I built an open-source Chrome sidebar for AI workflows. Here is what 6 weeks of solo dev taught me about distribution.
AI Buddy is a free, open-source Chrome extension that turns selected page text or annotated screenshots into prompts for ChatGPT, Claude, Gemini, DeepSeek, or Copilot. It lives in the Chrome side panel, so the source page stays visible while the AI conversation opens next to it.
I shipped the first version 6 weeks ago. Some honest notes for anyone building a similar tool:
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The product is not the hard part. Wiring a side panel and a content script takes a weekend. The hard part is finding the first 10 users who actually need it.
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Distribution channels vary in conversion per post. Posting in r/ChatGPTCoding with a real builder story got me one install. Posting in r/ClaudeAI with a screenshot workflow got me two. Posting in r/LocalLLaMA cost me a karma ratio I had to recover from. I now track every post against a CWS UTM link so I can see which channel actually converts.
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The CWS listing is the conversion node. If your store title is just the project name and the description is generic, people bounce. A listing that says "select text, send to ChatGPT, no copy paste" converts better than a listing that says "AI productivity assistant" with no specifics.
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Open source earns trust in a way paid tools do not. I see this in the comments more than I see it in installs. People say "I would not have tried this if I could not read the source" more than they say "the price is right." That is a moat, not a sales line.
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The next thing I am testing is the difference between a weekly build-in-public post and a daily reply-and-upvote grind. The first is harder to write, but I expect it to compound. The second is easy to do and I am not sure it moves installs.
If anyone is shipping a similar tool, the things I would love to compare notes on: side panel vs popup, screenshots vs text-only, and which AI destinations actually drive install clicks.
GitHub: https://github.com/mnbqwe10/ai_buddy
Chrome Web Store: https://chromewebstore.google.com/detail/ai-buddy/eigpaeoigklelmfgnkljhbjjbpohenpn
Answering the three open questions from the post after 6 weeks of user telemetry:
Side panel vs popup: side panel wins on retention, popup wins on first-click ease. Side panel keeps the source page visible while the AI conversation opens next to it, which matters when the trigger is "explain this chart" or "rewrite this paragraph" — the user goes back and forth. Popup feels snappier the first time but people stop using it after 2-3 days because they have to remember where the icon is.
Screenshots vs text-only: both, not either. Text-only triggers fire on selection, screenshots fire when there is no clean text (charts, dashboards, error UIs, code review screenshots). The volume of screenshot triggers was about 30% of my real usage — higher than I expected.
AI destinations that drive repeat use: the breakdown from my UTM tracking — ChatGPT ~45% (default mental model), Claude ~25% (long context), Gemini ~15% (grounded / web queries), DeepSeek ~10% (cost-sensitive code), Copilot ~5%. Same trigger, per-question routing is what makes it survive the next vendor change.
One thing I wish I had instrumented earlier: which AI destination leads to a second use within 7 days, not just first install. That is the repeat-use number, not the install number.
UTM tracking from day one is advice everyone hears and almost nobody actually does until they've already wasted a few weeks not knowing which channel is doing anything. By the time you realize you need the data, you've lost the window to collect it retroactively.
The concrete versus vague Chrome Web Store listing point is something a lot of extension builders get wrong because they write the listing like a feature list instead of describing the exact moment someone would reach for it. Specific beats impressive every time in a store full of vague promises.
The CWS listing line is exactly right. "Select text, send to ChatGPT, no copy paste" is concrete enough that someone can picture the workflow in two seconds. For tools like this, cutting out one annoying step usually matters more than sounding vaguely AI-powered. That's part of what I learned building DictaFlow too. People buy the saved motion, not the abstraction.
The UTM tracking per channel is smart. Most people guess which channel works. You're measuring it. The build in public post vs reply grind comparison is the real experiment. One compounds, the other doesnt. I've seen the same with IH comments vs posts.
The UTM tracking point is the one I would double down on. For AI workflow tools, I think there are really two conversion loops to measure: which channel brings installs, and which AI destination/model path actually becomes repeat usage.
Side panel vs popup is a UX question, but ChatGPT vs Claude vs Gemini vs local/open models quickly becomes an economics question too. Once users start sending larger screenshots or page chunks, it helps to know which workflow, model route, retry, and fallback created the spend instead of only seeing the final API bill.
That is the part we keep thinking about with Tokens Forge: if a product gives users easy AI access, the operator still needs a ledger behind it that explains route, model, balance bucket, and per-workflow usage. Otherwise it is hard to tell whether a distribution win is also a profitable workflow.
Really insightful post, this. I have experienced #4 myself. Browser extensions especially, open source isn't just about the "price," it's about the trust of not being a black box.
I’d definitely vote for the side panel over a popup. Being able to keep the source text in view while the AI works next to it is the only way this actually fits into a real workflow without the constant "copy-paste-toggle" fatigue.
The biggest takeaway wasn't the extension—it was treating distribution like an experiment instead of guesswork. Tracking every channel with UTMs and letting the data decide where to invest is a much better approach than chasing engagement metrics alone.