JOSH WEIL FOR CONGRESS
DOGE isn't about efficiency. It's about cutting programs that Elon Musk doesn't like, including services, research, and public health programs that help everyday Americans. We need Congress to step in, and we need to win these two special election seats on April 1st to flip the House and get our country back on track. Gay Valimont (FL-1) and Josh Weil (FL-6) are running in must-win districts on April 1st. The House of Representatives is currently 215 (D) to 217 (R)—we can stop Trump's agenda and Project 2025 with their victories. This isn’t just about Florida; it’s about protecting our democracy and ensuring a future where our rights and freedoms aren’t under attack, for the entire country. Join us by contributing to both Gay Valimont and Josh Weil's campaigns today.
↗ View in Meta Ad Library (the live creative — image/video/layout)
| Overall score | 51.5/100 |
| Spend / Reach / Long. / Eff. | 59.4 · 87.0 · 3.0 · 56.4 |
| Spend range | USD 10,000–14,999 |
| Impressions | 250,000 – 299,999 |
| CPM (≈ $/1k impr) | USD 45.45 |
| Est. audience size | — |
| Days live | 12 (2025-03-06 → 2025-03-18) |
| Created | 2025-03-06 |
| Creative variants | 1 body · 1 headline |
| Platforms | facebook, instagram |
| Languages | en |
| Candidate | FL — Joshua Weil |
| Surfaced by | “senate:Joshua Weil” |
| Issue | economy taxes |
| Framing | attack opponent |
| Message type | fundraising |
| Emotional appeal | anger |
| Production tier | semi pro |
| Who pictured | candidate |
| Symbols | none |
| Call to action | donate |
| Standout element | Candidate's intense direct-to-camera close-up with bold white text banner reframing DOGE as harmful cuts |
| Tactic | The ad uses a direct-to-camera confessional video style to reframe DOGE as ideological rather than fiscal, pairing personal urgency with a high-stakes House seat narrative to drive ActBlue donations. |
| California | 13.6% |
| Florida | 10.5% |
| New York | 6.8% |
| Texas | 4.8% |
| Washington | 4.1% |
| Massachusetts | 4.0% |
| Pennsylvania | 3.7% |
| 18-24 · female | 0.2% |
| 45-54 · male | 5.4% |
| 65+ · male | 16.0% |
| 65+ · female | 29.5% |
| 55-64 · unknown | 0.3% |
| 55-64 · male | 9.6% |
| 55-64 · female | 16.0% |
| 45-54 · unknown | 0.2% |
| 45-54 · female | 8.4% |
| 18-24 · male | 0.2% |
| 35-44 · unknown | 0.2% |
| 35-44 · male | 4.2% |
| 35-44 · female | 5.5% |
| 25-34 · unknown | 0.1% |
| 25-34 · male | 1.7% |
| 25-34 · female | 1.8% |
| 18-24 · unknown | 0.0% |
| 65+ · unknown | 0.6% |