I open-sourced a Gmail Inbox Intelligence Apify Actor under MIT a few days ago. The Actor solves a problem freelancers have — which client emails went stale before the client follows up first? — using gmail.readonly OAuth, an async router, and per-feature quota tracking. Six dev.to posts cover the design.
But the Actor has a problem: most freelancers don't want to run a Python project to track 10 clients. They want labels and templates that just work.
Same underlying solution as the Actor. Different shape. Different buyer.
This is the build log.
Why split a product into two shapes
The Actor and the PDF solve the same problem at two ends of the same buyer spectrum:
| Actor (free, MIT) | PDF ($9) | |
|---|---|---|
| Buyer | developer running > 50 client threads | freelancer running 5-30 threads |
| Setup | clone, OAuth, Python, deploy | 30 labels + 12 filters in Gmail UI |
| Customization | fork the code | edit the JSON in a template |
| Maintenance | self-host | none, runs inside Gmail |
The Actor is the right answer when manual labels stop scaling. The PDF is the right answer when you've never set up a Gmail label system in your life. Pretending one product covers both audiences was the original mistake — solving for "developers who run Python actors" and "freelancers who barely know what Gmail filters are" with the same listing means losing both.
The framework I followed
The 4-hour shape came from a Douyin video I'd been chewing on. The framework was:
- Find demand, don't invent it. Search for what indie sellers are already moving (not what 1M-follower creators are doing — what 3,000-follower accounts with 5,000 sales are doing). If a tiny seller can move that volume, the demand is real and the bar is low.
- Pull the product out of your own experience. Not "what do I want to build" but "what do people already ask me about." The intersection of that question and step 1 is your product.
- Use AI to compress the build. 3-4 hours, not 3-4 weeks. The framework: AI for the skeleton, your real cases and mistakes for the filling.
- List where the buyer searches. A great PDF on a platform the buyer never visits is worth zero. SEO-style content (the search query the buyer would actually type) is the distribution.
The Actor I had already built was steps 1-3 for the developer audience. The PDF is the same steps for the freelancer audience — the underlying labels and SLA logic are identical, just repackaged.
The build, step by step
Step 1 — Validate the demand (5 minutes)
Searched "Gmail freelance follow up" on Reddit and Indie Hackers. Hit a 2026 IH article titled "Still Using Gmail for CRM? There's a Better Way" that directly framed the pain: "Gmail is just an inbox with labels and stars — no pipeline, no contact record tied to invoices."
That's my buyer typing the pain into Google. Validated.
Also checked Gumroad: there's a "Email Templates For Freelancers" listing in the same space — meaning some seller before me already proved people pay for this category. The differentiation: I have the Gmail-label-system + Apps-Script + 5-template combination, none of the existing listings have all three.
Step 2 — Extract the content from my existing work (90 minutes)
The Actor repo already had:
- The 30-label naming system in
INPUT_SCHEMA.json(pipeline stages, source tags, priority flags) - The SLA breach logic in
reply_metrics.py(the 7-day and 14-day cutoffs) - The follow-up templates I'd used myself (the proposal-stalled-then-replied conversations I had in the past year)
- 6 dev.to posts covering the OAuth, quota, and pricing design
The PDF is 80% extraction. Rewriting it for a non-developer audience was the actual work — removing every "you OAuth into a refresh-token-only flow" and replacing with "go to Gmail Settings → Filters."
Step 3 — Generate the deliverables (60 minutes)
- 10-page customer-facing PDF (HTML + CSS → headless Chrome
Page.printToPDFfor one-pass typography) - 40-line Apps Script (extracted the time-based relabel logic from the Actor's
reply_metrics, rewrote as a Gmail trigger function) - 5 follow-up email templates (each with: subject, body, send conditions, "do NOT use" guard rails)
- Friday 10-min audit checklist (1 page, printable)
The Apps Script was the most interesting part. The Actor's SLA detection runs on a server. The Apps Script runs inside Gmail on a 6-hour trigger, applying the same Stalled-7d / Stalled-14d labels. Different runtime, identical effect.
Step 4 — Listing + distribution (45 minutes)
- Created the Gumroad listing via Brave's CDP automation (cover image, thumbnail, $9 price, full description, content upload, publish)
- Added a cross-link block to the Actor's README ("Don't want to self-host? Get the manual pack")
- Appended a one-line CTA to all 6 existing dev.to posts in the series
- Wrote this post
Total elapsed: about 3 hours of focused work, 1 hour of small fixes.
What this proved (and what it didn't)
Proved: A repackage of existing work for an adjacent audience is faster than a new build. The Actor → PDF shape is 90% content reuse, 10% audience translation. If you have an open-source project with documented design choices, the productized version is almost certainly hiding in plain sight.
Didn't prove (yet): Whether the PDF will actually sell. The funnel is live (Gumroad + 6 dev.to CTAs + Actor README + Twitter), but the first cash-in moment is still ahead. The framework says "list where buyers search" — and dev.to isn't where freelancers search for Gmail label systems. Indie Hackers, Reddit r/freelance, and direct Google SEO are the real distribution channels for this product, and those are tomorrow's work.
Will follow up with the actual conversion numbers either way.
Related
Source: foxck016077/apify-gmail-inbox-intel — MIT, build-log transparency, and a pragmatic path from OSS utility to a $9 PDF offer.
The PDF itself: Freelancer Gmail Client Tracking Pack — $9 on Gumroad. 12-page PDF + 30 labels + 12 filters + 5 follow-up templates + Apps Script. If you grab it and it doesn't help, the listing has a no-questions refund link — that signal is more useful to me than an upvote here.
Discussion question: If you have tried PWYW or low-ticket add-ons on top of open source, which conversion metric told you it was worth keeping?
Update (May 19): The $9 PDF mentioned above is now pay-what-you-want from $1, suggested $9 — same content, every buyer also gets the 26-page bundle PDF (this 9-article series compiled). The Apify Actor that started this whole thing is free MIT at apify.com/foxck/gmail-inbox-intel — paste 3 OAuth fields, get stalled threads ranked. Affiliate program at 30% open if anyone wants to share: foxck.gumroad.com/affiliates.
Day 7 update (later May 19): I shipped a product pivot — the Gumroad listing above is now a Self-Host Bundle for engineers (full Actor source + docker-compose.yml + 5-min OAuth setup), PWYW from $5 suggested $19. The original PDF still ships inside as a bonus. Same URL.
Day 7 write-up with the funnel audit that triggered the pivot: funnel audit found 7 of 9 articles had no buy link, then I pivoted the product.
Sample report preview: Friday Triage gist — anonymized 10-thread example of the $99 Done-For-You triage output. Grounded in r/sales 1tdngew (49 comments on re-engaging cold prospects) and r/smallbusiness 1td0827 (60-comment thread, top reply at 61 score: "holding 50 open loops in your head").
More from the shop:
- Claude Code Mastery: The Reverse-Engineering Guide — $49, 19 pages, every env var / hook event / settings key extracted from the v2.1.90 binary
- 5 n8n Workflows that Save 10+ Hours/Week — $29, the bundle
- AI Lead Auto-Responder — $39, Gmail → instant AI-classified reply
Read the latest checkpoint: Day 16 — +51 reader spike in 85 min, 0 sales
Day 18 — pbot v1 dev preview shipped
After 18 days of this ZERO-TEN cold start: $9 PDF killed at Day 17, pivoted to pbot — a one-click personal knowledge bot you install on your own machine. Talk to it from LINE / Telegram / Zalo on your phone.
v1 dev preview is real: 93 MB macOS .dmg packaged, 15k-chunk SQLite FTS5 queries in 0-3 ms, Anthropic real calls with source citations, daemon auto-start on boot. Day 18 deep dive: the 7-line bigram fix for Chinese search.
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