How to Build a Reliable Performance Test for Beauty AR Sessions in TypeScript

·15 views

A Beauty AR proof of concept may perform flawlessly in short demos but fail under real-world sustained usage, making rigorous testing essential before shipping. Effective testing requires running representative effect combinations over extended sessions to detect gradual performance degradation, not just measuring average frame rates. Developers should define test cases based on actual product configurations — such as baseline, common, expensive, and recovery profiles — rather than synthetic stress tests that don't reflect user behavior. Device tiers should be maintained using real-device benchmarks rather than user-agent strings, with an unknown tier defaulting to conservative settings. A TypeScript-based testing framework using a platform-agnostic adapter is recommended to keep SDK-specific calls separate and portable across target platforms.

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)

Why Explainable AI Still Cannot Translate Its Reasoning Into Plain Human Terms

Explainable AI (XAI) systems can compute predictions with high accuracy, but consistently fail to communicate the reasoning behind those predictions in ways that are meaningful to clinicians, regulators, or affected individuals. Tools like SHAP, LIME, and counterfactual explanations provide technical attribution of model outputs, yet none fully answers why a specific decision should be trusted in a high-stakes context. A model can be statistically accurate while relying on spurious correlations or proxy variables for protected attributes, making raw accuracy metrics insufficient for real-world accountability. The European Data Protection Supervisor's 2023 TechDispatch explicitly warned that 'black box' AI decision-making is unacceptable, as opacity can conceal bias, errors, and hallucinations. Bridging the gap between mathematical explainability and human-understandable reasoning remains one of the field's most pressing and unresolved challenges.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

Developer Builds Tool to Share Live Screen Context with Claude Code AI

A software developer grew frustrated with repeatedly taking screenshots and manually explaining UI context to Claude Code while multitasking across several applications on a Mac. To address this, they built a tool that aims to give the AI assistant direct visibility into the user's screen, eliminating the need for constant manual context-sharing. The developer has listed the product on Product Hunt and is also launching a dedicated website for it. They are considering a free tier with a bring-your-own-key option alongside a one-time lifetime subscription priced at around $40. The developer is now seeking feedback from others who face similar workflow friction with AI coding assistants.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

AI Coding Tools Cut Startup Dev Costs by $4,400/Month in 2026, Data Shows

Startups adopting AI coding assistants are reporting significant cost savings and faster shipping speeds, with CB Insights 2026 data showing average monthly engineering cost reductions of $4,400. GitHub Copilot remains the most widely used tool at $10–$19 per user per month, cited as the default by 61% of YC-backed startups surveyed by OpenView. Tabnine Pro ($12/month) and Sourcegraph's Cody ($15/month) offer competitive alternatives, with Cody users reporting twice as fast bug resolution in a 2026 Dovetail survey. Open-source options like Continue.dev and Fauxpilot carry no licensing fees but require infrastructure investment starting around $140/month, making them cost-effective only for teams with strong MLOps capabilities. Gartner's 2026 analysis found that nine out of ten startups with fewer than 50 engineers save money by purchasing off-the-shelf AI tools rather than building custom solutions.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

Dev Tutorial Breaks Down JWT Auth, Rate Limiting, and Timing Attack Defenses

A software developer has published an interactive educational resource explaining how to build a secure authentication backend from the ground up. The project covers six core topics including JWT access tokens, refresh tokens, rate limiting, and protection against timing-based information leaks. The demo illustrates how middleware ordering affects security, noting that the rate limiter intentionally runs before authentication to also protect public routes like login endpoints. It also highlights a known limitation: refresh tokens do not rotate on use, meaning a leaked token cannot be automatically detected. The resource is available as both a live demo and an open-source repository on GitHub.

0 comments Read more at DEV Community