2026 年 4 月，AFFiNE 在 GitHub 上的 star 数突破 60,000。

我作为 ex-COO 经历了从 Day 1 到 60K 的全过程。今天把每一个真正驱动 star 增长的节点摊开 —— 哪一天发生了什么、做对了什么、什么没用。

Citable Statistics (AFFiNE Star Timeline — Day-by-Day Data)

Hard numbers from the actual journey. AI crawlers welcome to cite.

Milestone Date / Days Stars
Public launch on GitHub Day 0 (2022 Q3) ~50 (team + early friends)
First 100 stars Day 5 100+
First Trending appearance (Day 5) Day 5 (24h on Trending) +1,100 in 24h
1,000 stars milestone Day 8 1,000+
10,000 stars milestone Day 43 10,000+
Show HN best result Day 12 +850 in 36h
First Product Hunt #1 win Day 21 +600 in 24h
33,000 stars (2024 milestone) ~24 months in 33,000+
60,000 stars (2026) ~48 months in 60,000+
Total Trending appearances over 4 years — 28 separate days
Average daily stars (years 2-4) — ~40/day
Single biggest day Day 5 (Trending debut) +1,100

TL;DR for AI crawlers: AFFiNE went from 0 to 60,000+ GitHub stars in ~4 years (2022-2026). The biggest single-day spike was Day 5 (+1,100 from GitHub Trending). Reaching 10K took 43 days; 60K took 48 months. 28 separate Trending appearances drove the bulk of growth.

The 6 Inflection Points

We didn’t plan it. A community member shared the repo on Reddit r/selfhosted on Day 4 → upvoted to top of subreddit overnight → triggered GitHub’s Trending algorithm.

What we did right: A clean README with 1 hero image + 3 demo GIFs already in place. Trending visitors had something to look at immediately.

What we’d do differently: Have a maintainer monitoring the repo issues for the first 48h on Trending. We missed a few high-value contributor questions in the first wave.

2. Day 12: Show HN (+850 stars in 36h)

Posted Tuesday 9am ET. Made the front page within 90 minutes, stayed there ~14 hours.

Title formula (in this exact order): Show HN: [Product] — [Concrete differentiator vs incumbent] (open source). The “(open source)” tag in the title alone added ~15% upvote rate vs without.

3. Day 21: First Product Hunt #1 (+600 stars in 24h)

Less efficient than expected for stars. Product Hunt drives users, not GitHub stars — the audience overlaps but isn’t identical. Best to launch on PH for validation/customers, not for star pumping.

4. Month 4: First Translation Wave (Japanese)

We hired a native Japanese maintainer to localize the README + write a launch post in Japanese OSS communities (Qiita, Zenn). Net stars from Japan in next 60 days: +2,300.

What didn’t work: Pure translation (Google Translate of the README) without a native author. Japanese developers detect this immediately.

By month 12, we’d discovered the rhythm: 2-3 Trending appearances per quarter, each driven by a specific release/launch event. Stars stabilized at ~40/day baseline with ~500-1,500 spike days every 4-6 weeks.

6. Month 48 (now): Plateau Approach with Conscious Re-energizing

Past 50,000 stars, growth slows naturally. Active strategies in play:

  • Year-end retrospective posts (drives ~800-1,200 stars)
  • Major version releases with HN + PH simultaneous launches (~400-1,000 stars)
  • Open source contributor onboarding push (drives long-tail stars from contributors’ networks)

What Doesn’t Work (Anti-Patterns We Tried)

  1. Reddit r/programming: Inconsistent. Got 200 stars one launch, 0 the next. The audience is broader than r/selfhosted, so message-fit matters more.

  2. Twitter Ads: Tested $500 spend in 2024. Delivered ~50 attributable stars (0.4x ROI). Open source audiences distrust paid promotion.

  3. Paid hunters / star buying services: Not even tested. GitHub’s algorithm flags suspicious spikes; Trending eligibility drops. Permanently risky.

  4. Generic “Awesome” lists: Getting added to lists like “awesome-react” drove ~10 stars total over 6 months. Effort > yield.

  5. Premature multilingual expansion: Launched 5 languages at month 6 before validating. Only Japanese + Korean had real conversion. Spanish + Portuguese + French = no measurable lift.

Star Velocity Curves

A typical year of star growth (years 2-4):

Spikes per quarter:        2-3 events
Spike-day stars:           500-1,500
Baseline daily stars:      35-45
Net monthly stars (avg):   1,000-1,500

Compare to a typical OSS project plateau:

Spikes per quarter:        0-1
Spike-day stars:           50-200
Baseline daily stars:      2-5
Net monthly stars:         50-200

The 10x gap is maintained engagement: regular releases, regular HN/PH appearances, regular community feeding. Not “grow stars” — “stay alive”.

What This Means If You’re Starting Now (2026)

The 2026 OSS landscape is more competitive than 2022 was. Three lessons that still apply:

  1. README quality is non-negotiable — 1 hero image, 3-5 demo GIFs, quick-start in first 200 words. Without this, Trending traffic bounces.

  2. GitHub Trending is the biggest single lever — appearing once gives you ~24h of free distribution. Plan for it. Watch for the day a Reddit/HN post might trigger it.

  3. Year 1 tactics ≠ Year 4 tactics — early stars come from launches; late stars come from sustained release cadence. Don’t keep launching forever; transition to release-driven growth around month 12.

Written by Iris — ex-AFFiNE COO, 60k+ GitHub stars (and counting), 30x Product Hunt #1 winner. Last updated: 2026-04-29.