PAIN TRIGGERS
Existing maps and tools do not account for local topography, elevation changes, and surrounding building heights when predicting sun/shade exposure.
SaaS· Cafe-goers who prefer outdoor seatingPain 7.00/10WTP 5.0/10Market 8.0/10Validation 8.0Confidence 85%Jul 16, 2026
SolSpot: Real-Time Sun & Shadow Map for Outdoor Seating
People wanting to sit outdoors struggle to know if specific cafe patios or benches will be in the sun or shade, as standard maps ignore dynamic shadows cast by buildings, trees, and terrain elevation.
3d-visualizationconsumersgeospatialmappingoutdoor-lifestyleproductivityreal-estatesaastravelurban-mobility
1
STAGE 01 · PROBLEM
Is the problem real?
CANONICAL PROBLEM
People wanting to sit outdoors at cafes, benches, or public spaces struggle to know if those specific spots will be in the sun or shade at a given time of day, as existing maps do not account for dynamic shadows cast by buildings, trees, and terrain.
FREQUENCY
Multiple repeated complaints in the post and comments.
INTENSITY
Users explicitly describe existing tools as bloated/overkill and mention workaround behavior.
EVIDENCE
I wanted to sit outside for coffee but there was no way to see which cafes are in the sun right now.
postShow HN: A map of cafes that are in the sun
72
predicting sun exposure is one thing, but taking into account slopes and surrounding buildings that might cast shadows in that area is another.
commentThat's a great idea, but the only thing I'm not clear on is that predicting sun exposure is one thing, but taking into account slopes and surrounding buildings that might cast shadows in that area is another. I think this is designed more for flat areas and single-story buildings. This is a problem I personally encountered in the past and couldn't solve, but I'll try it out and see if it actually works.
This is a problem I personally encountered in the past and couldn't solve
commentThat's a great idea, but the only thing I'm not clear on is that predicting sun exposure is one thing, but taking into account slopes and surrounding buildings that might cast shadows in that area is another. I think this is designed more for flat areas and single-story buildings. This is a problem I personally encountered in the past and couldn't solve, but I'll try it out and see if it actually works.
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STAGE 02 · CUSTOMER
Who feels this pain?
TARGET USERS
Cafe-goers who prefer outdoor seatingUrban Cafe Goers & Pedestrians
City dwellers who want to find outdoor cafe seating or public benches that are guaranteed to be in the sun (or shade) at specific times of day.
Context
Find outdoor seating spots (cafes, benches, public spaces) that are guaranteed to be in the sun (or shade) at a specific time of day.
Physically walking around a neighborhood to find a sunny spot by trial and error.
Attempting to build custom mapping projects or manual calculations to predict shadows.
Current Workarounds
Physically walking around a neighborhood searching for spots by trial and error
Guessing based on compass directions and general time of day
Using standard flat-map navigation apps which lack height and shadow data
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STAGE 03 · MARKET
Where's the gap?
EXISTING SOLUTION GAPS
Standard map and navigation apps (like Google Maps or Apple Maps) only show business locations but lack dynamic environmental data like real-time building/tree shadows.
Existing manual planning or basic sun-tracking tools fail to factor in complex local geometry like multi-story building heights, tree canopies, and sloping terrain.
OPPORTUNITY & VALUE
Why Now
Repeated complaints focus heavily on flat-area models failing in complex environments with multi-story buildings and terrain elevation changes.
Value Proposition
Unlike flat map services, SolSpot accounts for 3D building heights, elevation slopes, and dynamic astronomical math to calculate exact shadow intersections on micro-locations.
Product Direction
A web-based map application that combines 3D building geometry, terrain elevation data, and astronomical sun-position algorithms to render real-time, predictive sun and shadow overlays on top of local cafe patios and public benches.
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STAGE 04 · BUSINESS
How does it make money?
MONETIZATION
$4.99/moIndividual consumer subscription
Model
SaaS subscription
WILLINGNESS TO PAY
While casual users might start on a ad-supported free tier, high-frequency urban remote workers and cafe enthusiasts will pay a micro-subscription to save time and guarantee comfortable outdoor work/social sessions. Businesses may also eventually pay to claim 'sunny patio' badges.
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STAGE 05 · EXECUTION
How do you ship it?
MVP PLAN
“Find a sunny cafe table in seconds, not by trial and error.”
A web-based map application that combines 3D building geometry, terrain elevation data, and astronomical sun-position algorithms to render real-time, predictive sun and shadow overlays on top of local cafe patios and public benches.
Core Features
Interactive 3D shadow map overlay for selected high-density urban areas
Time-slider to predict sun/shade coverage up to 4 hours in advance
Curated directory of local cafes and public benches with live sun-status indicators
Simple geolocation to find the nearest currently sunny outdoor spot
Weekly Roadmap
1
W1-W2
Core 3D shadow engine renders on standard map interface for a single neighborhood.
- •Parse open-source 3D building data (OSM) for pilot area
- •Implement basic sun-position algorithm to cast shadows on map canvas
- •Create interactive map view with simple pan/zoom
2
W3-W4
Time slider and cafe data layer integration.
- •Build time-slider interface to recalculate shadows interactively
- •Manually map 30 local cafes and outdoor benches in the pilot area
- •Apply simple overlap logic to calculate if a point of interest is in shadow
3
W5
Mobile web optimization and private beta testing.
- •Optimize 3D rendering for mobile browsers
- •Add geolocation to center map on user
- •Share private beta link with 50 local neighborhood r/sub-reddit members
4
W6
Public launch of pilot area map with feedback system.
- •Launch on Product Hunt and local city social channels
- •Add 'Report incorrect shadow' feedback button to crowd-source model calibration
- •Track daily active users and outdoor-spot search conversions
Launch Strategy
Target local city subreddits (e.g., r/london, r/nyc, r/seattle) and launch on Product Hunt, focusing initially on a single, high-density, sun-sensitive metropolitan neighborhood.
RISKS & ASSUMPTIONS
Top Risks
3D Data Acquisition Costs
Acquiring high-resolution LiDAR or GIS 3D building and terrain data can be expensive or hard to source outside major cities.
Seasonality of Usage
User engagement may drop sharply in winter months when outdoor seating is unfeasible or closed in temperate climates.
Accuracy of Micro-obstacles
Temporary scaffoldings, umbrellas, and seasonal tree canopies can block the sun in ways the 3D model cannot easily predict.
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STAGE 06 · DECISION
Should you build it?
NEED A CLEARER CALL?
Run an Investment Memo to get a structured Go / No-Go verdict, competitor landscape, unit economics, and a 90-day validation roadmap for this opportunity.
Generate an investment memoWhat this score means
This idea scores in the upper-middle range of opportunities surfaced by MonetScope, with a validation sub-score of 8/10 against 3 independently sourced evidence signals. A "promising" rating usually indicates a real pain has been detected and discussed in the open, but the pipeline did not find enough signal to flag it as urgent or high-frequency. These opportunities can still produce excellent businesses — they often correspond to "boring" problems that established players have ignored — but the founder should expect a longer customer-development cycle to confirm willingness to pay.
Why this matters for SaaS founders
It sits at the intersection of "3d-visualization", "consumers", "geospatial", which makes it relevant to a specific subset of founders rather than a generic horizontal opportunity. SaaS opportunities at this stage tend to win on the strength of their initial wedge — a single workflow that the target user runs every week, where the existing solution is either spreadsheets, a clunky incumbent feature, or a manual process they hate. The build cost is moderate; the distribution cost is everything. The MonetScope pipeline surfaces this category alongside other saas signals, which is why it appears here rather than in a generic "trending ideas" feed.
Scores are derived from real forum discussions across Reddit, Hacker News and X, weighted by evidence volume and signal quality. How scoring works
Frequently asked questions
Is "SolSpot: Real-Time Sun & Shadow Map for Outdoor Seating" a real validated startup idea or just an AI-generated suggestion?
MonetScope does not generate ideas from a language model's imagination. Every opportunity on this site is anchored to specific source posts and comments from real public discussions — typically on Reddit, Hacker News, or X — where actual users describe the pain in their own words. The AI's role is structuring, scoring, and grouping those signals into a navigable opportunity, not inventing the problem.
How recent is the underlying data for 3d-visualization?
MonetScope's spider pipeline runs continuously and surfaces opportunities as new evidence accumulates. The "Updated" date in the header reflects the most recent re-scoring of this specific opportunity. Most saas opportunities visible in the public catalog draw from discussions in the last 30-60 days; older signals are de-prioritized because user pain shifts faster than most founders assume.
What's the difference between "overall score" and "validation score"?
Overall score is a composite across six dimensions — pain, urgency, willingness to pay, market size, defensibility, and execution ease — designed to give a single number for triage. Validation score is narrower: it asks "how cleanly does the same signal repeat across independent sources?" An opportunity can score high on overall but lower on validation when one or two large discussions dominate the evidence; conversely, validation can be high on a smaller-overall idea where the signal is consistent but the addressable market is modest.