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Liquid AI Releases LocalCowork Powered By LFM2-24B-A2B to Execute Privacy-First Agent Workflows Locally Via Model Context Protocol (MCP)

agentic ai, ai, AI (Artificial Intelligence), AI Agents, Artificial Intelligence, Editors Pick, Large Language Model, Model Context Protocol (MCP), New Releases, Staff, Technology, Uncategorized

Liquid AI has released LFM2-24B-A2B, a model optimized for local, low-latency tool dispatch, alongside LocalCowork, an open-source desktop agent application available in their Liquid4All GitHub Cookbook. The release provides a deployable architecture for running enterprise workflows entirely on-device, eliminating API calls and data egress for privacy-sensitive environments. Architecture and Serving Configuration To achieve low-latency execution […]

Liquid AI Releases LocalCowork Powered By LFM2-24B-A2B to Execute Privacy-First Agent Workflows Locally Via Model Context Protocol (MCP) Read More »

YuanLab AI Releases Yuan 3.0 Ultra: A Flagship Multimodal MoE Foundation Model, Built for Stronger Intelligence and Unrivaled Efficiency

ai, AI (Artificial Intelligence), AI Paper Summary, AI Shorts, Applications, Artificial Intelligence, Editors Pick, Language Model, Large Language Model, Machine Learning, New Releases, Open Source, Staff, Tech News, Technology

How can a trillion-parameter Large Language Model achieve state-of-the-art enterprise performance while simultaneously cutting its total parameter count by 33.3% and boosting pre-training efficiency by 49%? Yuan Lab AI releases Yuan3.0 Ultra, an open-source Mixture-of-Experts (MoE) large language model featuring 1T total parameters and 68.8B activated parameters. The model architecture is designed to optimize performance

YuanLab AI Releases Yuan 3.0 Ultra: A Flagship Multimodal MoE Foundation Model, Built for Stronger Intelligence and Unrivaled Efficiency Read More »

How to Build a Stable and Efficient QLoRA Fine-Tuning Pipeline Using Unsloth for Large Language Models

ai, AI (Artificial Intelligence), Artificial Intelligence, Editors Pick, Large Language Model, Staff, Technology

In this tutorial, we demonstrate how to efficiently fine-tune a large language model using Unsloth and QLoRA. We focus on building a stable, end-to-end supervised fine-tuning pipeline that handles common Colab issues such as GPU detection failures, runtime crashes, and library incompatibilities. By carefully controlling the environment, model configuration, and training loop, we show how

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Google Drops Gemini 3.1 Flash-Lite: A Cost-efficient Powerhouse with Adjustable Thinking Levels Designed for High-Scale Production AI

agentic ai, ai, AI (Artificial Intelligence), AI Shorts, Applications, Artificial Intelligence, Editors Pick, Language Model, Large Language Model, Machine Learning, New Releases, Staff, Tech News, Technology

Google has released Gemini 3.1 Flash-Lite, the most cost-efficient entry in the Gemini 3 model series. Designed for ‘intelligence at scale,’ this model is optimized for high-volume tasks where low latency and cost-per-token are the primary engineering constraints. It is currently available in Public Preview via the Gemini API (Google AI Studio) and Vertex AI.

Google Drops Gemini 3.1 Flash-Lite: A Cost-efficient Powerhouse with Adjustable Thinking Levels Designed for High-Scale Production AI Read More »

Alibaba just released Qwen 3.5 Small models: a family of 0.8B to 9B parameters built for on-device applications

agentic ai, ai, AI (Artificial Intelligence), Artificial Intelligence, Editors Pick, Language Model, Large Language Model, New Releases, Open Source, Small Language Model, Software engineering, Staff, Technology

Alibaba’s Qwen team has released the Qwen3.5 Small Model Series, a collection of Large Language Models (LLMs) ranging from 0.8B to 9B parameters. While the industry trend has historically favored increasing parameter counts to achieve ‘frontier’ performance, this release focuses on ‘More Intelligence, Less Compute.‘ These models represent a shift toward deploying capable AI on

Alibaba just released Qwen 3.5 Small models: a family of 0.8B to 9B parameters built for on-device applications Read More »

Google AI Introduces STATIC: A Sparse Matrix Framework Delivering 948x Faster Constrained Decoding for LLM Based Generative Retrieval

ai, AI (Artificial Intelligence), AI Shorts, Applications, Artificial Intelligence, Editors Pick, Large Language Model, Staff, Tech News, Technology

In industrial recommendation systems, the shift toward Generative Retrieval (GR) is replacing traditional embedding-based nearest neighbor search with Large Language Models (LLMs). These models represent items as Semantic IDs (SIDs)—discrete token sequences—and treat retrieval as an autoregressive decoding task. However, industrial applications often require strict adherence to business logic, such as enforcing content freshness or

Google AI Introduces STATIC: A Sparse Matrix Framework Delivering 948x Faster Constrained Decoding for LLM Based Generative Retrieval Read More »

A Complete End-to-End Coding Guide to MLflow Experiment Tracking, Hyperparameter Optimization, Model Evaluation, and Live Model Deployment

ai, AI (Artificial Intelligence), Artificial Intelligence, Editors Pick, Large Language Model, Machine Learning, MLOps, Staff, Tech News, Technology, Tutorials

In this tutorial, we build a complete, production-grade ML experimentation and deployment workflow using MLflow. We start by launching a dedicated MLflow Tracking Server with a structured backend and artifact store, enabling us to track experiments in a scalable, reproducible manner. We then train multiple machine learning models using a nested hyperparameter sweep while automatically

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A Coding Implementation to Build a Hierarchical Planner AI Agent Using Open-Source LLMs with Tool Execution and Structured Multi-Agent Reasoning

agentic ai, ai, AI (Artificial Intelligence), AI Agents, Artificial Intelligence, Editors Pick, Large Language Model, Open Source, Staff, Technology, Tutorials

In this tutorial, we build a hierarchical planner agent using an open-source instruct model. We design a structured multi-agent architecture comprising a planner agent, an executor agent, and an aggregator agent, where each component plays a specialized role in solving complex tasks. We use the planner agent to decompose high-level goals into actionable steps, the

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Sakana AI Introduces Doc-to-LoRA and Text-to-LoRA: Hypernetworks that Instantly Internalize Long Contexts and Adapt LLMs via Zero-Shot Natural Language

agentic ai, ai, AI (Artificial Intelligence), AI Paper Summary, AI Shorts, Applications, Artificial Intelligence, Editors Pick, Language Model, Large Language Model, Machine Learning, New Releases, Staff, Tech News, Technology

Customizing Large Language Models (LLMs) currently presents a significant engineering trade-off between the flexibility of In-Context Learning (ICL) and the efficiency of Context Distillation (CD) or Supervised Fine-Tuning (SFT). Tokyo-based Sakana AI has proposed a new approach to bypass these constraints through cost amortization. In two of their recent papers, they introduced Text-to-LoRA (T2L) and

Sakana AI Introduces Doc-to-LoRA and Text-to-LoRA: Hypernetworks that Instantly Internalize Long Contexts and Adapt LLMs via Zero-Shot Natural Language Read More »

Perplexity Just Released pplx-embed: New SOTA Qwen3 Bidirectional Embedding Models for Web-Scale Retrieval Tasks

Perplexity has released pplx-embed, a collection of multilingual embedding models optimized for large-scale retrieval tasks. These models are designed to handle the noise and complexity of web-scale data, providing a production-ready alternative to proprietary embedding APIs. Architectural Innovations: Bidirectional Attention and Diffusion Most Large Language Models (LLMs) utilize causal, decoder-only architectures. However, for embedding tasks,

Perplexity Just Released pplx-embed: New SOTA Qwen3 Bidirectional Embedding Models for Web-Scale Retrieval Tasks Read More »