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Google AI Edge Foresight Brings Offline AI Meeting Notes to Mac
Google AI Edge Foresight is an experimental meeting notes app for Apple Silicon Macs.
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- Google AI Edge Foresight is an experimental meeting notes app for Apple Silicon Macs.
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openai.comCreate a landscape editorial hero image for this Studio Global article: What is Google AI Edge Foresight, how does its experimental meeting-notes app compare with Granola’s combination of personal shorthand and A. Article summary: Google AI Edge Foresight is an experimental meeting-notes app for Apple Silicon Macs. Like Granola, it pairs a person’s shorthand with AI-written notes; its distinguishing approach is that meeting capture, notes and ques. Topic tags: general, documentation, general web. Style: premium digital editorial illustration, source-backed research mood, clean composition, high detail, modern web publication hero. Use reference image context only for broad subject, composition, and topical grounding; do not copy the exact image. Avoid: logos, brand marks, copyrighted characters, real person likenesses, fake screenshots, UI text, readable text, watermarks, charts with fak
Google AI Edge Foresight is an experimental meeting-notes app for Apple Silicon Macs. Like Granola, it puts your own shorthand alongside AI-generated notes, but its defining feature is that meeting capture and processing can happen offline on the Mac. 8
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How Foresight works
Foresight can capture audio from other apps or an in-person conversation, transcribe a meeting live and display the transcript alongside your notes. You can write shorthand as the conversation unfolds, review the full transcript later, and ask a Gemma 4–powered assistant questions about the meeting. 8
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The app can also use a local knowledge base of reference material—including documents and web bookmarks—to help answer questions during a meeting. The reported supported formats include PDFs, Google Docs, Microsoft Office files, plain text and Markdown. 14
What the 740-million-parameter model does
EmbeddingGemma 2 is a 740-million-parameter multimodal embedding model designed for on-device retrieval. In Foresight, it helps find relevant information in notes and reference material; it is not the model responsible for every step of the workflow. Speech transcription and answer generation are separate functions, with the meeting assistant powered by Gemma 4. 1
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That distinction matters: an embedding model helps locate relevant context, while the assistant uses context to respond. Google describes EmbeddingGemma 2 as a model for mapping information into a shared space to support on-device search and retrieval. 1
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Foresight vs. Granola
The clearest similarity is the note-taking workflow: both apps combine a person’s manual notes with AI-generated meeting notes. Foresight’s reported distinction is its offline, on-device design, along with a full transcript and a built-in assistant for questions about the meeting. 8
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That makes Foresight especially relevant to people who want meeting notes and questions to work without an internet connection. The available descriptions establish its local-first approach, but do not provide enough detail for a broader feature-by-feature comparison with Granola. 8
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An experiment, not a confirmed future Google service
Foresight is described as experimental. It follows an offline-first dictation app from the same Google team, extending local AI into meeting capture, note generation and retrieval. 10
The materials provided describe the Mac app and its local models; they do not establish plans for a future cloud-based Gemini note-taker integrated across Google services. That possibility should be treated as speculation, not an announced feature. 3
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Google AI Edge Foresight is an experimental meeting notes app for Apple Silicon Macs.
Sources
techcrunch.comGoogle releases a new local-first Granola competitor | TechCrunch
newsbytesapp.comGoogle launches AI note-taker that keeps meeting data on-device
developers.googleblog.comBring multimodal semantic search to the edge with ...
blog.googleEmbeddingGemma 2: an open, lightweight multimodal embedding model