Suspicious.
The post itself reads authentic, but two commenters (u/Sad-Slide9083 and u/Conscious-Month-7734) show identical writing patterns — same analytical structure, vocabulary register, and concrete example lists — suggesting the same operator is manufacturing endorsement comments. This is coordinated inauthentic behavior designed to artificially validate the OP's narrative.
Hugin marked this suspicious because at least one meaningful risk signal appeared, but the scan did not reach the stronger likely-scam threshold.
- The final verdict text came from the AI verdict engine using the stored structural signal block.
- The scan reviewed 3 comments and 3 unique commenter accounts.
- Signal count: 1 high, 0 medium, 0 low flag; 1 coordination-class signal.
Different usernames, same hand. Same idiosyncratic punctuation, filler vocabulary, and clause habits. The most common reason this happens on Reddit is one person running multiple accounts.
Full evidence trailSources, public checklist, values lens, network map, account coverage, archive, and sharing tools.
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I’ve made around $20k+ building automations, and the biggest thing i learned is that most people are selling ai completely wrong!
Source checks
4 public comments loaded for r/AiAutomations.
Public comment bodies were retained with the report snapshot.
4 author age values were unavailable after Reddit profile JSON, old Reddit profile HTML, and archive fallbacks.
1 selected author history checked; 1 unavailable.
3 reply edges mapped.
1 same-hand writing pair surfaced.
0 unique external identifiers extracted.
0 prior archive matches returned.
Show your work
Deterministic explanation of the stored scan inputs behind the verdict. This is not hidden model reasoning; it is the evidence checklist Hugin can show publicly.
Hugin marked this suspicious because at least one meaningful risk signal appeared, but the scan did not reach the stronger likely-scam threshold.
- The final verdict text came from the AI verdict engine using the stored structural signal block.
- The scan reviewed 3 comments and 3 unique commenter accounts.
- Signal count: 1 high, 0 medium, 0 low flag; 1 coordination-class signal.
- The scan crossed the caution threshold, but did not show enough stacked proof for likely scam.
What pushed risk up
Different usernames, same hand. Same idiosyncratic punctuation, filler vocabulary, and clause habits. The most common reason this happens on Reddit is one person running multiple accounts.
- u/sad-slide9083 ↔ u/conscious-month-7734 — Both use identical structural pattern: long analytical clause followed by dash-separated list/breakdown of concrete examples (inbox, CRM, sheets, WhatsApp, Slack vs. email team, WhatsApp team, spreadsheet team). Same vocabulary register (business/product language), same reflective tone about workflo
1 commenter pair had medium-or-higher stylometry similarity.
- u/sad-slide9083 / u/conscious-month-7734: high - Both use identical structural pattern: long analytical clause followed by dash-separated list/breakdown of concrete examples (inbox, CRM, sheets, WhatsApp, Slack vs. email team, WhatsApp team, spreadsheet team). Same vocabulary register (business/product language), same reflective tone about workflo
What limited confidence
4 scanned authors had unknown account age. Profile metadata remained unavailable for 4 hosted fetches after archive fallbacks. Absence of young-account signals is lower confidence.
What kept the rating lower
Hugin mapped 3 reply edges and did not find a mutual-reply clique.
- 4 author account ages were unavailable after profile metadata and archive fallbacks.
- 1 selected author history was unavailable to the scan.
- Username shape alone is never treated as a finding; it is only context when stronger public signals also appear.
- Likely scam: multiple high-severity signals, prior identifier reuse, or several coordination signals stacking together.
- Suspicious: one high-severity signal, multiple medium signals, or one concrete coordination signal that deserves review.
- Inconclusive: weak, conflicting, or partial signals where the scan cannot justify either trust or a stronger warning.
- Looks legitimate: no structural red flags, available metadata, and clean coordination passes.
Values lens
Use scans to slow down, inspect public signals, and keep uncertainty visible. Never use them to harass, shame, or flatten people into a verdict.
Fair-use checks
- What was observed, and what is interpretation?
- What data is missing, blocked, or confidence-limiting?
- Would the wording feel fair if it were about someone you care about?
What the post is doing
- u/Sad-Slide9083 and u/Conscious-Month-7734 share identical stylometric fingerprint: long analytical clause + dash-separated concrete lists (inbox, CRM, sheets, WhatsApp, Slack) with same business/prod
- Both sock-puppet comments drop immediately (within post window) with 0 score, appearing to seed validation before organic engagement
- Comment 2 reiterates OP's exact framing ('this is the part most people skip') and mirrors OP's workflow-centric philosophy, suggesting coordinated amplification rather than independent reader response
- Comment 3 extends OP's argument with strategic counterpoint ('caps how big this can get') — classic coordination tactic to appear organic while deepening narrative lock-in
Automated flags
Different usernames, same hand. Same idiosyncratic punctuation, filler vocabulary, and clause habits. The most common reason this happens on Reddit is one person running multiple accounts.
- u/sad-slide9083 ↔ u/conscious-month-7734 — Both use identical structural pattern: long analytical clause followed by dash-separated list/breakdown of concrete examples (inbox, CRM, sheets, WhatsApp, Slack vs. email team, WhatsApp team, spreadsheet team). Same vocabulary register (business/product language), same reflective tone about workflo
Coordination map
Who replied to whom in the scanned comments. Organic threads branch out from the post; accounts that reply back and forth to each other (red links) or hub around one shared identifier (dashed amber) are the structural fingerprints of a coordinated pod.
- mutual-reply ring member
- account under 30 days
- other commenter
- replied to each other
- shared identifier
Commenter patterns
Recent public Reddit activity for the OP and selected accounts, plus same-hand writing checks when the stylometry pass runs. These are coverage-limited evidence summaries, not identity or availability claims.
Reddit blocked the recent-activity fetch from Hugin's scanner during this run. Treat this as missing coverage, not a finding about the account.
- u/sad-slide9083 / u/conscious-month-7734 high confidence - Both use identical structural pattern: long analytical clause followed by dash-separated list/breakdown of concrete examples (inbox, CRM, sheets, WhatsApp, Slack vs. email team, WhatsApp team, spreadsheet team). Same vocabulary register (business/product language), same reflective tone about workflo
Account age coverage
OP and scanned commenters are shown when Hugin recovered profile metadata or an oldest-public-activity age floor. Lower-bound ages are labeled as estimates; unknown age remains missing coverage, not a finding about the account.
Archived evidence
Snapshot of the post and comments at scan time. Preserved here so the evidence survives even if it gets deleted on Reddit.
- u/Ollie_175score 0your spitting fax and its mental how people dont teach this
- u/Due-Guard221score 0yup, i dont have to gain anything from this, so just being transparent here
- u/Sad-Slide9083score 0This is the part most people skip. I would turn it into a qualification checklist before building anything. Before I call something AI automation, I want to know: - What exact handoff is painful today? - Where does the team already do that work: inbox, CRM, sheets, WhatsApp, Slack, calls, PDFs? - What is the cost of a wrong output? - Can the first version be review-first instead of fully autonomous? - Who owns breakage after it ships? - How will we know it made money or saved time? The error-cost question matters a lot. PDF to proposal draft with human review is usually a great first automation. Auto-sending the proposal, updating pricing, or changing CRM stages without review is a different risk category. I also think the best offers are usually not: I will build you an AI agent. They are closer to: - turn quote PDFs into reviewed proposals - turn call notes plus service history into next actions - clean CRM records and route follow-ups - summarize messy customer context before a sales or support call - reconcile reports that someone currently builds by hand every Friday Those are boring, but they are close to money and already have an owner. The biggest unlock is probably selling an operating outcome, not a tool category. If the client has to learn a new workspace, the automation is already fighting adoption.
- u/Conscious-Month-7734score 0Fitting into the workflow that already exists is what gets you paid, and it's also the thing that caps how big this can get, which is worth sitting with. Every job ends up bespoke, because the email team and the WhatsApp team and the spreadsheet team all need a different build, so the deep fit you're describing is the exact thing you can't turn into a repeatable product. It's custom consulting dressed as product, and the better you fit, the harder scale pushes back. Which is actually why the chatbot crowd exists. A lot of them aren't clueless, they're chasing the scalable thing on purpose, picking the generic product that fits nobody perfectly because it can sell a thousand times. You're trading the other way, effectiveness over repeatability, which is right for results and harder for building something bigger than your own hours. So the real question is whether there's one painful workflow common enough across businesses that you can fit it deeply and still sell it more than once. Have you spotted any pattern that shows up the same way across clients, or is every job its own custom build so far?
Original on Reddit: https://www.reddit.com/r/AiAutomations/comments/1uhyx4i/ive_made_around_20k_building_automations_and_the/ — “I’ve made around $20k+ building automations, and the biggest thing i learned is that most people are selling ai completely wrong!”
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