Mist-ic commited on
Commit
bc82eac
·
1 Parent(s): ffe4aa5

Fix Dockerfile COPY and README cleanup

Browse files

- Dockerfile: include README.md in builder stage (hatchling requires it for build)
- README.md: remove em-dashes and license section

Files changed (2) hide show
  1. Dockerfile +1 -1
  2. README.md +1 -5
Dockerfile CHANGED
@@ -6,7 +6,7 @@ WORKDIR /app
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  RUN pip install --no-cache-dir uv
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  # Copy dependency files first for cache efficiency
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- COPY pyproject.toml uv.lock ./
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  # Install dependencies
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  RUN uv sync --frozen --no-dev
 
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  RUN pip install --no-cache-dir uv
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  # Copy dependency files first for cache efficiency
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+ COPY pyproject.toml uv.lock README.md ./
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  # Install dependencies
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  RUN uv sync --frozen --no-dev
README.md CHANGED
@@ -6,7 +6,7 @@ Built with [OpenEnv](https://github.com/meta-pytorch/OpenEnv) for the **OpenEnv
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  ## Why SRE Incident Response?
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- Incident response is one of the most expensive and error-prone aspects of running production systems. Engineers must rapidly diagnose root causes from noisy signals, contain blast radius, and restore service health often under 3 AM pressure. SevZero provides a realistic simulation environment for training and evaluating AI agents on this critical task.
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  The environment models:
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  - **Realistic microservice topologies** with typed service layers (edge, identity, business, infrastructure)
@@ -155,7 +155,3 @@ models.py ← Pydantic API contract (Action, Observation, State)
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  ```
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  The simulator runs a tick-based loop: each step, failures evolve their metric signatures, propagation cascades through the dependency graph via queueing theory, pending remediation effects resolve after their delay, and the agent receives an updated observation.
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-
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- ## License
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-
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- MIT
 
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  ## Why SRE Incident Response?
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+ Incident response is one of the most expensive and error-prone aspects of running production systems. Engineers must rapidly diagnose root causes from noisy signals, contain blast radius, and restore service health, often under 3 AM pressure. SevZero provides a realistic simulation environment for training and evaluating AI agents on this critical task.
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  The environment models:
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  - **Realistic microservice topologies** with typed service layers (edge, identity, business, infrastructure)
 
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  ```
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  The simulator runs a tick-based loop: each step, failures evolve their metric signatures, propagation cascades through the dependency graph via queueing theory, pending remediation effects resolve after their delay, and the agent receives an updated observation.