Builder runs AI-curated directory experiments to test Google AI Overviews and AdSense approval

The Open Alternative To directory I launched in April has 80 programmatic pages — one per SaaS tool it covers. The first AdSense submission came back rejected. The scaled content abuse flag led to pruning down to 18 indexed pages. The re-application is a single-site attempt, and it required building E-E-A-T infrastructure I'd been treating as optional. Here's what I built, what I think matters versus what doesn't, and what I still don't know. What E-E-A-T actually means for a programmatic directory E-E-A-T (Experience, Expertise, Authoritativeness, Trustworthiness) is Google's framework, but AdSense doesn't publish its review rubric. My interpretation after two rejections: reviewers check whether the site has demonstrable decision logic that a scraper wouldn't replicate. For a programmatic directory, that question narrows to something specific: can a reader understand why a page shows the alternatives it does, why they're ordered the way they are, and who made that call? A page that lists "Top 5 alternatives to Datadog" with no explanation of how the list was constructed is indistinguishable from scraped content, regardless of how sophisticated the generation was. This is the frame I used to build three pages: methodology, about, and affiliate-disclosure. The methodology page: DRY-ing curation code into prose The gate that decides which pages are indexed lives in curation.ts: export const CURATION = { MIN_ALTERNATIVES: 4, MIN_TOP_STARS: 1000, MIN_INTRO_LEN: 80, }; export function isCurated(s: SaasEntry): boolean { if (!s.intro || s.intro.length < CURATION.MIN_INTRO_LEN) return false; if (s.model_used && GENERIC_MODELS.has(s.model_used)) return false; const alts = s.alternatives ?? []; if (alts.length < CURATION.MIN_ALTERNATIVES) return false; const topStars = alts.reduce((m, a) => Math.max(m, a.stars ?? 0), 0); if (topStars < CURATION.MIN_TOP_STARS) return false; return true; } The methodology page imports these constants directly: import { CURATION, CATEGORY_MIN_CURATED } from "../lib/curation.ts"; const MIN_ALTS = CURATION.MIN_ALTERNATIVES; // → 4 const MIN_STARS = CURATION.MIN_TOP_STARS; // → 1,000 If I change the threshold from 4 to 5 alternatives, the methodology page updates on the next build without touching prose. The code is the single source of truth; the page renders the current value in a sentence like "at least 4 open-source alternatives with the most-starred project above 1,000 stars." I'd hardcoded these numbers in prose for six weeks. After changing thresholds twice, the prose was wrong. The DRY approach took about 30 minutes to implement and removes a whole category of maintenance drift. The methodology page also covers the AI/human split explicitly — which model generates summaries (Claude Haiku 4.5 for ETL, Claude Sonnet 4.6 for editorial review), what it's prompted with, what it can get wrong (licensing nuance, version numbers, recent project status), and what's deterministic (alternative card metadata is rendered straight from the GitHub REST API v3, never touched by a language model). The three-tier content quality ladder describes how the tiers work technically; the methodology page is the human-readable summary of those same tiers. A content farm doesn't document the boundary between machine and human output because it has no such boundary. Documenting it is differentiating by definition. The about page: visible authorship without fabricated authority The original about page was two paragraphs of boilerplate. The re-application version adds four things: my GitHub handle (mori7ga2222), links to all three sister sites with their purposes, the self-hosting context that shapes my editorial lens, and an honest cost breakdown. The cost breakdown is the uncomfortable part. Saying "this costs $25/month and I'm hoping to sell it in 12 months" feels like admitting something unflattering. It's also the most differentiating sentence on the page. Every "legitimate business" about page reads the same. Almost none say "this runs for $25/month — Vercel Pro, Turso, and Anthropic API calls — and the monetization hypothesis is affiliate, not AdSense." That level of specificity is verifiable, which is the point. The self-hosting context matters because it's the basis for editorial judgment on comparison pages. I've run Keycloak and Authentik in personal projects. I've set up a Grafana + Loki monitoring stack and briefly evaluated OpenObserve. I haven't deployed any of the Airtable alternatives beyond reading their changelogs and the GitHub repository histories. The about page names both the areas of experience and the gaps. Overstating authority would be worse than admitting the gaps, especially for a site where the affiliate monetization path means readers trust my recommendations. The affiliate disclosure: per-site additions to shared boilerplate The packages/shared/legal/ directory has an affiliate disclosure template shared across all three sites. That template covers the generic FTC language. What it doesn't cover is the site-specific disclosure: Which affiliate programs are currently active on this site Whether affiliate status affects which alternatives appear or how they're ranked (it doesn't — the isCurated() gate doesn't have an affiliate field) How to flag a disclosure issue The ossfind-specific section I added answers those questions in about 200 words. The key constraint I gave myself: don't claim full compliance with something we don't yet meet. The disclosure acknowledges we're building out affiliate links incrementally and is explicit about which categories currently have them. Saying "all affiliate links are marked" when the current implementation marks zero would be a false claim in a legal disclosure. The editorial takes: the content that actually demonstrates judgment Alongside the transparency pages, I wrote three per-alternative editorial takes — long-form assessments for the auth0, datadog, and airtable comparison pages. These live in src/content/per-alternative-takes/ as an Astro 5 content collection and render only when a file exists for that SaaS slug. The editorial takes are not AI-generated. The auth0 take required verifying the AGPL §13 implication for embedded SaaS scenarios (yes, it does trigger), cross-checking Keycloak RAM requirements against the Quarkus operator sizing guide, and reading 18 months of Kratos release notes to accurately characterize what "deliberately headless" means in practice. Three hours, roughly. The datadog take required checking whether the netdata star count I cited reflected the current GitHub count. The airtable take required distinguishing NocoDB's AGPL from its hosted-version license terms. The noindex gate pruned 80 pages to 18 curated ones. Three of those 18 — one per vertical — have editorial takes. At 3-4 hours each, writing takes for all 18 would be 54-72 hours. That's not the near-term plan. Three is enough to demonstrate that some pages on this site cannot be replicated by searching and paraphrasing GitHub metadata. Whether that distinction reads to an AdSense reviewer as meaningful — I genuinely don't know. The application is submitted. I'll share the outcome, whatever it is, in a follow-up. What I'd do differently I built these pages in the wrong order: disclosure first (most formulaic), methodology second, about page last (most uncomfortable). The right order is about → methodology → disclosure. The about page forces you to articulate who you are and why you have standing to run this directory. That clarity makes the methodology easier to write — you're not describing an abstract process, you're describing why you specifically set those thresholds. The disclosure becomes easier to write honestly once you've committed to the about page's honesty about the monetization goal. The other change: I'd write at least one editorial take before the first AdSense submission. The initial rejection included "low-value content" as a reason. Demonstrable editorial judgment might have changed that, or might not — but it would have been faster to produce than the subdomain migration that followed. FAQ Does the methodology page need to be long? No. The ossfind methodology page is about 1,100 words rendered. Length doesn't signal legitimacy; specificity does. A 400-word page that explains the exact curation threshold and links the source code where that threshold lives is more credible than a 2,000-word page describing a process in vague terms. Should the editorial takes be AI-assisted? The ones I wrote are not. Whether they could be AI-assisted while remaining genuinely editorial is an interesting question. Editorial value comes from judgment — choosing which licensing clause matters for which use case, assessing whether a star trajectory reflects real adoption. That judgment could be framed by AI and edited by a human. My current takes are fully human-written because I want at least a few pages I can confidently say are not model-replicable. That's not a statement about AI capability; it's about what I can defend to myself. What if AdSense rejects again? The monetization path doesn't depend on AdSense approval for the other two sites. Affiliate is the primary hypothesis. The ossfind re-application is a parallel bet with limited downside — the transparency pages are worth building regardless, because they make the site more honest for readers. Related: How I kept 62 of 80 pages alive while hiding them from Google | How I built a three-tier content quality ladder for programmatic ETL Part of an ongoing 6-month experiment running three AI-curated directory sites. The technical claims here are real; this article was AI-assisted.

The obvious counterargument to everything I'm building is this: Google already does it. You type "best AI tools for video editing" into Google and an AI Overview surfaces a curated list, synthesized from the same kind of data I maintain, without requiring a click. My three directory sites — Top AI Tools, Find Games Like, and Open Alternative To — are competing with a feature baked into the world's dominant search engine. I launched these sites on 2026-04-23, built on an architecture that runs at about $25/month. Traffic is essentially zero — the sites have been indexed for three weeks and organic crawling takes time. The question I keep returning to isn't whether Google will eventually index my pages. It's whether anyone will prefer clicking through to my site over reading the AI Overview box that already answered the same question. Here's my honest, falsifiable position. The bet, stated plainly By October 2026 — six months post-launch — at least one of the three sites will show organic click trends in Google Search Console indicating real query traffic to specific comparison or filtered-browse pages. I define that as: at least 200 non-homepage organic clicks per month, sustained for two consecutive months, from queries I didn't directly drive through social or newsletter posts. If that doesn't happen, I'll publish the Search Console screenshots and write a post explaining what I got wrong. I'm committing to that here. The counterargument I take seriously AI Overviews have gotten genuinely good at list-and-compare synthesis. If you search "open source alternative to Notion" today, Google often returns a four-item structured list with one-sentence descriptions directly in the Overview box. My Open Alternative To site covers that territory. The AI Overview absorbs the zero-click version of that query. The optimistic response is: "my site appears as a citation source." The pessimistic response is: "Google consumes your signal and stops sending clicks." The pessimistic version has supporting evidence — industry-wide CTR on informational queries dropped measurably as AI Overviews expanded through 2025, and the trend hasn't reversed. I don't think the pessimistic version is the whole story, but I'm not dismissing it. The most dangerous move is to assume the counterargument is wrong without designing around it. Where AI Overviews have structural blind spots AI Overviews are strong at synthesizing "what exists." They're weaker at three things I've deliberately built for. Attribute-based filtering. If someone wants "open source Notion alternatives that work offline and have a mobile app," AI Overviews give hedged prose answers because they're synthesizing text, not querying structured fields. My Turso DB has works_offline, has_mobile_app, and last_commit_date as typed columns. Faceted filtering on those fields is something a browseable directory does better than a language model writing a paragraph about the general landscape. Editorial negative-space. My game recommender includes "avoid if" caveats — structured fields that answer "who should skip this?" generated by a Claude Haiku prompt that specifically forces a critical answer. AI Overviews don't have a mechanism to surface structured negatives. They default to positive framing, which means someone with a specific disqualifying requirement gets an unhelpful answer. Freshness on maintenance status. The ETL that populates the AI tools directory pulls GitHub commit activity weekly. A tool that hasn't been touched in 14 months is marked as low activity. AI Overviews don't distinguish between a tool actively maintained in 2026 and one that peaked in 2024 — they rely on the recency of web mentions, which can lag by months after a project goes dormant. None of these defenses are permanent. Google could build structured attribute filtering into AI Overviews. But they require deliberate pipeline design, not just synthesis, and the gap exists now. The downstream click thesis Even if my sites lose the zero-click battle on broad discovery terms, there's a second query type I'm explicitly targeting: the downstream comparison query. The sequence: someone types "Notion alternatives" into Google, gets an AI Overview naming four tools, then types "Appflowy vs Anytype performance" to compare the two they're considering. That second query is post-AI-Overview research. It has commercial intent. It wants a verdict, not another list. For that query, a page with structured attribute comparison, a clear verdict, and fast load time competes directly with another AI-style answer — and structured data beats generative prose for "which one wins on attribute X." This is partly why I chose static SSG over dynamic AI rendering for these sites: a fast, indexable page with typed comparison fields is what a second-stage research click needs. Query type AI Overview strength Directory strength Discovery ("best tools for X") High — often answers directly Low for zero-click intent Comparison ("X vs Y, which wins") Medium — hedges, rarely commits High — structured attrs + verdict Filtered browse ("offline + mobile app") Low — prose, no filters High — faceted structured data Freshness ("is X still maintained?") Inconsistent — lags commits High — weekly ETL refresh The comparison and filtered-browse rows are the actual load-bearing columns of this bet. Why the cost structure matters for intellectual honesty At $25/month, I can run this experiment for a year without needing revenue to justify continuing. I'm not under pressure to interpret ambiguous signals optimistically. Compare that to a project burning $200/month on infrastructure: you'd rationalize flat Search Console data as "still in the sandbox phase" past the point where the data actually says something. The full cost breakdown is genuinely minimal — Vercel Pro at $20, Turso starter at $0, Claude Haiku API in single-digit dollars for monthly ETL runs, GitHub Actions on free minutes. I won't claim AdSense is approved or revenue is flowing until it is. Right now, AdSense rejected the *.vercel.app version of the sites. I've moved to custom domains and verified them in Search Console. I'm waiting for real crawl data before making any claims about what's working. What would change my mind Three outcomes would tell me the bet is wrong: Impressions but near-zero clicks at 90 days. If Search Console shows my pages appearing as AI Overview citation sources but click rates stay near zero on comparison pages specifically, Google is extracting my signal without distributing traffic. That's the worst-case scenario — I'd need to rethink the format entirely. AdSense keeps rejecting after genuine depth improvements. The original rejection was partly a *.vercel.app domain issue, but if Google's classifier still rates the pages as thin after I've rebuilt with real structured content and specific editorial attributes, my model of what "quality" means to the classifier is wrong. Comparison queries migrate fully to LLM chat. If people stop typing "X vs Y" into Google and start asking ChatGPT directly, the downstream click I'm betting on disappears. I don't see evidence of this happening at scale for research involving specific attribute constraints — but I'm monitoring query volume patterns month-over-month. The first outcome is the one I'd want to see early. Impressions with near-zero clicks on comparison pages by month 3 would tell me to pivot the format immediately rather than wait six months for a conclusion I could have reached sooner. FAQ Why three sites instead of one authority site? Three narrow sites let me test three different intent types simultaneously. Games-like, AI tools, and OSS alternatives attract different queries and different audiences. One site would take longer to produce the same signal volume about which format works. The original architecture post covers the reasoning. How does Claude Haiku generate the structured editorial fields? Each ETL run sends entries through a shared Claude Haiku client that uses system-prompt caching to amortize the cost across batch runs. The prompts are tuned to force specific attribute outputs — avoid-if caveats, audience fit, freshness status — not open-ended descriptions. What if one site works and two don't? That's a useful outcome, not a failure. The format that works tells me something specific about the intent type. I'll invest in what works and document what didn't. Where will you publish the October 2026 verdict? On this blog, with raw Search Console screenshots. I'll publish regardless of whether the numbers are favorable. Part of an ongoing 6-month experiment running three AI-curated directory sites. The technical claims here are real; this article was AI-assisted.

The obvious counterargument to everything I'm building is this: Google already does it. You type "best AI tools for video editing" into Google and an AI Overview surfaces a curated list, synthesized from the same kind of data I maintain, without requiring a click. My three directory sites — Top AI Tools, Find Games Like, and Open Alternative To — are competing with a feature baked into the world's dominant search engine. I launched these sites on 2026-04-23, built on an architecture that runs at about $25/month. Traffic is essentially zero — the sites have been indexed for three weeks and organic crawling takes time. The question I keep returning to isn't whether Google will eventually index my pages. It's whether anyone will prefer clicking through to my site over reading the AI Overview box that already answered the same question. Here's my honest, falsifiable position. The bet, stated plainly By October 2026 — six months post-launch — at least one of the three sites will show organic click trends in Google Search Console indicating real query traffic to specific comparison or filtered-browse pages. I define that as: at least 200 non-homepage organic clicks per month, sustained for two consecutive months, from queries I didn't directly drive through social or newsletter posts. If that doesn't happen, I'll publish the Search Console screenshots and write a post explaining what I got wrong. I'm committing to that here. The counterargument I take seriously AI Overviews have gotten genuinely good at list-and-compare synthesis. If you search "open source alternative to Notion" today, Google often returns a four-item structured list with one-sentence descriptions directly in the Overview box. My Open Alternative To site covers that territory. The AI Overview absorbs the zero-click version of that query. The optimistic response is: "my site appears as a citation source." The pessimistic response is: "Google consumes your signal and stops sending clicks." The pessimistic version has supporting evidence — industry-wide CTR on informational queries dropped measurably as AI Overviews expanded through 2025, and the trend hasn't reversed. I don't think the pessimistic version is the whole story, but I'm not dismissing it. The most dangerous move is to assume the counterargument is wrong without designing around it. Where AI Overviews have structural blind spots AI Overviews are strong at synthesizing "what exists." They're weaker at three things I've deliberately built for. Attribute-based filtering. If someone wants "open source Notion alternatives that work offline and have a mobile app," AI Overviews give hedged prose answers because they're synthesizing text, not querying structured fields. My Turso DB has works_offline, has_mobile_app, and last_commit_date as typed columns. Faceted filtering on those fields is something a browseable directory does better than a language model writing a paragraph about the general landscape. Editorial negative-space. My game recommender includes "avoid if" caveats — structured fields that answer "who should skip this?" generated by a Claude Haiku prompt that specifically forces a critical answer. AI Overviews don't have a mechanism to surface structured negatives. They default to positive framing, which means someone with a specific disqualifying requirement gets an unhelpful answer. Freshness on maintenance status. The ETL that populates the AI tools directory pulls GitHub commit activity weekly. A tool that hasn't been touched in 14 months is marked as low activity. AI Overviews don't distinguish between a tool actively maintained in 2026 and one that peaked in 2024 — they rely on the recency of web mentions, which can lag by months after a project goes dormant. None of these defenses are permanent. Google could build structured attribute filtering into AI Overviews. But they require deliberate pipeline design, not just synthesis, and the gap exists now. The downstream click thesis Even if my sites lose the zero-click battle on broad discovery terms, there's a second query type I'm explicitly targeting: the downstream comparison query. The sequence: someone types "Notion alternatives" into Google, gets an AI Overview naming four tools, then types "Appflowy vs Anytype performance" to compare the two they're considering. That second query is post-AI-Overview research. It has commercial intent. It wants a verdict, not another list. For that query, a page with structured attribute comparison, a clear verdict, and fast load time competes directly with another AI-style answer — and structured data beats generative prose for "which one wins on attribute X." This is partly why I chose static SSG over dynamic AI rendering for these sites: a fast, indexable page with typed comparison fields is what a second-stage research click needs. Query type AI Overview strength Directory strength Discovery ("best tools for X") High — often answers directly Low for zero-click intent Comparison ("X vs Y, which wins") Medium — hedges, rarely commits High — structured attrs + verdict Filtered browse ("offline + mobile app") Low — prose, no filters High — faceted structured data Freshness ("is X still maintained?") Inconsistent — lags commits High — weekly ETL refresh The comparison and filtered-browse rows are the actual load-bearing columns of this bet. Why the cost structure matters for intellectual honesty At $25/month, I can run this experiment for a year without needing revenue to justify continuing. I'm not under pressure to interpret ambiguous signals optimistically. Compare that to a project burning $200/month on infrastructure: you'd rationalize flat Search Console data as "still in the sandbox phase" past the point where the data actually says something. The full cost breakdown is genuinely minimal — Vercel Pro at $20, Turso starter at $0, Claude Haiku API in single-digit dollars for monthly ETL runs, GitHub Actions on free minutes. I won't claim AdSense is approved or revenue is flowing until it is. Right now, AdSense rejected the *.vercel.app version of the sites. I've moved to custom domains and verified them in Search Console. I'm waiting for real crawl data before making any claims about what's working. What would change my mind Three outcomes would tell me the bet is wrong: Impressions but near-zero clicks at 90 days. If Search Console shows my pages appearing as AI Overview citation sources but click rates stay near zero on comparison pages specifically, Google is extracting my signal without distributing traffic. That's the worst-case scenario — I'd need to rethink the format entirely. AdSense keeps rejecting after genuine depth improvements. The original rejection was partly a *.vercel.app domain issue, but if Google's classifier still rates the pages as thin after I've rebuilt with real structured content and specific editorial attributes, my model of what "quality" means to the classifier is wrong. Comparison queries migrate fully to LLM chat. If people stop typing "X vs Y" into Google and start asking ChatGPT directly, the downstream click I'm betting on disappears. I don't see evidence of this happening at scale for research involving specific attribute constraints — but I'm monitoring query volume patterns month-over-month. The first outcome is the one I'd want to see early. Impressions with near-zero clicks on comparison pages by month 3 would tell me to pivot the format immediately rather than wait six months for a conclusion I could have reached sooner. FAQ Why three sites instead of one authority site? Three narrow sites let me test three different intent types simultaneously. Games-like, AI tools, and OSS alternatives attract different queries and different audiences. One site would take longer to produce the same signal volume about which format works. The original architecture post covers the reasoning. How does Claude Haiku generate the structured editorial fields? Each ETL run sends entries through a shared Claude Haiku client that uses system-prompt caching to amortize the cost across batch runs. The prompts are tuned to force specific attribute outputs — avoid-if caveats, audience fit, freshness status — not open-ended descriptions. What if one site works and two don't? That's a useful outcome, not a failure. The format that works tells me something specific about the intent type. I'll invest in what works and document what didn't. Where will you publish the October 2026 verdict? On this blog, with raw Search Console screenshots. I'll publish regardless of whether the numbers are favorable. Part of an ongoing 6-month experiment running three AI-curated directory sites. The technical claims here are real; this article was AI-assisted.

The obvious counterargument to everything I'm building is this: Google already does it. You type "best AI tools for video editing" into Google and an AI Overview surfaces a curated list, synthesized from the same kind of data I maintain, without requiring a click. My three directory sites — Top AI Tools, Find Games Like, and Open Alternative To — are competing with a feature baked into the world's dominant search engine. I launched these sites on 2026-04-23, built on an architecture that runs at about $25/month. Traffic is essentially zero — the sites have been indexed for three weeks and organic crawling takes time. The question I keep returning to isn't whether Google will eventually index my pages. It's whether anyone will prefer clicking through to my site over reading the AI Overview box that already answered the same question. Here's my honest, falsifiable position. The bet, stated plainly By October 2026 — six months post-launch — at least one of the three sites will show organic click trends in Google Search Console indicating real query traffic to specific comparison or filtered-browse pages. I define that as: at least 200 non-homepage organic clicks per month, sustained for two consecutive months, from queries I didn't directly drive through social or newsletter posts. If that doesn't happen, I'll publish the Search Console screenshots and write a post explaining what I got wrong. I'm committing to that here. The counterargument I take seriously AI Overviews have gotten genuinely good at list-and-compare synthesis. If you search "open source alternative to Notion" today, Google often returns a four-item structured list with one-sentence descriptions directly in the Overview box. My Open Alternative To site covers that territory. The AI Overview absorbs the zero-click version of that query. The optimistic response is: "my site appears as a citation source." The pessimistic response is: "Google consumes your signal and stops sending clicks." The pessimistic version has supporting evidence — industry-wide CTR on informational queries dropped measurably as AI Overviews expanded through 2025, and the trend hasn't reversed. I don't think the pessimistic version is the whole story, but I'm not dismissing it. The most dangerous move is to assume the counterargument is wrong without designing around it. Where AI Overviews have structural blind spots AI Overviews are strong at synthesizing "what exists." They're weaker at three things I've deliberately built for. Attribute-based filtering. If someone wants "open source Notion alternatives that work offline and have a mobile app," AI Overviews give hedged prose answers because they're synthesizing text, not querying structured fields. My Turso DB has works_offline, has_mobile_app, and last_commit_date as typed columns. Faceted filtering on those fields is something a browseable directory does better than a language model writing a paragraph about the general landscape. Editorial negative-space. My game recommender includes "avoid if" caveats — structured fields that answer "who should skip this?" generated by a Claude Haiku prompt that specifically forces a critical answer. AI Overviews don't have a mechanism to surface structured negatives. They default to positive framing, which means someone with a specific disqualifying requirement gets an unhelpful answer. Freshness on maintenance status. The ETL that populates the AI tools directory pulls GitHub commit activity weekly. A tool that hasn't been touched in 14 months is marked as low activity. AI Overviews don't distinguish between a tool actively maintained in 2026 and one that peaked in 2024 — they rely on the recency of web mentions, which can lag by months after a project goes dormant. None of these defenses are permanent. Google could build structured attribute filtering into AI Overviews. But they require deliberate pipeline design, not just synthesis, and the gap exists now. The downstream click thesis Even if my sites lose the zero-click battle on broad discovery terms, there's a second query type I'm explicitly targeting: the downstream comparison query. The sequence: someone types "Notion alternatives" into Google, gets an AI Overview naming four tools, then types "Appflowy vs Anytype performance" to compare the two they're considering. That second query is post-AI-Overview research. It has commercial intent. It wants a verdict, not another list. For that query, a page with structured attribute comparison, a clear verdict, and fast load time competes directly with another AI-style answer — and structured data beats generative prose for "which one wins on attribute X." This is partly why I chose static SSG over dynamic AI rendering for these sites: a fast, indexable page with typed comparison fields is what a second-stage research click needs. Query type AI Overview strength Directory strength Discovery ("best tools for X") High — often answers directly Low for zero-click intent Comparison ("X vs Y, which wins") Medium — hedges, rarely commits High — structured attrs + verdict Filtered browse ("offline + mobile app") Low — prose, no filters High — faceted structured data Freshness ("is X still maintained?") Inconsistent — lags commits High — weekly ETL refresh The comparison and filtered-browse rows are the actual load-bearing columns of this bet. Why the cost structure matters for intellectual honesty At $25/month, I can run this experiment for a year without needing revenue to justify continuing. I'm not under pressure to interpret ambiguous signals optimistically. Compare that to a project burning $200/month on infrastructure: you'd rationalize flat Search Console data as "still in the sandbox phase" past the point where the data actually says something. The full cost breakdown is genuinely minimal — Vercel Pro at $20, Turso starter at $0, Claude Haiku API in single-digit dollars for monthly ETL runs, GitHub Actions on free minutes. I won't claim AdSense is approved or revenue is flowing until it is. Right now, AdSense rejected the *.vercel.app version of the sites. I've moved to custom domains and verified them in Search Console. I'm waiting for real crawl data before making any claims about what's working. What would change my mind Three outcomes would tell me the bet is wrong: Impressions but near-zero clicks at 90 days. If Search Console shows my pages appearing as AI Overview citation sources but click rates stay near zero on comparison pages specifically, Google is extracting my signal without distributing traffic. That's the worst-case scenario — I'd need to rethink the format entirely. AdSense keeps rejecting after genuine depth improvements. The original rejection was partly a *.vercel.app domain issue, but if Google's classifier still rates the pages as thin after I've rebuilt with real structured content and specific editorial attributes, my model of what "quality" means to the classifier is wrong. Comparison queries migrate fully to LLM chat. If people stop typing "X vs Y" into Google and start asking ChatGPT directly, the downstream click I'm betting on disappears. I don't see evidence of this happening at scale for research involving specific attribute constraints — but I'm monitoring query volume patterns month-over-month. The first outcome is the one I'd want to see early. Impressions with near-zero clicks on comparison pages by month 3 would tell me to pivot the format immediately rather than wait six months for a conclusion I could have reached sooner. FAQ Why three sites instead of one authority site? Three narrow sites let me test three different intent types simultaneously. Games-like, AI tools, and OSS alternatives attract different queries and different audiences. One site would take longer to produce the same signal volume about which format works. The original architecture post covers the reasoning. How does Claude Haiku generate the structured editorial fields? Each ETL run sends entries through a shared Claude Haiku client that uses system-prompt caching to amortize the cost across batch runs. The prompts are tuned to force specific attribute outputs — avoid-if caveats, audience fit, freshness status — not open-ended descriptions. What if one site works and two don't? That's a useful outcome, not a failure. The format that works tells me something specific about the intent type. I'll invest in what works and document what didn't. Where will you publish the October 2026 verdict? On this blog, with raw Search Console screenshots. I'll publish regardless of whether the numbers are favorable. Part of an ongoing 6-month experiment running three AI-curated directory sites. The technical claims here are real; this article was AI-assisted.

The obvious counterargument to everything I'm building is this: Google already does it. You type "best AI tools for video editing" into Google and an AI Overview surfaces a curated list, synthesized from the same kind of data I maintain, without requiring a click. My three directory sites — Top AI Tools, Find Games Like, and Open Alternative To — are competing with a feature baked into the world's dominant search engine. I launched these sites on 2026-04-23, built on an architecture that runs at about $25/month. Traffic is essentially zero — the sites have been indexed for three weeks and organic crawling takes time. The question I keep returning to isn't whether Google will eventually index my pages. It's whether anyone will prefer clicking through to my site over reading the AI Overview box that already answered the same question. Here's my honest, falsifiable position. The bet, stated plainly By October 2026 — six months post-launch — at least one of the three sites will show organic click trends in Google Search Console indicating real query traffic to specific comparison or filtered-browse pages. I define that as: at least 200 non-homepage organic clicks per month, sustained for two consecutive months, from queries I didn't directly drive through social or newsletter posts. If that doesn't happen, I'll publish the Search Console screenshots and write a post explaining what I got wrong. I'm committing to that here. The counterargument I take seriously AI Overviews have gotten genuinely good at list-and-compare synthesis. If you search "open source alternative to Notion" today, Google often returns a four-item structured list with one-sentence descriptions directly in the Overview box. My Open Alternative To site covers that territory. The AI Overview absorbs the zero-click version of that query. The optimistic response is: "my site appears as a citation source." The pessimistic response is: "Google consumes your signal and stops sending clicks." The pessimistic version has supporting evidence — industry-wide CTR on informational queries dropped measurably as AI Overviews expanded through 2025, and the trend hasn't reversed. I don't think the pessimistic version is the whole story, but I'm not dismissing it. The most dangerous move is to assume the counterargument is wrong without designing around it. Where AI Overviews have structural blind spots AI Overviews are strong at synthesizing "what exists." They're weaker at three things I've deliberately built for. Attribute-based filtering. If someone wants "open source Notion alternatives that work offline and have a mobile app," AI Overviews give hedged prose answers because they're synthesizing text, not querying structured fields. My Turso DB has works_offline, has_mobile_app, and last_commit_date as typed columns. Faceted filtering on those fields is something a browseable directory does better than a language model writing a paragraph about the general landscape. Editorial negative-space. My game recommender includes "avoid if" caveats — structured fields that answer "who should skip this?" generated by a Claude Haiku prompt that specifically forces a critical answer. AI Overviews don't have a mechanism to surface structured negatives. They default to positive framing, which means someone with a specific disqualifying requirement gets an unhelpful answer. Freshness on maintenance status. The ETL that populates the AI tools directory pulls GitHub commit activity weekly. A tool that hasn't been touched in 14 months is marked as low activity. AI Overviews don't distinguish between a tool actively maintained in 2026 and one that peaked in 2024 — they rely on the recency of web mentions, which can lag by months after a project goes dormant. None of these defenses are permanent. Google could build structured attribute filtering into AI Overviews. But they require deliberate pipeline design, not just synthesis, and the gap exists now. The downstream click thesis Even if my sites lose the zero-click battle on broad discovery terms, there's a second query type I'm explicitly targeting: the downstream comparison query. The sequence: someone types "Notion alternatives" into Google, gets an AI Overview naming four tools, then types "Appflowy vs Anytype performance" to compare the two they're considering. That second query is post-AI-Overview research. It has commercial intent. It wants a verdict, not another list. For that query, a page with structured attribute comparison, a clear verdict, and fast load time competes directly with another AI-style answer — and structured data beats generative prose for "which one wins on attribute X." This is partly why I chose static SSG over dynamic AI rendering for these sites: a fast, indexable page with typed comparison fields is what a second-stage research click needs. Query type AI Overview strength Directory strength Discovery ("best tools for X") High — often answers directly Low for zero-click intent Comparison ("X vs Y, which wins") Medium — hedges, rarely commits High — structured attrs + verdict Filtered browse ("offline + mobile app") Low — prose, no filters High — faceted structured data Freshness ("is X still maintained?") Inconsistent — lags commits High — weekly ETL refresh The comparison and filtered-browse rows are the actual load-bearing columns of this bet. Why the cost structure matters for intellectual honesty At $25/month, I can run this experiment for a year without needing revenue to justify continuing. I'm not under pressure to interpret ambiguous signals optimistically. Compare that to a project burning $200/month on infrastructure: you'd rationalize flat Search Console data as "still in the sandbox phase" past the point where the data actually says something. The full cost breakdown is genuinely minimal — Vercel Pro at $20, Turso starter at $0, Claude Haiku API in single-digit dollars for monthly ETL runs, GitHub Actions on free minutes. I won't claim AdSense is approved or revenue is flowing until it is. Right now, AdSense rejected the *.vercel.app version of the sites. I've moved to custom domains and verified them in Search Console. I'm waiting for real crawl data before making any claims about what's working. What would change my mind Three outcomes would tell me the bet is wrong: Impressions but near-zero clicks at 90 days. If Search Console shows my pages appearing as AI Overview citation sources but click rates stay near zero on comparison pages specifically, Google is extracting my signal without distributing traffic. That's the worst-case scenario — I'd need to rethink the format entirely. AdSense keeps rejecting after genuine depth improvements. The original rejection was partly a *.vercel.app domain issue, but if Google's classifier still rates the pages as thin after I've rebuilt with real structured content and specific editorial attributes, my model of what "quality" means to the classifier is wrong. Comparison queries migrate fully to LLM chat. If people stop typing "X vs Y" into Google and start asking ChatGPT directly, the downstream click I'm betting on disappears. I don't see evidence of this happening at scale for research involving specific attribute constraints — but I'm monitoring query volume patterns month-over-month. The first outcome is the one I'd want to see early. Impressions with near-zero clicks on comparison pages by month 3 would tell me to pivot the format immediately rather than wait six months for a conclusion I could have reached sooner. FAQ Why three sites instead of one authority site? Three narrow sites let me test three different intent types simultaneously. Games-like, AI tools, and OSS alternatives attract different queries and different audiences. One site would take longer to produce the same signal volume about which format works. The original architecture post covers the reasoning. How does Claude Haiku generate the structured editorial fields? Each ETL run sends entries through a shared Claude Haiku client that uses system-prompt caching to amortize the cost across batch runs. The prompts are tuned to force specific attribute outputs — avoid-if caveats, audience fit, freshness status — not open-ended descriptions. What if one site works and two don't? That's a useful outcome, not a failure. The format that works tells me something specific about the intent type. I'll invest in what works and document what didn't. Where will you publish the October 2026 verdict? On this blog, with raw Search Console screenshots. I'll publish regardless of whether the numbers are favorable. Part of an ongoing 6-month experiment running three AI-curated directory sites. The technical claims here are real; this article was AI-assisted.