Introducing Mistral OCR 4
Mistral OCR 4 delivers enterprise document AI with 170-language support, bounding boxes, and self-hosted deployment.
Mistral AI
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The Professor of Outputmaxxing — Anjney Midha, AMP
We talk about how this legendary investor went from humble beginnings in Singapore to leading rounds in Anthropic, Mistral, Black Forest Labs, and Periodic Labs... and the AMP secret master plan!
Matching first names, full names and pronouns
I am working on a graph store of entities and relationships extracted from a factual test document of around 500 words. The first pass (NER) extracts named entities, the second extracts relationships (RE). For a given person, there are different references in the text: Maria, Maria Gotthard, Dr. Maria Gotthard and can also be referred to by 'she', for example 'she was rewarded by the company'. The goal is to merge all these references into one entity so that the relationship graph is not fragmented into different contexts. I have seen a few posts on different forums saying this is a very difficult problem, but hopefully someone out there has some insights or experience to share 🙂 To make things interesting, references to the same entity can occur in different chunks of text, making it impossible for the LLM (currently Ollama/Mistral) to process the cross-chunk context in one call. To address this, I have added a pass across all extracted entities, including exact text matching and a Levenshtein similarity check, but this does not handle first name v full name and comes with a host of other issues. It has a high risk of over-merging, for example if a set of entities consist of incrementally numbered items they will all be merged into one entity. I am wondering if there is a particular architecture for this problem, for example pre-processing a document to link related entities before extracting. Doesn't have to be LLM-based, heuristics and algorithms sometimes do the trick as…
Investigador CENIA participó en conversatorio GAM para debatir sobre identidad humana, creatividad e IA
En la biblioteca del Centro Cultural Gabriela Mistral (GAM), el investigador asociado de CENIA, Abel Wajnerman Paz, participó de Ventanal Alameda, un espacio de conversación y debate sobre temas de actualidad organizado por el GAM. En esta sesión, tres panelistas discutieron una pregunta cada vez más urgente: ¿es la creatividad un rasgo exclusivo de nuestra […] The post Investigador CENIA participó en conversatorio GAM para debatir sobre identidad humana, creatividad e IA appeared first on CENIA .
Introducing physics AI at Mistral: the foundation for engineering acceleration.
A new class of AI models that predict the behavior of physical systems, powering the engineers and hardware products of tomorrow.
AI Weekly Issue #496: Anthropic's Pentagon model is now everyone's model
Anthropic released Mythos to the public, collapsing the wall between cleared-contractor frontier AI and developer-grade frontier AI in a single press release. DeepMind's Demis Hassabis moved his AGI timeline from "five to ten years" to "a real possibility by 2029" and tied it explicitly to AlphaProof Nexus solving nine open Erdős problems for the cost of a steak dinner. Critical zero-days hit Starlette (a million AI agents on the wire) and CrowdStrike led a coordinated takedown of the Glassworm developer botnet across four C2 channels. BNP Paribas formalized a sovereign-AI security partnership with Mistral while Beijing froze overseas travel for top AI engineers at Alibaba and DeepSeek. And the AI-displaces-workforce arithmetic got honest: Uber burned its full-year AI token budget by April, ClickUp restructured to 1,000 humans alongside 3,000 internal agents, and Sam Altman publicly reversed his white-collar-apocalypse prediction.
Remote agents in Vibe. Powered by Mistral Medium 3.5.
Introducing Mistral Medium 3.5, remote coding agents in Vibe, plus new Work mode in Le Chat for complex tasks.