How R6 Stat Trackers Calculate K/D and Win Rate, and Why Sites Can Differ

·2 views

A developer breakdown explains how Rainbow Six Siege stat trackers compute core player figures such as K/D, headshot percentage and win rate. K/D is shown as the kill count when a player has zero deaths, which keeps the value sortable and avoids broken-looking displays. Win rate can use either a ranked-season denominator or a lifetime-matches denominator, which is a common reason two trackers report different numbers for the same account. The article also covers caching ranked data from an API with time windows tuned to each data type, and returning a dash rather than 0.0% when there is no sample. It uses the unofficial tracker SiegeRadar as its example, noting that the site is not affiliated with Ubisoft.

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)

AiRap.me turns a single photo into a lip-synced AI rap video

A developer built AiRap.me after finding that making an AI rap video required stitching together several separate tools. The platform lets users upload one photo, choose a style, and receive a finished rap video in roughly 20 to 30 seconds. The AI writes an original verse, produces the beat and vocals, and lip-syncs the face, and it supports people, pets, and cartoon or 3D characters across eight styles. Videos are delivered as watermark-free MP4s, and users are charged only when a video finishes, with free credits offered to new accounts. The developer is requesting community feedback on additional styles and the upload workflow.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

Developer builds free macOS app Shoji to add a paper-like texture to screens

After reading a research paper late at night, a backend developer found that the bright, glossy look of a Mac display felt harsh during long reading sessions. Existing paper-texture apps either required payment before any trial or came bundled in larger subscriptions, which he felt was inappropriate for something users can only judge through extended use. He built Shoji, a free app named after Japanese shoji screens, which places a transparent, click-through overlay across every display to render a light paper texture. The textures are procedurally generated in code to avoid visible tiling, and they are tuned separately for light and dark modes. The project also covered technical details such as hiding the overlay from screen sharing and navigating macOS notarization.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

Decoupling Frame Capture From Inference Cuts Edge Vision Latency in Python

The article addresses the problem that running object detection models such as YOLOv8 on every frame of an edge camera stream can overwhelm CPU or GPU resources. For slow-changing monitoring tasks like plant health or industrial gauges, processing each frame adds little diagnostic value. The author proposes a Python pipeline that separates frame capture from model execution using a bounded queue of size one, so the model always evaluates the latest frame and stale frames are discarded. The approach also includes a worker thread that paces inference to a target frame rate and a helper function for logging inference latency to monitor service health. The author argues that this design keeps resource usage predictable and enables local deployment without cloud round-trip delays.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

Python script pulls open EU public tenders from TED as flat JSON and CSV

The TED (Tenders Electronic Daily) portal publishes all EU public tenders above threshold values, but its expert query syntax and nested multilingual fields make it hard to use directly. A DEV Community post presents a Python script that uses an Apify actor calling the official TED Search API v3 to filter open tenders by keywords, buyer countries, CPV codes and deadlines. The actor returns flat, one-row-per-notice JSON that the script saves as a CSV, with deadlines sorted and values summed across lots. The author notes that estimated values are often missing and that CPV code prefixes are the most useful filter, and discloses that they built the actor used.

0 comments Read more at DEV Community