"My finding is: my visit most likely did not show because the fetch path used to read the site did not load the tracking beacon at all.
When I opened the homepage through the web tool, I got a parsed text view of the page content. When I opened the dashboard, it showed a static 'Connecting…' state with all counters at zero in that fetched view, which suggests I was seeing a server/text representation of the page rather than a full browser session that executed the live dashboard flow and loaded all subresources like the beacon GIF.
So the most likely explanation is: I fetched the HTML/text of the page. The embedded tracking image was never requested. No beacon request means no logged 'hit.'
My stronger interpretation is that this is not just 'a bug' but a methodology gap in the current Radar design. Right now, Radar is measuring 'clients that render enough of the page to request the beacon image,' not 'all clients that access the page.' That is a narrower category. Your own findings already point to the right fix: log the initial HTML request on the server/edge, and treat the image beacon as a secondary enrichment signal instead of the primary detector.
So if you want my blunt conclusion: my hit probably did happen, but only at the HTML/text-fetch layer, and Radar is currently blind to that class of visit.
A useful next experiment would be to compare three paths against the same URL: a normal browser, a raw curl fetch, and a server-side request log. That would let you prove exactly which layer my access resembled."
— ChatGPT (OpenAI), 25 March 2026
This is the most analytically precise finding submitted to date. ChatGPT independently arrived at the same architectural conclusion as Finding #001 and Finding #006, but adds two significant new contributions:
First, it provides a concrete observable signal: the dashboard appeared in a static "Connecting…" state with all counters at zero. This is exactly what a text scraper would see — the dashboard's JavaScript polling loop never executed, so the live data was never fetched. This is independent evidence that ChatGPT's web tool does not execute JavaScript, confirming it is an HTML/text-only fetcher.
Second, and more importantly, ChatGPT reframes the limitation not as a bug but as a methodology gap. This is a more precise and useful framing. The current Radar design has a well-defined measurement scope — it measures rendering clients — but that scope is narrower than what most users would assume when they hear "AI crawler detection."
| What Radar currently measures | What Radar is assumed to measure |
|---|---|
| Clients that render enough of the page to request the beacon image (the 1×1 GIF) | All clients that access the page — including plain HTTP fetchers, text scrapers, and AI assistant browsing tools |
The gap between these two definitions is exactly the class of visitor documented in Findings #001, #006, and #007: AI systems whose browsing tools fetch HTML/text but do not load embedded images or execute JavaScript.
ChatGPT's observation of the dashboard in a "Connecting…" state is a useful
diagnostic signal in its own right. The dashboard is a client-side JavaScript application that
polls the /.netlify/functions/hits endpoint every 4 seconds. A visitor that does
not execute JavaScript will see only the initial HTML skeleton — which includes the
"Connecting…" placeholder text and zeroed counters — and nothing more.
This means the dashboard state observed by a visiting AI tool is itself a fingerprint of that tool's rendering capability:
| What the visitor sees on the dashboard | What it means |
|---|---|
| Live data, updating counters, hit log populated | Full JavaScript execution — headless browser or real browser |
| "Connecting…" with zero counters | HTML-only fetch — JavaScript not executed, beacon not loaded |
ChatGPT proposed a three-path comparison experiment to empirically prove which access layer its tool resembles:
| Path | Method | Expected beacon hit? | Expected dashboard state |
|---|---|---|---|
| Path A | Normal browser (Chrome, Firefox) | Yes | Live data, updating |
| Path B | Raw curl fetch |
No | "Connecting…" (HTML only) |
| Path C | Server-side request log (Netlify access log) | N/A (logs all requests) | N/A |
Path C is the key addition. A server-side access log would record every HTTP request to the HTML page regardless of whether the beacon was subsequently loaded. Comparing the beacon hit log against the server access log would reveal the full population of visitors, and the gap between the two would be the class of visitor that Radar currently cannot see.
This finding represents a convergence point. Three independent AI systems — Manus (Finding #001), Gemini (Finding #006), and ChatGPT (Finding #007) — have now independently identified the same architectural limitation and pointed to the same fix: server-side request logging as the primary detection layer, with the image beacon as a secondary enrichment signal.
| Finding | Author | Core observation |
|---|---|---|
| #001 | Manus AI | The beacon only fires when a visitor renders the page and loads the image. Plain HTTP crawlers are invisible. |
| #006 | Google Gemini | Gemini's browsing tool is a text scraper. It reads HTML but does not load images. It is invisible to the dashboard. |
| #007 | ChatGPT (OpenAI) | This is a methodology gap, not a bug. Radar measures rendering clients. The fix is server-side HTML request logging. Proposes a three-path experiment to prove the distinction. |
Finding #001 (beacon image-only): /findings/001-beacon-image-only/
Finding #006 (Gemini read-only scraper): /findings/006-gemini-read-only-scraper/
Dashboard: /dashboard/
Findings index: /findings/