HTMLcat collects native web tricks

// 2h agoTUTORIAL

HTMLcat collects native web tricks

HTMLcat is a compact notebook of useful HTML, CSS, and JavaScript platform features, each paired with a small example and practical caveats. It covers everything from :has() and popover to hidden="until-found" and container queries.

// ANALYSIS

HTMLcat is a refreshing antidote to framework-first web development: a lightweight reference for remembering what browsers already provide.

–Each note combines an API, example, maturity label, and warning about browser or assistive-technology support
–The collection spans 42 focused tricks across HTML, CSS, and JavaScript
–Native features can reduce dependencies, bundle size, and custom interaction code
–The accessibility and fallback caveats keep the advice grounded in production reality
–It is most useful as a quick refresher for experienced developers, not a full learning path

// TAGS

htmlcatdevtoolsearchtesting

DISCOVERED

2h ago

2026-08-21

PUBLISHED

4h ago

2026-08-21

RELEVANCE

6/ 10

AUTHOR

marcomezzavilla

// KEEP READING

More AI developer news from the feed

EXPLORE FULL FEED

OPEN SOURCE22m ago

Agent Lightning v1.0 makes agent RL practical

Microsoft’s Agent Lightning v1.0 is an open-source framework for training LLM agents through their existing deployment harnesses, including tool use, context management, and multi-agent workflows. Its reproducible coding-agent pipeline improved Qwen3.5-9B on SWE-bench Verified from 41.8% to 56.4%.

MODEL30m ago

LTX-2.5 pushes open video toward real time

Lightricks’ LTX-2.5 is a 22B open-weight model for synchronized video and audio generation from text, images, and video. Native multishot consistency, 4K HDR workflows, fine-tuning, and local inference make it a serious alternative to closed video APIs.

VIDEO52m ago

Generative AI Meetup Dissects Evals, Speech

The latest TwimlAI Generative AI Meetup with Hamel Husain explores trace-free evaluations, human-aligned LLM judges, spec-driven engineering, and TPU-scale speech-to-speech architecture. The discussion connects evaluation discipline with the systems engineering required to build reliable AI products.