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●SUNSET — Five days until the imagen-4.0 family shuts down: imagen-4.0-generate-001 and two sibling models stop on August 17●SAMPLING — Since July 21 the temperature, top_p, and top_k parameters are deprecated; no shutdown date yet, but worth auditing your calls●API — The Interactions API is now generally available and is the recommended path to the latest models and features●ROBOTICS — gemini-robotics-er-1.6-preview shuts down on August 31; its successor, ER 2, has been in public preview since July 30●ENTERPRISE — The Gemini Enterprise mobile app reached GA for organizations authenticating through third-party identity providers●RENAME — NotebookLM Enterprise is now Gemini Notebook Enterprise, so the term to search the docs for has changed too●SUNSET — Five days until the imagen-4.0 family shuts down: imagen-4.0-generate-001 and two sibling models stop on August 17●SAMPLING — Since July 21 the temperature, top_p, and top_k parameters are deprecated; no shutdown date yet, but worth auditing your calls●API — The Interactions API is now generally available and is the recommended path to the latest models and features●ROBOTICS — gemini-robotics-er-1.6-preview shuts down on August 31; its successor, ER 2, has been in public preview since July 30●ENTERPRISE — The Gemini Enterprise mobile app reached GA for organizations authenticating through third-party identity providers●RENAME — NotebookLM Enterprise is now Gemini Notebook Enterprise, so the term to search the docs for has changed too
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◈ Gemini API/2026-06-19Advanced

When Your pgvector Search Quietly Gets Worse — Field Notes on Protecting Recall with Gemini Embeddings

A semantic search built on Gemini Embeddings and PostgreSQL pgvector tends to lose precision over months without throwing a single error. These are field notes on the real causes — model pinning, operator/index mismatch, HNSW reindexing, and recall collapse under filters — with working code.