When I shipped Trooper, a privacy-aware LLM proxy written in Go, I didn't have a marketing plan. I had GitHub traffic analytics and a habit of checking them obsessively.
Seven weeks later, I have something more useful than a viral moment: a ranked table of every traffic spike, what caused each one, and proof that the exact same playbook that worked at launch still works when you have something new to say.
What is Trooper?
Trooper sits between your app and your LLM provider. When your cloud quota runs out, it automatically falls back to a local Ollama instance with zero code changes on your end. It also tracks session context, so your agents don't go blind between calls.
It's not a chatbot wrapper. It's plumbing. Which makes the distribution story more interesting, because plumbing doesn't go viral the way demos do.
The Data
GitHub gives you 14-day rolling windows for clones and views. I screenshotted them obsessively and tracked every spike. Here's the full ranked table:
| Rank | Date | Clones | Unique Cloners | Views | Unique Visitors | Driver |
|---|---|---|---|---|---|---|
| 🥇 1 | May 13 | 375 | 173 | 1,113 | ~140 | Reddit wave peak |
| 🥈 2 | May 10-12 | 312 | 137 | 974 | 133 | Reddit launch spike |
| 🥉 3 | Jun 10 | 289 | 124 | 749 | 101 | "Escalate the model" r/ollama post |
| 4 | Jun 11 | 268 | 112 | 840 | 95 | Decaying from Jun 10 spike |
| 5 | Jun 12 | 240 | 99 | 739 | 74 | Decaying from Jun 10 spike |
| 6 | Jun 9 | 175 | 102 | 802 | 100 | Organic |
| 7 | Apr 25 | 174 | 71 | 664 | 113 | Early Reddit posts |
| 8 | Jun 7 | 171 | 110 | 876 | 110 | Organic recovery |
| 9 | Jun 6 | 163 | 104 | 755 | 102 | Organic recovery |
| 10 | May 29-30 | 122 | 73 | 610 | 83 | LinkedIn post |
| 11 | May 25 | 76 | 48 | 495 | 53 | Claude Code integration chat |
What I learned
1. Reddit is the only thing that moved the needle, and community fit matters more than size
The #1 and #2 peaks were both Reddit-driven. On May 10-11, I posted across r/ollama, r/LocalLLM, r/ClaudeCode, and r/Gemini simultaneously. Total views across those posts: ~7,000.
r/ollama alone drove nearly 4,000 of those views. Not r/LocalLLM. Not r/ClaudeCode. r/ollama, the smallest of the four communities.
The reason: Trooper solves an Ollama-specific problem. Quota exhaustion hitting your local Ollama fallback is something that community lives with daily. Posting to a larger but less relevant community got less traction, even with identical content.
Lesson: Precision beats reach. Find the subreddit where your exact problem is a lived experience, not just a relatable concept.
2. The post that doesn't feel like a post performs best
The r/ollama post that drove the May peak wasn't "I built a thing, please star it." It was structured around the problem first:
"I kept hitting Claude quota limits mid-session and losing context. So I built a proxy that falls back to Ollama automatically."
Nobody wants to read a launch announcement. Everyone wants to read about a problem they recognize.
Lesson: Lead with the pain, not the product.
3. Organic discovery is real, and it compounds quietly
Ranks #3 and #4 (the highest views since the May peak) happened with zero posts in the preceding two weeks. Pure organic discovery.
The referring traffic breakdown tells the story: github.com is the top referrer, meaning developers are finding Trooper by browsing related repos. Google is sending traffic too. Someone is searching for "LLM proxy ollama fallback" and landing on Trooper.
This doesn't show up immediately. It built slowly over six weeks. But it's now driving more daily traffic than the LinkedIn post did.
Lesson: SEO and GitHub organic discovery are slow, but they compound. Write a good README. Use keywords people actually search for.
3.5. The playbook worked a second time, on purpose
Three weeks after writing the first draft of this article, rank #3 happened: 289 clones and 124 unique cloners in roughly 24 hours. Second only to the original launch peak, and bigger than the entire Reddit launch week for views-per-day.
This time it wasn't a mystery. I had shipped a new feature: smart escalation, where Trooper bumps a request up to a bigger model mid-conversation when the local model can't handle it, instead of dropping context and starting over. I wrote it up using the exact same framing from lesson #2, problem first, no "I built a feature" language, and posted it to r/ollama again.
The title was "Escalate the model, not the conversation". Same subreddit as the original launch. Same problem-first structure. The referring sites confirmed it: reddit.com and com.reddit.frontpage combined for over 60 views and 16 unique visitors arriving via Reddit's front page.
Lesson: the launch playbook isn't a one-time unlock. It's a template. Same community, same problem-first framing, new thing to say, comparable result. The hard part was never "how do I get r/ollama's attention once", it's having something genuinely new and useful worth bringing back to them. If you ship something that actually matters to the community you launched in, the same channel that worked once will work again.
4. LinkedIn drove less than you'd expect
The May 29-30 LinkedIn post landed at rank #5. It did move the needle, 34 more clones than the pre-post baseline, but the effect faded within 48 hours and the unique cloner ratio was lower than Reddit.
My read: LinkedIn audiences share content but don't clone repos at the same rate as Reddit or HN. They're evaluating Trooper as a concept, not as a tool they're about to run.
Lesson: LinkedIn is good for reach and credibility. It is not a clone driver.
5. The view-to-clone ratio tells you who's actually landing
During the Reddit peak (May 13): ~140 unique visitors, 173 unique cloners. Ratio above 1.0, meaning people were cloning on multiple machines, or recommending it to colleagues.
During the organic recovery (Jun 7): 111 unique visitors, 106 unique cloners. Near 1.0, meaning developers landing on the page are converting almost immediately.
During the LinkedIn period: ratio dropped. More browsers, fewer cloners.
High view-to-clone ratio means right audience. Low ratio means wrong audience or unclear value prop.
What's next
The biggest shift in how I think about distribution: it's not "do one big launch and hope it compounds." It's "every time you ship something that solves a real problem for the community you launched in, go back and tell them, the same way you did the first time."
The escalation feature post worked because it followed the same rules as the original: problem first, no launch language, posted to the subreddit where the problem is lived daily. Two data points isn't a lot, but it's enough to convince me this is a repeatable loop, not a one-time stroke of luck.
I'm also shipping BRIEFING, a feature that lets agents carry context forward across sessions by reading a structured log on startup. Zero instrumentation, the agent doesn't know it's happening. Same plan as before: ship it, frame the problem it solves, post it to r/ollama.
If you're building open source infrastructure tools and wondering where to start with distribution: r/ollama if you're in the Ollama ecosystem, r/LocalLLaMA if you're broader. Write about the problem before you write about the solution, every time you have something new, not just at launch.
Trooper is open source and MIT licensed: github.com/shouvik12/trooper
Happy to answer questions about the proxy architecture, the session handling, or anything else in the comments.
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