GoKawiil
Tech News
A technical blog post examines the common practice of AI agent designers asking large language models to output a numeric confidence or probability score alongside their answers. The author argues there is no sound basis for trusting these numbers, contrasting them with how a human forecaster's confidence is grounded in historical data and a consistent updating process.
Research discussed in the piece examines what happens when AI systems that have performed reliably for extended periods suddenly make errors or stall, requiring a human reviewer to step in. Employees who have grown accustomed to rarely overriding accurate AI recommendations are described as being expected to catch and correct these rare failures, even though they remain accountable for outcomes.
A reader survey conducted by Android Authority found that camera quality and clean software topped the list of priorities for people buying new phones, ahead of performance and reliability. Ecosystem lock-in and customization options, features brands frequently tout, ranked far lower among respondents' concerns.
A developer's essay critiques how services report reliability using uptime percentages like 99.72% or 98.98%, arguing these numbers look deceptively similar to non-experts even though they represent vastly different amounts of actual downtime. The author references Jason Gorman's observation that reliability gains become exponentially harder near 100%, comparing uptime metrics to earthquake magnitude scales where small numeric differences hide huge real-world impact. The proposed fix: status pages should show actual downtime duration (e.g., '12 hours affected in the last 30 days') alongside or instead of raw percentages.
A developer built a standalone site that aggregates GitHub's published incident history and lets users filter it by which specific services were affected and how severe each outage was rated. The tool was created because existing status reporting from GitHub doesn't let engineers isolate the exact dependencies and reliability thresholds relevant to their own products.