A buyer's guide to AI visibility tools covers how ChatGPT, Gemini, Perplexity, and Google AI Overviews decide which brands to surface, plus the nine criteria that separate a real tool from a basic tracker.

Cloud Security Alliance research shows 82% of enterprises have AI agents they don't know about. Here's what your coding agent can actually touch and how to lock it down.

Developers are using AI coding agents to strip ads, trackers, and bloatware from Android TV and Google TV. Here are the tools worth knowing about.

Forever Security found AGENTS.md and CLAUDE.md files in a Downloads folder can manipulate AI coding agents into running malware or leaking auth tokens. Codex followed malicious instructions in over 90% of tests.

Forever Security found that a plain text file silently dropped into your Downloads folder can trick OpenAI Codex or Claude Code into running attacker commands. Here is what happened and how to protect yourself.

A podcast episode makes the case that developers using AI to write code risk skill decay, citing research from Anthropic, METR, and Microsoft/CMU. The fix is simple: never generate more code than you can read.

Microsoft released Scope, an MIT-licensed platform for evaluating how AI coding agents like GitHub Copilot and Claude Code actually use your APIs and docs. Here is what it found.

Google released the Elephant-Goldfish Model plugin under Apache 2.0, reframing AI-assisted coding so that design becomes the actual code rather than a prompt afterthought.

A three-app test using Gemini Pro found that even OWASP-guided prompts with a second AI security review still left vulnerabilities. Here is what the research found.

Google has open-sourced the Elephant-Goldfish Model plugin, a framework designed to blend AI-assisted and human coding while keeping developers intentionally in the loop.

A six-week case study tracked 118 AI coding agent failures across a shared markdown repo. The findings on drift and rule decay are worth reading before you build your next AGENTS.md.

Researchers mapped four recurring reasoning patterns in large reasoning models and used them to improve coding accuracy, identify tasks, and cut token usage with early stopping.