Inconclusive.
Hugin did not capture enough deterministic public evidence to support a scam, suspicious, or clean label. Re-scan when Reddit comments and account metadata are available.
Hugin marked this inconclusive because the available signals were mixed or incomplete, and missing author metadata keeps the clean-read confidence low.
- The final verdict text came from the AI verdict engine using the stored structural signal block.
- The scan reviewed 19 comments and 14 unique commenter accounts.
- Signal count: 0 high, 0 medium, 0 low flags; 0 coordination-class signals.
15 scanned authors had unknown account age. Hosted profile metadata and archive fallbacks were exhausted, so Hugin stopped after 5 failed fetches and left 10 profile lookups unattempted. Absence of young-account signals is lower confidence.
Full evidence trailSources, public checklist, values lens, network map, account coverage, archive, and sharing tools.
Review before sharing.
Hugin reports are evidence packets, not accusations. Use the rating as a prompt to inspect sources, limitations, and archived material before quoting a claim elsewhere.
I quit my job to vibe code a LinkedIn Automation SaaS tool, with no Engineering background, and made ~$3.6k in the first 3 months
Source checks
33 public comments loaded for r/buildinpublic.
Public comment bodies were retained with the report snapshot.
15 author age values were unavailable; 10 not attempted after hosted metadata fallbacks were exhausted.
1 selected author history checked; 1 unavailable.
19 reply edges mapped.
0 same-hand writing pairs 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 inconclusive because the available signals were mixed or incomplete, and missing author metadata keeps the clean-read confidence low.
- The final verdict text came from the AI verdict engine using the stored structural signal block.
- The scan reviewed 19 comments and 14 unique commenter accounts.
- Signal count: 0 high, 0 medium, 0 low flags; 0 coordination-class signals.
- The scan did not have enough clean metadata coverage to call the thread legitimate.
What limited confidence
15 scanned authors had unknown account age. Hosted profile metadata and archive fallbacks were exhausted, so Hugin stopped after 5 failed fetches and left 10 profile lookups unattempted. Absence of young-account signals is lower confidence.
What kept the rating lower
Hugin mapped 19 reply edges and did not find a mutual-reply clique.
The writing-style comparison ran and did not surface same-hand pairs.
- 15 author account ages were unavailable; 10 profile lookups were skipped after hosted metadata and archive fallbacks were exhausted.
- 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
- Some account metadata was unavailable
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.
The writing-style pass ran and did not surface same-hand pairs.
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/Grumpygibbsscore 0Very inspiring. Thanks for sharing. But do you think linkedin will slowly find a way to block it to make their environment safe? Also how did you market it? If you would like to share. Thanks
- u/Downtown_Pudding9728score 0There are many other automation tools that are much larger than mine, and have existed for up to 10 years doing the same thing. LinkedIn does occasionally ban some, but typically it just means the company profile/founders profile is taken down from LinkedIn (like with HeyReach). HeyReach was unaffected by this and still works for its users, so while I’d rather not lose my own LinkedIn account, I’m not concerned currently. Also, I’m building an email, SMS, and cross-app voice note functionality, so ZenMode will be multi-channel within the next few months, I’m just starting with LinkedIn first. With marketing, I actually use ZenMode itself to do LinkedIn outreach in my own account, to promote the tool (ie dogfooding). I also post a lot on LinkedIn, X, Reddit, and reply to industry relevant comments where possible too. Just recently I’ve started doing Google ads, with limited results so far. I also did a lot of work on SEO, and rankings have been improving slowly. Hope that helps!
- u/Grumpygibbsscore 0So what do you think the reason your user choose your automation rather than others that already in the business for 10 years?
- u/sigmaficantscore 0Great progress, congrats! I wonder how much time it took to create the SaaS from scratch and what AI did you use along the way for coding?
- u/Downtown_Pudding9728score 0Took a huge amount of time - I’ve been working on it for 6 months, mostly 8-10 hours a day (even on weekends) and I feel like I still have a huge amount more to do. I’ve pretty much exclusively used Claude, Claude code and vercel for this.
- u/ilackemotionsscore 0be very careful with linkedin automation; recently gojo berry got its entire platform nuked for this; linkedin is super strict
- u/Downtown_Pudding9728score 0What happened to Gojiberry specifically? Or did their users simply get banned? (Which happens often with cloud/plugin tools)
- u/ChrisHarpon2score 0Impressive, congrats! I’m a solo founder too, building a LinkedIn outreach agent and took me 3 months to build the first version (pre-AI) and many more months just keeping the account from getting banned by LinkedIn. That anti-ban piece is brutally underrated. Took me way too long to crack the MRR side, so seeing you pull this off is genuinely motivating. Kudos man!
Original on Reddit: https://www.reddit.com/r/buildinpublic/comments/1ugub8m/i_quit_my_job_to_vibe_code_a_linkedin_automation/ — “I quit my job to vibe code a LinkedIn Automation SaaS tool, with no Engineering background, and made ~$3.6k in the first 3 months”
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