Transforming Astronomy with AI: My Journey to Goeph
In my quest to streamline astronomical computations, I transformed a clunky Python pipeline using Skyfield into a Go ephemeris library, validated against stringent standards.
Key Highlights:
- The Challenge: Juggling vast CSV files and periodic reruns led to workflow inefficiencies and confusion.
- The Goal: Implement direct ephemeris computations in Go, eliminating Python overhead.
- AI as a Catalyst: Harnessing AI tools, I rapidly prototyped a proof of concept that matched Skyfield’s outputs within strict tolerances.
- Building a Library: Through tiered development, I created a robust toolkit named Goeph, enabling on-demand computations without cumbersome batch jobs.
Lessons Learned:
- Start small with measurable goals.
- Validation over assumptions is crucial.
- Planning and tight constraints prevent complexity from spiraling.
The future of astronomy workflows is here. 🚀 Explore the story behind Goeph and share your thoughts on AI in tech! Let’s connect and elevate the conversation!
