From Problem to 100 Users in 6 Weeks With Zero Budget
Six weeks ago I was two hours into a debugging session with Claude when it cut me off. No warning. No countdown. Two hours of context gone.
I searched for a tool that would show me how close I was before it happened. Found nothing that worked across more than one AI platform without requiring an API key or a separate dashboard.
So I spent six weekends building one.
This is the honest story of going from that frustration to 100 active users with zero marketing budget.
Week 1-2: Building the thing
My first instinct was wrong. I started designing a web dashboard where users could log in and see their AI usage. Three days in I stopped and asked: why would someone open a separate tab to check information they need while they are already inside Claude?
The right answer was obvious: put it directly in the page they are already on. A slim bar above the input box. Always visible, never in the way.
That realization saved me from building the wrong product entirely. The bar was simpler to build, more useful, and required no account creation.
I built the first version in two weekends. Claude only. Basic context window percentage and a rough rate limit estimate. I was using it myself every day by week two.
Week 3: The first real users
I did not do a launch. I pushed it to the Chrome Web Store and posted one comment on a Reddit thread where someone complained about Claude cutting them off. I linked the extension and explained what it showed.
That comment got 23 upvotes and drove the first 15 installs.
The lesson: the best distribution is being in the right place at the right time with the right answer. Not a launch. Not a product page. A genuine answer to someone who has the exact problem you solved.
I started watching r/ClaudeAI, r/ChatGPT, and r/webdev for posts mentioning rate limits or lost context. When I saw one I replied genuinely — with an explanation of how Claude's rate limits actually work, followed by a mention that I had built something to track them.
This drove roughly 30 installs over two weeks. Slow, but exactly the right users.
Week 4-5: Platform expansion
Most common question in early DMs: "does it work on ChatGPT?"
One weekend to add ChatGPT. Installs went from 30 to 55 in the week after.
Then Gemini and DeepSeek. Active users 55 to 70.
The pattern became clear: platform breadth drives installs more reliably than marketing at this stage. Each new platform is access to a new community of users who have the core problem.
Week 6: 100 users
Added Grok. Launched token-pulse.in as a proper marketing site. Active users crossed 100.
What actually drove installs
Reddit comments on relevant posts: ~45 installs
Highest-converting channel. One genuine helpful comment on a post where someone has the exact problem drives 2-8 installs reliably.
Chrome Web Store organic search: ~30 installs
People searching "Claude token tracker" directly. I did not drive these — store SEO did.
Platform announcements via extension updates: ~20 installs
Each new platform, existing users spread the word organically.
Personal LinkedIn (8.2k followers): ~10 installs
Lower than expected. LinkedIn audience not highly correlated with Chrome extension users.
Company social accounts: ~0 installs
Weeks of posting. Near zero measurable impact. No audience means no reach.
The mistakes
Building the dashboard first wasted three days. The tips panel nobody uses wasted two days. Posting on company accounts with no followers wasted weeks. Not asking for reviews until week five cost ranking momentum.
What is next
Pro tier: 90-day history, rate limit predictions, cross-device sync, VSCode extension, team features. Waitlist open at token-pulse.in.
Goal for month two: 500 installs and first paying Pro users.
Install TokenPulse free — Claude, ChatGPT, Gemini, DeepSeek, Grok.
Great breakdown, and the channel table is the useful part. One flag on "platform breadth drives installs more reliably than marketing," because it's true today and it quietly compounds the rented-moat problem from before: every platform you add is another company whose internals you depend on and don't control. Breadth drives installs, yes — and it also multiplies the number of landlords who can break you overnight. You're scaling acquisition and structural fragility with the same move. Width, when the thing you'd actually own is depth (the cross-tool timeline nobody can revoke).
But there's a sharper read of your own data hiding in it. Your #1 channel (Reddit comments, 45) and your platform-breadth strategy are the same mechanism: each new platform unlocks a new community — r/ClaudeAI, r/ChatGPT, r/Bard — where your best play (genuine answer to someone with the exact problem) works again on fresh people. So platforms aren't really the growth lever. Community access is. The platform is just the key to a room full of people with your problem.
That reframes the ceiling. It's not "how many AI platforms exist," it's "how many communities are loudly complaining about rate limits and lost context." Which tells you what to chase: not every model, but the platforms with the angriest, most active complaint threads, because that's where your one repeatable channel refires. A platform with the problem but no community to post in gives you install ceiling near zero.
So which of your five platforms drove installs per community-comment best, and is that the one with the most active rate-limit complaints? That ratio, not platform count, is your real map.
That’s a fair distinction. I probably framed “platform breadth” too much as the growth lever when the underlying lever is really access to communities where the problem is already being discussed.
I also agree on the rented-moat point. Adding platforms helps distribution today, but it also increases dependency on each ecosystem. The longer-term value I’m aiming for is the cross-platform usage history and intelligence layer, rather than simply supporting more platforms.
I haven’t tracked installs per community-comment rigorously yet, so that’s a good metric to add. My initial data is still small, but I’ll break it down by platform/community rather than looking only at total installs. That should make it much clearer which ecosystems are actually worth supporting.
The acquisition signal is pretty clear. The harder test now is monetization—do active users care enough about prediction/history/sync to pay, or are those features just nice-to-haves?
Exactly. I think acquisition is the least interesting question now because 100 active users can still mean very little commercially.
The next experiment is basically whether the features solve a painful enough problem to justify payment. History and sync may be useful, but prediction is the feature I’m most interested in testing because it potentially changes the product from “usage visibility” to “help me avoid getting interrupted.”
I’d rather validate that with a small number of paying users before building out the full Pro roadmap. If users consistently say “useful, but I wouldn’t pay for it,” that’s a much more valuable signal than another 500 free installs.
That willingness-to-pay test is the interesting next step. If you’re open to it, what’s the best email to reach you on?
Sure you can reach out at anup17508@gmail.com
The platform-expansion jump is a useful signal: it suggests the core job is stable, while each ecosystem supplies its own distribution. I’d watch activation by platform separately (install → first useful reading → return in week two) before optimizing for total installs; a new platform can inflate the top line while adding support cost. Did you see the same retention pattern across Claude, ChatGPT, Gemini, DeepSeek, and Grok, or is one ecosystem carrying most of the active usage?
This is something I should have tracked from the beginning.
Right now I’ve been looking primarily at installs and active users, so I don’t have a clean install → first useful reading → week-two retention breakdown by platform yet. That’s a gap in my analytics.
My suspicion is that the core use case is similar across platforms, but the frequency and severity of the problem probably aren’t. I’m going to start separating activation and retention by platform before adding more ecosystems.
I’d rather find out that one platform has 2x the retention of the others than keep adding platforms just to increase the top-line install number.