Find the evidence behind your next idea.

Use Arcmira to investigate this public argument about AI-written job applications: https://x.com/cremieuxrecueil/status/2099974541594292560. Its linked paper, Making Talk Cheap (https://jesse-silbert.github.io/website/silbert_jmp.pdf), studies Freelancer.com coding-job proposals. Read that paper through available web or file tools as external evidence, and separate observed results from modeled counterfactuals. Then search indexed podcast and video interviews with hiring managers, recruiters, labor researchers, and platform operators within [date range]. Do they describe the same loss of signal, a different response, or no change? Read nearby context. For each useful passage, record the hiring setting, date, speaker role, and what evidence the speaker gives. Keep anecdotes separate from measured effects. Return dated source moments, a relevant contrary account if available, and a short paragraph whose scope matches the evidence. State missing sources and access limits.

Use Arcmira to compare discussion of [topic, organization, or person] across two equal periods. Resolve the relevant entities and spelling variants first. For a current overview, use arcmira.momentum (MCP) or GET /v1/entities/{id}/momentum and keep each entity's returned counts, units, and as_of separate. Its as_of is the newest indexed media date, not the end of the comparison period. For exact historical periods, use arcmira.occurrences with after and before (MCP) or GET /v1/mentions/counts with published_after and published_before for both windows. Report the returned per-entity, per-channel counts and check has_more before treating the source mix as complete. Do not sum overlapping entities into a unique-video total. Only report a combined distinct-video count after retrieving and deduplicating the underlying video IDs through an available complete inventory. Then inspect dated transcript moments from each period to explain the discussion. Return a concise finding, the periods, the comparison method, and source moments. Distinguish indexed coverage from public opinion or adoption. The September 2026 datacenter brief is a frozen example of a deduplicated inventory comparison, not the output of today's momentum endpoint: https://arcmira.com/research/datacenters/2026-09.

Use Arcmira to investigate Brian Chau's claim here: https://x.com/brianchau57/status/2099580986879094916. Start with his September 15, 2026 MTS interview: https://www.youtube.com/watch?v=IouG8jXc5f0. Find the strongest contrary account and a perspective on defenders' access to capable models. Keep incidents separate. For each, distinguish the assigned task, stated permissions, reported actions, and claims about intent. Are the accounts disputing the same event and proposition? Read nearby context and return dated source moments. Treat interview claims as reported accounts. Identify the original incident report or logs needed to resolve the disagreement, and state what you could not find or access.

Use Arcmira to compare public statements about [company]'s revenue and customer growth within [date range]. Separate contracted ARR, live ARR, annualized run rate, recognized revenue, and gross margin. Check whether definitions changed, using dated source moments. If no comparable statements are found, say so. Check whether apparent contradictions disappear when period and metric match. Name the financial record still needed. Do not infer private finances or fraud from wording alone.