README.md

metadata

title: Agents Console
emoji: 📈
colorFrom: gray
colorTo: blue
sdk: gradio
sdk_version: 6.1.0
app_file: app.py
pinned: false
license: other
short_description: NEO alerting-satellite jitter reduction-Starship-style

Rendered Frame Theory (RFT) — Observer Agent Console

I built this Space to be completely open about what I’ve made, how it works, and where it succeeds or fails.

Rendered Frame Theory (RFT) is a framework I developed independently to solve a recurring problem I kept encountering in real predictive systems: many pipelines wait too long to act. They chase certainty, then react. By the time a correction happens, energy has already been wasted, instability has already grown, or the system has already triggered false positives.

RFT flips the priority. It treats timing, uncertainty, and decision “commit” (what I call collapse) as first-class variables instead of side effects.

This Space hosts working agents built using that approach. Nothing here is hidden. All code runs. All assumptions are stated. Results are shown honestly, including where the approach does not outperform conventional methods.

What RFT is (in practical terms)

RFT is not a new programming language, not a replacement for control theory, and not a belief system.

RFT is a decision-timing framework.

Most systems do: • predict state • minimise error • correct once confidence is high

RFT changes the order of importance: • predict state and delay cost • estimate uncertainty explicitly • commit actions earlier when the cost of waiting is higher than the cost of acting

In practice, this can lead to: • earlier meaningful corrections • fewer false positives • lower compute/actuation usage • more stable behaviour under noise

Important wording note: when I use “observer” in this Space, I mean an explicit decision mechanism (uncertainty → τ_eff → gate → commit/wait). I am not making a claim of machine consciousness here.

What this Space is NOT

This Space is not: • a certification claim • a flight-ready aerospace model • a physics replacement argument • a “trust me” pitch

It’s a transparent test harness that you can run, break, and compare.

What’s inside

This Space includes: • Near-Earth Object (NEO) observer agent (noisy alert filtering) • Satellite jitter observer agent (duty reduction / chatter control) • Starship-style re-entry & precision landing harness (simplified) • A Benchmarks tab with baseline vs RFT runs using the same seed • A Theory → Practice page mapping ideas directly to code behaviour • A Mathematics tab defining what the variables mean and how they map into implementation • An Investor / Agency walkthrough tab explaining what’s demonstrated here and what would be needed next for production

Every agent: • runs inside the Space • shows plots • exports CSV logs • uses reproducible seeds

Why I’m making this open

I’m choosing transparency over hype.

If this is “just clever coding”, that will be obvious once people run it. If it isn’t, that will also be obvious.

Either way, the code and logs speak louder than claims.

Run it. Change parameters. Break it. Compare it.

(Spaces config reference: https://huggingface.co/docs/hub/spaces-config-reference)