6 GitHub stars doesn't sound like much. But I didn't run a Show HN. I didn't launch on Product Hunt. I didn't email any newsletters. I just published an npm package and watched what drove people to the GitHub repo. The source of those stars surprised me.
The setup
textlens is a zero-dependency text analysis library for Node.js. It launched March 4. One command gets you Flesch-Kincaid readability scores, sentiment analysis, keyword extraction, and sentence statistics — no API calls, no setup, no config file.
npm install textlens
const textlens = require('textlens');
const result = textlens.analyze('Your text here.');
console.log(result.readability.fleschKincaid); // { grade: 8.1, readingEase: 62 }
As of today: 6 GitHub stars, 82 npm downloads this week, 15 dev.to articles totaling 355 views.
What I expected to drive stars
The strategy going in: publish a lot of dev.to content, get readers, convert some to GitHub stars.
I published 15 articles over 12 days. 355 total views. My best performer — "I Built a Free Hemingway Editor Alternative That Runs in Your Terminal" — hit 122 views on its own. I expected dev.to to be the main driver.
I also did a Product Hunt launch. 3 upvotes. I don't count that as a meaningful signal.
A Show HN was planned but never happened. The queue felt premature.
What GitHub referrer data actually showed
Here's the 14-day referrer table, pulled directly from the GitHub traffic API:
| Referrer | Views | Unique visitors |
|---|---|---|
| echojs.com | 36 | 18 |
| github.com | 32 | 3 |
| ckmtools.dev | 22 | 3 |
| 6 | 6 | |
| npmjs.com | 2 | 1 |
| Bing | 1 | 1 |
Echo JS is the top external traffic source by unique visitors. Not dev.to. Not Hacker News. Not Reddit. Echo JS.
The traffic pattern made the picture clearer. On March 10, GitHub views spiked to 23 unique visitors in a single day — that's more than the entire previous week combined. Days 11 and 12 stayed elevated. Those three days account for the majority of the 18 unique Echo JS visitors.
Stars went from 1 to 6 during that same 72-hour window.
dev.to sent 0 visitors to github.com/ckmtools/textlens despite 300+ article views.
Why Echo JS worked
Echo JS (echojs.com) is a developer news aggregator focused specifically on JavaScript. It's small, text-only, and curated. The audience is developers browsing for something interesting to look at — usually during work.
The difference from dev.to is the context. On dev.to, people read articles. On Echo JS, people discover things. When someone lands on Echo JS, they're in "what should I click?" mode. They follow links. They open GitHub repos. They star things they want to remember.
dev.to has the views but not the click-through. 122 views on my Hemingway article meant 122 people read the article. Almost none of them navigated to GitHub. The call-to-action worked fine inside dev.to's reading environment — it just didn't convert to GitHub behavior.
Echo JS has fewer views, but the viewers are primed to act on what they find.
What this changed about my strategy
Before looking at the referrer data, I was optimizing for dev.to views. After, I changed four things:
- Submit every article to Echo JS within the same hour of publishing. The traffic spike is time-sensitive — the front page moves fast, and visibility drops after a few hours.
- Write for discovery, not volume. One article that lands on Echo JS's front page outperforms ten articles that don't.
- Include 3+ GitHub links per article. Reader intent matters less than surface area. More links = more chances someone navigates over.
- Track GitHub referrers from day 1. npm downloads tell you about installs. GitHub referrers tell you about intent. They measure different things.
The npm download data (82 downloads this week) is encouraging, but it doesn't tell me where the interest is coming from. The referrer data does.
The takeaway
A zero-star package is invisible. Getting to 6 wasn't about volume or luck — it was about finding the channel where the right kind of attention lands.
If you're building an npm package and only tracking npm downloads, add GitHub referrer data to your monitoring. The distribution of traffic sources will probably surprise you.
More of what I've been building: github.com/ckmtools/textlens
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