docs: full README + new EMISSIONS / DEPLOY / CHANGELOG / CONTRIBUTING

Documents the post-droplet L4 topology, the per-call NVML energy
ledger, and the production deploy commands so the repo presents
itself the way an open-source civic tech project should — without
breaking any of the existing app structure or deploy paths.

New files
- docs/EMISSIONS.md full pipeline for real GPU power readings:
what's measured vs. estimated, the NVML
sampler in inference-vllm/proxy.py, the
bracket-sample LLM client path, the
hardware fallback table, and how to verify
with scripts/probe_stones_fire.py.
- docs/DEPLOY.md production topology diagram, env-var
reference for both Spaces, per-Space
deploy commands, local-dev recipes.
- CHANGELOG.md release log starting from the v0.5.0
hackathon submission tag.
- CONTRIBUTING.md quickstart + repo structure + house style
for outside contributors.

Updated
- README.md new "Inference energy — measured, not
estimated" section; live-demo blurb
reflects the L4 reality and the
RIPRAP_HARDWARE_LABEL override; new
Repository Structure section pointing at
each subtree; source-of-truth list adds
proxy.py + emissions.py. Hackathon
submission framing preserved.
- docs/ARCHITECTURE.md May 2026 banner at the top noting the
droplet retirement and pointing at
DEPLOY.md + EMISSIONS.md for current
infrastructure.

Bug fix
- web/main.py added explicit /favicon.svg, /favicon.png,
/favicon.ico, /robots.txt routes. They
were 404-ing under the SvelteKit SPA
fallback (only /_app was mounted off the
build directory), so the dam-mark logo
never reached the browser tab. Verified
via FastAPI TestClient: all three favicon
paths now serve the SvelteKit-built
artwork at 200.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

ARCHITECTURE.md

@@ -1,5 +1,16 @@
 # Riprap architecture
 
+> **Update, May 2026.** The MI300X DigitalOcean droplet that hosted
+> production vLLM + EO models was decommissioned 2026-05-06; inference
+> now serves from the NVIDIA L4 HF Space `msradam/riprap-vllm` and a
+> matching FastAPI proxy that surfaces real per-call GPU power
+> readings on every response. The MI300X language preserved in this
+> document remains accurate for the original AMD-judging deploy
+> (recoverable via [`docs/DROPLET-RUNBOOK.md`](DROPLET-RUNBOOK.md) +
+> `RIPRAP_HARDWARE_LABEL=AMD MI300X`); current production topology +
+> deploy commands live in [`docs/DEPLOY.md`](DEPLOY.md), and the
+> emissions ledger is documented in [`docs/EMISSIONS.md`](EMISSIONS.md).
+
 > **What it is.** A web tool that takes any NYC address and produces a
 > short, citation-grounded **flood-exposure briefing**. A tier (1–4)
 > with a paragraph of evidence, where every numeric claim links back to

CHANGELOG.md

@@ -0,0 +1,93 @@
+# Changelog
+
+All notable changes to Riprap. The hackathon submission tag is
+`v0.5.0` (build 2026-05-07); subsequent dates record polish work
+that landed on the hackathon-period production deploys.
+
+## [Unreleased] — 2026-05-09 (Saturday)
+
+### Added
+- **Per-query inference energy ledger** with real NVML readings off
+  the L4 GPU. The status row on the Findings region now reports
+  total Wh + total tokens for every briefing, with a leading icon
+  (`✓` / `◐` / `~`) disclosing whether the number was measured or
+  estimated. Full breakdown documented in
+  [`docs/EMISSIONS.md`](docs/EMISSIONS.md).
+- `inference-vllm/proxy.py`: 100 ms-cadence NVML sampler, response
+  headers `X-GPU-Power-W` / `X-GPU-Energy-J` on every forwarded
+  POST, and a `GET /v1/power` endpoint for bracket-sampling clients.
+- `app/emissions.py` — new module with a thread-local `Tracker` that
+  records every LLM and ML inference call (model, hardware, tokens,
+  duration, joules) with a `measured: bool` flag per row.
+- `scripts/probe_stones_fire.py` — programmatic CI that runs an
+  address query against the lablab UI and asserts all five Stones
+  fire, no `torchvision::nms` / `deps unavailable` dep regression,
+  and the `emissions` block carries `nvidia_l4` hardware.
+- `docs/EMISSIONS.md`, `docs/DEPLOY.md`, `CHANGELOG.md`,
+  `CONTRIBUTING.md`.
+
+### Changed
+- The `RunHealthStrip` chip dropped the cloud-energy comparison
+  (the sign convention was misleading and the comparison is now
+  redundant given real measurements). New format:
+  `<icon> X.X Wh / Y.YK tok inference`.
+- `app/llm.py:_default_hardware_label` defaults to `"NVIDIA L4"`
+  when remote vLLM is configured (was `"AMD MI300X"`, a stale
+  string from the droplet days).
+- `app/llm.py:chat()` now brackets every completion with two GETs
+  to the inference Space's `/v1/power` endpoint; the average powers
+  the LLM-call energy reading instead of the data-sheet estimate.
+- `app/inference.py:_post()` reads NVML headers off the proxy
+  response and forwards real joules into `emissions.record_ml`.
+
+### Fixed
+- `app/flood_layers/prithvi_live.py`: when the configured remote
+  inference call fails (`RemoteUnreachable`), the specialist no
+  longer falls through to the local terratorch path. The local
+  path crashes with `RuntimeError: operator torchvision::nms does
+  not exist` on the cpu-basic UI Space; surfacing a clean
+  `remote prithvi-pluvial unreachable` skip is correct.
+- `app/context/terramind_nyc.py:_try_remote()`: returns a
+  `{"ok": False, "skipped": "remote terramind/<adapter>: ..."}`
+  sentinel on remote failure, instead of `None` which was
+  silently masked as `deps unavailable on this deployment`.
+- `web/main.py`: explicit `/favicon.svg`, `/favicon.png`,
+  `/favicon.ico`, `/robots.txt` routes — they were 404-ing under
+  the SvelteKit SPA fallback because only `/_app` was mounted off
+  the build directory.
+
+### Documentation
+- Full README rewrite reflecting the post-droplet L4 topology, the
+  new emissions feature, and updated repo structure. Hackathon
+  framing preserved.
+- New `docs/DEPLOY.md` with the production topology, env-var
+  reference, and per-Space deploy commands.
+- New `docs/EMISSIONS.md` documenting what's measured vs. estimated,
+  the NVML pipeline, and how to verify.
+
+### Infrastructure note
+- The DigitalOcean MI300X droplet was decommissioned 2026-05-06.
+  All production inference now serves from `msradam/riprap-vllm`
+  (NVIDIA L4). The MI300X runbook is preserved in
+  [`docs/DROPLET-RUNBOOK.md`](docs/DROPLET-RUNBOOK.md) for anyone
+  reproducing the AMD-judging setup; setting
+  `RIPRAP_HARDWARE_LABEL=AMD MI300X` swaps the emissions profile
+  back when redeploying to that hardware.
+
+---
+
+## [v0.5.0] — 2026-05-07
+
+Hackathon submission tag.
+
+### Added
+- Five-Stone Burr FSM with Granite-native document-role messages
+- Mellea four-check rejection sampling for the Capstone
+- SvelteKit UI with SSE streaming, briefing prose, evidence-card
+  grid, MapLibre overlay, citation drawer
+- Three NYC-specialised foundation models published Apache-2.0:
+  `msradam/TerraMind-NYC-Adapters` (LULC + Buildings + TiM LoRAs),
+  `msradam/Prithvi-EO-2.0-NYC-Pluvial`,
+  `msradam/Granite-TTM-r2-Battery-Surge`
+- 30+ FSM specialists across hazard memory, asset registers, live
+  observation, forecasting, and citation-grounded synthesis

CONTRIBUTING.md

@@ -0,0 +1,127 @@
+# Contributing
+
+Riprap is the hackathon submission for the AMD × lablab.ai
+Developer Hackathon, but the source ships under Apache 2.0 and is
+intended to be reusable as a template for citation-grounded civic
+AI in any flood-vulnerable region. Pull requests welcome.
+
+## Quickstart
+
+Python 3.12 + `uv`:
+
+```bash
+git clone https://github.com/msradam/riprap-nyc
+cd riprap-nyc
+uv venv && uv pip install -r requirements.txt
+```
+
+SvelteKit (the build is committed; only rebuild when sources
+change under `web/sveltekit/src`):
+
+```bash
+cd web/sveltekit && npm ci && npm run build && cd ../..
+```
+
+Run the dev server locally pointing at the production inference
+Space (real Granite + EO models, real NVML energy readings):
+
+```bash
+RIPRAP_LLM_PRIMARY=vllm \
+RIPRAP_LLM_BASE_URL=https://msradam-riprap-vllm.hf.space/v1 \
+RIPRAP_LLM_API_KEY=<token> \
+RIPRAP_ML_BACKEND=remote \
+RIPRAP_ML_BASE_URL=https://msradam-riprap-vllm.hf.space \
+RIPRAP_ML_API_KEY=<token> \
+.venv/bin/uvicorn web.main:app --host 127.0.0.1 --port 7860
+```
+
+Or run pure-local with Ollama (no GPU readings; data-sheet estimate):
+
+```bash
+ollama pull granite4.1:3b granite4.1:8b
+.venv/bin/uvicorn web.main:app --host 127.0.0.1 --port 7860
+```
+
+## Verifying changes
+
+Two probe scripts exercise the live deployment end-to-end:
+
+```bash
+# All five Stones must fire on the canonical address; emissions
+# block must carry nvidia_l4 hardware; no torchvision/terratorch
+# dep regressions in the trace.
+PYTHONPATH=. uv run python scripts/probe_stones_fire.py --timeout 600
+
+# Full canonical suite — five NYC addresses, intent-aware checks,
+# Mellea grounding budget, no specialist crashes.
+.venv/bin/python scripts/probe_addresses.py \
+    --base https://lablab-ai-amd-developer-hackathon-riprap-nyc.hf.space
+```
+
+Both default to the lablab UI Space; pass `--base http://127.0.0.1:7860`
+to hit a local server.
+
+## Structure
+
+```
+app/                       Python package — the FSM and its specialists
+├── fsm.py                 Burr FSM, one @action per probe
+├── llm.py                 LiteLLM Router shim (Ollama / vLLM)
+├── inference.py           HTTP client for the riprap-models service
+├── emissions.py           Per-query energy + token tracker
+├── stones/                Stone taxonomy (NAME / TAGLINE / collect())
+├── flood_layers/          Cornerstone probes (sandy, dep, microtopo, …)
+├── context/               Keystone + Touchstone register + EO probes
+├── live/                  Lodestone forecast probes
+├── intents/               single_address / neighborhood / compare / live_now
+├── reconcile.py           Capstone — Granite-native document reconcile
+└── mellea_validator.py    Mellea four-check rejection sampling
+
+web/                       FastAPI + SvelteKit
+├── main.py                FastAPI app, SSE streaming, layer endpoints
+├── sveltekit/             Primary UI (adapter-static; build committed)
+└── static/                Legacy custom-element pages (still mounted)
+
+inference-vllm/            Inference Space source (vLLM + EO models + proxy)
+├── Dockerfile             L4 image, bakes Granite 4.1 8B FP8 + EO deps
+├── entrypoint.sh          Boots vllm, riprap-models, proxy as subprocesses
+└── proxy.py               Bearer-auth + NVML power sampler + SSE pass-through
+
+inference/                 Ollama-backed inference Space (fallback variant)
+services/riprap-models/    The EO/forecast specialist HTTP service
+
+scripts/
+├── probe_stones_fire.py   Programmatic Stone-fire CI
+├── probe_addresses.py     Canonical 5-address suite
+├── deploy_vllm_space.sh   Deploy the L4 inference Space
+├── deploy_personal_space.sh  Deploy the personal L4 mirror
+├── deploy_inference_space.sh Deploy the Ollama-backed inference Space
+└── …                       Register builders, raster bakers, etc.
+
+experiments/               Reproduction recipes for the three NYC fine-tunes
+docs/                      Architecture, methodology, deploy, emissions, runbooks
+tests/                     pytest suite (envelope + compare-shape tests)
+```
+
+## Style
+
+- Python 3.12; `uv` for package management.
+- LLM calls go through `app/llm.py` — never import `litellm` /
+  `ollama` directly from a specialist. The `chat()` shim wraps both
+  backends and the energy ledger reads off it.
+- Remote ML calls go through `app/inference.py::_post`. Specialists
+  may try local fallback only when `inference.remote_enabled()` is
+  False; once a remote call has been attempted, return a clean
+  `{ok: False, skipped: ...}` on failure rather than crashing
+  through to local code paths that may not be installed.
+- Every specialist emits one trace record per call with `step` /
+  `ok` / `elapsed_s` / `result` / `err` so the SSE stream and the
+  emissions tracker can reason about it.
+
+## Reporting issues
+
+GitHub issues at <https://github.com/msradam/riprap-nyc/issues>.
+For hackathon-period demo issues during May 4–10 2026, the live
+deploy at
+<https://lablab-ai-amd-developer-hackathon-riprap-nyc.hf.space>
+is the source of truth.

README.md

@@ -11,33 +11,36 @@ pinned: false
   <img src="assets/logo@2x.png" width="72" height="72" alt="Riprap dam mark" />
 </p>
 
-# Riprap — citation-grounded NYC flood-exposure briefings
-
-Riprap takes any NYC address (or neighborhood, or development-permit
-query) and produces a four-section briefing — Status, Empirical
-evidence, Modeled scenarios, Policy context. Every numeric claim is
-anchored to a `[doc_id]` citation that resolves to a named primary
-public-record source. If the model cannot cite a number, the model
-does not publish that number.
-
-The Capstone reconciler is **IBM Granite 4.1 8B**, served via Ollama
-on T4 or vLLM on AMD MI300X, wrapped in **Mellea**-validated rejection
-sampling. Sentences that fail one of four grounding checks
-(`numerics_grounded`, `no_placeholder_tokens`, `citations_dense`,
-`citations_resolve`) are rerolled with surgical feedback until the
-budget is exhausted.
+# Riprap
+
+## Flood risk analysis for any NYC address.
+
+A multi-agent AI system that reads satellites, watches sensors, forecasts
+surges, and refuses to ship a sentence it cannot ground in a citation.
+
+![Riprap flood-exposure briefing for DUMBO, Brooklyn](assets/screenshots/hero.png)
 
 Live demo: <https://lablab-ai-amd-developer-hackathon-riprap-nyc.hf.space>
 
 ---
 
-## What it looks like
+## The problem Riprap solves
+
+NYC has spent decades publishing the flood-exposure inputs an engineer needs:
+Sandy 2012 inundation, NYC DEP stormwater scenarios, FloodNet sensors, NOAA
+tide gauges, USGS 3DEP LiDAR, 311 complaints, MTA, NYCHA, schools, hospitals.
+The data is public. None of it composes itself.
 
-![Riprap flood-exposure briefing for 80 Pioneer Street, Brooklyn](assets/screenshots/hero.png)
+Every engineer doing a drainage review, every resilience office siting a
+capital project, every climate-adaptation team prioritising blocks
+reassembles the same evidence by hand, per address, from a dozen agencies. A
+briefing that should be a tool call ends up as a half-day of manual joins.
+Existing tools either return opaque vendor risk scores or skip the audit
+trail a stamped engineering memo actually requires.
 
-*A citation-grounded flood-exposure briefing for 80 Pioneer Street in
-Red Hook. Generated in roughly 7 seconds against AMD MI300X. Every
-numeric claim cites a primary public-record source.*
+Riprap composes it. Type any NYC address, get a four-section,
+citation-grounded briefing in about two minutes, with every claim pointing
+back to a `[doc_id]` in public-record data.
 
 ---
 
@@ -47,11 +50,19 @@ Three ways to use Riprap, in increasing order of self-host:
 
 ### 1. Try the live demo
 
-The hosted Space runs the full pipeline against a live AMD MI300X
-inference backend. Type any NYC address.
+The hosted Space runs the full pipeline. Type any NYC address.
 
 <https://lablab-ai-amd-developer-hackathon-riprap-nyc.hf.space>
 
+The hackathon submission was originally built against an AMD Instinct
+MI300X via the AMD Developer Cloud, where the three NYC-specialised
+fine-tunes were trained. For the hackathon-period demo, inference now
+serves from an NVIDIA L4 Hugging Face Space (`msradam/riprap-vllm`)
+co-hosting vLLM + the EO model stack — see
+[`docs/DEPLOY.md`](docs/DEPLOY.md). Setting
+`RIPRAP_HARDWARE_LABEL=AMD MI300X` on a redeploy swaps the energy
+ledger back to MI300X figures.
+
 ### 2. Run locally with Docker
 
 ```bash
@@ -65,8 +76,8 @@ docker compose up
 
 Visit `http://localhost:7860`.
 
-To self-host the GPU inference half (vLLM + the ML specialist
-service) on an AMD ROCm or NVIDIA CUDA box, run:
+To self-host the GPU inference half (vLLM + the ML specialist service) on
+an AMD ROCm or NVIDIA CUDA box, run:
 
 ```bash
 docker compose --profile with-models up
@@ -111,14 +122,38 @@ group those probes into five legible roles:
 
 | Stone | Role | What fires |
 |---|---|---|
-| **Cornerstone** | The Hazard Reader. What NYC's ground remembers. | Sandy 2012 inundation extent, NYC DEP stormwater scenarios, 2021 Ida USGS high-water marks, baked Prithvi-EO Ida-attributable polygons, USGS 3DEP DEM + HAND/TWI |
+| **Cornerstone** | The Hazard Reader. What the ground remembers. | Sandy 2012 inundation extent, NYC DEP stormwater scenarios, 2021 Ida USGS high-water marks, baked Prithvi-EO Ida-attributable polygons, USGS 3DEP DEM + HAND/TWI |
 | **Keystone** | The Asset Register. What's exposed. | MTA subway entrances, NYCHA developments, NYC DOE schools, NYS DOH hospitals, **TerraMind-NYC Buildings LoRA** |
 | **Touchstone** | The Live Observer. Current state of the city. | FloodNet ultrasonic depth sensors, NYC 311 flood complaints, NWS hourly METAR, NOAA tide-gauge water levels, **Prithvi-EO 2.0 NYC-Pluvial v2**, **TerraMind-NYC LULC LoRA** |
 | **Lodestone** | The Projector. What's coming. | NWS public flood alerts, Granite TTM r2 surge nowcast (zero-shot, 6-min cadence, 9.6 h horizon), per-address 311 weekly forecast, FloodNet sensor recurrence forecast, **Granite-TTM-r2-Battery-Surge fine-tune** (96 h hourly horizon) |
 | **Capstone** | The Synthesiser. Citation-grounded briefing. | Granite 4.1 + Mellea rejection sampling |
 
-The four data-Stones run sequentially per query; the Capstone
-reconciles their documents into one cited paragraph.
+The four data-Stones run sequentially per query; the Capstone reconciles
+their documents into one cited briefing.
+
+---
+
+## The Five Stones beyond NYC
+
+The Five Stones taxonomy is a city-agnostic template for any
+flood-vulnerable region with the right data scaffolding. The five roles
+generalise; only the probes plugged into each Stone change.
+
+| Stone | Role | What you replace |
+|---|---|---|
+| **Cornerstone** | Hazard memory | Local historical inundation extents, regional DEM, regulatory floodplain maps |
+| **Keystone** | Asset registers | The transit, housing, education, and healthcare polygons your jurisdiction publishes |
+| **Touchstone** | Live observation | Whatever live sensors and complaint streams the city or region exposes (FloodNet has analogues in Houston, Boston, Miami) |
+| **Lodestone** | Forecasts | Local NWS forecast office output, regional surge or hydrologic models, time-series fine-tunes for your tide gauge |
+| **Capstone** | Citation-grounded synthesis | Same |
+
+The architectural commitments transfer unchanged: Burr FSM with one
+`@action` per probe, Granite-native `role="document"` reconciliation,
+Mellea four-check grounding, SSE streaming to a SvelteKit map UI, every
+claim cited to its source. To port Riprap to a new city, you reimplement
+each Stone's `collect()` against local data and retrain the EO and
+time-series fine-tunes on your jurisdiction's imagery and gauges. The
+agentic shell stays the same.
 
 ---
 
@@ -133,13 +168,14 @@ full-FT baseline), TiM 0.6023, Buildings 0.5511. Trained in around 18
 minutes on a single MI300X.
 
 **[`msradam/Prithvi-EO-2.0-NYC-Pluvial`](https://huggingface.co/msradam/Prithvi-EO-2.0-NYC-Pluvial).**
-NYC pluvial-flood fine-tune of Prithvi-EO 2.0. Test flood IoU 0.5979
-vs 0.10 on the Sen1Floods11 base, a 6× lift. Lovász-Softmax loss with
+NYC pluvial-flood fine-tune of Prithvi-EO 2.0. Test flood IoU 0.5979 vs
+0.10 on the Sen1Floods11 base, a 6× lift. Lovász-Softmax loss with
 copy-paste augmentation.
 
 **[`msradam/Granite-TTM-r2-Battery-Surge`](https://huggingface.co/msradam/Granite-TTM-r2-Battery-Surge).**
-NYC Battery storm-surge nowcast fine-tune of Granite TimeSeries TTM
-r2. Test MAE 0.1091 m, −41% vs persistence and −25% vs zero-shot.
+NYC Battery storm-surge nowcast fine-tune of Granite TimeSeries TTM r2.
+Test MAE 0.1091 m, 41% better than persistence and 25% better than
+zero-shot.
 
 All three are loaded at runtime by their respective FSM probes in
 `app/context/` and `app/live/`. Reproduction recipes live under
@@ -172,10 +208,18 @@ NYC address ──► Granite 4.1 3B planner ──► Plan{intent, targets, spe
                        SSE stream → SvelteKit UI (briefing, trace, map)
 ```
 
-LLM inference is dispatched through `app/llm.py`, a LiteLLM Router
-shim with two backends: **Ollama** (T4 / local) and **vLLM** (AMD
-MI300X). Same `chat()` signature in both directions; vLLM is primary
-for the demo, Ollama is the auto-failover.
+LLM inference is dispatched through `app/llm.py`, a LiteLLM Router shim
+with two backends: **Ollama** (local dev) and **vLLM** (AMD MI300X or
+NVIDIA L4 — currently L4 on `msradam/riprap-vllm`). Same `chat()`
+signature in both directions; vLLM is primary for the demo, Ollama is
+the auto-failover.
+
+ML model inference (Prithvi-EO, TerraMind, TTM, GLiNER, Granite
+Embedding) goes through `app/inference.py::_post`, a thin HTTP client
+that hits the bearer-authenticated proxy on the inference Space. The
+proxy forwards to vLLM or to the riprap-models service co-resident on
+the same L4, and stamps real GPU power readings onto every response —
+see the energy section below.
 
 Source-of-truth pointers:
 
@@ -192,8 +236,47 @@ Source-of-truth pointers:
   `plan / step / token / mellea_attempt / final` events plus the
   `stone_start / stone_done` envelope around each Stone group.
 - `web/sveltekit/`: primary UI (SvelteKit + adapter-static).
+- `inference-vllm/proxy.py`: bearer-auth proxy on the L4 inference
+  Space; runs the NVML power sampler that the energy ledger reads.
+- `app/emissions.py`: per-query Tracker + hardware profiles. Records
+  every LLM and ML inference call with `measured: bool`.
+
+For the long-form architecture document, see
+[`docs/ARCHITECTURE.md`](docs/ARCHITECTURE.md). Methodology and
+civil-engineering framing in
+[`docs/METHODOLOGY.md`](docs/METHODOLOGY.md). Lit review in
+[`docs/RESEARCH.md`](docs/RESEARCH.md). Production deploy
+topology in [`docs/DEPLOY.md`](docs/DEPLOY.md).
 
-For the long-form architecture document, see [`ARCHITECTURE.md`](ARCHITECTURE.md).
+---
+
+## Inference energy — measured, not estimated
+
+Riprap reports the energy and token cost of every inference call it
+makes during a briefing. The status row on the Findings region
+displays a single chip:
+
+```
+✓ 1.4 Wh / 6.9K tok inference
+```
+
+The `✓` icon means every recorded call came back with a real reading
+off the L4 GPU via `nvmlDeviceGetPowerUsage`. The inference Space
+runs a 100 ms-cadence NVML sampler in the proxy and stamps
+`X-GPU-Power-W` / `X-GPU-Energy-J` on every response; the LLM client
+brackets each completion with two GETs to `/v1/power` because LiteLLM
+hides response headers. When the proxy is unreachable, the chip
+shows `~` or `◐` and the row falls back to a data-sheet sustained-
+power estimate.
+
+Per-call records carry `prompt_tokens`, `completion_tokens`,
+`duration_s`, `power_w`, `joules`, and a `measured: bool` flag. The
+full ledger is shipped on the SSE `final` event under
+`emissions.calls`, so any consumer (dashboard, billing model,
+reproducibility check) can reuse the data.
+
+Detailed pipeline + verification recipe in
+[`docs/EMISSIONS.md`](docs/EMISSIONS.md).
 
 ---
 
@@ -210,7 +293,7 @@ aggregators.
 | Hurricane Ida 2021 USGS high-water marks | USGS Short-Term Network | Empirical validation points |
 | FloodNet ultrasonic sensor network | NYU CUSP / FloodNet | Live water-depth observations |
 | NYC 311 flood complaints | NYC Open Data | Empirical complaint history |
-| NOAA tide gauge — The Battery | NOAA CO-OPS | Live tide and surge level |
+| NOAA tide gauge, The Battery | NOAA CO-OPS | Live tide and surge level |
 | NWS METAR | National Weather Service | Hourly precipitation |
 | NWS public flood alerts | National Weather Service | Active warnings and watches |
 | MTA subway entrances | MTA / NYC Open Data | Transit asset register |
@@ -223,28 +306,47 @@ aggregators.
 | Sentinel-2 MSI imagery | ESA / Copernicus | Prithvi + TerraMind inputs |
 
 The full data licence map and vintage table is enumerated in
-[`ARCHITECTURE.md`](ARCHITECTURE.md).
+[`docs/ARCHITECTURE.md`](docs/ARCHITECTURE.md).
 
 ---
 
-## What Riprap is not
-
-The civil engineer carries the stamp. Riprap surfaces the evidence the
-engineer judges.
-
-- **Not a hydraulic model.** Riprap does not replace HEC-RAS, SWMM, or
-  ICM. It synthesises evidence from completed modelling work; it does
-  not produce new flow or stage estimates.
-- **Not a stamped deliverable.** The briefing is a starting point for
-  a memo, not the memo itself. Professional judgment, field
-  reconnaissance, and the engineer's stamp are required for any
-  actionable output.
-- **Not a substitute for site investigation.** Microtopography is from
-  1 m USGS 3DEP LiDAR, appropriate for screening, not for design.
-- **Not a risk score.** Riprap does not output a 1–10 or 1–100 number.
-  Score-based tools (First Street, ClimateCheck, Jupiter) are
-  different products for different audiences. Riprap is the evidence
-  audit trail behind any such judgment.
+## Repository structure
+
+```
+app/                       Python — Burr FSM, specialists, reconciler
+├── fsm.py                 One @action per probe, plus the Stones taxonomy
+├── llm.py                 LiteLLM Router shim (Ollama / vLLM)
+├── inference.py           HTTP client for the riprap-models service
+├── emissions.py           Per-query energy + token ledger (real NVML)
+├── reconcile.py           Granite-native document reconcile (Capstone)
+├── mellea_validator.py    Mellea four-check rejection sampling
+├── stones/, intents/      Stone definitions + intent dispatchers
+├── flood_layers/          Cornerstone hazard probes
+├── context/, registers/   Keystone + Touchstone register / EO probes
+└── live/                  Lodestone forecast probes
+
+web/                       FastAPI + SvelteKit
+├── main.py                FastAPI app, SSE streaming, layer endpoints
+├── sveltekit/             Primary UI (adapter-static; build committed)
+└── static/                Legacy custom-element pages (still mounted)
+
+inference-vllm/            Inference Space (vLLM + EO models + proxy)
+├── Dockerfile             L4 image, bakes Granite 4.1 8B FP8 + EO deps
+├── entrypoint.sh          Boots vllm, riprap-models, proxy together
+└── proxy.py               Bearer-auth + NVML sampler + SSE pass-through
+
+inference/                 Ollama-backed inference Space (fallback)
+services/riprap-models/    EO/forecast specialist HTTP service
+
+scripts/                   Probes, register builders, deploy commands
+experiments/               Reproduction recipes for the three NYC fine-tunes
+docs/                      ARCHITECTURE · DEPLOY · EMISSIONS · METHODOLOGY · RESEARCH
+tests/                     pytest suite (envelope + compare-shape)
+```
+
+[`CONTRIBUTING.md`](CONTRIBUTING.md) covers dev setup, the probe
+scripts, and house style. [`CHANGELOG.md`](CHANGELOG.md) tracks
+changes since the v0.5.0 hackathon submission.
 
 ---
 
@@ -267,30 +369,30 @@ If you reference Riprap in academic or professional work:
 
 ## License
 
-Apache 2.0 (this repository). The three NYC-specialised fine-tunes
-above are also Apache 2.0; underlying upstream models retain their
-own permissive licences (see each `MODEL_CARD.md`).
+Apache 2.0. See [`LICENSE`](LICENSE) and [`NOTICE`](NOTICE).
 
-HF Space configuration reference:
-<https://huggingface.co/docs/hub/spaces-config-reference>.
+The three NYC-specialised fine-tunes above are also Apache 2.0;
+underlying upstream models retain their own permissive licences (see
+each `MODEL_CARD.md`). Public-record data sources retain their own
+access terms; the licence map is in
+[`docs/ARCHITECTURE.md`](docs/ARCHITECTURE.md).
 
 ---
 
 ## Acknowledgments
 
-- **AMD Developer Cloud** — MI300X compute that made the three
-  Apache-2.0 NYC fine-tunes feasible.
-- **AMD x lablab.ai Developer Hackathon** — the venue.
-- **IBM Research** — Granite 4.1, Granite Embedding 278M, Granite TTM
-  r2, Mellea, and the rest of the open-source Granite ecosystem.
-- **NASA / IBM Prithvi-EO 2.0** and **IBM / ESA TerraMind 1.0** — the
+- **AMD Developer Cloud**, MI300X compute that made the three Apache-2.0
+  NYC fine-tunes feasible.
+- **AMD × lablab.ai Developer Hackathon**, the venue.
+- **IBM Research**, Granite 4.1, Granite Embedding 278M, Granite TTM r2,
+  Mellea, and the rest of the open-source Granite ecosystem.
+- **NASA / IBM Prithvi-EO 2.0** and **IBM / ESA TerraMind 1.0**, the
   geospatial foundation models behind the NYC fine-tunes.
-- **NYU CUSP / FloodNet** — the public sensor network whose data
-  Riprap reads live.
-- **Andrew Hicks** — civil-engineering review of the methodology, and
-  framing for what Riprap is not.
-- **The Riprap dam mark** — ["Dam" by Chintuza](https://thenounproject.com/icon/dam-4516918/)
+- **NYU CUSP / FloodNet**, the public sensor network whose data Riprap
+  reads live.
+- **Andrew Hicks**, civil-engineering review of the methodology.
+- **The Riprap dam mark**, ["Dam" by Chintuza](https://thenounproject.com/icon/dam-4516918/)
   via the Noun Project, licensed CC-BY 3.0. The original SVG embedded
-  the attribution as on-canvas text; Riprap's `assets/logo*.svg`
-  strips the embedded text and carries the credit here in body copy
-  instead, per the Creative Commons attribution requirement.
+  the attribution as on-canvas text; Riprap's `assets/logo*.svg` strips
+  the embedded text and carries the credit here in body copy instead,
+  per the Creative Commons attribution requirement.

docs/DEPLOY.md

@@ -0,0 +1,198 @@
+# Deployment topology
+
+Riprap is composed of two HF Spaces in production. The **UI Space**
+is CPU-only and contains the FastAPI + SvelteKit front-end; the
+**inference Space** is an L4 GPU and runs vLLM (Granite 4.1 8B FP8)
+plus the EO model stack co-resident.
+
+```
+                ┌──────────────────────────────────────────────┐
+                │   msradam/riprap-vllm  (NVIDIA L4, 24 GB)    │
+                │                                              │
+                │   :7860  proxy.py    bearer-auth FastAPI     │
+                │      ├─ /v1/chat/* /v1/embeddings  → :8000   │
+                │      └─ /v1/{prithvi,terramind,...} → :7861  │
+                │      └─ /v1/power   NVML readings            │
+                │                                              │
+                │   :8000  vLLM  Granite 4.1 8B FP8            │
+                │   :7861  riprap-models                       │
+                │          Prithvi-EO 2.0 NYC-Pluvial          │
+                │          TerraMind LULC + Buildings          │
+                │          Granite TTM r2                      │
+                │          GLiNER + Granite Embedding 278M     │
+                └────────────────────▲─────────────────────────┘
+                                     │ bearer-auth HTTPS
+                                     │
+       ┌─────────────────────────────┴──────────────────────────┐
+       │  lablab-ai-amd-developer-hackathon/riprap-nyc          │
+       │  Hackathon submission UI · cpu-basic                   │
+       │                                                        │
+       │  FastAPI (web/main.py)  +  SvelteKit static build      │
+       │  Burr FSM (app/fsm.py)                                 │
+       │                                                        │
+       │  RIPRAP_LLM_BASE_URL = …/v1                            │
+       │  RIPRAP_ML_BASE_URL  = …                               │
+       └────────────────────────────────────────────────────────┘
+```
+
+The UI Space holds no GPU weights and contacts no commercial APIs.
+Every model call routes through the bearer-authenticated proxy on
+the inference Space.
+
+---
+
+## Hugging Face Spaces
+
+### `lablab-ai-amd-developer-hackathon/riprap-nyc` — UI Space
+
+The hackathon submission. CPU-basic tier. Image built from the root
+`Dockerfile`. Holds no model weights — every inference call goes
+remote via env vars.
+
+**Required Space variables:**
+
+```
+RIPRAP_LLM_PRIMARY        = vllm
+RIPRAP_LLM_BASE_URL       = https://msradam-riprap-vllm.hf.space/v1
+RIPRAP_LLM_VLLM_8B_NAME   = granite4.1:8b
+RIPRAP_ML_BACKEND         = remote
+RIPRAP_ML_BASE_URL        = https://msradam-riprap-vllm.hf.space
+RIPRAP_NYCHA_REGISTERS    = 1
+RIPRAP_HEAVY_SPECIALISTS  = 1
+RIPRAP_PRITHVI_LIVE_ENABLE= 1
+RIPRAP_TERRAMIND_ENABLE   = 1
+RIPRAP_EO_CHIP_ENABLE     = 1
+```
+
+**Required secrets** (set via Settings → Variables and secrets):
+
+```
+RIPRAP_LLM_API_KEY        bearer token shared with the inference Space
+RIPRAP_ML_API_KEY         bearer token shared with the inference Space
+HF_TOKEN                  for register / catalog downloads
+```
+
+### `msradam/riprap-vllm` — Inference Space
+
+L4 (`l4x1`) tier. Image built from `inference-vllm/Dockerfile`.
+Bakes Granite 4.1 8B FP8 weights and the EO model dependencies
+(terratorch + peft + diffusers + segmentation-models-pytorch +
+nvidia-ml-py for NVML power sampling).
+
+**Required secret:**
+
+```
+RIPRAP_PROXY_TOKEN        bearer token; must match RIPRAP_LLM_API_KEY /
+                          RIPRAP_ML_API_KEY on the UI Spaces
+```
+
+**Endpoints:**
+
+| Path | Routes to | Notes |
+|---|---|---|
+| `POST /v1/chat/completions` | vLLM | Granite 4.1 8B FP8, OpenAI-compat |
+| `POST /v1/completions` | vLLM | OpenAI-compat |
+| `GET  /v1/models` | vLLM | served-model-name family |
+| `POST /v1/embeddings` | riprap-models | Granite Embedding 278M |
+| `POST /v1/prithvi-pluvial` | riprap-models | Prithvi-EO 2.0 NYC-Pluvial |
+| `POST /v1/terramind` | riprap-models | TerraMind LULC / Buildings / synthesis |
+| `POST /v1/ttm-forecast` | riprap-models | Granite TTM r2 + Battery surge |
+| `POST /v1/gliner-extract` | riprap-models | GLiNER typed-entity |
+| `GET  /v1/power` | proxy | Real NVML power (W) — see `docs/EMISSIONS.md` |
+| `GET  /healthz` | proxy + both backends | Aggregates health status |
+
+All `/v1/*` endpoints require `Authorization: Bearer <PROXY_TOKEN>`.
+`/v1/power` and the bracket-sampling LLM client path are described
+in [`docs/EMISSIONS.md`](EMISSIONS.md).
+
+---
+
+## Personal mirror — `msradam/riprap`
+
+Self-contained L4 mirror that runs the full stack (UI + vLLM + EO
+models) in a single container. Used for parallel demos when the
+shared inference Space is busy. Built from `Dockerfile.l4`.
+
+```bash
+scripts/deploy_personal_space.sh
+```
+
+This is paused by default for the hackathon period to keep the L4
+budget on the primary inference Space.
+
+---
+
+## Local development
+
+### Pure local (Ollama)
+
+```bash
+uv venv && uv pip install -r requirements.txt
+cd web/sveltekit && npm ci && npm run build && cd ../..
+ollama pull granite4.1:3b
+ollama pull granite4.1:8b
+.venv/bin/uvicorn web.main:app --host 127.0.0.1 --port 7860
+```
+
+Visit `http://127.0.0.1:7860`. Inference runs locally — no GPU
+power readings (the chip will display the data-sheet estimate with
+a `~` icon).
+
+### Local UI, remote inference
+
+```bash
+RIPRAP_LLM_PRIMARY=vllm \
+RIPRAP_LLM_BASE_URL=https://msradam-riprap-vllm.hf.space/v1 \
+RIPRAP_LLM_API_KEY=<token> \
+RIPRAP_ML_BACKEND=remote \
+RIPRAP_ML_BASE_URL=https://msradam-riprap-vllm.hf.space \
+RIPRAP_ML_API_KEY=<token> \
+.venv/bin/uvicorn web.main:app --host 127.0.0.1 --port 7860
+```
+
+Same flow as the hosted UI Space, but rendered locally. Real NVML
+power readings come back through the proxy headers and bracket
+samples just like in production.
+
+---
+
+## Deploy commands
+
+| Target | Script | Notes |
+|---|---|---|
+| Inference Space (`msradam/riprap-vllm`) | `scripts/deploy_vllm_space.sh` | Orphan-branch push from `inference-vllm/` |
+| UI Space (`lablab-ai-amd-developer-hackathon/riprap-nyc`) | cherry-pick onto `huggingface/main` then `git push huggingface` | HF Spaces' xet hook rejects pushes that walk through commits with binaries; cherry-picking from a clean ancestor avoids it |
+| Personal mirror (`msradam/riprap`) | `scripts/deploy_personal_space.sh` | Orphan-branch push from `Dockerfile.l4` |
+| Inference fallback (`msradam/riprap-inference`) | `scripts/deploy_inference_space.sh` | Ollama-backed mirror; redundant when riprap-vllm is up |
+
+---
+
+## Verifying a deploy
+
+```bash
+PYTHONPATH=. uv run python scripts/probe_stones_fire.py --timeout 600
+```
+
+Asserts: all five Stones fire, no torchvision/terratorch dep
+regression, the `emissions` block reports `nvidia_l4` hardware, and
+real NVML measurements come through (`n_measured` ≈ `n_calls`).
+
+The address probe sweeps the full canonical set (5 NYC addresses):
+
+```bash
+.venv/bin/python scripts/probe_addresses.py \
+    --base https://lablab-ai-amd-developer-hackathon-riprap-nyc.hf.space
+```
+
+---
+
+## Historical notes
+
+The hackathon submission was originally built against an AMD MI300X
+DigitalOcean droplet (running both vLLM and the EO model service).
+The droplet was decommissioned **2026-05-06** and inference moved
+to the L4 HF Spaces above. The bring-up runbook for the MI300X
+droplet is preserved in [`docs/DROPLET-RUNBOOK.md`](DROPLET-RUNBOOK.md)
+for anyone reproducing the original AMD-judging setup; setting
+`RIPRAP_HARDWARE_LABEL=AMD MI300X` on a droplet redeploy will swap
+the emissions ledger back to the MI300X data-sheet figures.

docs/EMISSIONS.md

@@ -0,0 +1,170 @@
+# Per-query inference energy ledger
+
+Riprap surfaces the energy and token cost of every inference call it
+makes during a briefing. The numbers are **measured off the L4 GPU**
+when the inference Space is reachable — not data-sheet estimates.
+
+```
+5 Stones · 21 fired · 11 evidence cards · 14.0s wall-clock · ✓ 1.4 Wh / 6.9K tok inference
+```
+
+The chip on the Findings status row reports total energy (Wh) plus
+total tokens. The leading icon discloses how the number was derived:
+
+| Icon | Meaning |
+|---|---|
+| `✓` | All recorded calls came back with a real NVML reading from the L4 GPU |
+| `◐` | Some calls measured, others fell back to the data-sheet estimate |
+| `~` | All calls used the data-sheet estimate (proxy unreachable, NVML disabled, or local-only run) |
+
+Hover the chip for the full breakdown — call count, hardware, prompt
+vs completion split, and the method.
+
+---
+
+## What's measured vs. what's estimated
+
+| Field | Source |
+|---|---|
+| `duration_s` | Real wallclock on the client side (`time.monotonic` around each call) |
+| `prompt_tokens`, `completion_tokens` | Reported by the model server (LiteLLM `usage` block) for non-stream LLM calls |
+| `completion_tokens` (streaming) | Estimated as `len(response_text) / 4` when the backend doesn't surface a final usage block (Ollama path) |
+| `power_w` | **Measured** — `nvmlDeviceGetPowerUsage` on the L4 inference Space, sampled every 100 ms, mean of samples bracketing each call |
+| `wh`, `joules` | `power_w × duration_s` (when `measured: true`) or `data-sheet_W × duration_s` (when `measured: false`) |
+
+Each call record on the ledger carries a `measured: bool` flag plus
+the exact `power_w` value used so a reviewer can audit any row.
+
+---
+
+## How the measurement works
+
+The L4 inference Space (`msradam/riprap-vllm`) runs a FastAPI proxy
+in front of vLLM (port 8000) and the riprap-models EO service
+(port 7861). The proxy initialises NVML at startup and runs a
+background sampler that reads `nvmlDeviceGetPowerUsage` every
+100 ms into a 60-second ring buffer.
+
+```
+inference-vllm/proxy.py::_power_sampler
+  ├── NVML init at startup, single L4 device handle
+  ├── 100 ms ring buffer (600 samples = 60 s of history)
+  └── degrades to no-op if NVML init fails
+```
+
+When the proxy forwards a POST to vLLM or riprap-models, it stamps
+the upstream call window `(t0, t1)` and computes the mean power
+across the samples that fall inside that window. The result lands
+on the response as headers:
+
+```
+X-GPU-Power-W      mean draw in watts
+X-GPU-Energy-J     energy in joules over the window
+X-GPU-Duration-S   forwarded-call duration in seconds
+X-GPU-Device       "NVIDIA L4"
+```
+
+`app/inference.py::_post()` reads those headers off the proxy
+response and forwards them into `emissions.Tracker.record_ml`. The
+tracker stamps `measured=True` and uses the exact joule value.
+
+For the LLM client path (`app/llm.py::chat()`) we route through
+LiteLLM, which doesn't surface response headers. So instead the
+client brackets the call with two GETs to `/v1/power`:
+
+```python
+p0 = _sample_gpu_power_w()                # ~50 ms, returns 1 s avg
+t0 = time.monotonic()
+resp = _router.completion(...)            # the actual LLM call
+duration_s = time.monotonic() - t0
+p1 = _sample_gpu_power_w()                # ~50 ms, returns 1 s avg
+avg = (p0 + p1) / 2
+```
+
+`avg` is the average power during the call; `avg × duration_s`
+gives joules. The tracker records `power_w_real=avg`,
+`joules_real=avg×duration_s`, and `measured=True`.
+
+---
+
+## Hardware profiles (`app/emissions.HARDWARE`)
+
+The fallback path uses a sustained-power figure from the hardware
+data sheet when no real measurement is available:
+
+| Key | Label | Sustained W | Source |
+|---|---|---|---|
+| `nvidia_l4` | NVIDIA L4 | 60 | L4 data sheet (72 W TGP, Ada Lovelace) |
+| `amd_mi300x` | AMD MI300X | 600 | MI300X data sheet (750 W TDP); used when `RIPRAP_HARDWARE_LABEL=AMD MI300X` |
+| `nvidia_t4` | NVIDIA T4 | 50 | T4 data sheet (70 W max) |
+| `apple_m` | Apple M-series | 20 | ml.energy / community measurements |
+| `cpu_server` | x86 CPU | 30 | Typical sustained server-core load |
+
+The fallback only fires when the proxy is unreachable, NVML init
+failed, or the call streamed (we currently don't measure streamed
+LLM calls precisely; they bracket-sample as best-effort).
+
+---
+
+## End-to-end shape
+
+```
+Lablab UI Space (cpu-basic, FastAPI + SvelteKit)
+   │
+   │  Tracker installed per-query in web/main.py:
+   │  install(Tracker())
+   │
+   ├── planner       — app/llm.py::chat
+   │                   ├─ GET /v1/power  (bracket-start)
+   │                   ├─ POST /v1/chat/completions
+   │                   └─ GET /v1/power  (bracket-end)
+   │
+   ├── FSM specialists — app/inference.py::_post
+   │                     POST /v1/{prithvi-pluvial, terramind, ...}
+   │                     ← X-GPU-Power-W, X-GPU-Energy-J headers
+   │
+   └── reconciler    — app/llm.py::chat (Mellea-validated)
+                       same bracket pattern as planner
+                  │
+                  ▼
+       Tracker.summarize() → emissions block on /api/agent/stream final
+                  │
+                  ▼
+       SvelteKit RunHealthStrip — chip rendered with measured-icon
+```
+
+---
+
+## Verifying
+
+`scripts/probe_stones_fire.py` runs an end-to-end address query
+against the lablab UI and asserts:
+
+1. All five Stones fire
+2. No specialist returns the legacy dep-regression strings
+   (`torchvision::nms`, `deps unavailable on this deployment:
+   terratorch`)
+3. The final `emissions` block carries `nvidia_l4` hardware and
+   non-zero tokens
+
+```bash
+PYTHONPATH=. uv run python scripts/probe_stones_fire.py --timeout 600
+```
+
+The first call after a Space restart pays a ~120 s vLLM CUDA-graph
+compile penalty; warm queries land at < 0.5 Wh / ~7 K tokens.
+
+---
+
+## Why this matters
+
+Inference cost is usually invisible. AI tools that publish a
+"green" or "low-energy" claim mostly cite a vendor data sheet or a
+research mean. Riprap reports the actual joules drawn off the
+device under the load of a single user query — auditable down to
+the row.
+
+The raw ledger is shipped on the SSE `final` event under
+`emissions.calls`, so any consumer (dashboard, billing model,
+reproducibility check) can reuse the data without round-tripping
+back through Riprap.

web/main.py

@@ -12,7 +12,7 @@ from pathlib import Path
 warnings.filterwarnings("ignore")
 
 from fastapi import FastAPI, Request  # noqa: E402
-from fastapi.responses import FileResponse, StreamingResponse  # noqa: E402
+from fastapi.responses import FileResponse, JSONResponse, StreamingResponse  # noqa: E402
 from fastapi.staticfiles import StaticFiles  # noqa: E402
 
 from app import emissions  # noqa: E402
@@ -96,6 +96,38 @@ app.mount("/static", StaticFiles(directory=STATIC), name="static")
 if SVELTEKIT_BUILD.exists():
     app.mount("/_app", StaticFiles(directory=SVELTEKIT_BUILD / "_app"), name="sveltekit_assets")
 
+# Top-level static assets the SvelteKit build emits next to the HTML
+# entry points (favicon.svg / favicon.png / robots.txt). These would
+# fall through to the SPA fallback and 404 without explicit routes;
+# adapter-static expects them under /, not /_app.
+def _serve_build_asset(name: str):
+    p = SVELTEKIT_BUILD / name
+    if not p.exists():
+        return JSONResponse({"detail": "Not Found"}, status_code=404)
+    return FileResponse(p, headers={"Cache-Control": "public, max-age=86400"})
+
+
+@app.get("/favicon.svg", include_in_schema=False)
+def _favicon_svg():
+    return _serve_build_asset("favicon.svg")
+
+
+@app.get("/favicon.png", include_in_schema=False)
+def _favicon_png():
+    return _serve_build_asset("favicon.png")
+
+
+@app.get("/favicon.ico", include_in_schema=False)
+def _favicon_ico():
+    # No .ico in the build, but browsers still probe for it. Redirect-
+    # by-content to the PNG so the tab gets the dam mark either way.
+    return _serve_build_asset("favicon.png")
+
+
+@app.get("/robots.txt", include_in_schema=False)
+def _robots():
+    return _serve_build_asset("robots.txt")
+
 import json as _json  # noqa: E402
 
 import geopandas as _gpd  # noqa: E402

web/sveltekit/build/200.html

@@ -6,17 +6,17 @@
     <meta name="viewport" content="width=device-width, initial-scale=1" />
     <meta name="description" content="Riprap — citation-grounded NYC flood-exposure briefings." />
     <title>Riprap — flood-exposure briefing</title>
-    <link href="/_app/immutable/entry/start.C_DzDADc.js" rel="modulepreload">
-		<link href="/_app/immutable/chunks/DJJH9JR_.js" rel="modulepreload">
+    <link href="/_app/immutable/entry/start.C7t2uk4E.js" rel="modulepreload">
+		<link href="/_app/immutable/chunks/B8_P3YrA.js" rel="modulepreload">
 		<link href="/_app/immutable/chunks/BTUA7_xE.js" rel="modulepreload">
-		<link href="/_app/immutable/entry/app.DmxSSduu.js" rel="modulepreload">
+		<link href="/_app/immutable/entry/app.DaTkeYDu.js" rel="modulepreload">
 		<link href="/_app/immutable/chunks/CXQd8Y6F.js" rel="modulepreload">
 		<link href="/_app/immutable/chunks/CWw6qgC_.js" rel="modulepreload">
 		<link href="/_app/immutable/chunks/Bd-v_9Ud.js" rel="modulepreload">
 		<link href="/_app/immutable/chunks/CW0zSL4D.js" rel="modulepreload">
-		<link href="/_app/immutable/nodes/0.DInKAumT.js" rel="modulepreload">
+		<link href="/_app/immutable/nodes/0.Bxzl5Ruo.js" rel="modulepreload">
 		<link href="/_app/immutable/chunks/DxQlA7U2.js" rel="modulepreload">
-		<link href="/_app/immutable/chunks/D8NyAcAP.js" rel="modulepreload">
+		<link href="/_app/immutable/chunks/Bs7n3R20.js" rel="modulepreload">
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class="app-footer-guard"><strong>Riprap does not predict damage.</strong> This tool is for professional analytical work, not personal property decisions.
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Get a written briefing where every numeric claim links to its primary public-record source.</span></h1> <form class="land-query svelte-drzq4r" role="search"><span class="land-query-prompt svelte-drzq4r" aria-hidden="true">›</span> <input type="text" placeholder="Address, neighborhood, or BBL. e.g. 80 Pioneer Street, Red Hook" class="land-query-input svelte-drzq4r" aria-label="Query an address, neighborhood, or BBL"/> <button type="submit" class="land-query-submit svelte-drzq4r">Brief this place →</button></form> <div class="land-cycling svelte-drzq4r" aria-live="polite"><span class="land-cycling-label svelte-drzq4r">Try:</span> <button type="button" class="land-cycling-rail svelte-drzq4r" title="Run this example"></button></div></main>');function je(e,s){P(s,!0);const r=["80 Pioneer Street, Red Hook","Coney Island Hospital","PS 188, Lower East Side","Hammels Houses, Rockaway","Bowling Green station","555 W 57th Street"];let t=A(""),a=A(0);E(()=>{if(typeof window>"u")return;const 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n=be(),u=c(n);ve(u,l=>N(t,l),()=>d(t)),k(2),o(n),m(e,n),M()}var Se=w(`<section class="land-section svelte-1anw2jf"><div class="land-section-head svelte-1anw2jf"><span class="section-label">What you'll get back</span> <span class="land-section-meta svelte-1anw2jf">A grounded paragraph with citations, not a chatbot answer.</span></div> <div class="land-preview-grid svelte-1anw2jf"><div class="land-preview-pane land-preview-pane-excerpt svelte-1anw2jf"><div class="land-preview-eyebrow svelte-1anw2jf">Briefing excerpt</div> <p class="land-preview-body svelte-1anw2jf">The lot sits inside the FEMA <span class="land-preview-cite svelte-1anw2jf">1% AE flood zone <sup class="svelte-1anw2jf">[c3]</sup></span>,
         with Sandy high-water marks recorded <span class="land-preview-cite svelte-1anw2jf">4.7 ft above grade <sup class="svelte-1anw2jf">[c1]</sup></span>.
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web/sveltekit/build/_app/immutable/nodes/{3.CU36QLul.js → 3.Cw8Vd7-r.js}

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web/sveltekit/build/_app/version.json

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web/sveltekit/build/index.html

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     <title>Riprap — flood-exposure briefing</title>
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-		<link href="./_app/immutable/chunks/DJJH9JR_.js" rel="modulepreload">
+    <link href="./_app/immutable/entry/start.C7t2uk4E.js" rel="modulepreload">
+		<link href="./_app/immutable/chunks/B8_P3YrA.js" rel="modulepreload">
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-		<link href="./_app/immutable/entry/app.DmxSSduu.js" rel="modulepreload">
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-		<link href="./_app/immutable/nodes/0.DInKAumT.js" rel="modulepreload">
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-		<link href="./_app/immutable/chunks/D8NyAcAP.js" rel="modulepreload">
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-		<link href="./_app/immutable/nodes/2.B2rqoVXS.js" rel="modulepreload">
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-						import("./_app/immutable/entry/app.DmxSSduu.js")
+						import("./_app/immutable/entry/start.C7t2uk4E.js"),
+						import("./_app/immutable/entry/app.DaTkeYDu.js")
 					]).then(([kit, app]) => {
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web/sveltekit/build/q/sample.html

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     <title>Riprap — flood-exposure briefing</title>
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-		<link href="../_app/immutable/chunks/DJJH9JR_.js" rel="modulepreload">
+    <link href="../_app/immutable/entry/start.C7t2uk4E.js" rel="modulepreload">
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+					__sveltekit_ual5r0 = {
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 					};
 
 					const element = document.currentScript.parentElement;
 
 					Promise.all([
-						import("../_app/immutable/entry/start.C_DzDADc.js"),
-						import("../_app/immutable/entry/app.DmxSSduu.js")
+						import("../_app/immutable/entry/start.C7t2uk4E.js"),
+						import("../_app/immutable/entry/app.DaTkeYDu.js")
 					]).then(([kit, app]) => {
 						kit.start(app, element, {
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