Open-source AI workspace
TIA Studio
A desktop-native workspace for AI assistants, teams, channels, and local claws built for local-first operation.
Timeline
2026.03 - Present
Role
Open-source product engineering across desktop UX, assistant workflows, and channel-connected operations.
Outcome
Turned local assistant orchestration into a more usable desktop product by combining chat, team coordination, and channel operations in one workspace.
Overview
A desktop-native workspace for AI assistants, teams, channels, and local claws built for local-first operation.
Why it mattered
Developers needed a practical way to run assistants close to their tools, files, and real communication channels without depending on a cloud-hosted control plane.
Key decision
Keep assistants as the core primitive and model claws as assistant-plus-channel pairings so identity, scheduling, and runtime behavior stay simpler to manage.
Hard part
Balancing a full assistant workspace with local-first channel automation required careful boundaries between desktop UX, channel transport, and assistant runtime state.
Outcome
Turned local assistant orchestration into a more usable desktop product by combining chat, team coordination, and channel operations in one workspace.
My contribution
Role: Open-source product engineering across desktop UX, assistant workflows, and channel-connected operations.
- Built a desktop workspace for assistants, teams, threads, and channel-connected operations.
- Shaped the claw workflow around assistant and channel composition instead of introducing a separate runtime entity.
- Implemented a local-first product flow that supports channel integrations across Discord, Lark, Telegram, WhatsApp, Wecom, and Wechat-KF.
Outcome
- Made it faster to run local assistant operators from a native desktop workspace.
- Unified assistant chat, team coordination, and channel automation in one open-source product.
Tech stack
Product preview
A quick look at the shipped interface and workflow.
Product snapshot
A desktop-native workspace for AI assistants, teams, channels, and local claws built for local-first operation.
Implementation notes
Balancing a full assistant workspace with local-first channel automation required careful boundaries between desktop UX, channel transport, and assistant runtime state.
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