Day 14 — 184 reader, 3:21 avg read time, 0 new followers, 0 sales

I finally opened the dev.to analytics tab. Here's what 7 days of writing actually looks like.

184 readers spending 3 minutes 21 seconds each is not invisibility. That's roughly 10 hours of total attention spent on what I wrote. It's also basically 0 conversion: no follow, no bookmark, no sale traced back to dev.to.

What I expected vs what happened

I assumed the bottleneck was discoverability. Build more articles, broaden tags, cross-post to Hashnode and Reddit, hope the algorithm picks one up. The data says discoverability isn't the bottleneck. dev.to's internal feed sent me 2 visits in 7 days — the algorithm has made up its mind. The 184 readers are coming from somewhere else entirely:

• 152 external referrers — GitHub README links, gists, GitHub Topics page entries, Apify Store listing
• 30 bing.com — SEO indexing on specific technical phrases ("refresh-token-only OAuth Apify Actor", "per-feature KVS quota")
• 2 dev.to internal — basically nothing

That means the writing is doing its job as a credibility surface (someone lands on the GitHub repo, sees a dev.to series with 16 build-log posts, decides this is real). It is not doing its job as a discovery surface (dev.to's algorithm does not push my posts into anyone's feed).

The piece I had wrong

I have been pricing my time as if dev.to would compound — write 13 articles, the 14th gets distributed, the 15th gets distributed, snowball. The data says no. Each article is a one-shot inbound to whoever already knew about the repo. The 184 readers are not 184 new prospects; they're 184 visits from the same overlapping pool of GitHub visitors clicking through to read.

Which means the question I should have been asking earlier is: how does the GitHub repo itself get discovered by my actual target reader? Not "how do I get more dev.to algorithm love." Different problem, different funnel, different lever.

What changes

Reduces dev.to publishing cadence to a respectable signal-pace — write when there's something to say, not every day. The output for the dev.to surface to date:

• 13 articles published
• 1 GitHub star earned (thanks again @kuerdy)
• 1 reaction, 2 comments, 0 followers

That's the calibration. If 13 articles + 184 weekly readers translate to 0 followers added, I would be lying to myself to keep going at one-a-day pace. Day 14 might be the last daily entry in this series; the next post comes when there's a real signal change to report.

What does change behavior

The 6-hour CSV measurement loop keeps running. The Apify Store keyword rank (anonymous, no token-auth personalization trap) is the upstream metric that actually predicts whether a real visitor finds the product. That number is what I should have been watching from day 1, not "did I publish today."

Raw data

Every shipped surface, every engagement number, every audit finding from the past 13 days in one gist:
https://gist.github.com/foxck016077/18621168173229819e367fa71a6144ab

The Actor itself: https://apify.com/foxck/gmail-inbox-intel (free, MIT, build 0.1.36).

Cohort note: u/tino8383 on Indie Hackers just posted the same shape — 84 visitors, 0 sales, 3 weeks. There's a recurring pattern across solo launches: the surfaces that work as credibility don't work as discovery, and the metric that's easy to measure (visits) doesn't predict the metric that pays (follow / save / buy). If you're in this pattern, the thread is worth reading — the comments unpack the trap from a few angles.

Build 0.1.38 shipped while writing this: reply_metrics output now includes a priority_band field on every over-SLA thread — HOT (just past SLA), WARM (1.5-3× over), COLD (3×+, use the news-grounded reengage_angle workflow). Summary block returns priority_breakdown: {HOT: 3, WARM: 5, COLD: 12} so Friday triage starts with a one-line urgency split instead of paging through days_since_last_reply numbers. 16/16 tests pass. Changelog: https://github.com/foxck016077/apify-gmail-inbox-intel/discussions/17

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.

Join the pbot waitlist ($29 · first-100 get -30% → $20) →