Dev Guide Outlines Five-Phase Elixir-Rust Engine for Sub-Microsecond Crypto Arbitrage

·1 views

A technical roadmap published on DEV Community details how to build an ultra-low latency cryptocurrency triangular arbitrage engine using Elixir, Rust, and the Notification-Oriented Paradigm. The system is designed around five optimization phases, progressing from a core PON engine and adjacency registry through to private API authentication. It queries Binance's public exchange-info endpoint at boot and uses depth-first search to identify three-leg arbitrage cycles anchored to hub assets such as USDT, BTC, and ETH. To keep the hot path fast, console output is banned from rule engines, with telemetry events instead dispatched asynchronously to a dedicated terminal dashboard process. The dashboard aggregates and redraws metrics at 5Hz using a periodic timer, ensuring screen rendering never blocks trade evaluation.

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 Playwright Solution for Visual Testing with API Mocking

A developer has published their solution to Challenge 3, focused on visual testing combined with API mocking using Playwright. The approach addresses real-world problems such as missing chart renders and overlapping UI buttons caused by frequent API changes in dashboard applications. The solution involves mocking API endpoints to return fixed data, then capturing and comparing screenshot snapshots to detect unintended UI changes. A custom VisualHelper class was built with methods to check full-page or element-level snapshots, with configurable timeout and pixel-difference thresholds. Playwright's built-in toHaveScreenshot() function serves as the free alternative to paid visual testing tools like Percy, Applitools Eyes, and Sauce Visual.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

Developer recreates kulhad chai in pure CSS for frontend art challenge

A developer built a detailed CSS illustration of a kulhad of masala chai for DEV Community's Frontend Challenge: Comfort Food Edition. The artwork depicts a hand-thrown terracotta cup surrounded by whole spices and a half-eaten biscuit, rendered entirely using divs, gradients, clip-paths, and shadows — no SVG, images, or canvas. The kulhad's irregular silhouette was achieved with an 18-point polygon clip-path, while five stacked gradient layers simulate the texture of matte clay. Steam wisps required three iterations to look realistic, ultimately using individual horizontal origins and mix-blend-mode: screen to mimic rising vapour. A CSS mask-image technique was used to punch a clean bite-shaped hole through the biscuit, preserving its crumb texture and shadow details.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

DEV Community Post Shares CSS Code for Arabic-Themed Restaurant Landing Page

A developer published a front-end project on DEV Community showcasing a landing page concept called 'Comfort Table,' styled around an Arabic home-cooking theme. The post consists almost entirely of raw CSS and HTML code rather than written editorial content. The design uses a warm, earthy color palette with serif typography to evoke a cozy, home-style dining atmosphere. The project appears to be a personal or portfolio exercise demonstrating responsive web design techniques. No additional context, author background, or publication date was provided in the source material.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

Developer Compares Rule-Based and ML Anomaly Detection in Custom Log Analysis Tool

A software developer added an Isolation Forest machine learning model to Log Sentinel, a self-built Apache log analysis dashboard originally designed to detect brute force attacks and directory scans using rule-based logic. The experiment used a labelled dataset of 230 IP addresses — 200 normal and 30 simulated attackers — to fairly evaluate both detection approaches. Rule-based detectors achieved perfect precision and recall because the evaluation data was deliberately structured around known attack patterns the rules were written to catch. The ML model matched on recall by catching all 30 attackers but produced five false positives, flagging normal IPs with statistically unusual behaviour. The developer concluded both methods are complementary: rules reliably catch known threats with no false alarms, while ML can surface unexpected anomalies that no predefined rule would cover.

0 comments Read more at DEV Community