Four weeks ago, Batty had zero stars, zero downloads, and zero articles. Here's where it stands today — with real numbers, no spin.
The Dashboard
| Metric | Week 1 | Week 4 | Change |
|---|---|---|---|
| GitHub stars | 1 | 13 | +1200% |
| crates.io downloads | 16 | 76 | +375% |
| GitHub unique visitors (14d) | 27 | 84 | +211% |
| GitHub views (14d) | 127 | 293 | +131% |
| Forks | 1 | 3 | +200% |
| Dev.to articles | 1 | 15 | +1400% |
| X replies from @battyterm | 0 | 53 | — |
These aren't impressive numbers by any standard. But for an early-stage CLI tool with zero budget, they tell a story about what actually works.
What Worked
Dev.to articles (highest ROI by far)
15 articles published across Dev.to and Hashnode. Each one targets a different keyword cluster: "git worktrees AI agents," "sync vs async Rust daemon," "tmux agent orchestration," "AI agent task management."
The compounding effect is real. Google started indexing our articles within days. New stargazers increasingly have no social media presence — they're finding us through search, not X or Reddit. That's the SEO moat doing its job.
What surprised us: article quantity matters more than we expected. Each article is a permanent discovery path. A mediocre article that ranks for a long-tail keyword drives more stars than a viral tweet that dies in 48 hours.
X engagement (highest quality traffic)
53 replies across 18 rounds, targeting AI coding agent and Rust CLI threads. The strategy: add genuine technical value to other people's conversations, never promote directly.
The data: X drove the highest quality traffic. Nearly 1:1 visitor-to-unique ratio — when someone clicks through from an X reply, they're genuinely interested. Most other channels have a much higher bounce rate.
What worked specifically: replying to threads from accounts with 5K+ followers. Our reply appears under their post, visible to their entire audience. A single reply on a 20K-view thread drives more targeted traffic than a standalone post with 100 impressions.
YouTube comments (permanent and indexed)
3 comments on major Claude Code tutorial videos (Cole Medin, Ray Amjad). These are permanently visible, Google-indexed, and sitting under videos with hundreds of thousands of cumulative views. The comments add genuine technical perspective about multi-agent coordination challenges.
What surprised us: YouTube comments are underrated as a discovery channel. They're permanent, contextual, and appear exactly where someone is learning about the problem you solve.
What Didn't Work
Reddit (low engagement)
Three posts across r/commandline, r/rust, and r/rust's weekly thread. Total community engagement: near zero. The posts exist and are indexed, but Reddit's algorithm buried them quickly without early upvote velocity.
What we'd do differently: focus on commenting in existing threads rather than creating new posts. Reddit rewards participation in discussions, not announcements.
Bluesky (near zero traction)
Minimal traffic from Bluesky despite having an account. The developer audience there is growing but still small compared to X. We deprioritized it.
Show HN (blocked)
Our Show HN was posted but the author's account got flagged, killing all engagement. The post itself was solid — good title, strong first comment, architecture details. The account issue was the blocker, not the content. We'll retry with a clean account when timing is right.
What Surprised Us
Stars come from search, not social
Our most recent stargazers have no public X handles, no social profiles on GitHub. They're finding us through Google → Dev.to article → GitHub repo → star. The content moat works even when nobody shares it.
The 3-5 article threshold
After publishing 5 articles, Google started treating our Dev.to profile as authoritative for multi-agent coding topics. Articles published after that threshold indexed faster and ranked higher. The first 5 were an investment; articles 6-15 are compounding returns.
Consistency beats virality
No single piece of content "went viral." Our growth is steady: 2-3 stars per week, 5-10 new unique visitors per day. The consistency of publishing daily, replying in threads, and showing up in the same conversations created recognition. People started seeing @battyterm in AI agent discussions and clicking through.
The Honest Truth About Stars
13 stars in 4 weeks with zero budget is slow. Projects with existing audiences, Product Hunt launches, or HN front-page hits can get 100+ stars in a day.
But those stars come from a spike of attention that fades. Our 13 stars came from people who actually tried the tool or read the architecture deep-dive. The retention of attention matters more than the volume.
What's Next
- 15 stars is the next milestone (2 away)
- awesome-rust requires 50 stars — that's our medium-term target
- This Week in Rust PR is pending review
- SaaSHub listing is submitted (DR 77 backlink)
- Product Hunt launch prep is done but gated on 15+ stars
The strategy doesn't change: publish articles, engage in conversations, let search do the compounding. No shortcuts.
The tool: github.com/battysh/batty — supervised agent execution for software teams.
If you're building in public, I'd love to compare notes. What channels are working for you?
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