#Search

AILeapfrog, a small team doing high-volume AI visual and production work for fashion and brand clients, is building a "one brain" system that makes company knowledge and client context searchable through natural-language agents. The starter stack described is OpenClaw in a sandbox, a GitHub repository, Obsidian on the local machine, and Telegram as the access point. The system's research structure had roughly 1,200 files at the time of the talk.

AINVIDIA Dynamo uses a unified session-level identifier to make its inference stack aware of agent sessions, subagents, and their KV cache across turns and tool calls. On SWE-bench, two TP4 MiniMax-M2 replicas on one 8xH100 node gained roughly 12-16% throughput from program-aware scheduling over KV-aware routing alone. The post also describes experimental shared-pool indexing and a proposed KvHint interface for session-aware cache policies in vLLM and SGLang.

Why it matters: The post explains how session identifiers let an inference stack track agent working sets, with measured throughput gains on SWE-bench and agentic RL rollouts.

AIif there’s anything relevant, pick the coolest ideas and suggest how I can use it to improve my workflow

AIDatabricks has put Workday Data Connect federation into Beta in Unity Catalog, letting teams query Workday HR and finance data without copying it. Workday Data Cloud customers get zero-copy, read-only access to the shared tables, with Databricks running queries and Unity Catalog governing access, lineage, and auditing. Teams can combine current people and financial data with other enterprise data for analytics and AI, including Genie-powered natural-language exploration.

AIEarlier, Grok Bot would have to rely on web search or the X connector; with this release, it can match Grok's capabilities, where one prompt can trigger analysis of more than 100 X posts. I use Grok Bot on a daily basis to compose a Daily AI Brief - looks like it will get much better tomorrow. Testing time! 👀

AIPerplexity is open-sourcing pplx-embed-v2-late, multi-vector embedding models for text and images in one shared space, in 9B and 0.6B sizes. The 9B model can index multimodal data, the 0.6B model can run queries on device, and PDF pages can be searched without OCR. The author reports 92.4% on MADQA and 64% on BrowseComp+, with weights available on Hugging Face.

AI…ColBERT model, with fewer searches than any baseline.

AI…Recall@1000, against 69.3% for nemotron-embed-8b.

AIA corpus indexed with the 9B model can be searched with 0.6B queries. That lifts ViDoRe v3 from 62.3% to 63.5% with no added query cost.

AI…layout that text extraction drops.

AIpplx-embed-v2-late keeps a 128-dimensional vector per token and scores with MaxSim, so each query token is matched to its closest token in the document.

AI…space for cross-model querying. Both models achieve frontier performance and are publicly available on Hugging Face.

AIWe want you to help test it. Compass Cloud is currently in private beta for those who want excellent retrieval with less overhead. If you're an enterprise team interested in early access, click below to apply ⬇️

AIGoogle DeepMind has released EmbeddingGemma 2, an open 740 million parameter model that maps text, images, audio, and video into one embedding space. It is built on the Gemma 4 architecture under an Apache 2.0 license and supports an 8K token context window. The company reports a code benchmark gain from 68.76 to 78.68 on MTEB Code and says the model can run on-device with about 567MB of active RAM for the full multimodal version on a Google Pixel 11 Pro.

Why it matters: The release shows how a 740M-parameter embedding model can cover text, code, images, audio, and video on local hardware, with memory and storage figures to compare against other on-device options.

AI…1 to +1. the dashboard shows the split, how sentiment moved day by day, and the posts behind the score.

AIWhile the first-generation model delivered best-in-class text embedding, EmbeddingGemma 2 natively understands it all, connecting text, images, audio, and video in one shared space. This means you can search across different formats like text, images, audio, and video, without having to manually organize, translate, or label them first.

AIGoogle DeepMind released EmbeddingGemma 2, a 740M-parameter embedding model, under an Apache 2.0 license. The post says it is competitive across benchmarks and outperforms some specialist models more than twice its size, and that developers can use it for multimodal search or pair it with Gemma 4 for on-device RAG. Weights are available on Hugging Face and Kaggle.

AIDell is adding agentic AI capabilities to its AI Data Platform, including a Unified Semantic Layer with a searchable glossary and an Enterprise Knowledge Graph built with Nvidia's Auto-Ontology open source library. The features are designed to give agents shared context, reducing repeated token generation and compute costs. The platform's layers include the Data Orchestration Engine, Data Engines, and Storage Engines such as PowerScale, ObjectScale, and the Lightning File System.

AIGoogle released EmbeddingGemma 2, an open embedding model under the Apache 2.0 license that maps text, code, images, video, and audio into a shared 768-dimensional space. Developers can load a 270M-parameter text and code setup, or add vision and audio encoders up to a 740M-parameter full multimodal model. Matryoshka truncation to 256 or 128 dimensions reduces vector storage, with the guide noting quality losses on image, video, and speech retrieval at lower dimensions.

Why it matters: The guide gives concrete encoder sizes and dimension-storage tradeoffs, showing how to choose a configuration for text, code, image, video, and audio retrieval.

AIGoogle DeepMind launched EmbeddingGemma 2, an open-weight 740M parameter model that maps text, images, video frames, and audio into one vector space. The model can run on-device, with about 567MB active RAM for the full multimodal model on a Google Pixel 11 Pro, and is available through Google AI Edge Gallery, Google AI Edge Foresight on Mac, and MediaPipe Tasks, with ML Kit support coming in the weeks ahead.

Why it matters: The post names concrete on-device apps, memory footprints, and latency figures, showing how a multimodal embedding model can power local search without cloud calls.

AIDatabricks has made IP Functions generally available, letting users parse, validate, and join IPv4 and IPv6 addresses and CIDR blocks with built-in SQL functions optimized in Photon. In benchmarks versus another leading cloud data warehouse, CIDR joins ran up to 3.1x faster and cost up to 6.4x less. The functions support its Security Lakehouse vision for threat detection, investigation, and network analytics on one governed copy of data.

AICloudflare introduced a Web Search API through AI Gateway, partnering with Ceramic.ai, Exa, and Linkup to give agents fresh web results instead of guessed URLs. Requests appear in AI Gateway logs and draw from AI Gateway credits, with partners committing to Cloudflare's Verified bots crawling standards and including source links in results. Partners at list API pricing without markup are available via a REST endpoint or a Workers binding, with native server tools planned.

AIAi2 released AstaBrief 8B, a model that turns a research question and retrieved literature excerpts into a cited report, along with its training data. In Asta's Generate a report feature, Fast mode averages 51.1 seconds per report versus 178.5 seconds for Thinking mode, about 3.5x faster. The model is built on Qwen3-8B with supervised fine-tuning and DPO, and institutions can run its open weights on their own infrastructure.

Why it matters: The post explains the data filtering and one-pass generation choices behind a fast open-weights report model, showing what worked and what did not.

AIIt also beats voyage-context-4 on chunk retrieval while using 8x less storage per vector: 1 KB (1024 dims, int8) vs 8 KB (2048, float32).

AIDocument retrieval is closer, with our context v1 4B slightly ahead at Document@3 and Document@5.

AIIts queries, documents and capabilities are inspired by conversations with turbopuffer customers. It has 2,099 queries and 38,894 documents. We submitted for blind evaluation.

AIIt scores every document token against the query. Aggregated into chunk-level targets, those scores train the embedder to retrieve answer and supporting chunks.

AIContextual embedding models fix this by encoding the whole document once and pooling chunk vectors afterward. They are usually trained on one gold chunk per query.

AIpplx-embed-v2-context-9b-preview sets a new state of the art on ConTEB and @turbopuffer's new, privately held context-bench.

AIMeta is testing Forum, a standalone iOS and Android app in the US that syncs with users' Facebook Groups to consolidate their conversations in one place. The update adds a new top-contributor role replacing previous badges, an AI-powered Ask feature that surfaces group posts and comments, and topic labels for exploring interests.

AIThe United Nations system launched UN System Data Commons, an open-source platform built on Data Commons by Google that integrates siloed global statistics into one AI-ready knowledge graph. Users can query it in natural language, browse by location or theme, and use MCP-enabled AI agents to fetch verified figures and draft charts or reports. The UN plans to add more datasets, aiming to include 80% of UN system statistical datasets by 2027.