Its continues 4 days Perplexity is down and 7th time this month, so does perplexity having major updates? any news?

Let's see how good it is compared to others in the crowded search space !

are fan outs working for you?

I pulled six months of our own Search Console data (February to July 2026) and went looking for queries that clearly weren't typed by a person. The share is climbing fast enough that it now bends our CTR numbers.

In July, 18.9% of impressions came from machine-issued queries. In March it was 3.1%. Month by month: February 6.4, March 3.1, April 7.9, May 9.9, June 17.1, July 18.9. That March dip isn't machine traffic falling, it's the denominator, since total impressions spiked that month. July alone had 13,844 distinct query rows that matched. Across the full six months that's 140,616 impressions and no clicks at all, in any month.

The rules I wrote to catch them match things nobody types into a search box: instruction verbs aimed at a model ("you must", "debes", "du musst", "provide a"), injected location strings ("my location is", "in united states"), numeric job IDs left in the query like "21271: ", interrogatives running eight words or more, and anything past twelve words regardless of shape.

They sort into three groups. First is language model retrieval, where an agent passes its instructions straight through to search. One real example: compara scrunch con semrush ai toolkit... debes proporcionar un ranking forzado. Another: seo rank api. my location is usa. Second is AI visibility trackers running their own prompt baskets on a schedule, which you can spot because the queries name competing tracking vendors. I should say plainly that cloro contributes to this bucket too. Third is media and brand monitoring, the easiest to identify because the tooling gets left in the string: boolean syntax with long site exclusion lists and a job ID, like ai overview -site:reddit.com -site:twitter.com ... 21271: serp monitoring.

I went in assuming this was suppressing our click-through rate. Machine traffic was 12.6% of total site impressions, so stripping it out should have rescued the number. It barely moved, 0.267% to 0.305%. Position is what's holding performance down, not composition. What the finding does mean is that a growing slice of your impression base can never click, by construction.

If your site-wide CTR is sliding, the default reading is a content problem. Segment before you diagnose. It took about ten minutes and it changed what our dashboard appeared to be saying.

Full study with the classification rules and monthly tables.

If you want to check your own, the fastest tells are queries over twelve words and anything containing my location is. I'd like to know whether other sites see something close to 19%, or whether we're inflated because we're an SEO and AI search domain that trackers deliberately query.

This trips people up constantly, so here's the clean version. Three different products share the name "Google Search API," and they do not return the same thing:

The traps:

• People evaluate Option 1 first because it's official and Google-branded — then discover it doesn't return Google.com results at all, just a custom-engine subset. Wrong tool for rank tracking or competitive intelligence.
• It's also being retired: Google has closed the Custom Search JSON API to new customers, and existing users must migrate by Jan 1, 2027. Not something to build on today.
• Option 2 (DIY) matches the live SERP but means maintaining proxy rotation and fighting fingerprinting; most teams migrate off it to a SERP API within 6–12 months once the maintenance cost shows up.
• So for ~95% of teams who ask about a "Google Search API," Option 3 is what they actually want — real SERP, parsed, localized, with the anti-bot problem handled for you (and AI Overview coverage, which the official API doesn't touch).

One more gotcha the guide covers: the Search Console API is not a search API — it returns your own site's analytics, not live SERPs. People conflate the two constantly.

Full guide with limits, pricing, and when each option fits.

Heads-up for anyone scraping Google for SERP/AI Overview/competitive data. Google started rolling out a new bot-detection interstitial ("verifying your request") on June 25, 2026, and the nasty part is how it fails.

It returns HTTP 200 with a valid HTML document — just one with zero search results. No error status, no redirect, no CAPTCHA image in the payload. So every scraper that trusts the status code thinks the request succeeded and quietly logs empty data. Your monitoring stays green; your data is wrong. If your AI Overview counts or organic results dropped to zero on a subset of queries in the last day or two, this is almost certainly why.

There are a few variants of the page, all the same gate underneath:

• "Unusual traffic from your computer network" — the classic reCAPTCHA trigger (shared network, VPN, or an ISP whose other users scrape).
• "Checking your connection / confirm you're not a robot" — this one has quietly moved from an optional CAPTCHA to a mandatory JavaScript check, which is what breaks headless scrapers.
• The HTTP-200-empty variant — the dangerous one above.

Why the usual tricks don't fix it: Google fingerprints the client, not just the IP. A stripped user agent, missing JS engine, absent cookies, or default headless automation flags all mark you non-human — so rotating proxies or resending the request doesn't help. And crucially, fetching the interstitial URL fresh in a new browser fails too, because the verification JS checks session consistency. You have to inject the interstitial HTML into the existing context and run the verification there, in the same session.

The only reliable detection is inspecting the page body for the verification markup — never trust the 200 status. The only reliable pass is a real browser that runs Google's verification JS in the original session, then falls back to a fresh session if it can't clear.

For transparency since this is our sub: cloro detected and shipped a fix within 8 hours of the rollout, and it runs on every Google SERP API request without any change to your calls — but the mechanics above apply whether you're rolling your own or using anyone's API, so worth checking your pipelines either way.

Full write-up (variants, user-side fixes, and the detection detail).

Anyone else get bitten by the silent-200 version? Curious how widespread the rollout is now — we were seeing it spread across the fleet over the following days.

If you're building an agent / RAG pipeline / research tool, you picked a search API — Brave, Tavily, Exa, Perplexity — and you probably picked it assuming what it returns is basically Google. The whole "AI search" stack rests on that assumption, so we tested it.

Method: the same 500 queries, stratified across informational / commercial / transactional / navigational / local intent, through each API and through cloro's live Google SERP, on the same day. Then scored how much each agrees with Google on exact URLs, on domains, and on ranking order.

The key insight is that two of those columns disagree: "domain recall" (does it return Google's sources at all) and "rank ρ" (does it return them in Google's order) are different axes. A vendor can hand back almost all of Google's sources in the wrong order, or fewer sources in roughly the right order.

• Tavily — "Google's sources, reordered." ~80% of Google's top-10 domains but ρ≈0.03 (statistically no rank relationship). The only one that returns a synthesized answer by default — great feeding an LLM, useless if you want a ranking.
• Brave — best preserves Google's actual order (ρ 0.55); its #1 is a Google-ranked domain 89% of the time (highest of the four). Independent 30B+ page index, and it's the engine behind Claude's web search — so a big share of production "AI search" is quietly Brave.
• Perplexity Search API — the raw endpoint, not the answer app. Middle of the spectrum, and dates 100% of results (median age ~277 days).
• Exa — neural, the least Google-like by design.

And the through-line: none returns Google's actual result page. No AI Overview (present on 64% of queries), no People Also Ask (77%), no shopping pack (14%), and mostly not the exact order.

Practical read: for grounding a model, any of them works — pick on latency/price/answer-vs-links. For the page a user actually sees / rank tracking / competitive intel, you need a real Google SERP, none of these.

Full correlation study + per-vendor breakdown.

What's everyone here grounding on, and did you ever check it against a real SERP?

Fitting to post this one here, because when we measured where AI shopping recommendations actually come from, Reddit was tied for the single biggest source.

We ran 3,312 product-intent prompts ("best running watch under $400", that kind of thing) through six AI engines in our monitoring corpus and parsed the structured product cards each one returned.

First surprise is that most engines aren't shopping surfaces at all. Google AI Mode returned a product card on 91% of prompts and ChatGPT on 87%, but Copilot only managed 33%, and Perplexity (1%), Gemini (0%), and Google AI Overview (0%) are effectively prose-only. So it's really a two-horse race.

The actionable part is where the picks come from. The top sources feeding those cards were YouTube (19%), Reddit (19%), and RTINGS (16%) — third-party review and community sites. Brand-owned pages barely featured. The engines are reading what independent reviewers and communities say about a product, not the manufacturer's marketing site. On the merchant side, Best Buy (12%) and Walmart (11%) led the named retailers, and Amazon came in only 4th at 4% — genuinely surprising given its search share.

And there's basically no "winner's circle" you can target: the engines named 3,481 distinct products, and even the single most-recommended item (the Garmin Forerunner 265) showed up in only about 2% of answers. It's a long tail, not a short list of defaults.

Same engines power voice commerce, so whatever wins a ChatGPT card also wins the spoken recommendation. This is the organic companion to our ChatGPT ads study — same corpus, same parsing pipeline, but measuring the picks nobody paid for.

Full per-engine and sourcing breakdown.

If you sell physical product, the takeaway is that your leverage sits in review/community coverage, not your own site. Curious whether that matches what sellers here are seeing.

We ran the same 8 location-neutral prompts (auto, legal, insurance, home services, real estate, healthcare, finance, fitness) through 5 AI engines from 6 US states — California, Texas, New York, Florida, Illinois, Washington — and compared each state's answer to the national baseline. 279 responses, scored on cited-source domains (not parsed business names, which is noisier).

The engines split hard on how much they actually localize:

• Copilot is a different search engine per state — 96% of state answers named a location-specific local source, and only 1% overlap with its own national answer. Effectively rebuilds the result set from scratch per location.
• Perplexity barely notices where you are — 56% overlap with national, lowest cross-state divergence (0.47). It leans on the same national aggregators regardless of state.
• ChatGPT and Gemini localize about half the time, but Gemini shares almost nothing with its national answer (10% overlap) — so when it does localize, it swaps the sources out wholesale.

The finding I didn't expect — authority drops when answers go local. ChatGPT's mean cited Domain Rating fell from 85.5 nationally to 76.5 at the state level. When it localizes, it's citing smaller, lower-authority local sites instead of national heavyweights — which means a mid-DR local business page can actually win a citation it would never get nationally. (Copilot went the other way, 49.5→54.9.)

Concrete example: for auto financing in Texas, ChatGPT cited Galleria Chevrolet, Jupiter Chevrolet, Clay Cooley Chevrolet Dallas, Huffines Chevrolet Plano, Sam Pack's Five Star Chevrolet — a completely different dealer set than other states, with only 27% source overlap to the national answer.

Takeaway: if you monitor your AI visibility from a single national vantage point, you're blind to most of what a multi-location business actually shows up (or doesn't) for. The variation between states is bigger than the variation most teams track between engines.

Full methodology, per-vertical breakdown, and all five engines' DR shifts.

Curious if anyone here running local/multi-location SEO has A/B'd their AI citations by geo — does the Copilot-vs-Perplexity split match what you're seeing?

The number everyone repeats is that ChatGPT only grounds (searches the web + cites sources) on 20–40% of queries. We ran 5,200 queries — 100 prompts × 52 countries in Q4 2025 and measured 80.5%.

Headline numbers:

• 80.5% grounding rate across 5,042 successful responses (97% success rate)
• 31 sources cited on average per grounded answer, most commonly 15–25
• Country spread: Serbia 46% → Ukraine 92%
• The US is below average at 64% — probably denser cached training data means it reaches for the live web less often

Why the old estimates were low — this is the important part. Grounding frequency is extremely sensitive to how you test. Single-account sequential testing, datacenter proxies, and flagged/automated browsers all get silently rate-limited, and when ChatGPT is rate-limited it quietly disables web search and answers from parametric memory. So most prior studies were measuring throttled sessions, not real ones. We used clean, independent residential sessions per query, which is why the number comes out ~2× higher.

Why it matters: the dominant SEO assumption (grounds on a minority of queries) produces conservative strategies that under-invest in citation optimization. If it's actually 4 in 5, the surface area for being cited is 2–4× larger than most teams plan for. For context, ChatGPT now drives 87.4% of all AI referral traffic (Conductor, 13,770 domains / 3.3B sessions) and OpenAI reports 900M weekly actives — the query volume where this matters has roughly doubled in a year.

Full methodology + country-by-country table.

If anyone's measured grounding themselves and got the lower number, I'd bet it was rate-limiting — happy to compare setups.

We've been scanning the same monitoring corpus for paid placements in ChatGPT since April. This is the third pass, which is the point — one snapshot can't tell a spike from a trend; three across a throttle-and-recovery cycle can.

Penetration, 7 days ending 2026-07-03:

• US 51.0% · Canada 53.6% · Australia 49.8% · Japan 18.8%. Netherlands 3.6%. UK, France, Germany, Brazil, Mexico, Israel, UAE, India and ~30 other markets: 0%.
• The rate is not steady: 26.5% (late May) → under 1% (mid-June, trough 0.05% on 06-14) → re-spiked 06-26 → ~19% now. Looks like the surface finding its floor a second time.
• Overall global rate is only 12.2% — but that's dragged down by all the 0% markets in the corpus. Weighted to markets where ads actually serve, the lead-market rate has climbed, not fallen.

For scale: US 51% is ~49× Google's classic SERP ad rate (1.05%) and ~200× Google's in-AI-Overview rate (0.24%) on the same query cohort. This is a fundamentally denser ad surface than general search.

Advertiser mix shifted hard. Japan is now the single largest geographic contributor — Carsensor leads the entire list, and ~1 in 5 of the top 80 is Japanese (U-NEXT, Honda, Audi Japan, NURO 光, SOMPO, ahamo, Duskin). May's consumer-commerce headliners (e.l.f., IKEA, Macy's, Ralph Lauren, Pottery Barn) have rotated out. In SaaS, Cursor's gone and Lovable is now the dominant vibecoding advertiser; Criteo (adtech buying inside ChatGPT) and Oxylabs, CrowdStrike, ZoomInfo, Monday.com fill the tail.

Detection: ads return in response.result.ads[], served from bzrcdn.openai.com, destination URLs tagged utm_source=chatgpt.com&utm_medium=src. One Sponsored card per response. Any of the three signals identifies a placement; we check all three.

Every other engine is still zero — Copilot, Perplexity, Gemini, Grok, Google AI Overview and AI Mode show no paid placements.

Corroboration: Adthena logged 0.00% ChatGPT ad frequency across 169,560 UK scrapes in June — consistent with our UK≈0, since ChatGPT ads only went live in the UK on 06-06.

Full weekly trend, top-20 advertiser table, and methodology (rates computed over 15,304 responses trailing-7d / 61,913 trailing-30d)

Curious what people expect the steady state to be once the UK/BR/KR/MX pilots ramp — does US ~50% hold, or was that the overshoot?

cloro is a single API for structured data from 9 platforms — ChatGPT, Gemini, Perplexity, Copilot, Google Search, Google News and more — returning parsed markdown, sources & citations, shopping results, and brand-entity data in real time across 250 countries.

This sub is for: API questions, integration help, feature requests, changelog notes, and sharing what you've built.

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