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This post explores the limitations of the current Docker volume plugin ecosystem, emphasizing the difficulty in finding unprivileged solutions. The author details their journey in creating a custom volume plugin as a way to address this limitation. By Adam Faris.

The article focus is on:

• The lack of readily available unprivileged Docker volume plugins presents a challenge for many use cases, particularly those prioritizing security
• Building a custom plugin requires navigating complex build processes and leveraging specific tools like the Go plugin SDK.
• The author’s project provides a functional example of an unprivileged volume plugin that can perform basic file operations, demonstrating a viable approach to data persistence.
• This work underscores the need for more lightweight and flexible solutions within the Docker volume plugin ecosystem and offers valuable insights for developers interested in contributing to this area.

The author provides a comprehensive overview of the steps involved, including creating a root filesystem, building a Docker image, and enabling the custom plugin. This work offers a practical insight into developing lightweight Docker volume plugins and highlights potential areas for future exploration in this domain. Good read!

Observability is crucial for managing the increasing complexity of modern distributed software systems, especially those built with Docker containers and microservices. Traditional monitoring often falls short, leading to slow troubleshooting and increased Mean Time To Resolution (MTTR). End-to-end observability, particularly through distributed tracing, provides deep insights into system behavior, enabling proactive detection of performance issues and improved reliability. By Aditya Gupta.

Main sections in the article:

• Observability vs. Monitoring
• Challenges in Distributed Systems
• Distributed Tracing
• OpenTelemetry
• Integration benefits
• Advanced techniques
• CI/CD integration
• Future trends

The article highlights OpenTelemetry and Jaeger as key tools for achieving this. OpenTelemetry is an open standard for instrumenting applications and collecting telemetry data, while Jaeger is an open-source distributed tracing system that visualizes and analyzes this data. Their integration allows developers to pinpoint bottlenecks and issues that are often obscured by the transient nature of containers and asynchronous microservice communication.

Implementing observability involves instrumenting applications with OpenTelemetry, containerizing them with Docker, and deploying them with Jaeger using tools like Docker Compose. This approach transforms debugging from guesswork to a timeline-driven analysis, significantly reducing incident response times. Major tech firms already leverage these tools to enhance performance, user experience, and system reliability.

Docker is commonly used for server-side and command-line apps. However, with the right setup, you can also run GUI-based applications inside containers. These containers can include GUI libraries and display tools, which enable apps to run in a secure and isolated environment. Docker containers can run GUI applications by configuring display sharing with the host system. This approach packages applications with their dependencies while maintaining isolation, enabling consistent cross-platform deployment without cluttering the host system. By Anees Asghar.

The article further explains:

• Isolated environments prevent system conflicts
• Consistent behavior across different machines
• Lightweight alternative to virtual machines
• Easy testing and debugging capabilities
• Cross-platform Linux GUI support

Running GUI-based applications in Docker is a great way to extend what containers can do beyond the command line. With the right setup, you can launch desktop apps from a container as if they were installed on your system. It’s a simple yet powerful approach for testing, development, or exploring Linux tools in a clean environment. Good read!

Context-aware computing enables ultra-low-power operation while maintaining high-performance AI capabilities when needed. The rise of AI-driven IoT devices is pushing the limits of today’s microcontroller unit (MCU) landscape. While AI-powered perception applications—such as voice, facial recognition, object detection, and gesture control—are becoming essential in everything from smart home devices to industrial automation, the hardware available to support them is not keeping pace. By Todd Dust.

You will learn about:

• Traditional MCUs are Inadequate: The existing landscape of 32-bit MCUs cannot efficiently handle the computational and power requirements of modern AI-driven IoT applications.
• The Need for Energy Efficiency: Many current AI MCUs are not optimized for the ultra-low-power, always-on nature of IoT devices, leading to poor battery life and performance trade-offs.
• Multi-Gear Architecture is the Solution: A tiered architecture that dynamically shifts between ultra-low-power, efficiency, and high-performance compute domains is key to balancing power consumption and AI processing needs.
• Context-Aware Computing: The new approach enables devices to use only the necessary compute power for a given task, from simple environmental monitoring to complex AI inferencing, dramatically improving energy efficiency.
• Standardization is Crucial: Supporting common platforms like FreeRTOS and Zephyr helps standardize development, making it easier for designers to adopt these advanced MCUs in a rapidly evolving IoT space.

The rise of AI in IoT devices has exposed the limitations of traditional MCUs, which struggle with the performance and power demands of modern workloads. Current AI-ready hardware is often inflexible, proprietary, or repurposed from other domains, resulting in poor energy efficiency for always-on, battery-powered devices. This creates a significant gap in the market for a new class of processors.

To address this, a new multi-tiered MCU architecture offers a more intelligent solution. It uses a “multi-gear” approach with three distinct domains: an ultra-low-power “always-on” tier for constant monitoring, an “efficiency” tier for basic AI tasks, and a “performance” tier for demanding computations. This design dynamically allocates the right amount of power, ensuring high performance when needed while drastically conserving energy during idle or low-intensity periods. This context-aware computing represents a major step forward for creating scalable and efficient AI-enabled IoT devices. Nice one!

The traditional centralized model of cloud computing presents significant cybersecurity risks, creating a single point of failure and suffering from latency that can delay critical security updates. Edge computing emerges as a superior, decentralized solution that brings processing power closer to where data is generated. By Andrew Garfield.

Further in this article:

• Cloud’s Centralized Vulnerability: The centralized architecture of cloud computing creates a single point of failure, making it a prime target for large-scale cyber attacks.
• Edge Computing as a Decentralized Solution: Edge computing decentralizes processing, bringing it closer to the data source, which reduces latency and improves performance.
• Real-Time Threat Mitigation: By processing data locally, edge devices can detect and respond to security threats in real-time, minimizing potential damage.
• Reduced Attack Surface: Edge computing limits the transmission of sensitive data to the cloud, thereby shrinking the overall attack surface and reducing opportunities for data breaches.
• Growing Adoption in Critical Industries: Sectors like industrial automation, smart cities, and healthcare are already leveraging edge computing to enhance their security posture against sophisticated cyber threats.

This proximity enables real-time threat detection and response, significantly reducing the window for potential attacks. By processing data locally, edge computing also minimizes the attack surface, as less sensitive information needs to be transmitted to the cloud. This paradigm shift is already being adopted in critical sectors like industrial automation, smart cities, and healthcare, proving its effectiveness in safeguarding against modern cyber threats. Edge computing is not just an alternative but the future direction for a more resilient and secure digital infrastructure. The proactive approach to security enables businesses to stay ahead of the threat landscape and minimize the risk of data breaches. Good read!

Sockets threat research team uncovered a destructive supply-chain attack targeting Go developers. In April 2025, three malicious Go modules were identified, using obfuscated code to fetch and execute remote payloads that wipe disks clean. The Go ecosystem’s decentralized nature, lacking central gatekeeping, makes it vulnerable to namespace confusion and typosquatting, allowing attackers to disguise malicious modules as legitimate ones. By @socket.dev.

You will learn the following:

• Go’s open ecosystem, while flexible, is prone to exploitation due to minimal validation.
• Namespace confusion increases the risk of integrating malicious modules.
• Obfuscated code can hide catastrophic payloads like disk-wipers.
• Disk-wiping attacks cause permanent data loss, with no recovery possible.
• Proactive security, including audits and real-time threat detection, is critical for protection.

The payloads, targeting Linux systems, download a script that overwrites the primary disk with zeros, causing irreversible data loss and rendering systems unbootable. This attack highlights the severe risks in open-source supply chains, potentially leading to operational downtime and significant financial damage. Socket recommends proactive security measures like code audits and dependency monitoring to mitigate such threats. Good read!

Roxy Light shares an insightful journey of building a custom Lua interpreter in Go, highlighting the unique aspects of both languages. The project, spanning months, was driven by the inadequacy of existing Lua interpreters for specific needs. Lua, a dynamically typed language, supports various data types like nil, booleans, numbers, and tables, which are crucial for its functionality.

Lua is a dynamically typed language, so any variable can hold any value. Values in Lua can be one of a handful of types. The article also explains:

• Lua Language Overview: Dynamically typed with diverse data types.
• Interpreter Structure: Utilizes Go packages for a streamlined pipeline.
• Data Representation: Go interfaces effectively map to Lua values.
• Development Advantages: Go’s features ease interpreter construction.
• Challenges Faced: Notable issues in error handling and library compatibility.

The interpreter’s structure in Go is divided into packages for scanning, parsing, and execution, leveraging Go’s interfaces for Lua value representation. This design choice, along with Go’s garbage collector and testing tools, simplified development compared to the PUC-Rio Lua implementation. Challenges included error handling and compatibility issues with Lua’s standard libraries. Nice one!

Most developer time is spent in editors, and it’s where performance is most important. We want editors to load large projects quickly, and respond quickly in all situations. Modern editors like Visual Studio and Visual Studio Code have excellent performance as long as the underlying language services are also fast. With our native implementation, we’ll be able to provide incredibly fast editor experiences. By Anders Hejlsberg.

Microsoft is revolutionizing TypeScript with a native port of its compiler and tools, promising a 10x performance boost. Announced by Anders Hejlsberg, this initiative aims to enhance developer experience in large codebases by slashing build times, editor startup, and memory usage. The native implementation, expected by mid-2025, already shows impressive results, with build times for projects like VS Code dropping from 77.8s to 7.5s.

Main points made in the article:

• Performance Boost: Native TypeScript port offers up to 10x faster build times across various codebases.
• Editor Efficiency: Editor load times improved by 8x, enhancing developer productivity.
• Versioning Roadmap: TypeScript 7.0 will introduce the native codebase, with TypeScript 6.x maintained for compatibility.
• Future Prospects: Enables advanced refactorings and AI tools for an evolved coding experience.

When the native codebase has reached sufficient parity with the current TypeScript, it will be released as TypeScript 7.0. This is still in development and it’ll be announcing stability and feature milestones as they occur. Nice one!

This article explores using HCP Vault to anonymize sensitive data in retrieval augmented generation (RAG) workflows with IBM Granite and Ollama. It addresses the risk of large language models (LLMs) leaking personal identifiable information (PII) by employing Vault’s transform secrets engine for data masking and tokenization. A demo illustrates masking credit card numbers and tokenizing billing addresses for vacation rental bookings, ensuring safe data handling in a local test environment using Open WebUI. By Rosemary Wang.

Main points discussed:

• RAG and PII Risks: RAG enhances LLM output but risks exposing sensitive data like PII, a top concern in OWASP 2025 risks for LLMs.
• HCP Vault Solution: Vault’s transform secrets engine masks and tokenizes data to prevent leaks.
• Demo Setup: Uses Terraform to configure Vault, Python scripts for data generation, and Docker for local LLM testing with Ollama and Open WebUI.
• Data Protection: Masking hides credit card details (non-reversible), while tokenization with convergent encryption allows address analysis without revealing plaintext.
• Controlled Access: Authorized agents can decode tokenized data via Vault, ensuring security.

By masking or tokenizing sensitive data before augmenting a LLM with RAG, you can protect access to the data and prevent leakage of sensitive information. In this demo, an LLM in testing and other applications by default do not require access to sensitive information like credit card information or billing street address. They can still analyze and provide other information without leaking payment information. Good read!

In a startling breach of digital trust, researchers from the University of Zurich conducted a secret experiment on Reddit, deploying sophisticated AI bots to influence human opinion on the popular r/changemyview forum. These bots, operating without user or platform consent, adopted convincing human personas—from a rape victim to a Black man critical of the Black Lives Matter movement—and posted over 1,000 comments to sway discussions on contentious topics.

The key points discussed in the article:

• Non-consensual Research Carries High Risk: Conducting secret AI experiments on public platforms without user or platform consent can lead to severe ethical backlash, community outrage, and potential legal action.
• AI’s Persuasive Power is Advancing: The experiment demonstrates that AI bots can convincingly mimic complex human identities and engage in nuanced, persuasive arguments on sensitive and contentious topics, raising concerns about manipulation.
• Ethics vs. Academia: The incident highlights a growing tension between the pursuit of academic research and the ethical standards of online communities and platforms, which prioritize user safety and consent.
• Enforcement Gaps: Platform-level rules and academic ethics guidelines may not be sufficient to prevent controversial research, especially when institutional recommendations are not legally binding on the researchers.
• Demand for Transparency: There is a strong and clear demand from users and online communities for transparency and explicit consent when interacting with AI in social spaces, reinforcing the need for clear disclosure.

The revelation has triggered a significant backlash. Reddit has condemned the study as “deeply wrong on both a moral and legal level,” banning the bot accounts and threatening legal action against the university. The subreddit’s moderators, feeling their community was violated, filed an ethics complaint, demanding the research not be published. They emphasized that their forum is a “decidedly human space” and that users do not consent to being experimented upon by AI.

In response to the outcry, the University of Zurich has launched an investigation, and the researchers have agreed not to publish their findings. The incident serves as a stark case study on the ethical minefield of AI research in public online spaces, highlighting the growing conflict between academic inquiry and the fundamental rights of digital citizens to transparency and consent. Interesting read!