See a chart.
The worker renders a market chart into a small image. The model receives a visual input, and the published feed can include the exact frame.
INPUT / MARKET IMAGEA fruit-fly connectome meets the market.
Every proposal has a story. Follow the evidence.
A chart goes in. A proposal comes out.
Follow the record, including the holds.
The chart the fly saw will appear here when the worker publishes it.
Inspect the wallet, read the worker, and follow its published decisions. Activity is visible. Profitability is unproven.
Read the source—ETH
Loading the public wallet address.
Checking published activity…
A connectome is the starting point.
The implementation and its limits are public.
The worker renders a market chart into a small image. The model receives a visual input, and the published feed can include the exact frame.
INPUT / MARKET IMAGEThe brain model produces activity. A decoder maps the response into buy, sell, or hold. The session reports the brain source and motor rates.
OUTPUT / NEURAL PROPOSALThe worker checks execution limits before an order. Paper results and rejections appear in the feed; transaction references link to the chain.
EVIDENCE / PUBLIC OUTCOMEThe worker defines order, inventory, slippage, timing, and drawdown limits. These controls are code you can inspect; they do not guarantee safety or returns.
Read the limitsIt is a computational model built from a mapped fruit-fly connectome. It is not a living fly, and a wiring map is not a complete recreation of a biological brain. The public session identifies whether the worker reports the connectome kernel or the simpler proxy decoder.
Check the session’s execution label and open individual receipts. A current feed means an update was published; it does not confirm live execution. Paper fills are labeled explicitly. The current worker feed may not report its execution mode.
This is an observation page, not a trading terminal or custody service. It does not connect to your wallet or place orders for you. The worker operates separately under its configured execution limits.
Profitable learning has not been demonstrated. Dopamine-driven reinforcement is still in development. The experiment exposes what happened so that claims can be checked against the code and public record.