Computers turned into a
Digital Human.

DaemonLabs  /  Emulated Humanoid  /  macOS Native (Default)

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The Problem
Every AI agent that
sees your screen
is too slow.

Screenshot-based agents send images to the cloud, taking 4 seconds per click. They hallucinate, blow up context windows, and break when a UI button moves 10 pixels. That's a tech demo, not an enterprise product.

Our Approach
We read the OS.
At the kernel level.

We bypassed the cloud. Using native macOS-first accessibility hooks, our UI Targeting Engine pierces the UI tree in 1 millisecond. Combined with local semantic embeddings and procedural memory, we deliver deterministic, zero-latency automation.

02 — Architecture

Three core layers. Zero hallucination.

01
UI Targeting Engine
C++ · ControlFromPoint

We don't parse the whole screen. When the agent acts, a background thread instantly queries the OS for the exact accessibility element at that pixel. Deep nested web DOMs pierced in sub-milliseconds.

01
02
Procedural Memory
Workflow Compiler

Stop paying LLMs to guess where the "Send" button is. Record a workflow once, and our compiler saves the semantic UI targets as a JSON skill graph for instant, deterministic replay at machine speed.

02
03
Self-Healing UI
pplx-embed · INT8

Hardcoded string matching is brittle. We use lightweight, local quantized embedding models to match the semantic intent of UI elements. If "Chat" changes to "Messages", the agent self-heals anyway.

03
03 — Orchestration Layer

One Orchestrator. A Swarm of Specialists.

We ripped the "Brain" out from the "Hands." A heavy reasoning model plans the objective and routes atomic tasks to fast, specialized sub-agents. No more monolithic models exhausting their context windows.

01
GUI Driver
Powered by sub-second flash models. Blindly and rapidly executes UI clicks based on strict semantic targets provided by the Orchestration Engine.
02
Terminal Driver
Bypasses the GUI entirely. Executes native PowerShell commands to manipulate files instantly when clicking is too slow.
03
Validation Guardrail
Middleware that intercepts invalid tool calls and physically forces schema corrections into the prompt, preventing LLM death loops.
04
RAG Memory Agent
Uses local vector search to retrieve pre-compiled JSON skills matching the Orchestration Engine's current sub-goal.
Coordinator
Orchestrator
Live Task Trace
→ waiting for task
0ms
UI Targeting Latency
0px
Target Resolution Radius
10
Hallucination Loops
0%
Deterministic Replay
05 — Feature breakdown
01 / Perception
UI Targeting
Engine
Using macOS-first accessibility targeting, we pierce through nested app and web UI layers the exact millisecond a click happens. No waiting for background state updates.
auto.ControlFromPoint(x, y)
if not target:
  snap_to_nearest(60px)
OS Layer
Sub-1ms
02 / Resilience
Precision Target
Resolution
Humans click the empty padding inside buttons, not the text. Our Euclidean distance algorithm mathematically snaps lazy clicks to the nearest semantic bounding box.
dist = math.hypot(cx-x, cy-y)
if dist < 60:
  target = closest_el.Name
Math Layer
Active
03 / Orchestration
Multi-Model
Orchestration
The Orchestration Engine manages global context and routes tasks to the CLI Agent or the GUI Agent. Sub-agents only see their local context, saving massive token costs.
plan = Orchestration.plan(goal)
for task in plan:
  ui_driver.run(task)
Routing
Orchestrating
04 / Robustness
Validation
Guardrail
Small models stubbornly repeat bad tool calls. Our middleware intercepts invalid JSON actions and physically forces the correct schema into the error prompt to guarantee recovery.
if act not in tools:
  return "CRITICAL ERROR: Use exactly {'action': 'click'}"
Middleware
Shielded
05 / Memory
Workflow
Compiler
Replaces stateless guessing with procedural memory. Records deterministic JSON workflows natively, bypassing the cloud, and executes them at machine-speed.
{
 "action": "click",
 "semantic_target": "Send"
}
Memory
Compiled
06 — Under the Hood

Drives the macOS. Like a human.

Watch the Multi-Model Orchestration layer in action. The coordinator decides what to do, the Terminal Driver finds the files natively, and the GUI Driver executes the clicks. All protected by semantic embeddings.

› eh run "Email Q3 report to Boss"
✓ Coordinator routing to Terminal Driver...
✓ [Terminal] File found: C:/docs/Q3.pdf 4ms
✓ Coordinator routing to GUI Driver...
› GUI.click(semantic_target="Compose")
✓ Target resolution successful (dist: 12px)
› GUI.click(semantic_target="Send")
âš  UI changed: 'Send' not found. Running pplx-embed...
✓ Semantic match found: 'Submit' (similarity 0.94)
✓ Task complete. (Total tokens saved: 45,200)
›

Your machine. Your rules.

Stop relying on fragile cloud-vision tech demos. Deploy autonomous, deterministic, multi-agent swarms to run desktop workflows with Emulated Humanoid by DaemonLabs.

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