Developer Builds Apple Virtualization Framework Support Into Flutter Desktop App WSL Manager

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WSL Manager, a Flutter desktop app originally launched in 2021 to simplify Windows Subsystem for Linux management, has released a version 2 that also runs on Apple silicon Macs. The new macOS build manages native Linux and macOS virtual machines using Apple's Virtualization framework, installable via Homebrew. Because Flutter cannot directly hold a VZVirtualMachine object, the developer created a separate Swift command-line helper called vmctl, which runs as a detached daemon and communicates with the Dart frontend by printing JSON to stdout. A shared backend interface in Dart allows both the WSL and Apple VM helpers to be driven by the same codebase, with a capabilities record used instead of platform checks. The macOS implementation required solving several low-level challenges, including IP detection via DHCP lease files, SSH session handling, cloud-init seed generation without third-party tools, and in-process qcow2-to-raw disk conversion.

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Laptop GPU Outperforms 12-Core CPU by 4.3x Running Google Gemma 4 Model

A developer benchmarked Google's Gemma 4 E2B language model on a single laptop equipped with a 13th-gen Intel Core i7-1360P and an Nvidia GTX 1650 Ti (4 GB) GPU. The test used the llama.cpp server with identical settings on both arms, differing only by whether GPU layers were offloaded via the -ngl flag. Across eight test cells varying prompt and output lengths, the GPU achieved a median decode speed of 4.27 times faster than the CPU, with individual ratios ranging from 4.04x to 4.34x. The GPU also delivered prefill times roughly 3.63x faster, though time-to-first-token remained linearly dependent on prompt length for both devices. The results highlight that even an older, entry-level laptop GPU can offer substantial inference gains over a modern multi-core CPU for small language models.

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ProgrammingDEV Community ·

Developer uses MM/DD/YYYY dates to teach PostgreSQL extensions, indexes, and custom types

A developer at DEV Community has created an intentionally flawed PostgreSQL project that stores dates as plain MM/DD/YYYY text strings to demonstrate four advanced database features. The project covers building C extensions, expression indexes, custom operators, and specialized GiST index types. Using the awkward US-style date format as a teaching constraint, the tutorial walks through why a simple expression index often suffices and when a custom base type becomes necessary. The GiST index built in the project can efficiently answer queries filtered by month, day, or year independently, which suits seasonal or birthday-style lookups. The author explicitly warns against using this approach in production, framing the exercise purely as a hands-on learning tool.

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ProgrammingDEV Community ·

Radio lets AI agents from different providers chat in one shared channel

A tool called Radio, built by Plasma, allows AI agents from different providers to communicate directly without human go-betweens. Currently, users working with multiple agents like Claude Code, Codex, Cursor, and Grok must manually copy and paste outputs between them, acting as a middleman. Radio solves this by giving agents a shared URL-based channel where they can exchange messages in real time, across machines and devices. Setup requires just four steps: creating a channel at radio.plasma.ai, copying the link, pasting it into one or more agents, and sending an initial message. The tool requires no custom integrations or provider lock-in, working with any agent capable of fetching a URL.

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ProgrammingDEV Community ·

How Python Scripts Became a Low-Risk Toolkit for a Legacy Delphi ERP

A solo engineer maintaining a large Delphi-based ERP system for a footwear factory found a practical middle ground by adding Python tooling alongside the existing codebase rather than attempting a full rewrite. The Python scripts, stored in a separate tools directory and never deployed to users, handle tasks like schema dumping, SQL query analysis, and keyboard shortcut documentation that would be difficult or impractical to build in Delphi. A versioned JSON schema file proved especially valuable, giving the engineer a historical record of database changes and making AI coding assistants reliably accurate on a 342-table database. The approach treats the legacy source code as a queryable dataset, allowing new analytical and validation tooling to be built quickly without touching the core application. The engineer argues this strategy of augmenting rather than replacing a stable legacy system is a pragmatic solution for solo maintainers who cannot afford the risk of a parallel rewrite.

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