RFTs Forecasts — Open-Method Live Weather, Seismic, Magnetic & Solar Monitoring

Spaces
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Hey everyone :waving_hand: I’ve just published a new Space: RFTs Forecasts.

It’s a simple, transparent “live console” built around my Rendered Frame Theory (RFT) approach. You type a location, hit Run Forecast, and it pulls live public data and shows what RFT is predicting right now across four domains:

  • Atmospheric (location-based)
  • Seismic (region mode or local-radius mode)
  • Magnetic (global Kp)
  • Solar (global GOES X-ray flux)

What I’m trying to do here is keep everything easy to follow: the Space shows the actual inputs it used, the computed values (z → τ_eff → index), and the decision rule that triggered the label (stable / monitor / watch / warning, etc.). If data isn’t available, it disables that domain instead of guessing.

To make verification quick, the Space includes direct links to the official sources (NOAA SWPC / Open-Meteo / USGS) so anyone can check results instantly.

I’d genuinely love feedback—whether that’s feature ideas, UI improvements, or stress-testing the logic with different locations.

:backhand_index_pointing_right: RFTs Forecasts - a Hugging Face Space by RFTSystems

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This is a solid approach — especially the choice to disable a domain when data isn’t available instead of guessing. That alone puts this ahead of most “forecast” systems.

I also appreciate the explicit surfacing of:

  • raw inputs,

  • computed intermediates,

  • and the decision rule that triggered each label.

That transparency turns the Space into something closer to an inspectable system rather than a black-box predictor.

One thing I’m curious about: how do you think about durability over time?

In other words, if someone revisits a forecast later, what guarantees (if any) exist that:

  • the same inputs still resolve,

  • the same computation path applies,

  • and the result can be independently re-verified rather than just re-computed?

Not a criticism — just an interesting boundary between live prediction and verifiable historical claims. Overall, very clean work.

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Thanks a lot for taking the time to look this closely — I genuinely appreciate the rigor. I’ve been building this largely on my own, so feedback at this level is rare and it matters. You’re also completely right to flag durability over time — I’ll hold my hands up: I initially optimized for “live + inspectable” and underweighted “historical verifiability.” I’ve now added a Forecast Receipt system to close that gap. Each run can generate a downloadable JSON receipt that captures (1) the exact upstream source URLs, request parameters, and timestamps, (2) the raw inputs and computed intermediates (z, τ_eff, index), (3) the rule that triggered the label, and (4) an environment snapshot (app constants + library versions). On top of that, the receipt includes sha256 hashes for each upstream payload, and there’s an optional mode to embed the raw upstream payloads themselves (base64) so a third party can validate integrity offline rather than relying on a provider’s feed remaining unchanged. There’s also a “Verify Receipt” path: users can upload a saved receipt later and the Space will recompute z/τ_eff/index/labels from the stored intermediates and confirm it matches, and where raw payloads are embedded it can verify the hashes directly. That doesn’t magically guarantee external providers won’t revise data over time — but it does give a clean, auditable boundary: the original run is frozen, tamper-evident, and independently checkable. Really appreciate you pushing on this; it made the system better.

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