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Offline Computing
3 posts about Offline Computing. Every summary links to the original.
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How Notion handles concurrent editing with CRDTs
Notion replaced its text-editing system because its former last-write-wins storage could overwrite concurrent edits to the same block, with Offline Mode increasing the risk of lost changes. The new system uses a Conflict-free Replicated Data Type based on Replicated Growable Array, representing inserted text as uniquely identified items, retaining deletions as tombstones, and deterministically ordering concurrent inserts. Peritext-based annotation operations merge rich-text formatting such as bold, italics, and hyperlinks while preserving extendability rules. To keep edits correctly positioned when Enter splits a block, Notion introduced text slices, text slice trees, text instances, and compact search labels for locating slices across blocks. Deployed in July 2025, the system processes millions of CRDT operations per minute and supports Offline Mode and agent collaboration.
Bringing Gemma 4 12B to your Laptop: Unlocking Local, Agentic Workflows with Google AI Edge
Google DeepMind’s Gemma 4 12B is presented as an open model for running agentic, multimodal capabilities locally on everyday laptops through the Google AI Edge stack. The release covers Google AI Edge Gallery for local data analysis and coding, including Python generation, execution, chart rendering, and a reported 3D rendering workflow that can generate dependencies, code, and self-corrections in one turn. Google AI Edge Eloquent adds fully on-device dictation, transcription, and Voice Edit commands for rewriting or translating selected text, while LiteRT-LM’s new serve command exposes the model through a local, OpenAI-compatible endpoint for tools and frameworks. The post says Gemma 4 12B delivers a 60%+ overall quality improvement over prior models for Eloquent’s use case and keeps data on-device, with performance and memory benchmarks provided in the model card.
How we made Notion available offline
Notion's Offline Mode required turning a best-effort SQLite cache into storage that could guarantee a page and all its dependent records were available without a network connection. The system uses a forest of offline page trees backed by offline_page and offline_action tables, allowing independent reasons such as explicit toggles, favorites, automatic downloads, and inheritance to coexist while preserving hierarchy. To keep content current, clients subscribe to push channels emitted by the page version snapshot system, then use lastDownloadedTimestamp and server lastUpdatedTime to fetch only newer pages after reconnecting. When pages or databases change, the client reconciles fresh page and database snapshots with offline_action, adding or removing rows rather than rebuilding entire trees. This makes offline availability precise and eventually consistent, while supporting conflict resolution through the CRDT data model.
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