Developer Builds Responsive Dark Analytics Dashboard Using Only HTML, SCSS, and JS

·1 views

A developer has created a fully responsive dark-theme analytics dashboard without using any front-end framework, relying solely on semantic HTML, SCSS, and plain JavaScript. The project replicates sidebar mechanics from shadcn/ui in pure CSS, including a mobile drawer that activates on screens narrower than 768 pixels. Data visualizations are powered by Chart.js 4, featuring a radial chart, stacked bar chart, and half-circle gauge, while animated statistics are handled by countUp.js with reduced-motion support. An interactive orders table includes working select-all checkboxes with an indeterminate state, status badges, and horizontal scrolling for smaller screens. The codebase uses BEM naming conventions, SCSS design tokens, and container queries to manage responsive layouts, with visual design inspired by a publicly available Figma community file.

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 Shares Lessons on Secret Detection Rules and Real-World Coding Habits

A developer building Dotguard, an open-source secret-scanning tool, documented how writing each detection rule revealed distinct habits developers have around handling sensitive credentials. Common patterns observed included pasting authentication tokens into code for debugging and generating strong keys but storing them in plaintext files with obvious variable names. The project was designed with zero external dependencies and no network calls, making it fully auditable and suitable for restricted or air-gapped environments. False-positive suppression logic highlighted the challenge of balancing detection sensitivity with team trust, since a scanner that triggers too many false alarms risks being disabled entirely. The developer recommends distributing scanner hooks through version-controlled repository files so every new contributor automatically inherits the security behavior on first clone.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

Developer Builds Free Client-Side SEO and Social Media Preview Tool

A developer has launched OmniSEOtools, a free, open-source suite of SEO and meta tag testing utilities that runs entirely in the browser with no server-side processing. The tool includes previews for Twitter/X cards, Google SERP snippets, and LinkedIn post displays. Built with Next.js App Router and Tailwind CSS, it aims to address the slowness and paywalls common in existing social preview debuggers. Because all processing happens client-side, users get instant feedback without waiting on remote servers. The project is publicly available on GitHub, and the developer is seeking community input on additional SEO utilities to include.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

Self-Improving AI Still Far From Hype, Research Warns of Real Risks

A September 2026 article by Nokka, written with AI assistance via the Hermes Agent, examines the gap between hype and reality surrounding self-improving AI systems. A Princeton team led by Peter Kirgis and Sayash Kapoor ran a 'shadow evaluation' in August 2026, giving Claude Opus 4.8 six days and a $3,000 budget to produce NeurIPS-worthy research papers, but human authors rejected both AI-generated submissions. Separately, the S3Gym study found that AI agents can identify correct actions but struggle to convert that feedback into transferable, real-world improvement policies. Researchers conclude that while AI effectively assists humans at moderate task levels, it cannot yet independently produce top-tier original research or achieve compounding self-improvement. Key risks identified include reward hacking, model collapse from self-generated training data, and memory contradiction buildup in long-running agents.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

GVU Operator: Unified Framework Shows All AI Self-Improvement Shares One Structure

A 2025 research paper (arXiv 2512.02731) introduced the GVU Operator framework, proposing that every known AI self-improvement technique — from AlphaZero to Reflexion — follows the same three-step cycle: Generate, Verify, and Update. The framework argues that differences between techniques lie only in how verification and updating are implemented, not in any fundamental principle. Researchers highlight that the verifier is the most critical and failure-prone component, since unreliable self-evaluation can collapse the entire improvement loop. In May 2026, Anthropic published a report titled 'When AI Builds Itself,' formally acknowledging that Recursive Self-Improvement — where AI grows better at improving itself — is no longer theoretical. Anthropic described a spectrum of AI development autonomy and warned that current systems are already further along that spectrum than most people assume.

0 comments Read more at DEV Community