1-day full trial for new users
TalesRunner Automation
Run Smarter, not Harder with AloneAIOR
ALONEAIOR is a perception-driven automation architecture built for repetitive TalesRunner workflows. It reads the screen through OCR and visual cues, turns observation into policy decisions, coordinates behavior through finite-state control, and executes through stable system-level input orchestration.
ALONEAIOR is not just a script, but a gaming helper designed as a layered automation system. It operates from a human-visible perspective: reading state through OCR and visual cues, making decisions through adaptive policy logic, coordinating execution through a finite-state machine, and delivering actions through system-level input semantics.
Perception, policy, control, actuation, and backend in one stack
MANIFESTO
I did not enter this field because it existed. I entered because the problem already existed.
ALONEAIOR is built for the point where repetitive play has already hardened into measurable workflow.
It does not invent that condition. It gives that visible structural gap a stable, engineered response.
I did not begin from the desire to enter a so-called automation industry. I began as a long-time player with an engineering and systems background, and over time one fact became impossible to ignore: many core gameplay loops had already become repetitive, predictable, and structurally process-like.
For ordinary players, that repetition is paid in time, patience, and attention. For someone trained in automation and system design, the same pattern looks like a standard workflow problem waiting to be modeled.
This project is not built around the fantasy of breaking games. It is built around making a structural contradiction visible: when a game reduces too much of its value into measurable, repeatable labor, automation stops looking like an anomaly and starts looking like the natural consequence of the design itself.
THESIS
What ALONEAIOR actually responds to
Repetitive design creates machine-shaped problems.
When a game loop becomes predictable enough to be split into repeated states, timed transitions, and recoverable error patterns, it stops being purely expressive play. It becomes workflow. ALONEAIOR does not invent that transformation. It responds to it honestly.
A serious system is defined by stability, not by speed.
The real distinction between a toy macro and a mature automation system is not how aggressively it moves. It is whether it can observe uncertainty, degrade gracefully, recover from drift, re-synchronize after failure, and continue operating under noisy conditions.
Human time should return to human value.
The purpose is not to erase all play. The purpose is to remove dead labor so that human time can return to strategy, social interaction, exploration, judgment, and the parts of the experience that still meaningfully require a person.
ARCHITECTURE
Not just a macro, but an artificial intelligence system.
ALONEAIOR is designed as a full-stack automation architecture operating from a human-visible perspective. It does not rely on internal game state as its narrative center. Instead, it observes, interprets, decides, coordinates, executes, and synchronizes across five system layers.
Program Core
ALONEAIOR
One coordinated runtime for perception, policy, control, execution, and backend refinement.
Hover or focus a layer to inspect how each tier contributes to the full stack.
Layer 01
Perception
ALONEAIOR begins from the screen.
This layer performs frame sampling, OCR-based text reading, and visual cue detection to transform imperfect on-screen information into usable state. Because real interfaces are noisy, obstructed, and inconsistent, observation is treated as probabilistic rather than absolute.
- OCR reading for prompts, counters, result states, and interface labels.
- Pixel and UI-feature detection for transitions, targets, and interaction windows.
- Confidence-aware observation under blur, visual effects, occlusion, and unstable screens.
Layer 02
Policy
Observation becomes decision here.
Instead of replaying rigid loops, ALONEAIOR uses runpoint action-score logic, controlled exploration, fallback heuristics, and trap or oscillation memory to choose among candidate actions. The goal is not blind repetition, but stable selection under uncertainty.
- Runpoint action-score tables shaped by accumulated route knowledge.
- Exploration versus exploitation balance to avoid brittle local optima.
- Trap memory, oscillation awareness, and fallback heuristics for degraded states.
Layer 03
Control
This layer preserves coherence.
Finite-state orchestration, adaptive timing, recovery policy, and re-synchronization logic ensure that failure is treated as a recoverable event rather than a terminal collapse. This is where ALONEAIOR behaves less like a macro and more like a control system.
- Finite-state machine with entry rules, exit rules, timeouts, and downgrade paths.
- Adaptive timing control based on instability, failed progress, and environment noise.
- Recovery and re-sync logic for drift, no-progress states, and misread observations.
Layer 04
Actuation
Decision must become disciplined execution.
This layer translates selected actions into structured behavior through system-level input orchestration, timing profiles, and controlled rhythm. The point is not random clicking. The point is repeatable, interpretable output.
- Input abstraction for high-level action intent.
- Timing profile control for duration, interval, and sequence stability.
- Consistent executable behavior under changing conditions.
Layer 05
Backend
Local execution becomes an evolving product system here.
Activation, subscription control, policy synchronization, telemetry, and storage turn isolated automation into a maintainable and continuously refined platform.
- Auth and subscription services for access and device-level management.
- Policy sync for route knowledge download, upload, and refinement.
- Telemetry, metrics, and storage for reliability analysis and long-term iteration.
PROCESS
From onboarding to first stable run
01
💬
Enter the community
Join the appropriate channel, read current notices, and access the latest workflow-specific setup information.
02
▶
Review the environment setup
Watch the current system guide and align launcher, environment, and operational conditions before first execution.
03
🔑
Activate the device
Submit machine information, complete activation, and receive server-specific guidance where required.
04
🚀
Start with the trial
Run the full core workflow for one day and evaluate the system by resilience, coherence, and recovery quality, not only by speed.
CAPABILITIES
What the system can sustain under real conditions
🔐
OCR State Reading
Reads prompts, labels, counters, result states, and visible interface text so execution is grounded in actual observation.
🗺
Visual Cue Detection
Tracks transitions, menus, interaction windows, and screen-state changes through visual and pixel-level features.
👁
Policy-Based Decision Logic
Uses route knowledge, action scoring, exploration, and fallback logic to select behavior instead of replaying fixed loops.
🔍
Finite-State Workflow Control
Organizes long workflows into interpretable states with entry conditions, exits, timeout windows, and fallback paths.
⌨
Adaptive Timing and Recovery
Adjusts rhythm and recovery strategy when the system encounters drift, instability, or no-progress conditions.
↺
Reconnect and Long-Session Resilience
Handles interruptions, reconnect scenarios, and recovery continuity so sessions remain stable over time.
USE CASES
Built for players whose time has real constraints
ALONEAIOR is built for players who no longer confuse repetitive obligation with meaningful play.
In many long-running online games, daily and weekly progression is structured less around expression than around repeatable labor. For players with work, study, family responsibility, or limited attention, that structure becomes exclusionary long before it becomes rewarding.
ALONEAIOR addresses that gap by handing repetitive workflow to a coordinated system, so human time can return to the parts of the game that still matter: social interaction, strategy, experimentation, and actual enjoyment.
STRUCTURAL IMPACT
What automation reveals about games and communities
Automation does not only change efficiency. It reveals structure.
At the player level, it can compensate for game loops that demand too much repetitive labor from people with limited real-world time. At the community level, it can also intensify distrust, asymmetry, and suspicion when some players automate and others do not.
If a game's core progression can be reproduced through observation, policy, timing, and recovery, the uncomfortable question is no longer whether automation exists. The question is what kinds of gameplay have already become machine-shaped in the first place.
PLANS
Pricing
Server-specific pricing varies by workflow environment. New users begin with a 1-day full trial before choosing a plan.
TRIAL FIRST
Trial Access
Free for 1 day
- Full core workflow access
- Activation guidance
- Setup assistance
- Supported multi-server environments
- Evaluation based on real-session stability
After trial, server-specific plans and setup details are provided through the appropriate community support channels.
Enter CommunityTESTIMONIALS
What users actually notice
"I came for repetitive workflow, but what kept me here was the system's stability under messy conditions."
"The biggest difference was not how fast it ran. It was how coherently it recovered."
"It did not just save time. It returned attention to the part of the game that still felt human."
"This felt engineered from architecture upward, not stitched together from random features."
ABOUT
About ALONEAIOR
ALONEAIOR began from a simple but uncomfortable observation: a large share of modern online progression is no longer truly expressive play. It is repeated structure, repeated timing, repeated friction, and repeated labor.
The project was built from that contradiction. Its architecture combines visual perception, policy logic, finite-state control, adaptive timing, recovery flow, and backend synchronization into a coherent automation system designed to operate from a human-visible perspective.
For users, that means less wasted attention, more stable workflow, and more time returned to the parts of the game that still deserve real human presence.
FAQ
FAQ
How do I get started?
Join the relevant community channel, follow the current setup guide, download the latest build, and complete activation where required.
What makes this different from a macro?
A macro replays input. ALONEAIOR reads visible state, evaluates context, chooses among actions, adapts timing, recovers from drift, and maintains coherence across long workflows.
Why build around visual perception?
Because the project is designed to operate from the same kind of information a player can actually see. That means OCR, UI observation, and visual cues become the foundation of state, rather than pretending the screen does not matter.
Is there a free trial?
Yes. New users can evaluate the full core workflow through a 1-day trial before selecting a server-specific plan.
Which environments are supported?
Current workflow support covers Global, HK-TW, CN, KR, and TH environments, with setup guidance routed through the corresponding support channels.
Why does the project emphasize stability over speed?
Because real environments are noisy. A serious system is defined by how it observes uncertainty, recovers from failure, and continues coherently under imperfect conditions.
Need setup rules, TRFile examples, Alone.ini checks, and troubleshooting steps? Open the localized Technical FAQ page.
If a workflow has already been reduced into repeatable labor, the real question is no longer whether systems will emerge around it. The real question is whether those systems are shallow, brittle, and disposable, or architected to observe, decide, recover, and endure.