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Lobsters

How Notion handles concurrent editing with CRDTs

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  1. Article: Your Next DSL Author Is a Language Model

    Typed Domain Grounding (TDG) embeds a DSL inside a mainstream language the LLM already knows (e.g., Kotlin) and uses the host compiler as an oracle. The author describes five building blocks—embedding, choosing a host language with high training‑data frequency, compiler‑driven type safety, a generate‑compile‑repair loop, and an on‑demand teaching tool—and shows measured results from kUML, a Kotli…

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  2. Building a Reliable Script-to-Video Pipeline for Modern Web Applications

    A practical guide to building a reliable, observable script‑to‑video pipeline. It advocates a staged architecture (scene manifest → asset generation → voice → rendering → encoding), validates AI‑generated JSON with Zod, persists project and scene state, uses explicit workflow states, runs heavy work in background queues, limits concurrency, and stores voice, captions, and assets separately to ena…

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  3. Refinement Is Inherently Editable: Training-Free Prompt-to-Prompt Image Editing with Generative Refinement Network

    RefineEdit is a training‑free framework that edits images by refining binary image codes through a generative refinement network, using probability differences to pick edit locations. It outperforms prior methods on background preservation and CLIP scores across nine editing categories without extra training or masks.

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  4. The skills CLI now supports Notion hosted skills

    Vercel’s `skills` CLI (v1.7.0) now lets you install agent skills directly from Notion pages or shared Notion skill packs. Use `npx skills add notion` to browse packs, or `npx skills add <PAGE_URL>` for a single skill. Authentication is handled via the `ntn` CLI, which requires a Notion personal access token. Permissions follow Notion’s page sharing model, so only users with view access can instal…

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  5. OmniHarness: Harnessing Generalizable Visual Generation via Symbolic Policy Learning

    OmniHarness introduces a symbolic‑policy framework that extracts reusable procedural knowledge from multimodal LLM‑driven visual generation runs. By decoupling task logic from instance inputs, the system can instantiate, adapt, and compose policies for new visual tasks, using intermediate verification for on‑the‑fly refinement while keeping the underlying model frozen. Self‑directed practice task…

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  6. Shared Selective Persistent Memory for Agentic LLM Systems

    Apple proposes a memory architecture for agentic LLMs that selectively persists reusable context (specs, schemas, configs, constraints) across sessions and users. Shared workspaces with role‑based access and a zero‑token data‑refresh mechanism cut token usage by 97×, reduce task time by 14×, and raise task‑completion rates to 96% versus 71%‑79% for baselines.

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