AIBotOrNot
Community voting for spotting fake AI profiles across social platforms.
Tagline
Spot fake profiles before they fool you
A public signal for synthetic social identities
Stop guessing which creators are real
Crowd verdicts for suspicious profiles online
A public intelligence layer for spotting synthetic social identities before they spread.
The product is not doing model-based image forensics; it is organizing community suspicion into a searchable, vote-driven identity signal across platforms.
The alternative to manual sleuthing, fake-follower checkers, and gut feel.
Users can submit a URL, see real-time consensus, and inspect prior cases instead of bouncing between tools like HypeAuditor or doing manual profile archaeology.
A painkiller for creator fraud, impersonation, and AI influencer ambiguity.
The landing page emphasizes flagged profiles, verified human overrides, and famous confirmed AI accounts, which directly targets trust decisions around who is real online.
Primary user
Social media trust-and-safety or community moderation lead at a platform or brand monitoring team
ICP #1
Influencer marketing manager at a DTC brand running 20-50 creator deals per month
Pain
They waste budget on fake creators, inflated followers, and AI-generated personas that look legitimate until after the contract is signed.
Why this solves
AIBotOrNot gives a fast, public verdict workflow for suspicious profiles and a directory of known synthetic accounts, which is useful as a pre-check before outreach.
ICP #2
Trust & Safety analyst at a social platform with rising bot abuse
Pain
They need lightweight, human-sourced signals to triage suspicious profiles before escalating to internal review queues.
Why this solves
The product turns community reports into a live confidence score and lets moderators lock outcomes, making it a practical external signal layer for investigation.
ICP #3
Journalist or OSINT researcher investigating a public figure or creator
Pain
They need a quick way to test whether an online persona is authentic without spending hours manually comparing posts, metadata, and engagement patterns.
Why this solves
AIBotOrNot packages crowd suspicion, moderation review, and example cases into one browseable workflow that can speed up source vetting.
Strengths
- +The value prop is instantly understandable: submit a profile, get community and moderator-backed authenticity signals.
- +The live metrics and recent submissions make the product feel active instead of empty.
- +The famous AI influencer examples are smart educational proof points that help users understand the category.
Weaknesses
- −The page is too vague about how the confidence score is calculated, which makes the product feel more like a game than a reliable tool.
- −There is no clear explanation of who moderators are, how verification happens, or what standards they use.
- −The product appears to rely heavily on crowdsourcing, but the landing page does not address abuse, brigading, false accusations, or appeal flows.
- −The copy is broad across platforms, but the actual use case is narrow and not sharpened for a buyer with budget.
- −The page leans on beta hype and community language, but does not show any hard outcomes like accuracy, turnaround time, or false positive reduction.
Fix these
- Add a 'How confidence is calculated' section with visible signals, weighting, and moderation rules.
- Create separate landing page variants for creator vetting, trust & safety, and research use cases with tailored proof points.
- Show an audit trail for each verdict: vote history, moderator action, and reason tags.
- Add a trust policy section covering false reports, appeals, and anti-brigading protections.
- Replace generic beta hype with concrete outcomes, such as profiles reviewed, moderator response time, and percent of cases confirmed synthetic.
Drop-in replacement copy
Headline
Real profile or bot?
Crowd-check suspicious social accounts fast.
Fast verdicts for suspicious profiles
Submit a social profile URL and get a live vote-based read on whether it looks AI-generated or real. It’s built for the moment you need a quick answer before spending time or money.
Audit trail, not blind scoring
See votes, confidence changes, and moderator actions behind every verdict. That makes the result easier to trust, explain, and challenge when needed.
Verified Human can override the crowd
Moderators can lock in outcomes when the evidence is clear. That keeps the product from becoming just a popularity contest.
Learn the patterns from known AI accounts
Browse confirmed examples like Lil Miquela and Aitana Lopez to train your eye. Useful when you want to understand what synthetic profiles actually look like in the wild.
FAQ
How is the confidence score calculated?
It’s based on community votes, vote balance over time, and moderator actions. The page should show the weighting and the exact reason tags so the score feels useful, not magical.
Who are the moderators?
Moderators should be disclosed as named reviewers, platform staff, or trusted contributors with visible standards. Users need to know who can override the crowd and why.
What if people brigade a profile?
You need anti-abuse protections: rate limits, duplicate-vote checks, and review flags for suspicious vote patterns. The trust layer matters as much as the verdict itself.
Is this for everyday users or teams?
Both, but the paid value is clearest for creator vetting, trust & safety, and research workflows. Everyday users are the top of the funnel; teams are where budgets live.
Can I appeal a verdict?
Yes, there should be a clear appeal flow with evidence submission and moderator review. Without that, the product risks becoming a public accusation machine.
Fake profiles cost brands real money. Built AIBotOrNot: submit a suspicious social profile, get a live community verdict, and see moderator-backed Verified Human decisions. For creator vetting, T&S, and OSINT. If a profile feels off, now you can check it fast.
One URL. One verdict. Zero guessing. AIBotOrNot lets people flag suspicious profiles from Instagram, TikTok, YouTube, X, LinkedIn, and more. The crowd votes. Moderators can override. You get a public trail instead of vibes.
I kept seeing fake creators everywhere. So I made a place to submit suspicious profiles and crowdsource the answer. What surprised me: people don’t want another fancy detector. They want proof, history, and a clear trail of why a profile was flagged.
The hard part is trust, not AI. Anyone can build a score. Harder is explaining it, preventing brigading, and letting moderators lock in outcomes. That’s what I’m shipping next: audit trails, reason tags, and clearer standards for Verified Human.
Paid a creator who was fake? That one hurts. AIBotOrNot gives creator teams a fast pre-check before they sign deals. Submit the profile, inspect votes, and see if other people already flagged the account as suspicious.
Not sure if that account is real? That’s the whole problem. AIBotOrNot turns random suspicion into a live verdict: AI generated or real, with moderator override and a browseable history of cases.
Watch the confidence score move. Submit a profile URL, then see community votes update in real time as people weigh in. It’s like a public investigation queue for social identities, minus the spreadsheet archaeology.
This is what a fake profile looks like. AIBotOrNot includes known AI examples like Lil Miquela and Aitana Lopez so users can train their eye. Not everything is a bot. But once you’ve seen the patterns, you start spotting them everywhere.
People already use gut feel anyway. AIBotOrNot just makes the gut feel visible, searchable, and auditable. That matters when you’re deciding whether to trust a creator, escalate a suspicious account, or cite a profile in a story.
The internet needs receipts. So every verdict in AIBotOrNot keeps a trail: votes, moderator actions, and status changes. Less drama. More evidence. Useful if you care about what’s real online.
Angle: creator vetting
Most creator fraud tools start too late. By the time a fake profile shows up in a dashboard, the deal is already in motion. I built AIBotOrNot for the earlier moment: when a creator profile looks a little off and you need a fast sanity check before outreach, negotiation, or payment. The workflow is simple: - submit a suspicious URL - see community votes in real time - inspect the audit trail - let moderators override with Verified Human when needed What I’m trying to solve is not just detection. It’s decision speed. If you run influencer partnerships and have dealt with inflated profiles, impersonators, or AI-generated personas, I’d love feedback on the exact signals you’d want before you greenlight a deal.
Angle: trust and safety
Trust and Safety teams do not need more noise. They need usable signal. AIBotOrNot is my attempt at turning public suspicion into something that can actually help triage a profile. Instead of another black-box score, the product shows: - who voted - what they voted - when moderators stepped in - whether the outcome was confirmed human or flagged synthetic I’m aware of the obvious failure modes here: brigading, false accusations, and overconfident crowds. That’s why I think the trust layer matters as much as the detection layer. If you work in moderation or platform integrity, I’d especially value feedback on appeal flows, anti-abuse controls, and what would make this signal useful enough to sit next to an internal queue.
Angle: research and OSINT
A lot of researchers still verify online personas the hard way. Open tabs. Reverse image checks. Engagement pattern analysis. Cross-referencing posts across platforms. That process works, but it’s slow. AIBotOrNot is a public layer for the first pass: submit a profile, see the crowd’s read, check moderator notes, and browse confirmed examples to understand the patterns. I’m not claiming crowdsourcing replaces proper investigation. I am saying it can speed up the first 10 minutes. If you do OSINT, journalism, or source verification, I’d love to know what would make this genuinely useful instead of just interesting.
No visuals for this kit yet.
Tagline
Crowd-check suspicious profiles fast
Description
Submit a social profile URL and get a live community verdict, moderator override, and audit trail. AIBotOrNot helps creators, researchers, and trust teams spot synthetic identities before they become expensive mistakes.
Maker's first comment
I built this because I kept seeing the same problem over and over: a profile looks legit at first glance, the followers are there, the posts look polished, and then you realize you were dealing with a fake, an impersonator, or something AI-made. Most tools either feel too technical or too late. AIBotOrNot is my attempt at making the first check fast and public. You can submit a profile, see how people vote in real time, inspect the trail, and see when a moderator overrides the crowd. I also added confirmed AI examples because people learn patterns faster when they can compare cases. This is still early, and I’m very aware that community systems can be messy. I’d especially love feedback on how clear the verdict logic feels, whether the trust policy is strong enough, and what would make this useful for actual workflows instead of just curiosity.
Pinned maker comment
Would love feedback on the confidence score explanation, anti-brigading protections, and which use case should get its own landing page first: creator vetting, trust & safety, or research.
Meta
Hypothesis: creator fraud happens before outreach.
If your team vets creators manually, you’re probably catching fake or AI-generated profiles too late. AIBotOrNot lets you submit a suspicious profile URL, see a live community verdict, and review moderator-verified outcomes before money changes hands.
Google Search
Search intent: is this profile real?
AIBotOrNot helps people verify suspicious social profiles across Instagram, TikTok, YouTube, X, and LinkedIn. Use community votes, moderator overrides, and example AI accounts to make faster trust decisions.
Reddit Promoted
Hypothesis: people want receipts, not vibes.
When a profile looks fake, most tools give you a score and call it a day. AIBotOrNot shows the votes, the moderation trail, and the confirmed cases so you can judge for yourself.
Subreddits
r/SideProject
Show the product and ask for feedback on the trust flow, not vanity reactions
Rules: Must be a real side project; avoid spammy promotion; include what you built, why, and what feedback you want
r/indiehackers
Build story about launching a public trust layer for fake profiles
Rules: Founder story encouraged; be transparent; avoid pure promotion; ask specific questions
r/microsaas
Narrow utility: creator vetting and suspicious-profile triage for small teams
Rules: Keep it specific to the product; show use case and pricing or workflow; no generic marketing
r/EntrepreneurRideAlong
Progress update on validating demand for anti-fraud creator verification
Rules: Share journey and numbers; invite discussion; do not post a sales pitch
r/OSINT
Ask researchers whether a crowdsourced signal layer helps source verification
Rules: Educational/research framing; avoid doxxing; focus on methodology and legitimacy
Communities
Post a build log with screenshots, then reply to every comment with concrete details about confidence scoring, moderation, and early use cases.
X Creator Economy circles
Reply to creator-economy and brand-safety posts with a useful angle: how to vet suspicious profiles before deals, not a hard sell.
Trust & Safety / moderation Slack groups
Share the audit trail and anti-brigading idea, then ask what signal would be useful enough to triage rather than replace internal review.
OSINT and journalism Discords
Offer the directory of confirmed AI examples as a learning tool and ask investigators what metadata or provenance features they need next.
Cold outreach template
Hey {firstName} — saw you’re handling {context}. I built a tool that crowdsources verdicts on suspicious social profiles and shows an audit trail, not just a score. If you want, I can send you a few examples relevant to your workflow.
Product Hunt timing
Launch on Tuesday 9:00 AM PT, because PH traffic is strongest early in the week and you want a full day of comments, updates, and maker replies while momentum compounds.
Indie Hackers post ideas
- 01I built a public verdict layer for suspicious social profiles
- 02How I’m trying to turn fake-profile detection into a workflow
- 03What I learned building a crowd-powered trust signal for social accounts
Competitor alternatives
Current tone of voice
Playful, internet-native, and slightly gamified, with lines like 'Is that profile real, or just another bot?' and 'Founding Detectives.'
Your kit is ready. Sign up free to unlock, takes 10 seconds.
7 more X posts · 2 LinkedIn · Product Hunt copy · ad hooks · 100-user playbook · landing critique