log4cplus User Manual: C++ Logging Library Setup and Key Features

·1 views

log4cplus is a thread-safe C++ logging library modeled on the popular Java-based log4j framework. It can be installed via package managers like apt, built from source using CMake, or integrated into projects through the Conan package manager. The library supports multiple configuration methods, including property files that allow log levels and destinations to be changed without recompiling the application. Developers can route log output to custom callbacks, enabling integration with UI systems or telemetry pipelines. For performance-sensitive applications, log4cplus offers asynchronous appenders to offload I/O and avoid blocking the calling thread.

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)

Developer builds custom SQLite sync for React Native app, skipping third-party libraries

A developer building Skulpt, an open-source iOS and Android workout tracker, chose to write a custom offline sync layer in TypeScript rather than adopt existing solutions like PowerSync, ElectricSQL, WatermelonDB, or RxDB. Each established library was found to solve problems beyond what the app actually required, introducing unnecessary complexity or infrastructure dependencies. The custom solution centers on a local SQLite sync queue that batches pending operations and sends them to a lightweight HTTP API when connectivity is available. Conflicts are resolved using a simple last-write-wins strategy, deemed sufficient since workout data is not collaboratively edited in real time. A single environment variable controls whether sync is active, allowing the app to function as a fully offline tool when the variable is unset.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

SPM Modular Architecture Keeps Xcode Projects Thin and Scalable

A proposed iOS architecture uses Swift Package Manager (SPM) to move nearly all application code out of the Xcode project, leaving it with as few as 10–15 lines. Every feature, shared component, and UI element lives in fully self-contained Swift Packages, each carrying its own unit tests, mocks, and UI tests. The design enforces a strict separation between the UI layer, running on the MainActor, and an isolated concurrent layer built with Swift Structured Concurrency actors and Sendable models. Dependencies are declared explicitly in each Package.swift file and pinned to exact versions, avoiding the merge conflicts and opacity caused by Xcode's built-in package UI. A reference project and an agentic harness document are published on GitHub to help teams and AI coding agents contribute features while respecting the architecture's patterns.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

Developer uses same AI to write and review code, catching 3 self-made bugs

A developer building a Windows screenshot-translation app discovered that asking an AI to review its own freshly written code — by role-switching to a 'skeptical senior engineer' — uncovered three real bugs the same model had missed during writing. The bugs emerged in a fix for an OCR pipeline that was skipping white text on dark backgrounds, and included flawed shape logic, over-aggressive noise removal, and incorrect brightness measurement. The developer notes that writing and reviewing code engage different cognitive orientations: writing checks intent fulfillment, while reviewing questions whether the intent was correct to begin with. Crucially, not every finding from the AI review was acted on immediately; each was triaged as a real bug, a design choice, or a false positive before any change was made. The experiment suggests that role framing — not model capability — can meaningfully shift what an AI notices in its own output.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

Developer Builds Minimal Open-Source AI Meeting Assistant to Demystify the Tech

A developer is creating a stripped-down, open-source AI meeting assistant to demonstrate how such tools work under the hood, without the complexity of commercial products like Otter or Fireflies. The project focuses on core functionality: recording audio, generating transcripts, summarizing meetings, and extracting action items and decisions. It uses a React and TypeScript frontend, a FastAPI backend, SQLite for storage, and OpenAI Whisper for transcription. The codebase follows a monorepo structure and will be published on GitHub so other developers can follow, contribute, and learn. Intentionally excluded features include Zoom or Teams integrations, mobile apps, analytics dashboards, and AI agents, keeping the scope manageable for a working first release.

0 comments Read more at DEV Community