AstroGuide:Portfolio Chris Hollander’s Astrophotography

AstroGuide destinations

Recommendation engine transparency

Where does the advisor actually send you?

This report runs every telescope pairing through every possible answer pattern, then measures where the recommendation lands. It is a behavioral audit of the engine—not a market survey, product ranking, or claim that every scenario is equally likely in real life.

13,440complete scenarios
15selectable telescopes
4platform ecosystems
64answer patterns per pair

The fairest platform comparison

Cross-platform acceptance rate

For each current platform and candidate platform, this measures how often the final recommendation comes from the candidate’s ecosystem. It removes the distortion caused by platforms having different numbers of models in the advisor.

Row platform currently owned Column platform being considered Cell candidate platform recommendation rate
Current ↓ / Candidate → Seestar DWARFLAB Vaonis Unistellar
Seestar 4 models — 73.4% 752 of 1,024 49.2% 378 of 768 53.7% 550 of 1,024
DWARFLAB 4 models 73.1% 749 of 1,024 — 43.0% 330 of 768 48.6% 498 of 1,024
Vaonis 3 models 66.1% 508 of 768 63.4% 487 of 768 — 42.7% 328 of 768
Unistellar 4 models 69.7% 714 of 1,024 71.5% 732 of 1,024 35.7% 274 of 768 —

A 73% cell does not mean that platform is “73% better.” It means that, across the equally weighted telescope pairs and answer patterns represented by that cell, the engine selected a telescope from the candidate platform 73% of the time.

Confusion-matrix view

Where recommendations land

Rows are the platform currently owned; columns contain the final recommended telescope’s platform. Percentages are normalized within each row because platforms contribute different numbers of current models.

Current ↓ / Recommended → Seestar DWARFLAB Vaonis Unistellar
Seestar 3,584 runs 53.1% 1,904 scenarios 21.0% 752 scenarios 10.5% 378 scenarios 15.3% 550 scenarios
DWARFLAB 3,584 runs 20.9% 749 scenarios 56.0% 2,007 scenarios 9.2% 330 scenarios 13.9% 498 scenarios
Vaonis 2,688 runs 18.9% 508 scenarios 18.1% 487 scenarios 50.8% 1,365 scenarios 12.2% 328 scenarios
Unistellar 3,584 runs 19.9% 714 scenarios 20.4% 732 scenarios 7.6% 274 scenarios 52.0% 1,864 scenarios

The diagonal includes both “keep your current telescope” and “choose another model on the same platform.” It should not be interpreted as a pure platform-loyalty score.

Inside the diagonal

Keep, switch models, or switch platforms?

This separates an exact “keep what you own” result from a move to another model in the same ecosystem and a move across platforms.

ZWO

Seestar

38.3%Keep the current telescope

1,372 scenarios

14.8%Choose another model on the same platform

532 scenarios

46.9%Move to another platform

1,680 scenarios
DWARFLAB

DWARFLAB

42.1%Keep the current telescope

1,509 scenarios

13.9%Choose another model on the same platform

498 scenarios

44.0%Move to another platform

1,577 scenarios
Vaonis

Vaonis

47.7%Keep the current telescope

1,281 scenarios

3.1%Choose another model on the same platform

84 scenarios

49.2%Move to another platform

1,323 scenarios
Unistellar

Unistellar

48.7%Keep the current telescope

1,746 scenarios

3.3%Choose another model on the same platform

118 scenarios

48.0%Move to another platform

1,720 scenarios

Methodology and limitations

Exactly what this does—and does not—measure

Platform capability ratings explain the ownership experience. They do not contribute points to Upgrade Advisor recommendations.

  1. 01

    Enumerate every ordered telescope pair

    Each of the 15 telescopes is treated as the current telescope and compared with every other telescope: 15 × 14 ordered pairs.

  2. 02

    Run every answer pattern

    The advisor currently asks 6 two-choice questions, producing 64 possible answer patterns for every pair. Together, that creates 13,440 complete runs.

  3. 03

    Record the final recommended telescope

    The report groups that telescope by platform, preserving both exact-retention and same-platform model changes. Cross-platform alternatives are restricted to the platform the user selected as the candidate.

  4. 04

    Weight everything equally

    No usage analytics, sales data, or inferred customer profile is used. Rare and common answer patterns receive identical weight, as do every current and candidate telescope pairing.

This is not user telemetry

The report describes possible engine behavior. It does not claim that real visitors choose platforms or answers in these proportions.

The catalog affects the matrix

Platforms with more selectable models create more pairings. The acceptance-rate matrix compensates for that; the destination matrix deliberately shows the complete engine surface.

Non-current models remain included

Discontinued telescopes and clearly labeled speculative hardware remain selectable because the advisor supports used-market and exploratory comparisons.

Capabilities are explanatory

Mosaics, citizen science, digital eyepieces, editing tools, and other platform features appear in comparison tables but never contribute recommendation points.