ │ │ │ ▼ ▼ ▼ │ │ ┌──────────┐ ┌──────────┐ ┌──────────────────┐ │ │ │Observa- │ │Online │ │ Policy Engine │ │ │

Why API Breaking Changes Still Reach Production Even With CI/CD A few years ago I watched a "tiny" API change take down checkout for about forty minutes. The change was a one-liner. The pull request had two approvals. CI was green across the board. And it still broke production, because the thing that actually mattered was never tested. If you run microservices at any real scale, you have lived some version of this. Let's talk about why it keeps happening even with a mature pipeline, and what the teams who don't keep getting paged do differently. The Problem Here's the change that caused the outage. A payments service had a response that looked like this: { "status" : "ok" , "transaction_id" : "txn_8842" , "amount_cents" : 4200 } Someone renamed amount_cents to amount and switched it to a decimal, because "cents is confusing." Cleaner field, better docs. The producing service's tests were updated to match, everything passed, it shipped. The problem: three downstream services still read amount_cents . One of them was the order service, which now received undefined , multiplied it by a quantity, and wrote NaN into the database. The failures didn't even surface in the payments service. They surfaced two hops away, in a service the original author had never opened. This is the core issue. A breaking change is not defined by the service that makes it. It's defined by the consumers who depend on it. And the producer's CI pipeline has no idea those consumers exist. Why Existing Approaches Fail The natural reaction is "we need more tests." But look at what each layer actually checks. Unit tests verify the code does what the author intended. The author intended to rename the field. The unit tests were updated to expect amount . They passed because they were testing the new, broken behavior. Green unit tests told us nothing. Integration tests verify the service works with its own dependencies — its database, its cache, the APIs it calls. They almost never spin up the services

Rethinking fake content in Symfony projects A prototype web page displaying pure placeholder content When building early UI prototypes or shaping design systems in Symfony, placeholder content becomes a constant companion. Lorem ipsum text. Dummy profile photos. Placeholder videos. Silent audio. Temporary avatars. Realistic fake user data. Every project needs them — and yet most setups rely on a patchwork of libraries, links and hardcoded values. Omnia Ipsum aims to fix that by giving Symfony developers a single, elegant toolkit for placeholder content of all kinds. In this article, I will walk you through the motivation behind the project, the conceptual patterns it follows, and its most advanced features — all designed to make your prototyping workflow faster, cleaner and more maintainable. Motivation: Why a placeholder library? Most Symfony projects start the same way: You add lorem ipsum text manually into Twig templates. You grab placeholder images from an external service. You generate avatars using yet another site. You paste in temporary YouTube or stock video URLs. You install Faker separately whenever realistic data is needed. The result is inconsistent, fragmented and difficult to maintain. And even worse: placeholder content often leaks into production unless guarded carefully. The idea behind Omnia Ipsum was simple: “If your UI needs placeholder content, it should come from one place — predictable, configurable, and accessible directly from Twig.” This cuts down on boilerplate, cognitive overhead, and the "temporary chaos" of early-stage templates. Quick Start Prerequisite Go to github.com/symfinity/recipes and follow the instructions to add the required recipe repository. Installation composer require --dev symfinity/omnia-ipsum Usage Use the Twig functions immediately: <img src= " {{ omnia_image ( 600 , 400 ) }} " alt= "Placeholder" > <img src= " {{ omnia_avatar ( 'John Doe' , 100 ) }} " alt= "Avatar" > <video src= " {{ omnia_video ( 1920 , 1080 ) }}

The Problem Typography should be one of the simplest parts of a project. In reality, it often ends up scattered across multiple layers: Bootstrap: $font-family-base variables Tailwind: JavaScript configuration TypeScript: type definitions Design systems: W3C Design Tokens The same font information gets copied and maintained in several places. Every update means touching multiple files, hoping everything stays in sync. It's repetitive, error-prone, and easy to get wrong. So I built Font Manager. Define your fonts once and export them in whatever format your project needs — CSS, Bootstrap variables, Tailwind configuration, TypeScript definitions, design tokens, and more. The Solution A simple Twig function: {{ font_manager ( 'Ubuntu' , '400 700' ) }} Configuration: symfinity_font_manager : export : formats : - scss_bootstrap - tailwind_config - typescript_definitions One lock command: php bin/console fonts:lock Every format, automatically generated. Perfectly synced. Bootstrap Example Before: // Manually copy font name $font-family-base : 'Ubuntu' , sans-serif ; // ❌ Duplication @import 'bootstrap/scss/bootstrap' ; After: symfinity_font_manager : export : formats : [ scss_bootstrap ] php bin/console fonts:lock // app.scss @import './assets/styles/fonts-bootstrap' ; // ← Auto-generated @import 'bootstrap/scss/bootstrap' ; Bootstrap uses your fonts automatically. No manual mapping. No duplication. Tailwind Example symfinity_font_manager : export : formats : [ tailwind_config ] // tailwind.config.js const fonts = require ( ' ./assets/fonts-tailwind.config.js ' ); // ← Auto-generated module . exports = { theme : { extend : { fontFamily : fonts . fontFamily } } }; <p class= "font-sans" > Your custom font, via Tailwind. </p> TypeScript Example symfinity_font_manager : export : formats : [ typescript_definitions ] import { fonts , type FontFamily } from ' ./assets/fonts ' ; applyFont ( element , ' sans ' ); // ✓ Valid applyFont ( element , ' invalid ' ); // ✗ TypeScript erro

According to Automotive News, the cult-favorite Honda Element could return in 2029 as a small hybrid SUV.

Google's GKE Labs has introduced OpenRL, an open-source project that provides a self-hosted API for post-training and fine-tuning Large Language Models (LLMs) on standard Kubernetes clusters. By Sergio De Simone

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