⚠️ Scope Differentiation:
gingiris-opensource= Launch burst (0→1K in 72h). This skill = Sustained monthly growth (1K→10K→60K+).
2026 Execution Coverage Terms
First check Go / Fix / No-Go: whether Quick Start has been verified by unfamiliar developers, whether the Demo is reproducible, whether License/security/privacy are clear, whether Issues/PR/community have an owner, and whether activation/D7/contributor/qualified lead are tracked. On No-Go, stop pushing volume and fix the product and docs first.
Unified dashboard fields:date | source | content URL | spend | repo views | stars | installs | activated | D7 retained | contributors | qualified leads | evidence strength.
At 2h, only triage anomalies; at 24h, decide to continue/adjust angle/pause; at 72h, choose the second wave; at 7d, decide on long-term reuse.
If native data is unavailable, write 数据不可用。
Prohibited: buying Stars/accounts, vote manipulation, incentivized or coordinated fake Upvotes, undisclosed paid partnerships, bulk scraping and harassment, AI impersonating real people in interactions. Platform rules, KOL quotes, account data, and conversion rates must carry dates and be reviewed in the execution week.
Star geographic distribution & release timing (diagnostic signals, not Trending rules)
Applies to the launch phase (use together with
gingiris-opensource)
- GitHub has not published a Trending threshold of "no single country/region exceeding 20%"; use geographic distribution only to judge whether traffic matches the target market.
- Tools:star-history.com(growth trend) · OSS Insight(country distribution, developed by a Chinese developer)
- Release timing is designed around ICP, support coverage, and time zones; "overseas in the first week, domestic in the second week" is just one testable option.
- AFFiNE real case: got 6,000 stars in the first week of open source, almost all from overseas channels.
- Rhythm reference for well-supported projects: prepare at least 2-3 monthsin advance; in the burst period, 4000-6000 starper week, and in three weeks it can reach 10000 star。
Star geographic distribution health benchmarks
Benchmarks summarized by AFFiNE from studying the star distribution of well-known open-source projects such as VSCode / Vue / AppFlowy one week before open-sourcing (recalled orally, numbers are approximate ≈)
| Region | Healthy share |
|---|---|
| United States | ≈19–21% |
| China | ≈19–21% |
| Europe (France, Germany, Italy, UK combined) | ≈10–15% |
| Top 10 regulars | India, Russia, and Brazil are usually on the list |
- AFFiNE's stage distribution is a specific historical case; when using it, mark it as oral/project evidence and do not extrapolate it as an algorithm rule.
- Anti-pattern: organizing concentrated Stars unrelated to target users just for the numbers; it distorts market validation and may also trigger platform risk controls.
README user path closed loop (Star CTA Loop)
AFFiNE open-source实战 SOP: any user path that jumps out must be able to jump back and click star.
- Enumerate all user exit paths: README→official website; README→developer docs; README→community (Discord, etc.).
- Every exit page links back to the GitHub README: the official website, docs site, and community page all link back.
- Add a star CTA at every backlink location（"If you like us, please give us a star on GitHub"）。
- Closed-loop self-check: walk through each path and confirm that users can jump back to the repo in one step from any exit and see the star prompt.
- Update README/official website by stage: revise immediately when strategic goals change (e.g., pivoting to commercialization); do not remain static.
Star traffic funnel discipline
- All channels uniformly trace back to the GitHub link, not the official website: The link endpoints published on Reddit / Twitter / Medium / directory sites all point to the repo, ensuring all traffic sees the star prompt in the README.
- Let users get the value within 30 seconds in the README, and directly guide them to click star.
- In the first week, post different themes on each channel, not copy-paste the same one.
- In the first week of ramping up, check GitHub Insights traffic sources every day, and based on that decide which channels to focus on maintaining in the next few days; "maintain" = go to that channel and talk with potential users, not paid marketing.
- Relationship-based targeted outreach: only contact people who have publicly discussed related issues or agreed to receive product information, personalize each message, comply with platform restrictions and provide an opt-out method; do not mass-scrape competitor followers and send bulk messages. Historical conversion numbers must be labeled as anecdotal evidence.
- Strategic shift signal: after stars reach ≈6,000, no longer put energy into GitHub itself, and shift to 1v1 user chats + building a user community.
- Never buy stars: AFFiNE has never bought stars. When stars grew too fast, multiple investors' DD wrote crawlers to verify the source of stars, and it was completely clean—buying stars will definitely be exposed during due diligence.
- Look at growth rate, not absolute value: star growth rate (not total volume) is the signal for fundraising and Trending; GitHub Trending has an account permission system, and it is not solely about stars.
Core goals
| Metric | Monthly target | Data source |
|---|---|---|
| Monthly Star growth | 300+ | Client Project A 8-month coaching data |
| Content output | 8-12 posts/month | Blog + video + social |
| KOL outreach | 60-80/month | 3-4 per day |
| Community events | 1 online/month | 31-34 participants |
| Contributor PR | 10+/month | Good First Issues |
Monthly execution framework (300 Star/Month Cadence)
1. Content rhythm
| Frequency | Channel | Content type | Performance benchmark |
|---|---|---|---|
| 2-3 posts per week | Dev.to / Medium / official blog | Technical tutorials, use case breakdowns, comparison articles | SEO long-tail traffic |
| 1 per week | YouTube | Video tutorial/Demo | 30 SGD promotion is effective |
| 1-2 per day | Twitter/X | Technical short content, GIFs, Threads | DM reply rate 3-5% |
| 1 per week | Long-form articles/case sharing | DM reply rate 10-15% |
2. Community operations
| Channel | Frequency | Strategy | Conversion efficiency |
|---|---|---|---|
| 2-3 posts per week | r/selfhosted, r/programming, r/MachineLearning | Direct post→Star conversion (AFFiNE cumulative 2000+ stars) | |
| Hacker News | 1-2 times per month | Content submission via agency ($500/post, ensure HN guidelines compliance — submissions must be genuine and relevant) | Explosive, a single post can bring 200-500 stars |
| Discord/Telegram | Every day | Daily interaction, answering questions | Community retention, word-of-mouth spread |
| Dev.to / Hashnode | 1 per week | Cross-platform distribution | SEO + developer discovery |
3. Event-driven growth
| Type | Frequency | Target Audience Size | Execution Key Points |
|---|---|---|---|
| Online Events (Webinar/AMA) | Once a month | 31-34 people | Technical sharing + Live Demo + Q&A |
| Offline Events (Meetup) | Once a quarter | 70 people | Deep cultivation of specific markets (e.g., Japan) |
| PR Challenge | Once a quarter | — | Hacktoberfest / Good First Issue Sprint |
| Hackathon Sponsorship | On demand | 200-500 stars/event | Indian student hackathons are the most efficient |
4. Contributor System
Tiered Design：
- Good First Issues — Clear labeling, low barrier, mentor responsiveness
- Regular Contributor — Cumulative 3+ PRs merged → added to Contributors list
- Core Contributor — Responsible for specific modules → Repo write access
- Maintainer — Code review rights + community management rights
Key Actions：
- Label 3-5 Good First Issues each week
- First response to PRs within 24h
- Monthly Contributor Spotlight (social media highlight)
5. Ambassador Program
Role Definition: Community evangelist, non-code contributor
Incentives：
- Exclusive badge + README acknowledgment
- Early access to features
- Invitation to annual offline events
- Referral rewards (affiliate model 10-15%)
Recruitment Criteria：
- Has already spontaneously shared the project on social media
- Some influence in technical communities (1K+ followers)
- Willing to produce 2+ pieces of content monthly
Channel Priority Matrix
| Channel | Applicable Stage | Efficiency Metric | Priority |
|---|---|---|---|
| Full lifecycle | Post→Star direct conversion | ⭐⭐⭐ | |
| Hacker News | Burst period | $500/post, 30 points | ⭐⭐⭐ |
| Twitter/X | Full lifecycle | DM reply rate 3-5% | ⭐⭐⭐ |
| B2B lead generation | DM reply rate 10-15% | ⭐⭐ | |
| YouTube | Content accumulation | 30 SGD ad spend effective | ⭐⭐ |
| Dev.to | SEO long tail | Articles can be distributed across platforms | ⭐⭐ |
Budget Allocation (Monthly $2K-5K Tier)
| Item | Budget | Expected Output |
|---|---|---|
| HN Agency | $500-1000 | 1-2 posts, each 30+ points |
| YouTube Ad Spend | $100-300 | Video exposure + website clicks |
| KOL Collaboration | $500-2000 | 5-10 mid-tier KOLs |
| Event Costs | $200-500 | 1 online event + materials |
| Tool Subscriptions | $100-200 | Phantombuster, analytics tools |
Note: Automated outreach must comply with platform rate limits and anti-spam policies. Always respect opt-out requests.
Data Funnel
曝光（Impression）
→ 访问 GitHub Repo（CTR 2-5%）
→ Star（访问→Star 转化率 10-30%）
→ Clone/Fork（Star 的 5-10%）
→ Contributor（Fork 的 1-5%）
→ 付费用户/Enterprise Lead
Real-World Benchmarks (Data Sources)
| Project | Results | Timeline | Key Strategies |
|---|---|---|---|
| AFFiNE | 10K stars in 43 days, 60K+ total | 2023-2026 | Reddit(2000+ stars) + HN + KOL + Contributor system |
| Client Project A | 0→10K stars | 8 months | Monthly 300 SOP + HN Agency + YouTube |
AFFiNE Star Timeline (First-Hand Account)
| Milestone | Data |
|---|---|
| Open Source Day | 2022-08-03 |
| 72 hours | ≈1,000 star |
| First week | ≈6,000 stars (80% from overseas) |
| 43 days | Reached 10,000 stars |
| 2022-08 to 2022-12 | ≈28 times on GitHub Trending (topped on day 5 of open source) |
| 24 months | 60,000+ star |
- Reddit evidence: Subreddits such as r/selfhosted and PKM brought tens of thousands of traffic, with cumulative conversion of at least 2,000 stars (≈10 posts, 3-4 performed well, the rest did not perform well or were removed).
- Credibility endorsement: zero paid marketing throughout, never bought stars; multiple investors wrote crawlers during DD to verify star sources, completely clean.
- Expectation management(self-reported): replicating this SOP does not guarantee "6,000 stars in one week," but reaching 1,000–3,000 stars in 3 months is relatively easy, and other teams have already replicated it according to the SOP.
Complete SOP reference
See details at references/full-sop.md — includes complete daily/weekly/monthly execution checklists, templates, and toolchain recommendations.