TaiYi objects and ObjectVerse: sharing frozen research vs notebooks

Sharing research usually boils down to a choice between static notebooks or live-linked dashboards. Notebooks on GitHub are the standard for reproducibility, but they often break when library dependencies shift or data APIs change. You end up with a wall of dependency errors instead of a working model.

Platforms like ObjectVerse take a different approach by treating research as frozen objects. When you package a TaiYi object, you are essentially snapshotting the logic, the parameters, and the specific data slice used at that moment. This is great for peer review because it removes the 'it works on my machine' variable. However, the trade-off is inflexibility. You can't easily tweak a live data stream or pivot the parameters once the object is sealed. It's a static artifact, not a living sandbox.

TradingView ideas sit somewhere in the middle, focusing on visual communication rather than deep algorithmic verification. They are excellent for quick signal sharing, but they lack the rigor of a full backtest environment. If you need to show your work, here is how the trade-offs generally stack up:

• Notebooks: High transparency but high maintenance and potential for silent failures.
• TaiYi Objects: High reproducibility and clean state, but rigid and harder to iterate.
• TradingView: High accessibility and visual clarity, but often lacks depth on execution logic.

The biggest pitfall with frozen research is the assumption footnote. If you don't explicitly document your slippage model or trade execution constraints, the frozen object looks more successful than it would ever be in production. Do you prefer the transparency of raw code in a notebook, or do you find that frozen artifacts make it easier to actually audit someone else's logic?