Google Introduces Speech-to-Retrieval (S2R) Approach that Maps a Spoken Query Directly to an Embedding and Retrieves Information without First Converting Speech to Text
Google AI Research team has brought a production shift in Voice Search by introducing Speech-to-Retrieval (S2R). S2R maps a spoken query directly to an embedding and retrieves information without first…
A Coding Implementation of Secure AI Agent with Self-Auditing Guardrails, PII Redaction, and Safe Tool Access in Python
In this tutorial, we explore how to secure AI agents in practical, hands-on ways using Python. We focus on building an intelligent yet responsible agent that adheres to safety rules…
5 Most Popular Agentic AI Design Patterns Every AI Engineer Should Know
As AI agents evolve beyond simple chatbots, new design patterns have emerged to make them more capable, adaptable, and intelligent. These agentic design patterns define how agents think, act, and…
Sentient AI Releases ROMA: An Open-Source and AGI Focused Meta-Agent Framework for Building AI Agents with Hierarchical Task Execution
Sentient AI has released ROMA (Recursive Open Meta-Agent), an open-source meta-agent framework for building high-performance multi-agent systems. ROMA structures agentic workflows as a hierarchical, recursive task tree: parent nodes break…
A Coding Guide to Master Self-Supervised Learning with Lightly AI for Efficient Data Curation and Active Learning
In this tutorial, we explore the power of self-supervised learning using the Lightly AI framework. We begin by building a SimCLR model to learn meaningful image representations without labels, then…
Meet OpenTSLM: A Family of Time-Series Language Models (TSLMs) Revolutionizing Medical Time-Series Analysis
A significant development is set to transform AI in healthcare. Researchers at Stanford University, in collaboration with ETH Zurich and tech leaders including Google Research and Amazon, have introduced OpenTSLM,…
Liquid AI Releases LFM2-8B-A1B: An On-Device Mixture-of-Experts with 8.3B Params and a 1.5B Active Params per Token
How much capability can a sparse 8.3B-parameter MoE with a ~1.5B active path deliver on your phone without blowing latency or memory? Liquid AI has released LFM2-8B-A1B, a small-scale Mixture-of-Experts…
Meta Superintelligence Labs’ MetaEmbed Rethinks Multimodal Embeddings and Enables Test-Time Scaling with Flexible Late Interaction
What if you could tune multimodal retrieval at serve time—trading accuracy, latency, and index size—simply by choosing how many learnable Meta Tokens (e.g., 1→16 for queries, 1→64 for candidates) to…
Agentic Context Engineering (ACE): Self-Improving LLMs via Evolving Contexts, Not Fine-Tuning
TL;DR: A team of researchers from Stanford University, SambaNova Systems and UC Berkeley introduce ACE framework that improves LLM performance by editing and growing the input context instead of updating…
Google Open-Sources an MCP Server for the Google Ads API, Bringing LLM-Native Access to Ads Data
Google has open-sourced a Model Context Protocol (MCP) server that exposes read-only access to the Google Ads API for agentic and LLM applications. The repository googleads/google-ads-mcp implements an MCP server…
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