Hands-on reviews of AI tools, measured against real tasks.
Run a five-question gate on schema access, promotion control, model switching, audit trail, and cost/time delta versus using the assistant outside the PaaS.
Use a probe for freshness, citations, ranking, latency, and cost before wiring Brave Search API into an AI agent.
Run a repeatable audit of cost, latency, and failure modes before a cheaper Claude Code worker model becomes the team default.
A better AI code reviewer pays for itself only when bug lift, review burden, token cost, privacy terms, and security coverage clear your bar.
A practical bake-off for support-ops teams to compare agent memory tools on recall, latency, cost, and leakage before production rollout.
Stop choosing agent platforms by docs score; run a messy invoice benchmark and audit failures, cost, observability, and human controls.
Run a repeatable live-agent eval for injection, exfiltration, and false positives before putting a semantic firewall in front of production LLM agents.
Stop buying agent memory on download counts; run a support-corpus bake-off for recall, latency, cost, updates, and poisoning.
Use a live share-price search to test an AI tool, then separate verified facts, reported risk, and analyst expectation before acting.