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Pep AI

A No-Code Student Built a Peptide Tracker in 2 Weeks and Hit $125K in 80 Days

A 22-year-old business major spotted the exploding ‘peptides’ niche on TikTok, vibe-coded an app in two weeks, lit it up with influencer marketing, and hit $125K total in 80 days.

Published: Jun 22, 2026Figures last checked: Aug 13, 20262 min readPrimary-source verified · 3
Monthly (est.)
$60k/mo
Time to grow
3 months
Launched
2026
A No-Code Student Built a Peptide Tracker in 2 Weeks and Hit $125K in 80 Days

Key takeaways

  • Confirm where you can distribute and that demand exists — before building (distribution-first)
  • Win the first 100 on Reddit by naming the pain (‘you track doses in messy spreadsheets’)
  • Find the winning influencer pattern, then scale into paid ads ($30K in → $60K same month, 2X ROAS)

The pain point, and how they found it

It started with a roommate saying ‘let’s take peptides,’ then peptide videos flooding his TikTok feed. He found a booming trend where users tracked doses and side effects in spreadsheets and messy notes. ‘This is huge, yet there’s no proper tracker’ — that gap was the opening.

Background & product

Cedric Roberge is a 22-year-old University of Oregon business major with no coding background. His first app, a student marketplace called UniYard, got 800 users but zero revenue. After repeated failures, he nearly gave up.

The turning point was niche discovery. After a roommate said ‘let’s take peptides,’ his TikTok feed filled with peptide/GLP-1 content. The trend was booming, yet users tracked doses and side effects in spreadsheets and messy notes — that gap was the opening. He tried competitors, read their reviews, fed the collected reviews to Claude, and asked it to ‘design the ideal peptide app.’ That’s the AI-era way to build.

He built it in ~2 weeks on Replit with Claude as his ‘senior developer’ (payments via RevenueCat). True to ‘embarrassingly simple,’ he started with dose logging and reminders; today it spans an AI meal scanner, body scanning, 15+ tracked side effects, and Apple Health sync. The biggest wall was Apple review of a medical topic, cleared by studying how existing GLP-1 trackers word disclaimers (‘for tracking and educational purposes only, not medical advice’). The app now holds 4.7★ across 2,400+ ratings, and he has gone from solo founder to a team of three.

Pricing

$9.99/mo or $44.99/yr with a 3-day free trial. Logging doses and basic reminders are free; the AI food scanner and unlimited cycle management are premium.

Based in: United States (Oregon)

App screens

  • Home dashboard summarising dosing: time until the next dose (1 d 23 h for BPC-157), the last dose of 0.3 mg, current level and time to peak, the day’s stack marked as logged, and an application-site picker with a body diagram, under the headline “Peptide & GLP-1 Tracker”
  • Promotional graphic listing supported compounds as tags — Tirzepatide, Wegovy Pill, Zepbound, Semaglutide, Ozempic, Retatrutide — ending with “…And 30+ More”, under the headline “Works With Any GLP-1”; no app UI is shown
  • “Lifestyle” tracking screen: below a day-of-week selector, cards for protein (80/128 g), calories (1600), water (64 oz), a weight chart at 165 lbs, steps (480) and calories burned (16), under the headline “Track Your Progress”
  • Dose “Calculator” screen with Reconstitution and Nutrition tabs: with 10 units set on the syringe scale, it computes a concentration of 10 mg/mL, a 10% syringe fill and 10 doses per compound, with fields for peptide amount, BAC water added and dose amount below, under the headline “Calculator For Precise Math”

Images are served directly by the App Store / the product's own site (not rehosted here) — Pep AI

Pep AI growth channels and tech stack

How it was built

  1. Sniff out a fast-growing niche (peptides / GLP-1) on TikTok, then try every competitor app and read through their reviews
  2. Hand the collected reviews to Claude and ask it to design the ideal peptide app
  3. Put Claude in the 'senior developer' seat on Replit and finish in about two weeks (payments via RevenueCat)
  4. Ship with dosage logging and reminders only, under the motto 'embarrassingly simple'
  5. Medical topics are an App Review choke point, so study how existing GLP-1 trackers word their disclaimers ('for tracking and education only, not medical advice') and pass review with the same design
Time to first release
14 days
What AI did
Claude did the heavy lifting from design through implementation, used as a 'senior developer' on Replit — the founder had never programmed

How it got users

  1. Confirm where you can distribute and that demand exists before building (distribution first)
  2. Win day-one's 100 downloads from posts in peptide, biohacking and GLP-1 subreddits, naming the reader's own pain: 'you're all tracking doses and injection sites in a spreadsheet, right?'
  3. Make biohacking micro-influencers the main channel (one Instagram story alone produced $1,000; larger collaborations exceeded $10,000 per post)
  4. Once the winning pattern is visible, drop $30K at once across TikTok, Instagram and YouTube creators (same month: $60K in revenue, 2X ROAS)
  5. Give away dosage logging and reminders to maximize inflow, then upgrade users to paid with AI meal and body scanning once their medical data has accumulated
First users
Day-one's 100 downloads, from peptide, biohacking and GLP-1 subreddits
Early ad spend
$0 at the start (Reddit posts and micro-influencers only); after the winning pattern was confirmed, $30K spent and $60K recouped the same month

The repeatable playbook

  1. 1Confirm where you can distribute and that demand exists — before building (distribution-first)
  2. 2Win the first 100 on Reddit by naming the pain (‘you track doses in messy spreadsheets’)
  3. 3Find the winning influencer pattern, then scale into paid ads ($30K in → $60K same month, 2X ROAS)
  4. 4Sniff a fast-growing niche from social trends; feed competitor reviews to AI to design the ideal app
  5. 5Freemium: let users bank medical data, then upgrade them via AI analysis (meal/body scans)
  6. 6In strict-review categories, study and mimic approved apps’ disclaimer wording to pass

The hard parts

His first app UniYard earned $1 despite 800 users, and several others flopped. This win came after a stretch where he nearly quit.

Deep dive

【Deep dive】Pep AI’s growth is a textbook case of ‘distribution-first’ — confirming where you can distribute before you build. Here it is, stage by stage.

Confirm demand before code

He didn’t build on a whim. He sniffed out the fast-growing peptide niche on TikTok, swept through competitors and their reviews, and identified the unmet complaints before starting. Knowing ‘who and where’ he would reach *before* building is what set him apart.

First 100 users from Reddit — name the pain

Day-one’s 100 downloads came from posts in peptide / biohacking / GLP-1 subreddits. He named the readers’ own pain — ‘you’re all tracking doses and injection sites in messy spreadsheets’ — and offered the fix. In his words: ‘Go where your users are. If you solve a real pain point, they will beg you to take their money.’

Find the influencer that works, then scale into ads

Early on, biohacking micro-influencers were the core channel. One Instagram story post alone drove $1,000; a bigger collaboration produced over $10,000 from a single post. Once the winning pattern was clear, he poured $30K into TikTok/Instagram/YouTube creators and returned $60K that month (2X ROAS). Because insiders of the exact audience do the talking, it lands at low CAC.

Freemium — monetize *after* they’ve entrusted medical data

Free dose logging, reminders, and basic tracking lower the barrier (maximizing influencer-driven inflow). Once users start banking their own medical data, premium tools — AI meal scanner, body scanning, deep analysis — pull them to paid. Pricing is $9.99/mo, $29.99–$44.99/yr.

Beat heavy regulation by mimicking ‘approved’ wording

Medical topics are an Apple-review chokepoint. He studied how existing GLP-1 trackers phrase disclaimers (‘for tracking and educational purposes only, not medical advice’) and used the same design to pass review. A niche’s ‘scariness’ becomes the very moat that keeps competitors out.

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