Tres bon recap et regroupement sur le comment Google choisi le sens d'une recherche

Le protocole robots.txt respecté et utilisé par Googlebot... Mais pas les LLMs !

Les features de GA en 2026 qui font en sorte que le tool redevient interessant à differents aspects. Avinash, toujours au top :)

Google scinde ses videos de search en 2 categories : SEO fundamentals + Technical Search fundamentals.

Google Search Central Live Deep Dive, Barcelona - Day 3 Recap - A review of the 3rd and final day.

Aperçu du temps moyen que prend Google pour crawler, indexer et servir

Google remplace le bouton Show more à la fin de l'AI Overview par un champ Google AI Mode. Ils veulent vous forcer à rester dedans.

Tester le language OKF de chez Google.

ersion 0.2

OKF is an open, human- and agent-friendly format for representing knowledge: the metadata, context, and curated insight that surrounds data and systems. It is designed to be authored by people, generated by agents, exchanged across organizations, and consumed by both.

The format is intentionally minimal: a directory of markdown files with YAML frontmatter. There is no schema registry, no central authority, and no required tooling. If you can cat a file, you can read OKF; if you can git clone a repo, you can ship it.

S'attaquer et tester des trucs chouettes avec le nouveau format de Markdown de chez Google

serp analysis with AI - Ou les gens cliquent vs ou ils portent leur attention sans cliquer

Google et leur nouveau protocole eCommerce

Mise à jour de la doc officielle Google en matière de SEO pour AI Overview (pas Gemini)

La fete est finie chez Gmail.

Un podcast très grossier mais pas inintéressant pour comprendre comme l'AI impacte la recherche dans Google. Conversation avec un ingénieur Google

Here are the nine reasons he cited for canonical choices:

Exact duplicate content
The pages are fully identical, leaving no meaningful signal to distinguish one URL from another.
Substantial duplication in main content
A large portion of the primary content overlaps across pages, such as the same article appearing in multiple places.
Too little unique main content relative to template content
The page’s unique content is minimal, so repeated elements like navigation, menus, or layout dominate and make pages appear effectively the same.
URL parameter patterns inferred as duplicates
When multiple parameterized URLs are known to return the same content, Google may generalize that pattern and treat similar parameter variations as duplicates.
Mobile version used for comparison
Google may evaluate the mobile version instead of the desktop version, which can lead to duplication assessments that differ from what is manually checked.
Googlebot-visible version used for evaluation
Canonical decisions are based on what Googlebot actually receives, not necessarily what users see.
Serving Googlebot alternate or non-content pages
If Googlebot is shown bot challenges, pseudo-error pages, or other generic responses, those may match previously seen content and be treated as duplicates.
Failure to render JavaScript content
When Google cannot render the page, it may rely on the base HTML shell, which can be identical across pages and trigger duplication.
Ambiguity or misclassification in the system
In some cases, a URL may be treated as duplicate simply because it appears “misplaced” or due to limitations in how the system interprets similarity.

La solution Google pour generer du contenu brandé rapidement et facilement pour ceux/celles qui n'ont ni le temps, ni les moyens, ni l'expertise. Gratuit pour le moment, mais j'imagine que ça ne va pas durer.

Je trouve néanmoins l'idée tres chouette

Implementations are described herein for augmenting a traditional search session with stateful chat—via what will be referred to as a “generative companion”—to facilitate more interactive searching. In various implementations, a query may be received, e.g., from a client device operated by a user. Contextual information associated with the user or the client device may be retrieved. Generative model (GM) output may be generated based on processing, using a generative model, data indicative of the query and the contextual information.

At least utilizing a custom corpus of documents to condition a large language model (LLM) when generating a response to a user query. In some implementations, a user query associated with a client device is received. An API query for an external application is generated by an LLM based on the user query.

A machine-learned system for modeling contextual data such as long user historical data is provided. The system includes a machine-learned embedding model and a machine-learned sequence processing model.

Google Patent

An example method for prompt-based query generation is provided. The method includes receiving, by a computing device, at least two prompts associated with a retrieval task to be performed on a corpus of documents associated with the task. The method includes applying, based on the at least two prompts and the corpus of documents, a large language model to generate a synthetic training dataset comprising a plurality of query-document pairs, wherein each query-document pair comprises a synthetically generated query and a document from the corpus of documents.

Tips pour Google Locale. Ca va me servir pour un client local qui a un événement dans quelques temps