CodeIsGo.com

AWS’s 2025 was a pivotal year, marked by the rise of Agentic AI, custom silicon advancements, and real multicloud integration, fundamentally altering how developers build and deploy software. By Damien Gallagher.

Some key points in the article:

• Agentic AI: AWS introduced autonomous agents like Amazon Nova 2 models and Bedrock, enabling developers to build intelligent systems that perform tasks independently.
• Custom Silicon: Graviton5 and Trainium3 offer improved performance and energy efficiency, making custom silicon a cornerstone of AWS’s compute strategy.
• Multicloud Integration: Partnerships with Google and Azure provide practical multicloud solutions, enhancing interoperability and flexibility.
• Developer Experience: Updates like Lambda Durable Functions and Kiro IDE improve developer productivity and simplify complex workflows.
• Global Infrastructure: New regions in Mexico, Thailand, Taiwan, and New Zealand expand AWS’s global footprint, ensuring low-latency and data residency compliance.
• Storage Enhancements: S3 Vectors and S3 Tables offer scalable and cost-effective solutions for managing vector embeddings and running analytics.
• Strategic Deprecations: AWS’s decision to deprecate services like AWS Cloud9 and AWS WAF Classic reflects a commitment to modernizing its service offerings.
• Customer Feedback: The reversal of CodeCommit deprecation demonstrates AWS’s responsiveness to customer needs and feedback.

AWS’s 2025 is a testament to the company’s forward-thinking approach to cloud computing. The focus on agentic AI, custom silicon, and multicloud integration represents a significant advancement in the field, providing developers with more powerful and flexible tools. The strategic deprecations and global infrastructure expansions further solidify AWS’s position as a leader in the cloud market. Overall, the advancements made in 2025 set a new benchmark for what developers can expect from cloud services, making it a pivotal year for AWS and the broader tech community. Nice one!

Enhance your Angular forms with metadata and ARIA attributes for improved user experience, inclusivity and accessibility. By Danny Koppenhagen.

The article covers:

• Assigning metadata to form fields enhances user guidance and experience.
• Metadata keys are created using createMetadataKey() and assigned within the form schema.
• The FormFieldInfo component displays field information, validation errors, and loading states.
• The FieldAriaAttributes directive automatically manages ARIA attributes for improved accessibility.
• ARIA attributes managed include aria-invalid, aria-busy, aria-describedby, and aria-errormessage.
• The article includes a demo application on GitHub and Stackblitz for further exploration.

This article provides a thorough guide on enhancing Angular forms with metadata and ARIA attributes, making it a valuable resource for developers aiming to improve form accessibility and user experience. It represents a significant advancement in leveraging Angular Signal Forms for creating inclusive and user-friendly applications. Good read!

The OpenAI report reveals a rapid adoption of AI in enterprises and ChatGPT usage is increasing significantly, with users saving up to 60 minutes a day. The report suggests that AI usage is directly correlated to an increase in efficiency. By Mark McCormick.

OpenAI’s “The State of Enterprise AI” report reveals that enterprise AI adoption is rapidly accelerating. Key findings include a 9x increase in ChatGPT Enterprise seats year-over-year and a 320x increase in token consumption per organization. The report underscores that AI helps solve complex enterprise problems, requiring reliability, safety, and security at scale. This phase of enterprise AI adoption is said to be entering a phase where significant economic value is created through scaled use cases.

The report also highlights that AI usage directly correlates with time savings. Enterprise workers sending 30% more ChatGPT messages since November 2024 are saving 40 to 60 minutes per day. Frontline workers in the 95th percentile of adoption, especially in coding, writing, and analysis, generate significantly more messages than median users. The report concludes that the benefits of AI scale with the depth of use, making it crucial for enterprises to integrate AI across multiple tasks to maximize efficiency. Interesting read!

Nvidia’s Rubin platform cements its dominance in AI infrastructure, locking customers into a vertically integrated ecosystem that promises unmatched efficiency at a cost. By Marcus Schuler.

Nvidia’s Rubin platform, announced at CES, represents a bold move towards vertical integration in AI infrastructure. Comprising six specialized chips, Rubin is designed for optimal performance when used together, locking customers into Nvidia’s ecosystem. The platform promises a 10x reduction in inference token cost, a significant advantage for running large language models. However, this efficiency comes with strings attached, requiring the use of Nvidia’s entire hardware stack.

Rubin’s impact extends beyond data centers to automotive AI, with Mercedes-Benz adopting Nvidia’s autonomous driving stack. This move undercuts Tesla’s pricing and offers a safer, more integrated solution. The coordinated endorsements from tech CEOs highlight Nvidia’s market power and the strategic importance of securing GPU supply.

For developers and DevOps engineers, Rubin presents both opportunities and challenges. While it offers unmatched performance, it also deepens dependency on a single supplier. UX designers will need to consider the implications of this dependency on AI-driven features and user experiences. Nice one!

Master GitHub Copilot for IaC by wiring repository‑scoped custom instructions, tightening naming and tooling rules, and treating the AI as a co‑pilot—not a replacement—for safer, faster Terraform deployments. By Lukas Rottach.

The blog post explains some good practices:

• Precision – use exact wording and list every allowed version to avoid ambiguous guesses.
• Conflict avoidance – ensure new rules don’t contradict existing ones.
• Structure exposure – map the directory layout so Copilot can locate entry points quickly.
• Example‑driven guidance – embed short snippets illustrating conventions.
• Iterative rollout – start with core rules, validate Copilot’s adherence, then expand.

The author warns that overly large instruction files (> 1 000 lines) exceed the model’s context window, causing inconsistent behavior. He stresses that developers retain full accountability for security, stability, and architectural decisions; Copilot merely accelerates routine coding tasks.

Lukas Rottach’s post is a practical handbook for DevOps engineers who want to embed GitHub Copilot Agents into their IaC processes, particularly Terraform projects on Azure. After a brief personal backstory, he positions Copilot as a “co‑driver”: it can surface patterns, generate boilerplate, and respect project‑wide policies, but the engineer remains the ultimate decision‑maker. Good read!

Amazon Bedrock’s AgentCore Gateway now supports API Gateway, enabling seamless integration of existing REST APIs into agentic applications using the Model Context Protocol (MCP), enhancing security and observability. By Sparsh Wadhwa, Dhawalkumar Patel, and Heeki Park.

The article provides a detailed walkthrough of setting up an existing REST API with API Gateway as a target for AgentCore Gateway. It covers the prerequisites, including an AWS account with an existing REST API and necessary IAM permissions. The walkthrough includes steps for setting up inbound and outbound authorization, with options for IAM-based authorization and API key authorization. Code examples using Boto3 are provided for creating a gateway and configuring targets, along with examples of target configurations and credential provider configurations.

The integration supports IAM and API key authorization, ensuring secure connections between AgentCore Gateway and API Gateway. Observability is a key feature, with detailed logs and metrics available through Amazon CloudWatch, AWS CloudTrail, and AWS X-Ray. The article also includes a section on testing the gateway with the Strands Agent framework, demonstrating how to list and call available tools from the MCP server.

The article concludes by emphasizing the benefits of this integration, such as simplifying the connection between API Gateway and AgentCore Gateway, eliminating the manual export/import process, and enabling the use of existing REST APIs as tools for agentic applications. It also highlights the built-in security and observability features, making it easier for developers and DevOps engineers to modernize their API infrastructure for AI-powered systems. Nice one!

Unlock the power of ChatGPT within your terminal with this practical tutorial demonstrating how to build a real-time command explanation tool using the OpenAI Realtime API. By Surya Bhaskar Reddy Karri.

Developers spend a huge chunk of their time in the terminal like running commands, reading logs, debugging scripts, working with git, managing servers, and automating tasks.

Author will walk you through:

• How to Build an LLM-Powered CLI Tool in Python
• Why AI Belongs in the Terminal
• How to Bring AI-Native Interactions Directly Into Your Terminal
• What Is the OpenAI Realtime API?
• Project Overview: Building llm-explain
• Project Structure
  • Step 1: Implement the Realtime Client
  • Step 2: Create the CLI Tool
  • Step 3: Run the Tool
  • Step 4: Optional — Add Tool Calling (AI That Executes Commands)

The article tackles the frustration of traditional CLI environments – reliance on memorization, syntax errors, and time-consuming debugging – by proposing an AI-augmented solution. It introduces the OpenAI Realtime API as a key component, allowing for low-latency, token-by-token streaming of model responses directly into the terminal, mimicking a ChatGPT experience within the command line. The tutorial provides a step-by-step implementation using Python and a lightweight UI, showcasing how to send prompts, receive explanations in real time, and manage complex commands. The inclusion of “tool calling” opens possibilities for creating more sophisticated agents capable of executing actions – such as fixing Git commands or analyzing logs. This approach transforms the terminal into an interactive assistant, drastically reducing developer friction. Nice one!

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!