How I built a 30,000-venue platform without writing code

Ziv Goldvasser — Tue, 30 Jun 2026 10:04:18 +0000

Six months ago I would have laughed at that sentence. Today it's just my morning routine.

Gayout is an LGBTQ+ travel directory — bars, saunas, hotels, Pride events, mega-events across 120 countries in 11 languages. About 30,000 venues, hundreds of thousands of indexed pages, ~$300/month total infrastructure cost. Built and operated solo. I can read PHP slowly. I cannot write a non-trivial query without help.

This post is the actual stack and the patterns that make it work — not a high-level "AI is amazing" rant.

## The stack

Boring web: PHP 8 + MariaDB on LiteSpeed shared hosting. Cloudflare in front. No frameworks. Long-running cron jobs do all the interesting work.

The interesting layer is the AI orchestration:

Claude Sonnet → Haiku for city content generation (40 cities/night, A-Z cycle)
Gemini 2.5 Flash for translation across 11 languages + venue descriptions
Anthropic for the trip planner (free user-facing feature)
Google Places + DataForSEO + Eventbrite + Meetup + OutSavvy + AllEvents + TripAdvisor + Skiddle + RA + SeatGeek + Ticketmaster for venue/event data ingestion

Each pipeline runs on its own cron schedule with three guarantees baked in: flock single-instance lock, hard runtime cap, batch size limit. Without those, one bug took the entire site down at 3am once — a lesson I now apply religiously.

The pattern: nightly content + nightly cleanup

Every night, 40 city pages get refreshed. For each city the cron runs about 15 sequential audit steps, wrapped in try/catch so one failure doesn't break the rest:

foreach ([
    ['lgbtq backfill',     fn() => CityAuditor::backfillLgbtqType($vid)],
    ['archive stale',      fn() => CityAuditor::archiveStaleVenues($vid)],
    ['miscat fix',         fn() => CityAuditor::fixMisCategorized($vid)],
    ['spa/sauna split',    fn() => CityAuditor::reclassifySpaSauna($vid)],
    ['cruising detect',    fn() => CityAuditor::detectCruising($vid)],
    ['recurring events',   fn() => CityAuditor::detectRecurringEvents($vid)],
    ['translate non-Latin', fn() => CityAuditor::translateNonLatinVenues($vid)],
    ['enrich events',      fn() => CityAuditor::enrichVenueEvents($vid)],
    ['city Q&A',           fn() => CityAuditor::generateCityQA($vid)],
    ['review summary',     fn() => CityAuditor::summarizeVenueReviews($vid)],
    ['humanize guide',     fn() => CityAuditor::humanizeCityGuide($vid)],
    ['hotel coverage',     fn() => CityAuditor::ensureHotelCoverage(...)],
    ['local-lang venues',  fn() => CityAuditor::discoverLocalLanguageVenues(...)],
] as [$label, $fn]) {
    try { $auditSummary[] = $fn(); }
    catch (\Throwable $e) { $auditSummary[] = "$label=ERR(...)"; }
}

By morning: 40 city pages have fresher intros, new FAQ blocks, archived closed venues, deduped recurring events, re-tagged categories, and freshly discovered venues from local-language Google Places searches ("schwule Bar Berlin" surfaces results "gay bar Berlin" misses).

The actual hard part: data quality

The content generation isn't hard. AI handles that for fractions of a cent per page. The hard part is everything around the content. A few real examples:

Pleasuredrome's English page had a Hebrew sentence in it. A translation pipeline wrote into the source EN column instead of translations table. Found by a one-time scan for non-Latin chars in description_long using a byte-length-to-char-length ratio (Hebrew is 2 bytes/char, English ~1):

SELECT id, name FROM venues
WHERE status = 'approved'
  AND LENGTH(description_long) > CHAR_LENGTH(description_long) * 1.3

Vivares in Madrid had sddfsd as its description. Found with a gibberish detector — short, all-lowercase, no spaces:

SELECT id, name FROM venues
WHERE CHAR_LENGTH(TRIM(description_short)) BETWEEN 3 AND 30
  AND description_short REGEXP '^[a-zA-Z]+\.?$'

The card grid showed "Drag Brunch with Mizery × 8". Recurring weekly events imported as separate rows. Collapsed at the display layer (not DB — imports keep adding) with a (venue_id, normalized_title) dedup that strips month prefixes:

function dedup_recurring_events(array $rows, int $cap = 0): array {
    $out = []; $seen = [];
    foreach ($rows as $e) {
        $t = $e['title'];
        $t = preg_replace('/^\s*(Jan|Feb|...|Dec)[a-z]*\s+\d{0,2}[,\s]*\d{0,4}\s*[-–—]\s*/i', '', $t);
        $t = preg_replace('/\s+\d{1,2}(st|nd|rd|th)?\b/i', '', $t);
        $t = strtolower(preg_replace('/[^\p{L}\p{N}]+/u', '', $t));
        $key = ($e['venue_id'] ?? 0) . '|' . $t;
        if ($t === '' || isset($seen[$key])) continue;
        $seen[$key] = true;
        $out[] = $e;
        if ($cap > 0 && count($out) >= $cap) break;
    }
    return $out;
}

These aren't sexy AI features. They're the actual product.

The humanizer (or: how to keep AI text out of your AI text)

Every AI writer trips the same tells: vibrant, nestled, rich tapestry, hidden gem, bustling, whether you're a seasoned, in conclusion. A second pass uses a senior-editor prompt to strip them. The prompt is the entire architecture:

You are a senior travel editor at Condé Nast Traveler — known for a sharp,
skeptical, human voice that strips marketing language and replaces it with
real observation. You are revising an AI-generated draft.

🚫 BANNED PHRASES — delete or replace any of these on sight:
  vibrant · nestled · rich tapestry · delve into · bustling · hidden gem ·
  melting pot · feast for the senses · something for everyone · world-class ·
  iconic · plethora · myriad · navigate · landscape · journey · embrace ·
  immerse yourself · at the heart of · a haven for · a must-visit ·
  whether you're a · seasoned · in conclusion · overall · plays a crucial role

✍️ HOW HUMANS WRITE TRAVEL:
  - Specifics over generics. "€8 cocktails" beats "reasonable prices."
  - Short sentences mixed with long ones. Vary aggressively.
  - Use contractions: "it's", "don't", "you'll".
  - Mild opinion is good. AI never has opinions. Humans do.
  - One-line paragraphs are fine.
  - Cut every sentence that doesn't earn its place.

Then a one-time scan flags published content that still contains banned phrases and queues it for re-humanization. Iteratively, the corpus gets better.

Cost discipline matters more than the AI

This week I cut a DataForSEO bill by ~75% with five-minute changes. The pattern:

Four search keywords had ~80% overlap. "gay events" + "pride" + "drag" + "lgbtq" returned mostly the same rows. Cut to 2. → 50% saving.
Sunday "weekly drain" pulled every city. Filtered to cities with ≥3 approved venues. → Another big saving on Sundays.
Saturated-city cooldown. If the last 3 runs of a city all returned 0 new inserts, skip that city for 30 days:

SELECT city_id FROM (
    SELECT city_id, inserted_count, posted_at,
           ROW_NUMBER() OVER (PARTITION BY city_id ORDER BY posted_at DESC) AS rn
    FROM dataforseo_tasks
    WHERE status = 'complete' AND keyword LIKE 'gay events%'
) t
WHERE rn <= 3
GROUP BY city_id
HAVING COUNT(*) = 3
   AND SUM(inserted_count) = 0
   AND MAX(posted_at) > NOW() - INTERVAL 30 DAY

Day-of-week skip. Top-cities cron stopped running Tue/Thu/Sat. Events don't appear hourly; daily polling was overkill. → 43% extra saving.

Total: from ~$10/month to ~$2.50/month. The same pattern applies to every API in the stack — most cost is wasted on data you already have.

What "no developer" actually means

It doesn't mean no engineering happens. It means a single non-engineer can:

Specify clearly. "Make a thing that dedups events" doesn't work. "Find events where (venue_id, normalized_title) matches another future event, keep the earliest, archive the rest, and add a 30-day cooldown so imports don't immediately re-create them" works.
Read output critically. I can't always spot a logic bug, but I can almost always spot when AI output doesn't match what I asked for. That's enough to iterate.
Pay attention to failure modes. Every job has hard caps on runtime, batch size, and dollars spent. Without them, one bug = one expensive incident.
Be honest when stuck. "I have no idea why this is broken, here's the error and the last three things I changed" is the fastest fix at 11pm.

The bigger picture

The platform I run today would have required a $500K seed round and a team of four three years ago. Now it's me, $300/month in AI/API costs, and a hosting bill smaller than my coffee budget.

Live data is at gayout.com. 30,000+ venues, 120 countries, 11 languages, including content in Russian, Hebrew, Arabic. Bug reports welcome.

If you're sitting on an idea you've told yourself you can't build because you can't code — that excuse is getting weaker every quarter.

Ziv runs Gayout.com — an LGBTQ+ travel platform built solo with AI tools. Based in Tel Aviv. Follow on Twitter/X and let me know what you want to know.

**Tags:** #ai #buildinpublic #showdev #indiehacker