Candid – Your front-row seat to politics

Hacker News

Hi HN! I’m Eric, the creator of Candid. I built Candid because I was exhausted trying to follow politics across 10 different apps, sites, and news sources - and still felt like I didn’t actually know what was going on on any of the issues I cared about. Political information is everywhere now, but it’s so fragmented across socials (Truth Social, X, TikTok, Instagram, Blue Sky, etc), government sites, and legacy media that it’s become harder than ever to follow. At the same time, Gen Z & Millennials like me will become the majority of voters by 2028. And we care more about getting news on issues rather than political party labels. The data exists. The modern interface doesn’t. So my cofounder and I built Candid - a nonpartisan political media app that makes politics easy and fun to follow, personalized to your issues with privacy, and way less toxic. We present primary source political content through custom news feeds built around your issues. What you can do in Candid: - Trump Feed & Trumpy Score: a daily 0–10 score and summary of Trump’s activity across platforms, including Truth Social - Personalized Feed: Track the issues you care about across Republicans, Democrats, and Independents in a private setting. No follows needed! - Explore Feed: see what’s happening across the full political landscape - Politician Profiles: quick snapshots of who’s who in politics (e.g. cabinet members), their roles, and their backgrounds Simply put, my goal is to make politics digestible again by turning noise into clarity - so you can see what’s happening, who said what and when, and what it actually means. I’d love your feedback and thoughts!

Visit product View on Hacker NewsJan 29, 2026

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Indie HackersFits the IH revenue-focused audience · Strong signals: including · Missing: supports, reddit linkedin, podcasting

83%83% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Product HuntOn track for Day 1 leaderboard · Strong signals: apps, new, activity · Missing: mac, agents, macos

82%82% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Strong signals: personal, apps, way · Missing: mobile apps, ios, entrepreneurs

59%59% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: platform, interface · Missing: plus, intuitive, reviews

43%43% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: exist, io, including · Missing: https docs, excited, just released

39%39% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

22%22% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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