docs(skill): regenerate hindsight-docs skill mirror

- Revert the Idle RAM column on the Docker Image Variants table; the
  Hardware subsection already carries that detail.
- Reword the CPU/GPU line: CPU is fine for dev and basic workloads, but
  the local cross-encoder reranker typically benefits from a GPU under
  production traffic — or offload reranking to an external provider.

hindsight-docs/docs/developer/installation.md

@@ -45,6 +45,24 @@ Configure which one to use with `HINDSIGHT_API_VECTOR_EXTENSION`. See [Configura
 
 You need an LLM API key for fact extraction, entity resolution, and answer generation. See [Models](./models) for supported providers, model recommendations, and configuration.
 
+### Hardware
+
+Hindsight is designed to run on commodity hardware. The footprint depends mainly on whether the **full** image (which bundles local embedding and reranker models) or the **slim** image (which delegates those to external providers) is used.
+
+| Component | Minimum RAM | Recommended RAM | Notes |
+|-----------|-------------|-----------------|-------|
+| **API — Full image** | 1.5 GB | 2 GB | Loads local BGE embedder (~130 MB) and MiniLM cross-encoder (~90 MB) into memory, plus PyTorch/ONNX runtime arenas. Idle RSS settles around 0.8–1.0 GB; expect 1.2–1.5 GB under load. |
+| **API — Slim image** | 512 MB | 1 GB | No local models. Steady-state RSS is dominated by Python runtime and DB connections. Requires [external embedding and reranker providers](./configuration#embeddings) (e.g. TEI, OpenAI, Cohere). |
+| **Control Plane (UI)** | 128 MB | 256 MB | Next.js process, lightweight. |
+| **Worker** (if separated) | Same as API image variant | Same as API image variant | Workers load the same models as the API server. |
+| **PostgreSQL** | 512 MB | 1 GB+ | Scales with the number of memories and indexes. |
+
+:::tip Reducing the footprint
+The bulk of the full image's memory comes from the bundled embedding and reranker models and their PyTorch/ONNX runtimes. To shrink the deployment to a few hundred MB of RAM, switch to the **slim** image and configure [external embedding and reranker providers](./configuration#embeddings).
+:::
+
+CPU vs GPU: 2 vCPUs on CPU-only is fine for development and basic workloads. For production traffic, the local reranker (cross-encoder) is the main bottleneck and typically benefits from a GPU to keep recall latency reasonable; alternatively, offload reranking to an [external reranker provider](./configuration#embeddings) (e.g. TEI, Cohere) on dedicated GPU hardware.
+
 ---
 
 ## Docker

skills/hindsight-docs/references/developer/installation.md

@@ -45,6 +45,24 @@ Configure which one to use with `HINDSIGHT_API_VECTOR_EXTENSION`. See [Configura
 
 You need an LLM API key for fact extraction, entity resolution, and answer generation. See [Models](./models) for supported providers, model recommendations, and configuration.
 
+### Hardware
+
+Hindsight is designed to run on commodity hardware. The footprint depends mainly on whether the **full** image (which bundles local embedding and reranker models) or the **slim** image (which delegates those to external providers) is used.
+
+| Component | Minimum RAM | Recommended RAM | Notes |
+|-----------|-------------|-----------------|-------|
+| **API — Full image** | 1.5 GB | 2 GB | Loads local BGE embedder (~130 MB) and MiniLM cross-encoder (~90 MB) into memory, plus PyTorch/ONNX runtime arenas. Idle RSS settles around 0.8–1.0 GB; expect 1.2–1.5 GB under load. |
+| **API — Slim image** | 512 MB | 1 GB | No local models. Steady-state RSS is dominated by Python runtime and DB connections. Requires [external embedding and reranker providers](./configuration#embeddings) (e.g. TEI, OpenAI, Cohere). |
+| **Control Plane (UI)** | 128 MB | 256 MB | Next.js process, lightweight. |
+| **Worker** (if separated) | Same as API image variant | Same as API image variant | Workers load the same models as the API server. |
+| **PostgreSQL** | 512 MB | 1 GB+ | Scales with the number of memories and indexes. |
+
+:::tip Reducing the footprint
+The bulk of the full image's memory comes from the bundled embedding and reranker models and their PyTorch/ONNX runtimes. To shrink the deployment to a few hundred MB of RAM, switch to the **slim** image and configure [external embedding and reranker providers](./configuration#embeddings).
+:::
+
+CPU vs GPU: 2 vCPUs on CPU-only is fine for development and basic workloads. For production traffic, the local reranker (cross-encoder) is the main bottleneck and typically benefits from a GPU to keep recall latency reasonable; alternatively, offload reranking to an [external reranker provider](./configuration#embeddings) (e.g. TEI, Cohere) on dedicated GPU hardware.
+
 ---
 
 ## Docker