Audrey Schoonwater

Start building with Gemini Embedding 2, our most capable and first fully multimodal embedding model built on the Gemini architecture. Now available in preview via the Gemini API and in Vertex AI.

🚨🔴CYBERALERT 🇫🇷FRANCE🔴 | 💣 Cyberattaque BERCY : le fichier FICOBA piraté... 1,2M de comptes bancaires consultés et copiés par un cybercriminel... ⤵️ Voici une nouvelle déflagration ! Bercy communique sur un piratage hors-norme après qu'un cybercriminel ait eu accès à l'un des fichiers les plus sensibles de l'administration fiscale française... le fichier FICOBA, le FIchier national des COmptes BAncaires. Le ministère de l'Économie fait état de 1,2 million de comptes bancaires consultés depuis la fin du mois de janvier. Là encore, un fonctionnaire s'est fait piégé et ses identifiants ont été usurpés... la faute à PAS DE MFA... Pour rappel, on parle de l'ensemble des comptes bancaires ouverts dans les établissements bancaires français et qui contient des données sensibles comme : 👉🏾 les coordonnées bancaires (RIB/IBAN) 👉🏾 l'identité du titulaire 👉🏾 l'adresse postale 👉🏾 l'identifiant fiscal de l'usager Autre info, depuis quelques jours, les applications pour les pros de la DGFIP comme ficoba, ficovie, cadastre, étaient inaccessibles... Ce qui se passe actuellement dans le numérique en FRANCE est grave. On a l'impression d'être une passoire... Après la cyberattaque du Ministère de l'intérieur en décembre, cette nouvelle cyberattaque va faire bcp parler... Cybèrement vôtre, SaxX ¯\_(ツ)_/¯

I think about this SEO legend every day

BREAKING: Google launched a vibe coding platform that just wiped out most paid app builders. You can build AI-powered web apps and use them instantly. No subscriptions, no fee, no paywall. Here is how it works 👇

Are you a news publisher? This is basically like Top Stories, but in ChatGPT. A dedicated carousel with articles from news and media publishers. Oh, and Google's there too. :)

Researchers built a new RAG approach that: - does not need a vector DB. - does not embed data. - involves no chunking. - performs no similarity search. And it hit 98.7% accuracy on a financial benchmark (SOTA). Here's the core problem with RAG that this new approach solves: Traditional RAG chunks documents, embeds them into vectors, and retrieves based on semantic similarity. But similarity ≠ relevance. When you ask "What were the debt trends in 2023?", a vector search returns chunks that look similar. But the actual answer might be buried in some Appendix, referenced on some page, in a section that shares zero semantic overlap with your query. Traditional RAG would likely never find it. PageIndex (open-source) solves this. Instead of chunking and embedding, PageIndex builds a hierarchical tree structure from your documents, like an intelligent table of contents. Then it uses reasoning to traverse that tree. For instance, the model doesn't ask: "What text looks similar to this query?" Instead, it asks: "Based on this document's structure, where would a human expert look for this answer?" That's a fundamentally different approach with: - No arbitrary chunking that breaks context. - No vector DB infrastructure to maintain. - Traceable retrieval to see exactly why it chose a specific section. - The ability to see in-document references ("see Table 5.3") the way a human would. But here's the deeper issue that it solves. Vector search treats every query as independent. But documents have structure and logic, like sections that reference other sections and context that builds across pages. PageIndex respects that structure instead of flattening it into embeddings. Do note that this approach may not make sense in every use case since traditional vector search is still fast, simple, and works well for many applications. But for professional documents that require domain expertise and multi-step reasoning, this tree-based, reasoning-first approach shines. For instance, PageIndex achieved 98.7% accuracy on FinanceBench, significantly outperforming traditional vector-based RAG systems on complex financial document analysis. Everything is fully open-source, so you can see the full implementation in GitHub and try it yourself. I have shared the GitHub repo in the replies!