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A long list of free AI offers is useful only if it helps answer a personal question: which of these can I actually use, under the terms that apply to me? I built ai_resets in Python with PostgreSQL to track AI usage resets, plan changes, promotions, free credits, and student offers. Its discovery sources include official pages, RSS, public social posts, and aggregators such as freetokens.custats.info, described in my source catalog as covering 250+ offers. That number describes the discovery pool, not benefits I have redeemed or programs available to every reader. This article explains the cataloging workflow as of November 11, 2026. It does not promise current balances or universal eligibility. Offer categories must be distinguished before comparing headline values. Aggregators help discover candidates, but verification is claim level and requires official evidence for the stored benefit. Deadlines must stay attached to the right claim. Student eligibility remains an unresolved condition until proven. Verification states should stay visible, and a practical shortlist should filter by product, eligibility, and evidence before comparing nominal value.
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Jeremy Koski (2026) studied this question.
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