Image and video decision

Golf Shot Tracer

A capable developer can build a limited desktop/CLI shot-tracing tool using open libraries in a few weeks, but reproducing the polished, GPU-accelerated mobile one-tap app and distribution is substantially more work and UX engineering, so keep paying for the full mobile experience unless you only need a narrow desktop workflow.

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SubscriptionCustom pricing
Initial build60 hours
Monthly upkeep1 hours + $0
Evidence2/3 runs agree

Open-source builds that already do this

Every project below is open source and already does this job today. Fork one, self-host it, or take the parts you need - the build prompt further down assumes an empty file, and this is the shortcut past that. Licences differ; check the one on each card before you ship. All Golf Shot Tracer alternatives, with the arithmetic →

What a replacement has to do

  • Import/record video → initialize ball location → run frame-by-frame tracking → render trajectory overlay frames → encode/export shareable video

What it still won’t have

  • Polished native mobile UI and App Store / Play Store distribution
  • GPU-accelerated, on-device inference optimizations for mobile
  • One-tap UX with robust touch-to-track experience and presets
  • Built-in social export integrations and share shortcuts
  • Edge-case camera formats and wide codec/device support testing

What remains hard

  • Product polish and ongoing maintenance
Read the build prompt

First-year cost

No published price

Golf Shot Tracer does not publish a price we could read, so there is nothing to compare against. What building costs is below.

Money you would actually spend

Keep paying

Subscription price × seats × 12

Build it

AI build APIs + hosting

Time you would spend

What you would spend

What we assumed

The verdict above measures whether you could build it. This one is only about money.

Runnable build prompt

Not run yet
Build a minimal desktop/video-tool replacement in Python: use moviepy for decoding/encoding, OpenCV for frame I/O and tracking, and NumPy/SciPy for trajectory smoothing. Core features in scope: (1) open/import mp4, (2) UI or CLI step to click/tap initial ball position on a frame, (3) per-frame tracking using an OpenCV tracker with fallback re-detection, (4) compute a smoothed 2D trajectory and render colored glow overlay per frame, (5) encode/export mp4 at original resolution and frame rate, (6) include basic presets for color/width and a preview. Explicitly out of scope: mobile native app, App Store/Play Store packaging, GPU-accelerated on-device NN optimization, social sharing integrations, and advanced UI polish. Include error handling for unsupported codecs and missing frames, unit tests for the tracking/render pipeline, and a small end-to-end integration test that verifies import → track → export.
How we checked5 sources · 2/3 runs agreed · evidence score 60

How the score was reached

  • Partly verdict base52
  • An open-source build was found+5
  • 5 cited sources+3
  • Evidence score60

The base comes from the verdict. Everything under it is a check that either happened or did not, and each one is a fact frozen in this record rather than a judgement made at render time - so the same evidence always produces the same number.

How scoring works →

Cited sources · 5

Every page the run actually retrieved.

Integrity checks

What held up, and what did not.

! 2 of 3 runs agreed; the verdict is the majority✓ Citations limited to fetched pages! 1 moat recorded