SShortSingh.
Back to feed

MOKSHA v0.1.1 Cuts Mobile CPU and GPU Load with Render Optimizations

0
·1 views

The open-source game project MOKSHA released version 0.1.1 on August 18, 2026, focused on improving rendering performance on mobile devices. Repeated trigonometric calculations in the draw loop were replaced with lookups from a pre-built 2048-entry table, significantly reducing per-frame CPU cost. Gradient and offscreen sprite caching was introduced to eliminate redundant GPU allocations each frame. Shadow blur calls were reduced and isolated with explicit state resets, with low-end devices able to disable the effect entirely via quality-tier controls. All changes were merged through three dedicated pull requests before the v0.1.1 release PR was finalized.

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)

Log in to join the discussion and vote.

Log in

Related stories

0
ProgrammingDEV Community ·

Three methods to automate prepaid API balance monitoring for fintech platforms

A developer working on a platform dependent on prepaid APIs — including SMS gateways and identity verification services — built three solutions after their manager requested balance checks three times daily. The options range from a manually updated Excel spreadsheet to a Python script with a cron job for full automation, and a semi-automated approach using Office Scripts combined with Power Automate. The article highlights common spreadsheet design mistakes, such as storing multiple balances in one cell or using wide column layouts, and recommends a structured row-per-service format with plain numeric values. Rather than fixed low-balance thresholds, the author suggests tracking days of remaining runway based on average daily spend. The guide uses African fintech APIs — Termii, Dojah, and Africa's Talking — as examples, but notes the approach applies to any service with a balance endpoint.

0
ProgrammingDEV Community ·

How to Automate Code Formatting at the Repository Level

Inconsistent code formatting across teams often stems from relying on individual developer IDE settings, which vary by editor, platform, and personal preference. A practical solution begins with locking down line endings using a .gitattributes file, preventing cross-platform issues that can corrupt commit histories or break scripts. Universal style rules can then be enforced project-wide through an .editorconfig file, ensuring the repository dictates formatting rather than each developer's local environment. Automated pre-commit hooks add a final enforcement layer, either fixing or rejecting non-compliant code before it ever reaches the shared codebase. The overall goal is to make formatting automatic and invisible, reducing team friction while maintaining a measurable baseline of code quality.

0
ProgrammingDEV Community ·

HackCanton Season 2 Draws 360 Builders, Awards $10K Cash and 100K CC in Rewards

HackCanton Season 2 attracted over 360 builders from more than 60 countries, with 75-plus teams participating across the program's entry funnel. Of those, 30 projects advanced to judging and 18 teams pitched live at the Grand Final. The hackathon distributed $10,000 in cash, $25,200 in NaaS credits, and 100,000 CC through ecosystem challenges sponsored by BitSafe and OnRails. Winning submissions demonstrated substantive use of Canton's privacy, authorization, and settlement capabilities rather than superficial blockchain integration. The event generated a library of reusable Canton development patterns spanning institutional trading desks, tokenized funds, RWA products, and developer tooling.

0
ProgrammingDEV Community ·

ARIMA, SARIMA, and Prophet Tested for Predicting CPU and Infrastructure Spikes

A developer built a predictive health check layer for infrastructure monitoring by testing three time-series forecasting models — ARIMA, SARIMA, and Facebook Prophet — against five real-world CPU and memory usage patterns. Prophet performed best at detecting gradual trends like slow memory leaks, while SARIMA excelled at forecasting predictable daily workday spikes when given proper seasonal configuration. All three models failed to reliably predict monthly spikes unless trained on years of historical data, as too few occurrences made it impossible to distinguish a pattern from a random anomaly. Completely random traffic bursts also defeated every model, with ARIMA and SARIMA prone to misreading sudden spikes as the start of a sustained upward trend. The findings suggest that no single model fits all infrastructure scenarios, and selecting the right forecasting tool depends heavily on the specific usage pattern being monitored.

MOKSHA v0.1.1 Cuts Mobile CPU and GPU Load with Render Optimizations · ShortSingh