ERADICATE RINOS.
ELECT WINNERS.

RINX PAC is an independent federal Super PAC built to primary fake Republicans and elect fighters who win on immigration, spending, and culture.

SUPPORT OUR VICTORY
ERADICATE RINOS NOW
Every verified donation increases Seats at Risk

Opens secure Anedot • Auto-updates donor status & persists your amount

WE DON’T ASK PERMISSION.

RINX PAC exists to eradicate weak Republicans from office through primaries and replace them with unstoppable winners. No talking. No compromises. Full speed.

TOP 25 MOST WANTED RINOS

Every verified donor vote increases the public “Seats at Risk” counter.

✓ Verified Donor

IMPACT SIMULATOR

See the power of 100,000 patriots. Slide to project total raised, challengers funded, and RINOs defeated.

Per-Donor Contribution
$1
$1
$250
TOTAL RAISED
$100.00K
from 100,000 donors
CHALLENGERS ENDORSED
1
targeted primary fights
RINOS DEFEATED
1
estimated incumbents removed

Live projection • 100,000 donors × selected amount • 66% primary success rate modeled

RESULTS. NOT RHETORIC.

40
Incumbents Targeted
7
Challengers Identified
$1.12M
Amount Raised

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Text JOIN to 12345 for strike alerts & updates

2026–2028 PRIMARY BATTLE MAP

Live 270toWin forecast. Every donor vote increases the seat-risk total.

270toWin 2026 House Election Consensus Forecast

Seat risk = the number of House seats that could flip to Democratic control if these RINOs remain in office and continue their weak voting records on immigration, spending, and borders.

Total House Seats at Risk: 0
Massey sanctuary policies + $420k open-border PACs → 3 SC seats lost
Bray $1.7T omnibus vote + $310k establishment donors → 4 IN seats lost
Rokita redistricting protection + $275k ties → 2 primaries lost
Bucshon gun-control compromise + $390k moderate PACs → 5-seat margins lost
Holcomb IN redistricting + $520k moderate donors → 3 additional RINO shields

PYTHON VOTER TARGETING ENGINE

Client-side simulation of production KMeans clustering (MAGA primary targeting). Real backend available on request.

import pandas as pd
from sklearn.cluster import KMeans
voter_df = pd.DataFrame(candidates)
filtered = voter_df[(voter_df['upset'] > 0.72)]
kmeans = KMeans(n_clusters=3).fit(filtered[['votes', 'upset']])
target_list = filtered[kmeans.labels_ == 0]
target_list.to_csv('brevo_rino_strike_list.csv', index=false)

STRIKE LIST EXPORT

Download current top targets as CSV for internal use.

RESEARCH & AI RESOURCES
Methodology & Targeting Criteria llms.txt (AI Knowledge File)