honuro

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[honuro @duneruo89]

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[honuro @duneruo89]

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[honuro @duneruo89]

@asjustinX Parásito total..

[honuro @duneruo89]

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[Jay4Finance @jay4surveys]

⚠️ If you're always active between 12AM and 6AM

There’s an online night role paying $200 per hour, paid daily in USD,
For 30 days

Most people don’t even know these openings exist.

The company is currently recruiting for the next 7 days.

Full details will be sent to you if interested.

Please don’t DM me. Just RT & drop a comment if you’re interested.

(Must be a follower too)

[honuro @duneruo89]

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[honuro @duneruo89]

@alexgroberman AI SEO Cheat Codes

[Alex Groberman @alexgroberman]

Google just accidentally revealed how its AI search systems actually work.

In the process, they validated what SEO Stuff (https://t.co/wKpf0EILTx) has been doing all year to get customers more traffic + sales.

Now that none of it is a secret, let's talk about it.

That way you can apply these learnings to your businesses ASAP.

But before we get into it…

If you want free cheat codes for getting inside ChatGPT, Gemini, Perplexity, and Google AI Mode within the next 30 days, just RT this, follow me, and reply “AI SEO Cheat Codes.” I’ll DM them to you.

You must do all 3 for the DM.

Alright, let's jump into it.

Metehan Yesilyurt, who previously went viral when he expertly analyzed Perplexity's ranking factors, recently broke down Google AI ranking factors in a blog post.

(Link in the replies.)

It was fascinating.

Basically, as noted by Yesilyurt, by selling the underlying infrastructure through a product called Google Cloud Discovery Engine (Vertex AI Search), Google revealed a lot about its AI systems work.

If you understand what Discovery Engine exposes, you understand how Google AI Mode, AI Overviews, and future AI search features are likely ranking and retrieving your content.

I'll talk about the 7 ranking signals below, but I advise you to read the entire blog post I'm linking to because it goes into way more helpful technical detail:

Base Ranking

The core algorithm’s initial relevance score.

Gecko Score (Embedding Similarity)

Vector similarity between your content and the query.

Semantic match.

Jetstream (Cross-Attention Relevance)

A more advanced model that understands negation, contrast, context, and nuance better than embeddings.

BM25 Keyword Matching

Kind of self-explanatory. Yes, keyword matching still matters.

PCTR (Predicted Click-Through Rate)

A three-tier prediction model:

Tier 1: Popularity

Tier 2: PCTR

Tier 3: Personalized PCTR (unlocked only after 100,000+ queries)

Freshness

Time-sensitive recency scoring.

Boost / Bury Rules

Manual ranking adjustments based on business logic.

This is the most transparent look we’ve ever had into Google’s AI ranking pipeline.

Discovery Engine also exposes the retrieval pipeline:

Max chunk size: 500 tokens (~375 words)

Optional: ancestor headings travel with each chunk

Tables and images get parsed

Layout parser + Gemini-enhanced understanding (LLM-augmented indexing)

This means: every important point needs to live inside a 500-token block with clean headings and clear structure.

If your content is one massive wall of text, you're done.

Also, I hate to be the "I told you so" guy on this, but schema matters.

For some reason it has become controversial to say this on social media, but it was obvious and now it is confirmed.

Discovery Engine shows Google processes structured data with three separate flags:

Searchable (affects recall)

Indexable (affects filtering + ordering)

Retrievable (affects what the model can output)

These are independent.

Meaning:

A field can influence ranking without being visible, or be visible without influencing ranking.

A massive hint at how Google uses structured data for AI Mode.

Also, Google revealed the 4-stage AI search pipeline:

Prepare

Query understanding, synonym mapping (time-aware), autocomplete, NLU.

Retrieve

Chunking, layout parsing, schema extraction, embeddings.

Signal

The 7 signals above.

Serve

Gemini 2.5 Flash generates the final answer, applies instructions, safety filters, related questions, and grounding rules.

Traditional Search to AI Overviews to AI Mode are simply different configurations of this same pipeline.

So what does all this mean?

Well, it means you must optimize for three layers at once:

Layer 1: Semantic similarity (Gecko)

Your content needs to clearly match the intent of the prompts you want.

Layer 2: Cross-attention relevance (Jetstream)

Jetstream rewards:

clear definitions

direct answers

contrast statements

“X vs Y”

“Best for ___”

“Without ___”

Layer 3: Chunk-level clarity

Your content must be extractable in 500-token blocks with:

question-based headings

two to three sentence answers

TLDR summaries

clean HTML

factual claims

lists and comparisons

This is exactly what AI systems quote.

And this is exactly why SEO Stuff (https://t.co/wKpf0EILTx) works so well in AI search.

The Discovery Engine findings validate the entire SEO Stuff approach from long before this documentation was public.
Let me break down the packages through the lens of Google’s architecture:

SEO Stuff Gold Plan

https://t.co/yEFyM0Ze7W

10 long-form, comparison-based, extractable articles

Structured in 500-token blocks

Question H2s

Two to three sentence direct answers

TLDR blocks

FAQ schema + product schema

3 DR50+ backlinks to strengthen entity signals

Gold Plan maps to:

Gecko (semantic match)

Jetstream (cross-attention relevance)

BM25 (keyword match)

Freshness

Entity trust (for Boost/Bury)

This is the fastest path to appearing in ChatGPT, Gemini, Perplexity, and Google AI Mode.

SEO Stuff Premium Content Bundle

https://t.co/4CAnUt07PO

60 comparison-driven articles

Structured to match the exact pattern LLMs extract

Category-defining content

Builds topical coverage + entity clarity

Creates a deep corpus for Jetstream + embeddings

Premium Bundle maps to:

Retrieval depth

Structured chunking

Ancestor heading clarity

Embedding similarity

AI model grounding

This is how you train AI systems to associate your brand with your category.

SEO Stuff Premium Backlink Bundle

https://t.co/Z9m9D7TjES

3 DR50+ backlinks from domains LLMs already trust

Reinforces brand consistency across the web

Boosts entity recognition

Backlinks help with:

Base ranking

PCTR (popularity + trust)

Boost/Bury eligibility

Entity clarity

This is why so many customers reorder.

It works.

Google is not hiding its AI search architecture.

They literally exposed:

The signals

The ranking layers

The chunk sizes

The parsing logic

The semantic models

The engagement tiers

The answer generation flow

The brands that understand this and structure their content accordingly will run through the next era of search like absolute beasts.

And SEO Stuff (https://t.co/wKpf0EILTx) was built specifically to map to this architecture.

If AI is replacing the first click, your content must replace the first impression.

Plain and simple.
If you want cheat codes for getting inside ChatGPT, Gemini, Perplexity, and Google AI Mode within 30 days…

RT this, follow me, and reply “AI SEO Cheat Codes.”

You must do all 3 for the DM.

[honuro @duneruo89]

@robecolby Si lo suben o te ponen a estar 90 días más avisa a mi me pusieron a esperar 90 días más y ni deuda tengo con ellos

[honuro @duneruo89]

@lucylotuss Lucy mrry me please I want to lick your ass