Local-first

"Local-First" is a software design philosophy systematically articulated by Ink & Switch laboratory in its eponymous paper, with the core proposition that user data sovereignty should belong to the users themselves and that software should prioritize completing computation and storage on local devices.

Local-First 的核心主张是用户数据应首先存储在本地设备，云端仅作为同步和备份的辅助手段

The local proxy model means that all data remains within the user's own infrastructure and is not sent to third-party cloud services.

The Local-First concept was systematically articulated by the Ink & Switch lab in their 2019 paper 'Local-First Software'

The core premise of local-first architecture is that user data should primarily be stored and processed on local devices rather than defaulting to cloud servers

Obsidian and Logseq are typical cases of knowledge management tools that rose to prominence under the local-first philosophy.

A local-first architecture brings benefits to finance apps such as privacy protection, no subscription barrier, offline availability, and data portability.

Running local AI models typically requires 8–16 GB or more of memory, a relatively recent CPU or a GPU-accelerated graphics card, and sufficient storage space.

Pure local solutions forgo the real-time cross-device synchronization capability that cloud-based solutions inherently possess, which is the trade-off of a local-first architecture.

The trade-off of a local-first architecture is that users must maintain their own local environment, and compute performance is limited by local hardware, representing an objective gap compared to cloud-based large models.

Local-first implementations in AI coding tools typically rely on two approaches: running open-source large models locally (e.g., loading Llama or Mistral via Ollama or LM Studio), or completing analysis logic via CLI tools already installed on the user's machine.

The perpetual license model is especially coherent for local-first applications, as the software does not rely on server operations and developers have no infrastructure cost justification for continuously charging subscription fees.

The local-first philosophy emphasizes long-term data accessibility, so that even if the developer stops maintaining the service, users' data remains fully in their own hands.

Pizza Bot adopts a local-first architecture, where the state and data of agent work are primarily stored locally rather than relying entirely on the cloud.

Meterless.ai adopts a local-first design, where AI workloads run on the user's device and data and processes are controlled by the user.

A purely local solution means more manual maintenance, requiring users to enter transactions and update holdings themselves.

The Local-first concept was systematically elaborated by the Ink & Switch laboratory in their 2019 paper of the same name, with core tenets including user data ownership, offline availability, and end-to-end encryption.

The Local-first architecture fundamentally eliminates the risk of server-side data breaches, because the server does not hold user data.

Yunque adopts a Local-first architecture, where all user data is stored only on the local device and is not uploaded to the cloud.

Yunque adopts a Local-first architecture in which all data is stored only on the user's local phone and is not uploaded to the cloud.