Qubes OS Tips: File Transfers, Xen Store Commands, and Network Fixes

·1 views

A developer who used Qubes OS as their primary professional operating system for two years has shared a collection of practical tips and commands from an unpublished draft guide. The article covers file transfer between virtual machines using qvm-copy commands, managing Xen Store metadata via xenstore and qubesdb utilities, and restarting NetworkManager in the sys-net qube from dom0. It also explains how to configure local network settings using startup scripts and qubesdb-read commands within individual qubes. The author notes that while Qubes OS is functional, its complex and unreliable upgrade process led them to abandon it for business use, retaining only a test machine to monitor improvements.

Read the full story at DEV Community

This is an AI-generated summary. ShortSingh links to the original source for the complete article.

Discussion (0)

How gRPC Works Internally and Why It Outperforms Traditional RPC Methods

gRPC is a modern Remote Procedure Call framework developed by Google, designed to make service-to-service communication faster and more efficient than older approaches like CORBA or SOAP. At its core, gRPC relies on Protocol Buffers, a binary serialization format that produces smaller payloads and faster encoding compared to text-based formats like JSON or XML. Developers define service contracts in a .proto file, which specifies methods, request types, and response types, serving as a shared blueprint across services. The framework supports multiple programming languages including Go, Java, Python, C++, and Node.js, making it broadly accessible for polyglot microservice architectures. Its combination of compact data encoding, schema-driven contracts, and HTTP/2 transport makes gRPC a high-performance choice for inter-service communication.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

Developer Builds AI Reverse Hiring Platform Where Companies Pitch Candidates Anonymously

A developer has built a reverse hiring platform that flips the traditional recruitment model by requiring employers to seek out candidates rather than the other way around. Built with Next.js 15, Supabase, OpenAI GPT-4o-mini, and hosted on Vercel, the platform lets candidates upload resumes that AI parses into anonymous profiles, hiding real identities behind codes like 'JN-2847'. Employers browse the talent pool, receive AI-generated match scores based on cosine similarity of text embeddings, and must disclose salary upfront before sending any interview request. A candidate's true identity is only revealed if they choose to accept the employer's request. The project addresses what the developer describes as a broken hiring system where candidates face automated rejections and companies spend roughly €25,000 per hire on recruiting agencies.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

Tool Lets Developers Detect Regex Denial-of-Service Vulnerabilities Before Deployment

A developer has released ReDoScan, a free web checker and REST API designed to identify regular expressions vulnerable to catastrophic backtracking before they reach production. Catastrophic backtracking, classified as CWE-1333 or ReDoS, occurs when certain regex patterns create an exponentially growing search space on crafted non-matching inputs, pinning CPU usage at 100%. The vulnerability affects all major backtracking-based runtime engines, including Node.js, Python, Java, and Ruby. Common triggers include nested quantifiers, overlapping alternation, and prefix overlap — patterns that frequently appear in real-world validators. The tool offers static analysis with a five-level risk rating and integrates with CI/CD pipelines via a RapidAPI-hosted endpoint, with a free tier allowing 1,500 scans per month.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

Why AI Agents Struggle to Complete Long Workflows Without Human Help

Despite major advances in AI models and infrastructure over the past three years, very few AI applications can autonomously run complete business workflows without human oversight. The core problem, according to builders of the Mano-AFK autonomous app platform, is not executing individual actions but sustaining a reliable decision loop across dozens or hundreds of steps. Agents frequently fail by making an error, failing to detect it, and then compounding it over subsequent steps — a pattern the team addressed by adding a separate adversarial reviewer agent to catch misaligned decisions in real time. Another key finding was that giving agents access to external memory via bash tools boosted task success rates from 56% to 90%, as models tend to lose focus on early constraints after 20–30 steps even within a sufficient context window. These insights suggest that robust AI agents require not just better models, but smarter loop management, error verification, and persistent state tracking.

0 comments Read more at DEV Community