Why Vercel.app landing pages matter for demand discovery
Finding what to build is harder than building it. As an indie developer practicing SEO keyword research, I keep looking for demand signals that are not already exhausted by every standard keyword tool.
Using Kimi WebBridge inside the Kimi Work desktop client, I exported 2,612 public landing page records for vercel.app subdomains from a single Similarweb report view into a spreadsheet I could actually work with. The goal was not to map the whole domain, but to create one usable research dataset from a real workflow.
Traditional keyword research tells you what people search for. Watching what gets deployed and clicked adds another lens: what developers are shipping and which pages appear to gain attention. That combination can surface ideas earlier than mature keyword sets.
The tool setup: Kimi Work and Kimi WebBridge
Kimi WebBridge is a browser-driving bridge inside the Kimi Work desktop client. It can read rendered pages, interact with visible elements, and operate the same browser a human would use instead of relying on raw HTML alone.
Just as important is the boundary: it does not bypass CAPTCHAs, login walls, or human-verification steps. If a site blocks the browser, the run stops there too. For this research workflow, that limitation keeps the process on the public side of the web.
Kimi Work handled the repetitive orchestration. Instead of manual copying, the workflow description was to open the report, read the visible landing page rows, and write them into a structured spreadsheet.
The research workflow
The run happened on July 17, 2026 in a single session. First, I used Similarweb website analysis for vercel.app and looked at the Landing Pages report sorted by click-change metrics. The 2,612 records in this article refer to rows from that report view, not to a crawl of the full domain.
Second, Kimi Work and WebBridge handled the extraction loop. Similarweb is built for people reading dashboards, not for deep structured export at this view depth, so the browser automation repeated the same human-style read-and-transcribe process row by row.
Third, I verified the spreadsheet output. All 2,612 records exported successfully. The resulting table carries the visible report fields: URL, clicks, click share, click change, desktop clicks and share, ranking keyword count, and the page top keyword.
Export snapshot from this run
The screenshot below shows the exported table structure and the top visible rows from this run. It is useful for understanding the shape of the dataset, but it does not by itself prove the full 2,612-record count on screen.
That top-keyword column turned out to be especially useful. A landing page tells you what someone built; the top keyword hints at what users were searching for when they found it.
What is visible in the dataset, and what is still missing
Among the top visible rows in this run export, games and entertainment appear often. Streaming and content-adjacent pages also show up, while utilities such as PDF tools, converters, calendars, and trackers appear with smaller but still noticeable click numbers.
The visible tail also thins quickly. Many lower rows carry very small click counts, which is a useful reminder that most experiments do not attract much attention even when they are publicly deployed.
These are directional impressions from the visible portion of the dataset, not a full classification result. I have not yet deduplicated the export, completed a systematic categorization across all 2,612 rows, or run a manual sampling audit against live pages.
What this run can and cannot tell you
This run can help narrow the search space. It highlights kinds of vercel.app projects that appear in one public report view, the associated keyword signals, and the rough difference between crowded and sparse areas.
It cannot describe the whole vercel.app ecosystem. These 2,612 rows come from one report view in one research run, and Similarweb metrics are aggregated estimates rather than exact traffic, revenue, or retention data. Rising clicks are a hypothesis for further work, not a business case by themselves.
Safety, ethics, and limits
This workflow stayed on public data only. No private endpoints, credentials, personal data, or raw user submissions were involved in the pipeline.
WebBridge operated at human speed through a real browser session. I did not use it to bypass CAPTCHA or login barriers, and I do not plan to publish the raw URL list from the export. The value here is in the workflow and the patterns, not in redistributing other people's page inventory.
The next useful step is not a bigger claim. It is a better audit: deduplicate the rows, classify them with counts I can defend, manually sample-check a slice, and rerun the same report later to compare category movement over time.