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Settlement is a village strategy game built around six residents whose jobs, blocked work, memories and recent actions can be inspected. Its default campaign uses deterministic game AI—not a hidden language model—while an optional, explicitly configured research path uses Claude-generated proposals that must pass game constraints before they change state.
What Settlement lets you inspect
Kaushikreddy Lingi’s DEV Community article, posted October 1, 2026, describes Settlement as both a village game and an inspectable simulation lab. A player chooses an objective, such as preparing for a raid or restocking supplies, and a planner turns it into jobs.
The six residents bid for jobs based on role suitability, distance and bounded experience. Each job has one owner. Residents then travel to workplaces, gather resources and coordinate recruitment. If a resident rests, that resident can release work for someone else to take over; agents may also reconsider assignments when resources or workplaces change.
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- Current jobs and who owns them.
- Blocked work, so a task that cannot proceed is distinguishable from one that is simply unassigned.
- Spending limits, including the objective’s allowance.
- Recent memories and an action log.
The article says the research view also supports matched seeds, JSON and CSV exports, and replay verification. These features make it possible to examine what happened in a run; they do not by themselves establish that the system’s decisions are good or safe.
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Default campaign versus the optional research path
| What to compare | Default campaign | Optional research path |
|---|---|---|
| Model involvement | Deterministic game AI; the article says it makes no hidden model calls. | Claude proposals are available only in explicitly configured local-worker research experiments. |
| How actions are applied | Game logic assigns jobs and processes actions. | Proposals are subject to checks on funds, capacity and the objective’s spending allowance before the treasury or army changes. |
| What can be inspected | Jobs, blocked work, spending limits, memories and recent actions. | The article reports the same inspection focus, plus research-oriented matched seeds, exports and replay verification. |
| Evidence provided | A playable deterministic simulation. | Tools for comparing and replaying experiments; the article reports no measured quality or performance advantage. |
That distinction matters: Settlement’s ordinary campaign does not understand arbitrary natural-language goals or train a neural network. Claude proposals belong to a separate, configured research workflow, not an always-on feature of the village game.
How to inspect a job handoff
The article proposes a practical experiment: see whether a job changes owners when a resident rests, identify why the replacement received it, and check whether a resulting action stays within the objective budget. Treat this as an inspection exercise, not as a reported test result.
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- Start the project and open it in a browser, following the setup below.
- Choose Village orders → Prepare for a raid.
- Open View plan → Shared jobs and note a resident’s current job and its owner.
- Inspect that resident before assigning rest, then assign rest and check whether another resident takes over the work.
- Look for the replacement’s assignment reason, any blocked-work status, and the relevant spending limit and action-log entry.
This sequence can help you distinguish task ownership from the explanation for an assignment, and an allowed action from one that should be blocked. The article does not supply outcomes for this exercise, so none should be assumed in advance.
Reported setup instructions and their limits
The article reports this local setup for Node.js 24 or later:
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- Clone the Settlement-Village repository.
- From the project directory, run
npm ci. - Run
npm run dev. - Open
http://localhost:5173.
The article is the source for these commands; they were not independently tested here, so current repository state and compatibility are not confirmed.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What the scenarios can—and cannot—show
The authored scenarios are a bounded testbed for inspecting task assignment, memory, spending and action constraints. They should not be treated as a general AI safety benchmark. The article reports no research statistic or measured comparison demonstrating decision quality, performance or safety across broader systems.
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Settlement’s clearest contribution, as described in the article, is making selected pieces of a multi-agent simulation visible: who owns a task, what work is blocked, what a resident remembers and what actions were logged. Those details make a run easier to examine, but they are not a substitute for broader evidence about how an AI system behaves outside the authored scenarios.
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