Developer Builds Professional Four-Wheel Color Grading Tool Inside a Browser Editor

·1 views

A developer has added a full color-grading workflow to Timeline Studio, an open-source, browser-based video editor. The implementation features four color wheels — shadows, midtones, highlights, and global offset — each with independent hue, saturation, and luminance controls. Color wheels allow editors to adjust color direction and intensity across specific luminance ranges, avoiding the problem of a single control affecting all areas of a frame equally. The tool also supports independent keyframes for all 15 grading properties and applies the same animated grade across preview, transitions, and final export. The project is publicly available on GitHub and aims to bring desktop-grade color tools to a local-first, browser-run editing environment.

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)

Guide: Migrate Laravel 13 and Next.js App to Zero-Downtime VPS Deployments

A technical tutorial outlines how to migrate a live Laravel 13 and Next.js monorepo on a single Ubuntu VPS to a zero-downtime releases-and-symlink deployment pattern. The approach replaces direct file overwrites with timestamped release directories, so the live path is updated via an atomic symlink switch that takes only microseconds. Key steps include pausing queue workers, creating shared directories for persistent files like .env and Laravel storage, and copying the existing app as the first named release. The guide ensures the app stays operational throughout migration, with no Nginx configuration changes needed since it follows symlinks transparently.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

Design skills dominate Claude Code's fastest-rising installs, data shows

According to data pulled on August 12, 2026, from Skillselion — a live catalog tracking agent skill installs — four of the seven fastest-rising Claude Code skill searches are focused on visual and interaction design quality. The four design-oriented skills, including Anthropic's own Frontend Design and community entries like Design Taste Frontend and Impeccable, collectively account for over 1.6 million installs. Search growth for these skills ranges from 20% to 70%, suggesting developers are actively seeking ways to make AI-generated interfaces look less generic. The remaining three trending skills — Grill Me, Caveman, and Ponytail — reflect a parallel demand for restraint, pushing agents to plan carefully, reduce token usage, and minimize unnecessary output. Taken together, the trend points to developers prioritizing quality and discipline over raw output volume from their AI coding agents.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

Why AWS Lambda Cold Starts Can Spike Your Go API Response Time to 800ms

AWS Lambda functions written in Go may appear fast under normal conditions, often logging response times as low as 12ms, but can experience dramatic latency spikes reaching 800ms. This phenomenon, known as a cold start, occurs when a Lambda function is invoked after being idle, forcing the cloud environment to initialize a new execution container from scratch. The gap between measured and real-world performance can mislead developers into underestimating their API's latency profile. Understanding cold start arithmetic is critical for engineers building serverless Go services on AWS who require consistently low response times.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

NVIDIA Triton Inference Server: Open-Source Tool to Speed Up AI Model Deployment

NVIDIA's Triton Inference Server is an open-source software platform designed to simplify and accelerate the deployment of trained AI models in production environments. It supports multiple popular frameworks including TensorFlow, PyTorch, ONNX Runtime, and TensorRT, allowing a single server instance to serve models from different origins. Triton works across both CPUs and NVIDIA GPUs, with performance optimized significantly on GPU hardware. The tool uses techniques such as dynamic batching to group incoming requests and maximize hardware utilization, reducing inference latency. Deployment is primarily handled via Docker containers, making it accessible for teams looking to standardize their AI serving infrastructure.

0 comments Read more at DEV Community