How Docker Compose Simplifies Local Dev Environments With One YAML File

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Setting up local dependencies like databases, queues, and caches can be tedious for developers, but Docker Compose addresses this by defining all services in a single docker-compose.yml file. A sample configuration can include an app service built from a local Dockerfile, a PostgreSQL 16 database, and a Redis 7 cache, all linked together with environment variables. Source code is mounted as a volume inside the container, so code changes are reflected instantly without rebuilding the image. Developers can start all services in detached mode using 'docker compose up -d', monitor output with 'docker compose logs -f', and shut everything down cleanly with 'docker compose down'. Adding the '-v' flag to the down command also removes persistent volumes, which is useful when a fresh test database is needed.

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Google AI System Lets Quantum Computer Calibrate Itself During Operation

A Google-led team has demonstrated a quantum computer capable of continuously self-calibrating while running, using a reinforcement learning system published in Nature. Tested on Google's Willow superconducting processor, the AI analyzes error-correction signals in real time to adjust over 1,000 control parameters without halting computation. The system made logical error rates 3.5 times more stable under simulated hardware drift and reduced them by roughly 20% compared to conventional expert tuning. Current quantum systems must periodically pause to recalibrate, a limitation that grows more problematic as algorithms become longer and more complex. The research also sets new benchmark metrics for surface-code and color-code error correction on superconducting hardware, addressing a key engineering hurdle on the path to fault-tolerant quantum computing.

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ProgrammingDEV Community ·

Flutter Powers Cross-Platform Gaming Marketplace With Single Shared Codebase

A development team built a gaming marketplace mobile app for both Android and iOS using Flutter, maintaining a single shared codebase throughout the project. The app required features such as smart search, secure checkout, real-time order tracking, and push notifications, all while delivering native-like performance. Responsibilities were separated by keeping business logic off the mobile client and distributing it across independent backend services. Performance was improved through techniques like lazy loading, local caching, infinite scrolling, and background synchronization to reduce unnecessary network requests. The team concluded that choosing scalable architecture patterns early was the most critical factor in managing new requirements and keeping development efficient over time.

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ProgrammingDEV Community ·

How a LinkedIn SDUI Overhaul Broke Job-Application Extensions and Forced a Rebuild

LinkedIn rolled out a major frontend update transitioning its Easy Apply flow to a Server-Driven UI (SDUI) architecture, which instantly broke multiple job-application browser extensions including JobEasyApply, LazyApply, and JobCopilot. The update moved the Easy Apply modal into a same-origin iframe with form elements partitioned behind Shadow DOMs, making them invisible to standard DOM query selectors. Developers found that programmatic clicks stopped working and form validators ignored autofilled text, as traditional static selectors could no longer locate or interact with the fields. To restore functionality, the JobEasyApply team replaced conventional selectors with a recursive tree-walking engine capable of crossing shadow root boundaries and accessing iframe document contexts explicitly. The episode highlights how large-scale framework changes by platforms can simultaneously disrupt entire ecosystems of third-party automation tools, forcing developers to build deeper, framework-aware solutions.

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ProgrammingDEV Community ·

Healthcare Chatbot Fails Safety Audit After Ignoring Patient's Chest Pain Report

A WhatsApp chatbot deployed at a healthcare clinic was stress-tested using four simulated patient personas and scored 78 out of 100, masking serious safety failures beneath an apparent passing grade. Most critically, when a patient mentioned chest pain, the bot responded only with 'I can't provide medical advice,' offering no direction to emergency services or an A&E department. The audit also found the bot accepted a completely invalid date of birth without any validation, potentially corrupting downstream patient records used for scheduling, billing, and clinical history. A frustrated patient who reported two weeks of failed contact attempts was asked to re-enter personal details from scratch rather than being escalated to a human agent. While the bot showed strengths in resisting prompt injection and maintaining a professional tone, auditors concluded that failing to recognize and escalate emergency symptoms represents a patient safety and legal liability issue, not merely a user experience flaw.

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