Presentation: Local First – How To Build Software Which Still Works After the Acquihire

2026-04-08 · Source: InfoQ · Field: Technology & Digital — Software Development & Engineering, Emerging Technologies & Innovation, Cloud Computing & IT Infrastructure · Depth: Advanced, extended

Summary

Alex Good's presentation addresses the fragility of modern cloud-dependent applications, emphasizing issues like development complexity, user dependence on servers, and maintenance challenges. He advocates for "local-first" software, a design principle where data primarily resides on the user's device, enabling network-optional functionality and enhanced user agency. The technical approach leverages Git-like Directed Acyclic Graphs (DAGs) and fine-grained data models, exemplified by the Automerge library, to achieve automatic, conflict-free merging of changes across devices. This infrastructure simplifies engineering workflows by abstracting distributed system complexities and provides new capabilities through rich change history. While offering significant benefits for document-editing applications, challenges remain in areas such as generic authentication, partial synchronization, and schema evolution, with opportunities for integration with LLM-based workflows.

Key takeaway

Embrace local-first software to build resilient, server-agnostic collaborative applications, leveraging Git-like DAGs and libraries like Automerge to make on-device data the primary source of truth. This approach simplifies development by abstracting distributed system complexities, enabling network-optional functionality and deterministic conflict resolution for fine-grained data. It empowers developers of document-editing and creative tools with enhanced user agency and new capabilities like versioned AI workflows, though generic auth/auth and schema evolution remain key challenges.

Topics

• Local-First Software
• Automerge Library
• Commit DAGs
• Conflict Resolution
• Distributed Systems

Best for: Software Engineer, AI Architect, Director of AI/ML

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Editorial summary, takeaway, and curation by AIssential. Original article published by InfoQ.