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v4.10.0 — One AI budget, clearer Findings

A shared monthly AI allowance and focused Finding detail make usage, evidence, and resolution easier to understand.

NEW Somente em English

v4.4.0 — Notion Sources, cross-source edges, and conflicting claims

Connect Notion beside GitHub, auto-propose cross-source relationships, and surface conflicting claims with both Evidence trails side by side.

Galileo 4.4.0 connects Notion beside GitHub, proposes cross-source relationships automatically, and surfaces conflicting claims — so lying documentation shows up with both Evidence trails side by side.

New

  • Notion Connector — connect a Notion workspace as a Source; every page the integration can read lands as a document Artifact with bounded page-block Evidence.
  • Cross-source Edges — after Sync, Galileo proposes Notion→GitHub describes Edges (LLM suggests, deterministic grounding decides), so Findings can span both Sources.
  • Conflicting-claims Findings — when two Sources disagree about the same service, Galileo opens one Finding on that service with both grounded quotes side by side — never picking a winner for you.
  • Source edit — rename a Source or rotate its credential without deleting it. Identity stays immutable; a credential change is re-verified against the same system.
  • Duplicate-Source protection — connecting the same GitHub repo or Notion workspace twice is blocked at creation with an immediate form error.

Improved

  • Realistic README grounding — GitHub READMEs are stored as structured blocks (like Notion pages), so cross-source quotes match what you actually read — not raw markdown markup.
  • Notification delivery — invitation emails honor email opt-outs for existing users; bulk changelog announcements are safely re-runnable without double delivery.
  • Admin and org tables — Graph, Findings, and admin indexes share one DataTable driver for search, filters, and live refresh.

Fixed

  • Transferring ownership to yourself no longer leaves the Org without an owner.
  • Cross-source proposal calls have more headroom so thinking models are less likely to truncate mid-response.