Source-led, practical analysis of the AI products, models, and tools attracting attention now.
A source-led review of Callstack's 12 React Native skills, with static audits, routing accuracy, a 500-item performance A/B test, progressive-disclosure measurements, and adoption limits.
A source-led review of Expo’s 23 official skills, with static audits, routing accuracy, Expo Router A/B tests, EAS cost boundaries, and adoption guidance.
A source-led review of 183 community Apple development skills, with static audits, routing accuracy, SwiftUI A/B tests, governance findings, and adoption guidance.
A source-led review of Google’s Android Skills collection, with static audits, trigger-accuracy tests, A/B output checks, version risks, and adoption guidance.
An evidence-led OpenClaw review combining official docs, independent hands-on tests, community workflows, and security guidance—without presenting third-party results as our own.
Browser Use can turn a language model into a browser operator, but the useful question is not whether it can click. It is whether the task, permissions, recovery loop, and audit trail are safe enough for your workflow.
The official MCP servers repository is useful for learning and prototyping. A production integration still needs an explicit tool inventory, least-privilege credentials, logging, and a threat model.
The openai/skills repository treats agent skills as discoverable folders of instructions, scripts, and resources. Here is how to install one without turning a convenient shortcut into an opaque permission grant.
A source-first comparison of GPT-5.6 Sol, Claude Opus 5, Gemini 3.1 Pro, and Llama 4 Maverick using only specifications published by their developers.
Search momentum can tell you where attention is moving. It cannot tell you what is true, useful, or worth publishing. This framework shows how to turn a spike into defensible AI coverage.
A transparent workflow for turning fast-moving search interest into a sourced page that can be reviewed, updated, and eventually scored as a successful or failed forecast.
A repeatable test plan for judging AI tools on real work: output quality, reliability, cost, privacy, and failure modes—not launch-day excitement.