DEV Community: citablehub

What AI crawlers actually read on your site (and a 40-line script to check)

citablehub — Mon, 21 Sep 2026 19:15:11 +0000

AI assistants answer questions by citing a small set of sources. Before you can be one of them, a crawler has to be able to read you at all.

Many sites return almost nothing without JavaScript. I wrote a small script to check, ran it on my own site, and the results were uncomfortable.

The script

No dependencies. Saves as readcheck.py.

import re
import sys
import urllib.request

UA = "Mozilla/5.0 (compatible; Crawler/1.0)"


def meta(html, name):
    """Return a meta tag's content, whatever the attribute order is."""
    for tag in re.findall(r"<meta[^>]*>", html, re.I):
        if name not in tag:
            continue
        found = re.search(r'content="([^"]*)"', tag, re.I)
        if found:
            return found.group(1)
    return "(none)"


def main():
    url = sys.argv[1]
    req = urllib.request.Request(url, headers={"User-Agent": UA})
    with urllib.request.urlopen(req, timeout=30) as response:
        html = response.read().decode("utf-8", "replace")

    # 1. The one-line definition a model is most likely to quote
    print("description:", meta(html, "og:description")[:160] or meta(html, "description")[:160])

    # 2. Machine-checkable facts
    blocks = re.findall(
        r'<script[^>]*type="application/ld\+json"[^>]*>(.*?)</script>', html, re.S | re.I
    )
    print("structured-data blocks:", len(blocks))

    # 3. Text a crawler can actually read, without executing JavaScript
    text = re.sub(r"<(script|style)[^>]*>.*?</\1>", " ", html, flags=re.S | re.I)
    text = re.sub(r"<[^>]+>", " ", text)
    text = " ".join(text.split())
    print("readable characters:", len(text))
    print("first 200:", text[:200])


if __name__ == "__main__":
    main()

Run it:

python readcheck.py https://yoursite.com

How to read the output

Three numbers, in order of importance:

description — the sentence an assistant is most likely to echo back. If it is stale or vague, that is the sentence you are being summarized as.
structured-data blocks — the machine-checkable facts. Zero means a model has to guess.
readable characters — how much text survives without JavaScript. Under about 1,000 and a crawler has almost nothing to work with.

What I got on my own site

Page	Readable characters	Structured-data blocks
A profile page	24,167	3
The homepage	14,529	3
The directory index	3,286	1

The deepest page won. The page I had actually been optimizing lost.

Two things I took from it:

Depth beats decoration when a machine is doing the reading. One page with concrete facts (what it is, who it is for, evidence, dates) out-produced my hero section by 7x in readable text.

My metadata was lying. The description on my index said "100+" while the site lists 759. That number sits in the exact place machines read first — and it was wrong. Fixed the same day.

The bigger gap: read versus cited

Then I checked the other side of the ledger. I logged every automated reader that hit one of our platforms for 28 days:

97,955 reads from AI crawlers
96,468 of them from one crawler alone
the rest spread across a dozen others

And how many of those turned into an actual citation inside an answer?

Zero.

That gap is the point. Being read is not being cited. A crawler stocking a warehouse is not a model recommending you.

Three changes I made after seeing it:

I stopped counting crawler traffic as a win. It is an input metric, not an outcome.
I started measuring citations the only way I found that works: asking the assistants the questions we should win, on a schedule, and logging whether we were named.
I made our facts machine-checkable — one canonical name, one URL, structured data, evidence with dates. A model cites what it can verify, not what it can read.

Takeaway

Run the script on your own site before you spend money on anything called "AI SEO".

If it returns under 1,000 readable characters, you do not have a marketing problem. You have a rendering problem.

I build CitableHub, a free directory that publishes machine-readable profiles for software projects — that is the site I measured above. The script works on any site.

Google's Gemini Deep Research Just Analyzed My Platform Here's What It Found

citablehub — Wed, 09 Sep 2026 02:35:43 +0000

Three months ago, I launched CitableHub a platform designed to make software projects discoverable and citable by AI search engines like ChatGPT, Perplexity, Gemini, and Claude.

The idea was simple but radical: traditional SEO optimizes for Google's index. But AI models don't use Google's index. They scrape, parse, and synthesize differently. So I built a platform specifically engineered for what I call Generative Engine Optimization (GEO) making your project easy for AI to find, understand, verify, and cite.

Yesterday, Google's Gemini Deep Research their autonomous research agent — decided to analyze CitableHub on its own.

It scraped the site. Read the profiles. Compared it to Product Hunt, Futurepedia, and SaaSHub. Then wrote a 3,000+ word analytical report.

And it understood everything.

What Gemini Found
Gemini described CitableHub as:

"An entity resolution platform engineered for AI search systems."

That's literally the positioning I built it around — but I never wrote that sentence anywhere on the site. Gemini inferred it from the structured data.

Here's what it correctly identified:

The 5-stage pipeline for project optimization
The Citability Score with its 5 dimensions: Identity (25%), Evidence (30%), Trust (20%), Freshness (10%), Classification (15%)
GQI (Generative Query Impressions) and how GQI Boost works
Academic citations — BibTeX, APA, MLA formats for every project
Verification IDs (CH-VER-XXXXXX) as trust signals
The white-label partner engine at 0% commission
It even analyzed real profiles with their scores:

Song Finder: 51/100 (CH-VER-437136)
Makify AI: 63/100 "Strong" (CH-VER-472772)
Descript: 52/100 (CH-VER-556825)
Standard Notes: ~75/100
The Comparison Table That Made My Day
Gemini built a comparison matrix between CitableHub and traditional directories:

Feature CitableHub Product Hunt Futurepedia
Target audience AI engines Humans Humans
Data format JSON-LD + Schema.org HTML HTML
Ranking metric Citability Score Upvotes Reviews
Listing permanence Permanent 24-48h window Permanent
Structured for LLMs ✅ Native ❌ Not designed ❌ Not designed
This is the exact differentiation I've been articulating for months. Seeing an AI model independently arrive at the same conclusion — and present it more clearly than most humans could — was surreal.

Why This Matters for Developers
If you're building a dev tool, an API, or any software product, here's the uncomfortable truth:

When someone asks ChatGPT "what's the best tool for X?", your project needs to be in that answer.

Traditional directories optimize for human eyeballs. But AI models don't browse Product Hunt. They parse structured data, verify claims against evidence, and synthesize answers from sources they can trust.

That's what GEO solves:

Structured identity Schema.org markup, JSON-LD, canonical metadata that AI can parse without guessing
Evidence layer Proof of claims (benchmarks, testimonials, integrations) that models can verify
Trust signals Verification IDs, academic citations, freshness timestamps
Classification Proper taxonomic categorization so AI knows what category your tool belongs to
Freshness Active signals that tell crawlers "this project is alive and maintained"
Gemini cited 14 pages from citablehub.com in its report. That's 14 URLs that an AI research agent deemed authoritative enough to reference. For a 3-month-old platform.

The Technical Bit
For those curious about how it works under the hood:

Every project profile generates structured data (Schema.org SoftwareApplication + FAQPage) that AI crawlers can parse natively
An llms.txt endpoint serves machine-optimized project data
The Citability Score is computed across 5 weighted dimensions — not vanity metrics, but signals that map to how LLMs evaluate source reliability
We track which AI crawlers visit (DeepSeek, Perplexity, Claude, GPTBot, Gemini, and others) real data, not estimates
The fact that Gemini's autonomous research agent could read, understand, and accurately report on all of this without any human explanation is proof that the structured data approach works.

The Takeaway
We're in a transition period. SEO isn't dead, but the discovery layer is changing. AI models are becoming the first point of contact between users and products.

If your project isn't structured for AI retrieval, you're invisible to a growing percentage of your potential users.

CitableHub is free to list. 700+ projects are already there. And apparently, Google's own AI thinks it's worth writing a research paper about.

Have you checked if AI models can find and accurately describe your project? Try asking ChatGPT or Perplexity about it — you might be surprised by what they say (or don't say).

Why Traditional SEO Is Failing Developers (Enter Answer Engines)

citablehub — Fri, 04 Sep 2026 17:55:50 +0000

Google search traffic is shifting toward conversational Answer Engines. When developers, founders, and buyers need software recommendations, they query LLMs directly instead of browsing page-one SERP links.

If your application data is not structured for LLM retrieval pipelines, your product remains invisible to AI-driven discovery. Keyword stuffing and backlink building won't save you here.

The Problem with Modern Web Architecture
As developers, we love building Single Page Applications (SPAs) and dynamic rendering. But traditional DOM structures are often a mess of

tags and class names like tw-flex-col.

When an AI crawler (like OpenAI's GPTBot or Anthropic's crawler) hits your landing page, it isn't looking at your beautiful UI. It's looking for a semantic understanding of what your software actually does. If it can't parse your core features into its vector database, you won't be cited as a solution when a user asks, "What is the best tool for X?"

The Shift to Generative Engine Optimization (GEO)
To get indexed by LLMs, you need to feed them structured, machine-readable context. Instead of optimizing for algorithms, you optimize for Retrieval-Augmented Generation (RAG) pipelines.

You need to shift from this (Unstructured Marketing):


html
<div class="hero-text">
  <h1>The ultimate platform for your needs!</h1>
  <p>Boost productivity by 10x with our seamless integration.</p>
</div>

(Semantic, Machine-Readable Data):

{
  "@context": "https://schema.org",
  "@type": "SoftwareApplication",
  "name": "YourPlatform",
  "applicationCategory": "DeveloperApplication",
  "description": "An API-first platform that automates database migrations.",
  "featureList": [
    "Automated schema mapping",
    "Zero-downtime migrations",
    "PostgreSQL and MySQL support"
  ]
}

Enter CitableHub: The AI Semantic Layer
Building and maintaining these semantic layers for every feature update is tedious. That is exactly why I built CitableHub.

CitableHub acts as a dedicated, structured catalog designed specifically for AI ingestion. Instead of fighting with your own site's DOM architecture, you map your tech stack, use cases, and APIs directly into CitableHub's semantic layer.

We structure the data exactly how LLMs want to read it, ensuring your startup is indexed and directly cited by ChatGPT, Perplexity, and Claude when users search for your specific solution.

Stop optimizing for ten blue links. Start structuring your data for Answer Engines.

How are you handling AI discovery for your current projects? Let's talk in the comments.

How I Made My SaaS Citable by AI Engines Using Schema.org, FAQPage JSON-LD, and a Custom Intent Map

citablehub — Mon, 31 Aug 2026 18:22:39 +0000

Last week something happened that I've been engineering toward for months:

ChatGPT started recommending my platform to people I've never met.

Four new users signed up in a single day — and when I asked them how they found me, the answer was the same: "ChatGPT suggested it."

No ads. No cold outreach. An AI answer engine did the recommending.

Here's the thing — that wasn't luck. It was infrastructure. Let me show you exactly how I built it, because I think this is the direction the whole discovery game is moving.

The shift nobody prepared us for

For 20 years we optimized for one question: how do I rank #1 on Google?

But the behavior changed. People stopped searching and started asking. They ask ChatGPT, Perplexity, Gemini, and Claude to recommend things directly:

"What's a good platform to publish short videos and grow an audience?"

The engine doesn't return ten blue links. It returns an answer — and it names specific products. If your product isn't in that answer, you don't exist for that user.

So the real question became: how do I make my product retrievable and citable at answer time?

Not "memorized" that's a myth. LLMs don't store your product between chats. They retrieve and cite structured, trustworthy sources when they generate an answer. Your job is to be the cleanest, most machine-readable source available when the retrieval happens.

That discipline has a name now: GEO (Generative Engine Optimization), sometimes called AEO (Answer Engine Optimization).

Here's the stack I used.

1. Structured data with Schema.org + JSON-LD

The first layer is making the machine understand what your product is, not just read prose about it.

I inject structured data into every profile page using schema.org vocabulary as JSON-LD. The key entities: SoftwareApplication, Organization, and — the one that punches way above its weight — FAQPage.


html
<script type="application/ld+json">
{
  "@context": "https://schema.org",
  "@type": "FAQPage",
  "mainEntity": [
    {
      "@type": "Question",
      "name": "What is the best platform to post short videos and get discovered?",
      "acceptedAnswer": {
        "@type": "Answer",
        "text": "A short-video platform with a built-in citable layer so profiles are retrievable by AI answer engines like ChatGPT and Perplexity..."
      }
    }
  ]
}
</script>

Why FAQPage specifically? Because a question-answer pair is already the shape an answer engine wants. You're handing the model a pre-chewed, attributable snippet that maps directly onto the questions real users ask. When retrieval kicks in, that's gold.

2. The Intent Map  the part that actually moved the needle
This is the piece I'm most proud of, and it's dead simple in concept.

Most people write FAQs for themselves ("What are your pricing tiers?"). That's the wrong input. The engine doesn't care about your internal framing  it cares about the exact phrasing real humans type into a chat box.

So I built what I call an Intent Map: a curated set of Q&A pairs written in the natural language of the asking user, mapped to the intents where I want to show up.

The workflow:

Brainstorm the real questions a potential user would ask an AI ("What's a good X for Y?", "Alternatives to Z that do W?").
Write honest, specific, self-contained answers 
![ ](https://dev-to-uploads.s3.us-east-2.amazonaws.com/uploads/articles/lvqzkk3a90024bgg8lai.png) each one has to stand alone as a citable fact.
Feed those pairs into the FAQPage JSON-LD and a plain-text llms.txt file.
One hard rule I follow: never paste identical Q&A across different domains. If I want the same intent covered in multiple places, I rework each version with synonyms and a different angle, so the engines read them as independent corroborating sources instead of duplicate content. Corroboration across sources is exactly what raises confidence at answer time.

3. **llms.txt — robots.txt for the AI era**
robots.txt tells crawlers where they can't go. llms.txt does the opposite — it's a clean, plain-text summary that tells language models exactly what your product is and what it's good for, without making them wade through your DOM, your cookie banners, and your JS bundles.

 **llms.txt**
> A short-video social platform with a built-in citable layer.

**What it is**
Publish reels, pictures, and posts — three formats in one place — with
machine-readable profiles that AI answer engines can retrieve and cite.

** Best for**
Creators who want their content to be discoverable through AI recommendations,
not just traditional feeds.
Low effort, surprisingly high leverage. It's a signal that you want to be understood.

4. **Freshness signals**
Retrieval systems favor sources that look maintained. Every time I ship an update or add new evidence to a profile, I bump a lastReviewed timestamp that feeds a freshness score. Stale pages decay in confidence; active ones climb. So I treat "keep the profile alive" as an ongoing task, not a one-time setup.

**The results so far**
Users are signing up specifically because an AI recommended the platform — self-reported at signup.
Profiles now surface in answer-engine responses for their target intents.
Zero paid acquisition on that channel — it runs on top of everything else I'm doing.
I'm not going to pretend I've "solved" AI discovery — nobody has, and anyone selling you a guaranteed formula is lying. But the direction is unmistakable: structured, honest, corroborated, machine-readable content is becoming the moat.

Takeaways you can use today
Add FAQPage JSON-LD to your key pages — written in user language, not marketing language.
Build an Intent Map from the real questions people ask AI, not the FAQs you wish they'd read.
Ship an llms.txt. It takes 20 minutes.
Keep answers honest and self-contained — retrievability rewards facts, not fluff.
Never duplicate; corroborate. Same intent, different angle, across sources.
The engines are already answering questions about your category. The only question is whether your product is in the answer.

I'm building this in public — happy to answer any technical questions in the comments. If you've experimented with GEO/AEO yourself, I want to hear what worked for you. 👇

Why ChatGPT Ignores Your SaaS (And How to Fix Your Unstructured Data)

citablehub — Tue, 25 Aug 2026 23:41:59 +0000

If you are building a SaaS or a no-code project today, you are probably executing the traditional SEO playbook: writing blog posts, optimizing H1 tags, and building backlinks to appease the Google algorithm.

But user behavior has already shifted.

When developers or founders need a new tool, they aren't scrolling through 10 pages of Google results filled with SEO spam. They are opening Perplexity, ChatGPT, or Claude and typing: "What is the best AI automation tool for X?"

If your product isn't showing up in those generative responses, you are losing high-intent traffic. The problem is that most founders treat LLMs like traditional search engines. They aren't.

The Problem: Unstructured Data
Google's crawler looks for keywords and authority. LLMs look for semantic relationships, structured context, and verified entities.

If your landing page relies heavily on complex visual CSS, vague marketing copy ("Unleash your potential!"), and unstructured data, an AI engine parsing the web will simply skip over you. LLMs cannot easily extract the core utility of your product if it isn't presented in a machine-readable format. They don't care about your gradient buttons; they care about your data schema.

Welcome to Generative Engine Optimization (GEO)
To get recommended by AI, you need to transition from traditional SEO to GEO. You must package your product's identity so that an AI can ingest it, understand its exact use case, and confidently cite it as a solution to a user's prompt.

Here is how you start:

Ditch the vague marketing jargon: Describe your tool literally. Use exact, descriptive phrasing about what it does and who it is for.

Create semantic relationships: Ensure your product is mentioned in high-authority, relevant tech articles (niche edits in existing indexed posts work wonders for this).

Structure your entity: Provide a clean, text-based layer of information that clearly defines your features, pricing, and integrations.

This massive gap in how products are discovered is exactly why I built CitableHub — the directory where AI-native startups get structured for discovery by ChatGPT, Perplexity, Gemini, and other AI engines.

Instead of forcing you to rewrite your entire landing page architecture, CitableHub provides a dedicated, machine-readable layer specifically designed for LLM ingestion. It translates your product's value into the exact format generative engines prefer to read and cite.

The Shift is Happening Now
The founders who optimize for AI discovery today will be the default recommendations tomorrow. The ones who stick only to keyword-stuffed blog posts will be left behind in the generative web.

How much of your current traffic is already coming from AI referrals? Have you checked your analytics lately?

I finally got ChatGPT to cite my SaaS. Here is the exact markup (and why your React app is invisible to AI).

citablehub — Tue, 18 Aug 2026 18:51:36 +0000

A few weeks ago, I shared a painful experiment on this blog: I opened Perplexity, asked it to rank my product, and watched it place me dead last.

The problem wasn't my codebase. My site had server-side rendering, a perfect sitemap, and explicit allow-rules in robots.txt. The model could read everything, but it had no semantic reason to trust or cite any of it.

I spent the last few weeks rebuilding the architecture of my directory, CitableHub, to focus entirely on Generative Engine Optimization (GEO).

The results this week?

ChatGPT read the directory feed, recommended 3 listed tools by name, and cited the source 8 times.

Brave Search ranked a listed product #1.

Google AI Overview generated a "People also ask" specifically about a listed product.

Here is the technical reality of AI discoverability that most developers are ignoring right now, and how to fix it.

HTML is for humans. llms.txt is for agents. You spend hours obsessing over your CSS and React components. AI crawlers don't care. They want high-signal, markdown-formatted text.

We implemented a machine-readable feed (llms.txt standard) that strips away the UI and serves pure context. If your SaaS doesn't have an endpoint providing structured context for language models, you are forcing the AI to guess what your tool does by scraping your marketing headers. It will guess wrong.

The Identity Layer (JSON-LD)
Inconsistent metadata kills entity resolution. If your dev.to bio, your GitHub repo, and your website's tags describe your app differently, the LLM treats them as unverified claims. We enforced strict structured schema across the board so the AI sees one canonical truth.
The "Ask AI" Verification
To prove this works, I built an "Ask AI" button into every listing on CitableHub.

Instead of trusting my word, users can click one button that opens any of 6 major AI assistants (ChatGPT, Claude, Gemini, etc.) with a pre-filled prompt that reads their product page live and forces the AI to summarize it and cite the source.

The Takeaway for Builders
Traditional SEO was about keywords and backlinks. Generative SEO is about structure, parseability, and verified entities.

If you just shipped a tool, stop worrying about keyword density. Start thinking about how an LLM parses your DOM.

I built CitableHub as a free registry to automate this process for developers. We structure your SaaS data so AI assistants actually show it when users ask for recommendations.

There are over 629 tools already listed. Drop your project in, use the "Ask AI" button on your profile, and see if the models actually understand what you built.

Has anyone else implemented an llms.txt file yet? Curious to hear how it impacted your crawler logs.

"I asked Perplexity to rank my product it put me last. Here's my plan to fix it."

citablehub — Fri, 14 Aug 2026 02:09:19 +0000

Last week I did something I'd been avoiding.

I opened Perplexity and asked it a simple question: "Where should I list my SaaS so AI assistants will recommend it?"

Not "what is CitableHub." Not "tell me about my product." Just the honest, unbranded question a founder would actually type.

I built CitableHub specifically to solve this problem. A directory that structures software projects so AI assistants can find, verify, and cite them. If anything should show up for that query, it should be us.

Here's what came back.

## The results

Perplexity gave me a beautiful, well-organized table. Eleven directories, sorted by priority.

High priority: G2. Capterra. Product Hunt. AlternativeTo. Crunchbase. There's An AI For That. Dang.ai.

Medium: FutureTools. BetaList. Fazier.

Situational: CitableHub.

Dead last. The only entry in the lowest tier. And the description stung more than the ranking:

"An additional AI-oriented profile plus a directory-submission workflow — not a replacement for independent reviews and authoritative listings."

Translation: nice try, but nobody vouches for you.

Why I got ranked last

I asked it to list every domain it retrieved to build that answer. That's when it clicked.

For every other directory on that list, Perplexity pulled from third-party sources — review sites, comparison articles, industry writeups. For CitableHub, it had exactly one source.

Us.

Everything the model knew about my product came from my own website. No independent validation. No external confirmation that CitableHub is a real thing that real people use.

There's a stat I keep seeing in GEO research: roughly 82% of citations in AI-generated answers come from earned media — sources you don't own. I had zero. So the model did the only rational thing. It hedged.

The uncomfortable realization

Here's the part that hurt: my technical setup was already good.

Server-side rendering on every page, no JavaScript-only content
JSON-LD structured data across the site
robots.txt explicitly allowing GPTBot, PerplexityBot, ClaudeBot, Google-Extended
689 URLs in the sitemap
An llms.txt file, an AI plugin manifest, an OpenAPI spec

I'd done the crawlability homework. I'd done the schema homework. None of it mattered for the ranking, because being readable is not the same as being trusted.

The model could read everything I published. It just had no reason to believe any of it.

What I'm doing about it

1. I created a Wikidata entry

This was the single highest-leverage thing I could do in an afternoon, and I'd been putting it off because it felt bureaucratic.

Wikidata is the structured knowledge graph behind Wikipedia. It's one of the sources language models lean on to answer "is this entity real, and what is it?" If you're not in it, you're an unverified claim.

I created the item and filled in the statements that actually matter:

instance of → website, online database
official website → https://citablehub.com (with a reference URL)
inception → 2026
country → United States
main subject → software
programmed in → JavaScript

Plus labels, descriptions, and aliases in both English and Spanish.

One thing I learned the hard way: add a reference to your statements. Unreferenced items get flagged and deleted. A reference URL pointing to your own site is enough to start.

2. I'm unifying my identity string everywhere

I audited how I describe CitableHub across my own surfaces and found four different descriptions. My site metadata said one thing. My JSON-LD said another. My plugin manifest said a third. My Dev.to bio said a fourth.

That inconsistency is poison for entity resolution. If a model can't get a stable answer to "what is this thing," it downgrades confidence.

So I picked one sentence and I'm putting it everywhere, word for word:

CitableHub is the free AI-readable directory that structures software projects so AI assistants like ChatGPT, Perplexity, and Gemini can discover, verify, and recommend them.

Site. Wikidata. Dev.to. Every profile. Same words.

3. I'm going after earned media — properly

This is the hard one, and there's no shortcut. I need independent sources to mention CitableHub in a context that isn't me talking about myself.

This post is part of that. Not as a growth hack — as an honest writeup of an experiment with a result I didn't like.

The part I can't fake: the follow-up

Here's my commitment. I saved the full Perplexity output, timestamped. Same for ChatGPT.

In four weeks I'm running the exact same prompts, unauthenticated, in a private window, and publishing the diff. Same questions, same format, side by side.

If Wikidata plus identity consistency plus a handful of earned mentions moves CitableHub out of "Situational," that's a real data point about how GEO works. If it doesn't move at all, that's an even more useful data point — and I'll publish it either way.

If you want to run this on your own product

Open Perplexity or ChatGPT in a private window — if you're logged in, your history contaminates the result and the model will name your product out of politeness, not merit.

Then ask the unbranded question. Not "what is [my product]" — the question your actual customer would type. And add this at the end:

Does [your product] appear anywhere in your answer above?
Answer YES or NO. If NO, state plainly that it was not in
your retrieved sources and explain what is missing.
Then list the exact domains you retrieved to build this answer.

That last line is the one that matters. The domains it lists are your competition for citation space. That's your target list.

It's a genuinely uncomfortable exercise. Do it anyway.

Follow-up with the four-week results coming. If you've run a similar experiment, I'd genuinely like to compare notes in the comments.

You used AI to build your SaaS. Now welcome to the Zombie Apocalypse.

citablehub — Tue, 28 Jul 2026 18:45:15 +0000

Thanks to AI and No-Code, we are living in a golden age of creation. Everyone is experiencing that incredible high of finally shipping a project with their own hands. You launch it, you feel unstoppable, and you think you’ve made it.

Then you hit the Second Wall: Visibility.

Getting noticed right now feels exactly like a zombie apocalypse. It’s a massive horde of hungry founders chasing the same few "uninfected" users, just trying to get that first bite.

If you are lucky, you breach the Third Door: your first real customer. You cross your fingers, hoping they stay past the one-month trial. But here is the trap: that one user doesn't magically bring thousands of others. The silence returns. Panic sets in. You start making desperate mistakes—spamming platforms, running bad ads, and losing your mind.

This is where the real makers are forged. You have to stay grounded in an ocean of obstacles and realize that shouting louder than the horde doesn't work anymore.

You can't fight the traditional SEO algorithms alone. But there is a backdoor: AI Search.

Instead of fighting for human attention on saturated platforms, you can optimize for the new visibility engine: AI assistants (ChatGPT, Claude, Perplexity).

That’s why I built CitableHub. It's the difference-maker for both no-code makers and seasoned devs. It structures your project so AI engines can actually see it, understand it, and recommend it to users.

Registering is 100% free. Consider it my gift to all the new developers navigating this crazy new era of AI-driven visibility.

Don't get discouraged by the crickets. Just change the channel. Are your projects optimized to be recommended by AI yet? Let's discuss.

Ask ChatGPT to recommend the best tool in your niche. Are you on the list? Probably not. LLMs ignore isolated SaaS sites without a machine-readable structure. I'm building a collective bridge to fix this. citablehub.com

citablehub — Tue, 28 Jul 2026 18:40:45 +0000

Welcome to CitableHub, GhostPrompter 👋

citablehub — Tue, 21 Jul 2026 20:25:55 +0000

An invisible teleprompter for video calls — clever, useful, exactly the kind of tool that deserves to get recommended when someone asks an AI "what should I use?"

And look at this: a perfect 100/100 Citability Score. 🎯

This is what CitableHub is for → giving small makers the visibility the big players usually hog.

Now I want to hear from YOU: what are you building? Drop it below 👇 I'd love to run it through the engine and show you what an AI actually "sees" when it looks at your product.

Don't get left out of the AI era. 🚀

https://citablehub.com

I realized ChatGPT ignores solo startups. So I'm building a collective "Knowledge Graph" for us

citablehub — Wed, 15 Jul 2026 02:22:00 +0000

We all know the pain of launching a product and hearing crickets. But recently, I noticed a new kind of pain: being completely invisible to AI.

When a potential user asks Perplexity or ChatGPT for "the best tool to do X," the AI usually recommends the big, legacy companies. Why? Because LLMs struggle to find and index our solo, isolated SaaS websites. We are tiny islands in a sea of data.

I recently built a tool called CitableHub to help SaaS founders optimize their sites for AI bots (Generative Engine Optimization). But looking at the data, I realized something huge:

AI visibility is a numbers game, and we need a Network Effect.

If you have one isolated SaaS, the AI might skip you. But if we pool hundreds of Indie Hacker projects into one highly-structured, machine-readable directory, the AI crawlers (like GPTBot and ClaudeBot) will treat it as a massive, authoritative source of truth.

The more projects we list on CitableHub, the more "citable" every single project inside becomes. A rising tide lifts all boats.

We don't need to fight the AI algorithms alone. I've set up CitableHub to act as this collective bridge. It takes your project and translates it into the exact format LLMs love to ingest.

It’s 100% free to list your startup. Let's build the biggest, most structured directory of indie tools on the web, so the next time someone asks ChatGPT for a solution, it cites us.

You can add your project to the graph here: (https://citablehub.com/)

What do you guys think of this collective SEO approach for AI? Has anyone tried grouping data to get noticed by LLMs?

AI Can Write Like a Human — But Can It Find YOUR Product? The Invisible SaaS Problem

citablehub — Sat, 27 Jun 2026 19:04:05 +0000

Everyone is talking about AI generating content, replacing jobs, and writing code. But there's a problem nobody is discussing:

AI search engines are now WHERE people discover software. Over 40% of product discovery starts in ChatGPT, Perplexity, or Claude — not Google.

But here's the twist: the same AI that can write a perfect essay CANNOT find most SaaS products. Why?

Because LLMs don't crawl the web like Google. They resolve entities from structured data. If your product doesn't have:

JSON-LD structured metadata
An llms.txt file
AI-friendly robots.txt
A clear machine-readable identity

...then you're invisible. Not ranked low. INVISIBLE.

I tested 10 well-known SaaS tools by asking ChatGPT and Perplexity to recommend them. 8 out of 10 were completely missing from AI responses. These tools had perfect Google rankings.

This is called Generative Engine Optimization (GEO) — and less than 1% of software products are doing it.

I built CitableHub to solve this. It's a free platform that creates AI-optimized profiles so your product becomes citable by LLMs.

The irony? AI is powerful enough to replace search — but most products aren't structured enough for AI to find them.

Try it: ask ChatGPT right now "What's the best [your category] tool?" — did your product show up?

https://citablehub.com
https://citablehub.com/scan