reunios2024/cortex-sentinel-trading-nexus — explained in plain English
Analysis updated 2026-05-18
Get a second opinion before committing capital to a prediction market trade.
Evaluate a DAO governance proposal across multiple voter bases at once.
Fold crowd sentiment data into macro investment research.
Check how reliable a crowd wisdom claim is for fact checking.
| reunios2024/cortex-sentinel-trading-nexus | charan820/phantomcrowd-simulacra | gainubi/note-slides | |
|---|---|---|---|
| Stars | 152 | 151 | 151 |
| Language | HTML | HTML | HTML |
| Setup difficulty | moderate | moderate | easy |
| Complexity | 4/5 | 4/5 | 2/5 |
| Audience | researcher | pm founder | vibe coder |
Figures from each repo's GitHub metadata at analysis time.
The README has no installation section and does not say how to obtain API access to the six data sources.
SolaraX is a sentiment aggregation system built for people who want a single confidence score instead of digging through many separate opinion sources. It pulls in six kinds of inputs: prediction markets like Polymarket and Kalshi, social media sentiment from Twitter, Reddit and Telegram, on-chain governance votes, macroeconomic survey data, and forecasts from a large time-series language model called Kronos. Rather than simply averaging these numbers, the project uses three AI agents that argue a case: one argues for the highest probability outcome, one argues the contrarian view, and a third weighs both sides and produces a final score. This debate is saved as a transcript so a person can review the reasoning instead of only seeing a number. Alongside the consensus score, the system reports how much the six sources agree with each other and flags situations where the inputs disagree enough to undermine confidence in the result. The output is delivered through a local web API in a simple JSON format containing the question, the consensus probability, a conviction label, and a flag for uncertain cases. The project states clearly that it does not place trades, connect to crypto wallets, or handle private keys. It describes itself as a signal processor only. A companion web dashboard shows the individual data sources as gauges, displays the debate history with color coded arguments, and tracks how much the sources agree with each other over time. The interface supports several languages. The README describes its intended users as prediction market traders, people who analyze DAO governance proposals, macro focused investors, academic researchers, and journalists checking claims about crowd sentiment. Use of the project for non commercial research or personal purposes is free, while commercial use by trading firms or funds requires a paid license. The repository lists HTML as its primary language in its metadata, carries an MIT license badge, and includes a disclaimer stating it provides no financial advice and does not execute trades or manage funds. The README does not include setup or installation instructions.
A sentiment aggregation tool that combines prediction markets, social media, on chain votes, and other signals into one confidence score using AI agents that debate the result.
Mainly HTML. The stack also includes Python, REST API, WebSocket.
Free for personal and non commercial research use, but a for-profit trading firm or fund must pay for a commercial license, the code itself carries an MIT badge.
Setup difficulty is rated moderate, with roughly 1h+ to a first successful run.
Mainly researcher.
This repo across BitVibe Labs
Don't trust strangers blindly. Verify against the repo.