Reference Images

Looking at real photographs

WebSearch returns text and WebFetch returns markdown — neither gives you an image. To see a photo you must download it and Read the file:

mkdir -p ref/strand
# 1. SEARCH — Openverse: CC-licensed, no API key. KEEP QUERIES TO 2–3 WORDS.
#    Every term is ANDed, so "miami south beach art deco dusk" returns ZERO results.
curl -s -A "agent/1.0" \
  "https://api.openverse.org/v1/images/?q=art+deco+hotel&page_size=8" \
  | python3 -c "import json,sys; [print(r['url']) for r in json.load(sys.stdin)['results']]"

# 2. DOWNLOAD
curl -sL -A "agent/1.0" -o ref/strand/deco1.jpg "<url from above>"

# 3. LOOK at it — Read the local file; images render for you.

Then read your own screenshot of the same subject and name the gap out loud: "real facades have setbacks, AC units and stained concrete; mine are flat", "the real kerb has a ramp, a drain and a meter every third car; mine is a clean extrusion", "real asphalt is bluer, patched, and the lane paint is worn through in the wheel tracks", "the real light is warmer and lower and the shadows are longer". A named gap is a work item; "make it better" is not.

Sources that need no key (all verified working)

• Openverse — keyword photo search, direct image URLs. The everyday default. https://api.openverse.org/v1/images/?q=<2-3+words>&page_size=10 Trap: multi-word queries are ANDed. Two or three words, or you get nothing.
• Wikimedia Commons search — landmarks, aerials, named buildings. https://commons.wikimedia.org/w/api.php?action=query&generator=search&gsrsearch=<query>&gsrnamespace=6&gsrlimit=5&prop=imageinfo&iiprop=url&iiurlwidth=1600&format=json Send a User-Agent. gsrnamespace=6 is required. Do NOT add filetype:bitmap — it breaks the query and no query key comes back. Read imageinfo[0].thumburl.
• Wikimedia geosearch — every photo taken near a real coordinate. This is how you study a real place rather than a word. https://commons.wikimedia.org/w/api.php?action=query&generator=geosearch&ggscoord=<lat>%7C<lon>&ggsradius=1000&ggslimit=10&ggsnamespace=6&prop=imageinfo&iiprop=url&iiurlwidth=1600&format=json
• KartaView — street-level photography from a car windscreen, at any coordinate. https://api.openstreetcam.org/2.0/photo/?lat=<lat>&lng=<lng>&radius=400 Read result.data[].fileurlProc. The most valuable source you have: it is your exact game camera — driver eye height, kerb to kerb, real parked-car spacing, real pole and cable spans, real sky. One of these tells you more about how a street reads than an hour of guessing.
• Aerial/satellite imagery of a real city: see the map-data source doc.

Use it for far more than "the city"

Vehicle proportions, paint and glass tint, wheels, plates. Road markings and their wear. Kerbs, drains, ramps. Traffic signals and how they're mounted. Street-lighting colour by era. Overhead cables. Palm and street-tree shapes. Awnings and their frames. Shopfront signage typography. Roof clutter, aerials, dishes, laundry, bins, pallets. Tide lines and beach litter. Wet asphalt at night, neon reflections, dusk sky gradients. Crowd density and how people actually cluster on a pavement.

Anything you are about to guess at, fetch three photos instead.

Keep what you download. A handful of images in ref/<subject>/, linked from the design document that used them with a line on what you took from each — so a later session's answer to "why is this district this colour" is a photograph, not a vibe.

The tell of a generic build is what memory leaves out: nothing is stained, nothing is repaired, nothing sags, nothing is bolted on afterwards, nothing is worn where feet and tyres go. Photographs are full of exactly that, and it is most of what makes an image read as real.