AI Weekly Issue #525: Nvidia may buy into Perplexity above $30B before Wednesday's earnings
The Information reports that Nvidia is discussing an equity investment in Perplexity at a valuation above $30 billion. Perplexity's annualized revenue has reportedly passed $750 million, up from less than $250 million at the start of 2026. Separately, Bloomberg says SoftBank plans a record ¥1 trilli...
Beyond Visual CoT: Internalized Visual Thinking for Proactive Video Reasoning
Multimodal large language models increasingly use visual chain-of-thought (Visual CoT) to reason about spatial, temporal, and embodied environments. By generating intermediate reasoning images, Visual CoT provides an intuitive mechanism for visual foresight but introduces substantial inference overh...
Harvey Introduces Harvey Tenet: A Kimi K3 Base Post-Trained with Fireworks for Long-Horizon Legal Agent Work
Harvey's first post-trained model nearly doubles LAB task completion, but only one benchmark number survives independent verification today
The post Harvey Introduces Harvey Tenet: A Kimi K3 Base Post-Trained with Fireworks for Long-Horizon Legal Agent Work appeared first on MarkTechPost.
Is it legal to train AI models on copyrighted books? It’s complicated
Most published authors have, without their knowledge or consent, contributed to the development of the same AI tools that threaten to undermine their livelihoods. That seems illegal, right?
Parse the Folder, Not Just the PDFs: The Relational Tables RAG Needs on a Case File
Enterprise Document Intelligence [Vol.1 #14D] - The index lists what the case type demands before any folder is opened, and the two questions worth building for are not retrieval questions at all
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Spec-Driven Development with Claude Code: Writing Bulletproof Specs
I have written enough specs for Claude Code now to have hit the failure mode nobody warns you about. The spec was fine. The plan was fine. Claude worked through the tasks, ran the test suite, and reported everything passing. I looked at the diff properly the next morning and found it had converted ...
Meet FreeToken: An Edge-Native MoE Serving Engine that Runs 753B GLM-5.2 on a Single Workstation GPU
FreeToken splits MoE cache misses between PCIe fills and CPU execution using measured bandwidths, unlocking frontier models locally
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Building an End-to-End Document Intelligence Pipeline with deepDoctection
Build an end-to-end document intelligence pipeline with deepDoctection. This tutorial covers configuring layout analysis, DocTR OCR, and table extraction, while demonstrating how to implement custom services for entity recognition and generate structured JSONL data for your RAG workflows.
The post B...
Vercel Introduces ‘Is Agentic’, a Free Agent-Readiness Scoring Tool That Audits Public Websites Using Ora’s 100+ Checks
Vercel and Ora launched Is Agentic, a free audit scoring website readiness for AI agents across 118 checks.
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Inherent, founded by DeepMind alumni, says its AI ‘teammate’ just outperformed Anthropic and OpenAI at replicating research
Built by DeepMind alumni, British AI lab Inherent released Faraday, an AI agent whose ability to replicate scientific papers could be a stepping stone for innovation.
Building a Proper Backend for My LangGraph AI Agent
Turning a demo agent into something that can keep real booking data
The post Building a Proper Backend for My LangGraph AI Agent appeared first on Towards Data Science.
Frontier AI labs still won’t say how they’d contain a rogue model
A new study finds leading AI labs have few publicly documented plans for containing rogue models, raising questions about preparedness as AI systems increasingly demonstrate unexpected and potentially dangerous behavior.
Multi-Document RAG: A Folder of Unrelated PDFs Is One Long Document with a Nested Outline
Enterprise Document Intelligence [Vol.1 #14B] - No shared fields means no index to build. One summary line per file plus each file’s own table of contents, and retrieval routes down two levels
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Decoding AI’s Open-Source Course Maps Three Ways to Run an Agent Loop and the Provider Economics Behind Each
Most teams treat ‘which model’ as the important decision. The harness engineering literature keeps pointing somewhere else. In LangChain’s Terminal-Bench experiment, changing only the harness—same model throughout—moved a coding agent from roughly 30th place into the top 5. That result reframes the ...
Agentic AI research in 2026 has moved past the basic question of whether a model can be called a tool. The harder questions are whether an agent can finish long workflows, survive live websites, verify its own work, recover from failure, and improve its process over time. The five papers below map t...
Anthropic forbids its Claude models from generating sexually explicit content. But a series of tests conducted by TechCrunch found that it didn't take much to get past the restriction.
Building Agentic Document Intelligence Pipelines: Creating Scientific Figures with AutoFigure
This tutorial explores AutoFigure, a practical toolkit for generating professional scientific figures directly from text descriptions and research papers. We walk through setting up the environment, configuring an API-backed generation workflow, and converting complex document intelligence pipelines...
Anthropic Brings Claude Mythos 5 to Claude Security: Enterprise Teams Get Frontier Vulnerability Scanning Without Direct Model Access
Anthropic has moved its most cyber-capable model into a product security teams can switch on themselves. Claude Security scans now run on Claude Mythos 5, in public beta for Claude Enterprise customers with no separate model add-on. The scan connects to a GitHub repository, traces data flows across ...
Nvidia just showed that the harness, not the AI model, is now the real hero
Nvidia research shows that AI agents can perform well, and not go off the deep end, through fine-tuning, even if the AI model isn't that great at the task.
Turning Codex from an interactive assistant into a programmable automation component
The post Running Codex as a Headless Agent appeared first on Towards Data Science.
Estimating from No Data: Deriving a Continuous Score from Categories
A walkthrough of and the maths behind using low-capacity networks to acquire fine-grained scoring when only categorical labelling is available for training
The post Estimating from No Data: Deriving a Continuous Score from Categories appeared first on Towards Data Science.
Samuel Yeboah, Francesco Di Chiara and Mingliang LiuToday, Netflix runs two Flink autoscalers. That is exactly one more than we want. We built the first one in-house years ago, when there was no mature option suited to our platform. The second came from the Apache Flink community, and it can scale w...