Local-native Agent · Cloud Runtime for multi-environment collaboration
Give Slai a direction and goal. It coordinates controlled nodes to turn complex real-world work into completed results.
Slai began as a local-native Agent instance developed on a consumer Windows computer, with deployment across Windows, Linux, macOS, and Android. Cloud Slai is a multi-tenant Agent Runtime for multi-environment collaboration and controlled cross-platform remote execution. It understands the task in the cloud, plans the steps, and uses the computers, phones, and other controlled nodes you authorize to execute, verify, and continue complex work. A single conversation can sustain long-running tasks for hours or even days, while a larger project can be maintained continuously for days or months. Turn one-off conversations into a work thread you can keep moving forward. You do not need to repeat your background, preferences, or completed work each time. Use simple language to complete complex tasks. Slai is a long-term private AI assistant for individuals.
No prompt engineering, Skill setup, or coding knowledge required. Give Slai a direction and goal; within executable, authorized bounds, it plans, uses tools, and coordinates your approved device nodes to move the work forward.
Windows, Linux, macOS, Android, and other devices can connect under your account; the cloud control plane routes work by permission rather than exposing devices directly to an Agent.
Keep task state, execution results, and evidence so complex work can be checked, paused, resumed, and continued.
Slai keeps your conversations, tasks, and memory connected to your account. Local-native instances can be deployed across Windows, Linux, macOS, and Android; after authorization, these devices become capability nodes with different execution roles. Continue the same conversation and pick up long-running tasks across environments. Available capabilities depend on the device you connect.
Slai combines a local-native Agent instance developed on a consumer Windows computer with a cloud Agent Runtime for multi-environment collaboration. It connects model capability, tools, and user-authorized controlled nodes while preserving task, evidence, and permission boundaries across multi-tenant cloud operation and cross-platform execution.
Use it for browser work, file handling, device collaboration, automation, personal experiments, and long-running tasks. You describe the goal; Slai plans and advances the work through account authorization, controlled nodes, and traceable task state across the platforms you choose to connect.
Public evaluation results & engineering contributions
Slai Agent Runtime brings model capability into public evaluations and real engineering work: running benchmark tasks, submitting public records, and sending discovered fixes upstream. It has completed or advanced more than one hundred public engineering, evaluation, and open-source collaboration efforts; merged fixes, PRs under review, and evaluation submissions are verified on their respective public pages.
Two consecutive public upstream milestones for Slai. AMD's official GAIA v0.25.3 release notes and v0.25.2 release notes each list @huong8373tt-beep in their Contributors section. In v0.25.3, the account is the first named community contributor, credited for chat-workspace file resolution, daemon-update restarts, home-first search, gateway-token restoration, relay handling, documentation, and test and CI reliability.
This is a small, publicly verifiable record of upstream recognition: the contribution work was not only merged upstream, but was credited in official release notes across two consecutive releases.
Wish-driven programming
Wish-driven programming is not about skipping reality. Developers do not need to begin with functions, frameworks, or commands; describe the rough idea, goal, and boundaries, and Slai keeps advancing through real tool feedback.
You provide direction, make key decisions, and define boundaries that must not be crossed. Slai keeps moving the work toward real results instead of stopping at advice that merely looks correct. It does not rely on one generation being completely right: it can read real files, call tools, receive error feedback, correct direction, and continue.
During continuous, non-cold-start execution, Slai’s input Token cache hit rate can remain around 95–99%. The actual rate varies with the model service and task structure; this is a request-efficiency metric, not a task-accuracy or success-rate claim.
Proven methods, acceptance criteria, and tool experience can be retained as reusable capabilities, helping the next task move forward more reliably. Within user-authorized bounds, Slai gradually adapts to the working methods you approve. This is recursive evolution in an engineering sense: capability comes from real execution, actual modification, and acceptance feedback—not from changing the weights of the connected model. Open-ended exploration, aesthetic work, and product judgment still depend more on flagship-model reasoning; engineering tasks with code, tests, logs, interfaces, and real-environment feedback place comparatively lower demands on model capability.
Contact: wlyo@outlook.com · huong8373tt@gmail.com
