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Gadget’s platform enables Swift developers to build scalable backends with minimal code, using auto-generated GraphQL APIs and Apollo iOS for seamless app integration. By Gabe Braden.

The main featured sections:

• Gadget’s platform enables Swift developers to create scalable backends with minimal code, eliminating traditional backend development overhead
• The tutorial demonstrates building a pushup tracking app with a Swift frontend connected to a Gadget backend
• Apollo iOS generates type-safe Swift code from Gadget’s auto-generated GraphQL schema
• Proper authentication setup ensures users can only access their own data through session tokens stored in the Keychain
• The article addresses common Swift concurrency issues by configuring Xcode’s “Default Actor Isolation” setting
• Request interceptors simplify adding authentication headers to all API calls
• This approach allows developers to focus on app functionality while relying on Gadget’s infrastructure for scalability and security

This article offers an in-depth exploration of building scalable backends for Swift mobile applications using Gadget’s platform, presenting a complete end-to-end tutorial for a pushup tracking app. The process begins with leveraging Gadget’s infrastructure to rapidly create a database and API through its web-based editor, eliminating hours of traditional backend development. The tutorial guides readers through creating a pushup data model with proper relationships to user accounts and implementing access controls to ensure data privacy. The article then transitions to the Swift client side, demonstrating how to use Apollo iOS to generate type-safe Swift code from Gadget’s auto-generated GraphQL schema. Good read!

An AI assistant dramatically improves the usability of bionic hands, boosting success rates in delicate tasks and reducing the cognitive load on users. By Jacek Krywko.

The article describes a novel approach to bionic hand control – an AI-powered co-pilot system. Unlike traditional methods relying solely on user input interpreted via EMG signals, this system uses AI to predict the user’s intended actions and assist in their execution. The core methodology involves training a machine learning model on EMG data collected during attempted object manipulations. This model then anticipates the user’s movements, providing subtle corrections and adjustments to the hand’s actuators.

Lab testing with both amputee and intact-limb participants showed a remarkable increase in success rates for delicate tasks. The AI also demonstrably reduced the cognitive effort required to operate the prosthetic, freeing up mental resources for other tasks. Researchers emphasize that while robotics themselves are reaching a high level of dexterity, the bottleneck remains the interface between the user’s nervous system and the prosthetic device.

Challenges include the inherent noisiness of surface EMG and the need for more invasive, yet accurate, neural interfaces. The team is actively pursuing research into internal EMG and neural implants to improve signal quality and control precision. They also seek industry partnerships to move the technology from the lab to real-world clinical trials and eventual commercialization. Nice one!

This article explores how massive backend licensing agreements between tech giants and AI providers are transforming digital search and assistants, highlighting their impact on competition, innovation, and developer opportunities. By SmarterArticles.

Some main point author explains:

• Backend deals ensure default search placement and revenue streams for platforms.
• AI integration via OpenAI and Microsoft’s Copilot redefines user expectations.
• Regulatory actions aim to curb monopolistic practices and promote fair competition.
• Smaller players face significant challenges in competing with infrastructure-heavy incumbents.
• Strategic partnerships are becoming essential for survival in the evolving digital economy.

The £20 billion annual payment from Google to Apple underscores the critical role of platform control in shaping digital ecosystems. As AI-powered search and assistants evolve, companies like OpenAI, Microsoft, and Alphabet are leveraging these deals to embed advanced capabilities directly into operating systems and search interfaces. While this strengthens their market positions, it also raises concerns about reduced competition and barriers for smaller players. Developers must navigate a landscape where access to users, data, and infrastructure is tightly controlled by a few dominant entities. The shift toward AI-driven experiences demands new strategies in UX design, integration, and compliance with evolving regulations. Nice one!

Europe boasts a strong grassroots open-source community, yet struggles to translate that activity into commercial value. This article, based on Linux Foundation research, explores the disconnect between European developer contributions and funding, highlighting the need for greater C-level recognition of open-source’s value and a more robust ecosystem to foster commercial ventures. It argues that bridging this gap is crucial for Europe’s digital sovereignty in an increasingly geopolitically charged landscape. By Olimpiu Pop.

Based on research from the Linux Foundation, this article explores the surprising reality of Europe’s open-source landscape. While European developers are highly active in open-source projects – contributing more than the US or China – the region struggles to capture the commercial value generated. This is attributed to limited funding, a less developed ecosystem, and a lack of understanding of open source’s strategic importance among European executives. The piece connects this issue to the growing emphasis on digital sovereignty, arguing that a stronger European open-source ecosystem is vital for maintaining technological independence. Good read!

The article details a two-part CI/CD Pipeline Architecture Framework designed to guide teams from basic automation to a mature development platform. The first part, the “Golden Path,” is a linear, six-stage workflow that forms the essential, reliable backbone: Code Commit (with branching strategy), Automated Build (ensuring environment parity), Automated Testing (using a test pyramid), Staging Deployment (mirroring production via IaC), Production Deployment (with health checks and rollback), and Monitoring & Feedback (closing the loop with observability). By Kamil Chmielewski.

What you will learn:

• A robust CI/CD pipeline requires both a reliable core workflow (“Golden Path”) and strategic enhancements (“Pipeline Pillars”).
• The Golden Path’s six stages (Commit, Build, Test, Stage, Deploy, Monitor) must be automated, repeatable, and provide fast feedback.
• The seven Pipeline Pillars (e.g., Feature Flags, Advanced Testing, Security) are modular capabilities that address specific scaling and operational challenges.
• Implementation is progressive: master the foundational Golden Path first, then selectively adopt pillars based on team needs.
• Pipeline success should be measured using developer experience metrics and business outcomes (e.g., deployment frequency, lead time).
• The CI/CD pipeline should be treated as an internal product, with developers as the primary customers.
• Practical checklists are provided for each Golden Path step and Pipeline Pillar to guide implementation.
• The framework aims to create a platform that enables high-velocity, reliable software delivery without sacrificing security or developer productivity.

This article provides exceptional value as a comprehensive, structured guide. It successfully synthesizes established DevOps principles into a clear, actionable framework. While not introducing novel concepts, its significant contribution is the practical “Golden Path + Pillars” model and accompanying checklists, which offer teams a clear roadmap for incremental maturity. It represents a highly effective compilation of best practices for platform engineering. Great read!

A 20-line PowerShell snippet scans every subscription, flags ADF factories that haven’t executed a pipeline in 30 days, and hands you a clean-up hit-list in seconds. By Dieter Gobeyn.

This blog post provides overview of:

• Zero-run ADF factories still accrue IR, logging, and governance costs.
• “Unused” = no pipeline execution in last 30 days (configurable).
• One self-contained PowerShell script; read-only, no extra modules.
• Iterates all subscriptions, outputs table with subscription, RG, name, tags.
• Safe for Reader roles; can be scheduled in Azure Automation.
• Extend script to pipeline-level or different time windows as needed.
• Clean-up decisions remain manual—pair output with tagging/owner process.

Stale ADF pipelines bloat your tenant: they burn IR capacity, inflate log ingestion, confuse engineers, and widen the blast-radius of a security breach. Gobeyn’s post supplies a single, self-contained PowerShell script that enumerates every subscription, queries the ADF activity log for runs in the last 30 days, and returns a table of “zero-run” factories together with resource-group, subscription, and tags. Run it, eyeball the list, delete or disable—no external tools, no cost, no excuses. Perfect for FinOps squads, SREs, and data-platform owners who need a quick hygiene win before the next funding review. Nice one!

Deploying a Hugo static site to AWS S3 using the AWS CLI provides a robust, scalable solution for hosting your website. This guide covers the complete deployment process, from initial setup to advanced automation and cache management strategies. By About Rost Glukhov.

The article details deploying Hugo static sites to AWS S3 using the AWS CLI, offering a comprehensive guide from initial setup to advanced optimization. Key steps include generating static files with Hugo’s build command, configuring AWS CLI with proper credentials and IAM permissions, and setting up an S3 bucket for static website hosting.

Some key points mentioned:

• Use hugo --gc --minify to generate optimized static files.
• Configure AWS CLI with IAM permissions for S3 and CloudFront.
• Create and set up an S3 bucket for static website hosting.
• Apply bucket policies for public access or CloudFront integration.
• Deploy using aws s3 sync with --delete and --cache-control.
• Implement advanced cache control strategies for different file types.
• Set up CloudFront for CDN, SSL/TLS, and custom domains.
• Automate deployments with CI/CD pipelines (e.g., GitHub Actions).
• Monitor with CloudWatch and S3 logging.
• Troubleshoot common issues like cache invalidation and permissions.

Security considerations like restricting S3 access and using CloudFront as a CDN are highlighted. The guide explains using aws s3 sync with parameters like --delete and --cache-control to manage files and caching. Advanced strategies for cache management, such as setting different TTLs for HTML and assets, and selective CloudFront invalidation to reduce costs, are covered. Automation via CI/CD pipelines, including GitHub Actions, is demonstrated, along with monitoring through CloudWatch and troubleshooting common issues. The overall focus is on scalable, secure, and cost-effective deployment practices for static sites. Good read!

Observo AI revolutionizes privacy protection by dynamically identifying and securing sensitive data in telemetry across all organizational layers. By observo.ai.

Observo AI offers an AI-driven solution to detect and safeguard sensitive information within dynamic telemetry data. It addresses the growing challenge of hidden PII in logs, metrics, traces, and events, crucial for organizations grappling with stricter regulations and escalating breach disclosure timelines.

The article takes on journey exploring following:

• Invisible risk of sensitive data hidden in telemetry
• Why field-dependent tools can’t keep up
• How AI-Native data pipelines detect and secure PII
• What Observo AI delivers
• Real-world example: Hospital system secures PII and simplifies compliance

Observo AI offers a transformative approach to securing sensitive data in modern telemetry, addressing critical gaps in traditional tools. By leveraging AI-native pipelines, it provides real-time detection, protection, and cost-effective retention, significantly reducing compliance risks and operational burdens. This solution represents a substantial advancement in data security, appealing to organizations managing complex, dynamic data environments. Good read!

Black Friday’s chaos is a perfect example of how message queues can transform a fragile system into a resilient one, smoothing out traffic spikes and preventing system crashes. By Jakub Slys.

This blog post explains the core function of message queues in distributed systems, illustrating how they decouple producers and consumers to handle uneven workloads. It highlights the problem of system overload during peak demand (like Black Friday) and how queues act as a buffer, absorbing excess requests and preventing failures. The article targets developers and DevOps engineers interested in understanding how to build more robust and scalable applications. Essentially, queues are a critical tool for managing asynchronous communication and improving system stability.

Key Points:

• Decoupling: Message queues separate producers and consumers, allowing them to operate independently.
• Buffering: They absorb traffic spikes, preventing system overload.
• Asynchronous Communication: They enable non-blocking operations, improving responsiveness.
• Scalability: Consumer groups allow scaling out processing capacity.
• Fault Tolerance: Queues ensure messages are not lost even if consumers are temporarily unavailable.
• Idempotency: Producers and consumers need to handle potential message duplicates.
• Event-Driven Architecture: Queues are a foundational element of this architectural style.

Ultimately, the article argues that understanding message queues is essential for building modern, scalable, and fault-tolerant distributed systems, moving beyond simply handling immediate requests to embracing a more reactive and resilient approach to software design. Nice one!

This article compares Java and C++ performance, debunking myths and revealing Java’s strengths in memory management, execution speed, and optimizations. Find out why Java might be the unsung hero of high-frequency trading and server applications. By Johnny’s Software Lab LLC.

The main observations in teh article:

• Java’s garbage collection and compaction improve memory locality and reduce fragmentation.
• Java’s high-tier JIT-compiled code can match C++’s performance, but warm-up time and mixed code execution can slow down Java.
• Java’s latency is less predictable than C++ due to GC pauses and mixed code execution, but it can achieve low latency with proper tuning.
• Java’s runtime profiling and deoptimization checks enable aggressive, speculative optimizations.
• Java can emit more efficient instructions based on the runtime environment.
• The choice between Java and C++ depends on the specific use case and requirements

You will read about valuable insights into Java’s performance capabilities, challenging the notion that C++ is always superior. It highlights Java’s strengths in memory management and optimizations, making a strong case for its use in specific scenarios like high-frequency trading and long-running server applications. The article represents a significant step towards demystifying Java’s performance and encouraging informed language choices based on specific use cases.

The author concludes that Java and C++ have their strengths and weaknesses. While C++ excels in predictable latency and resource efficiency, Java shines in long-running server applications and high-frequency trading systems. The choice between the two depends on the specific use case and requirements. Good read!