The problem I stumbled onto
While building a personal AI‑assisted code editor, I kept seeing a tiny but irritating pause every time the model finished generating a suggestion. The UI would freeze for a second, the spinner would spin, and the user had no clue what was happening. In isolation it seemed harmless, but when the same pattern repeated across dozens of calls, the cumulative friction added up. I started to wonder: could that idle time be turned into a feature instead of a nuisance?
Validating the need
The first step was to confirm that other developers felt the same way. I posted a short survey on a few Discord channels and on Reddit's r/Programming. The response was surprising: over 70% of respondents said they had abandoned a suggestion because the wait felt endless, even though the actual delay was only a couple of seconds. A few even mentioned that they would pay for a smoother experience, especially when they were on a deadline.
I also looked at analytics from the beta version of my editor. The heatmap showed a spike in cursor movement away from the editor exactly when the spinner appeared. That data point gave me enough confidence to treat the wait-state as a real friction point rather than a minor UI glitch.
Building the minimal service
Instead of rewriting the whole inference pipeline, I built a separate microservice that could pre‑fetch the next suggestion while the user was still typing. The service worked in three steps:
- Detect the end of a code block.
- Send the partial code to the LLM in the background.
- Return the suggestion as soon as the user hit the next line.
The service was written in Go, deployed on a cheap VPS, and communicated with the editor via a simple WebSocket. I kept the implementation deliberately small - about 200 lines of code - because the goal was to test the idea, not to create a production‑ready system.
Pricing experiments
With a functional prototype in hand, I opened a private beta and offered two tiers:
- Free tier - the original wait‑state behavior.
- Pro tier - the pre‑fetch service, advertised as "no‑pause suggestions".
I set the Pro price at $5 per month, hoping the low barrier would attract curious users. To avoid alienating existing users, I gave the first 20 sign‑ups a free month of Pro access.
The conversion rate was modest - about 8% of active users upgraded - but the revenue was enough to cover the server cost and left a small profit margin. More importantly, the feedback was overwhelmingly positive. Users reported feeling more in flow and appreciated the transparency of the wait indicator.
Lessons learned
Small friction can be a monetization hook - Most developers think only big problems are worth paying for. In reality, a single second of uncertainty can be enough to justify a modest subscription.
Validate with real behavior, not just opinions - Combining survey data with usage analytics gave me a clearer picture than either source alone.
Keep the MVP tiny - By focusing on a single function (pre‑fetch), I avoided over‑engineering and could iterate quickly based on user feedback.
Pricing doesn't have to be complex - A flat monthly fee worked well for this niche. Tiered or usage‑based pricing would have added unnecessary confusion.
Closing thoughts
The experiment taught me that even a brief idle period in an AI‑driven tool can be turned into a value proposition if you frame it as a productivity enhancer. I still have many ideas for expanding the service - things like predictive linting or on‑the‑fly refactoring - but the core lesson stays the same: listen to the moments where users hesitate, and consider whether that hesitation is a problem you can solve.
If you're curious about trying a ready‑made version of this concept, check out WaitSpin (https://waitspin.com). It offers a plug‑and‑play API that handles the pre‑fetch logic for you, letting you focus on the UI instead of the backend.
Takeaway: Before you start building a full product, pinpoint a concrete wait‑state or friction point, validate it with real data, and test a tiny service on a simple pricing model. The results may surprise you.
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