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Alibaba Open-Sources Zvec: An Embedded Vector Database Bringing SQLite-like Simplicity and High-Performance On-Device RAG to Edge Applications

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Alibaba Tongyi Lab research team released ‘Zvec’, an open source, in-process vector database that targets edge and on-device retrieval workloads. It is positioned as ‘the SQLite of vector databases’ because it runs as a library inside your application and does not require any external service or daemon. It is designed for retrieval augmented generation (RAG), semantic search, and agent workloads that must run locally on laptops, mobile devices, or other constrained hardware/edge devices The core idea is simple. Many applications now need vector search and metadata filtering but do not want to run a separate vector database service. Traditional server style systems are heavy for desktop tools, mobile apps, or command line utilities. An embedded engine that behaves like SQLite but for embeddings fits this gap. https://ift.tt/65kCdQi Why embedded vector search matters for RAG ? RAG and semantic search pipelines need more than a bare index. They need vectors, scalar fields, full CR...
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How to Build a Privacy-Preserving Federated Pipeline to Fine-Tune Large Language Models with LoRA Using Flower and PEFT

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In this tutorial, we demonstrate how to federate fine-tuning of a large language model using LoRA without ever centralizing private text data. We simulate multiple organizations as virtual clients and show how each client adapts a shared base model locally while exchanging only lightweight LoRA adapter parameters. By combining Flower’s federated learning simulation engine with parameter-efficient fine-tuning, we demonstrate a practical, scalable approach for organizations that want to customize LLMs on sensitive data while preserving privacy and reducing communication and compute costs. Check out the  FULL CODES here . Copy Code Copied Use a different Browser !pip -q install -U "protobuf<5" "flwr[simulation]" transformers peft accelerate datasets sentencepiece import torch if torch.cuda.is_available(): !pip -q install -U bitsandbytes import os os.environ["RAY_DISABLE_USAGE_STATS"] = "1" os.environ["TOKENIZERS_PARALLELISM...
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Microsoft AI Proposes OrbitalBrain: Enabling Distributed Machine Learning in Space with Inter-Satellite Links and Constellation-Aware Resource Optimization Strategies

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Earth observation (EO) constellations capture huge volumes of high-resolution imagery every day, but most of it never reaches the ground in time for model training. Downlink bandwidth is the main bottleneck. Images can sit on orbit for days while ground models train on partial and delayed data. Microsoft Researchers introduced ‘OrbitalBrain’ framework as a different approach. Instead of using satellites only as sensors that relay data to Earth, it turns a nanosatellite constellation into a distributed training system. Models are trained, aggregated, and updated directly in space, using onboard compute, inter-satellite links, and predictive scheduling of power and bandwidth. https://ift.tt/7fWK2vp The BentPipe Bottleneck Most commercial constellations use the BentPipe model. Satellites collect images, store them locally, and dump them to ground stations whenever they pass overhead. The research team evaluates a Planet-like constellation with 207 satellites and 12 ground station...
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Meet OAT: The New Action Tokenizer Bringing LLM-Style Scaling and Flexible, Anytime Inference to the Robotics World

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Robots are entering their GPT-3 era. For years, researchers have tried to train robots using the same autoregressive (AR) models that power large language models (LLMs). If a model can predict the next word in a sentence, it should be able to predict the next move for a robotic arm. However, a technical wall has blocked this progress: continuous robot movements are difficult to turn into discrete tokens. A team of researchers from Harvard University and Stanford University have released a new framework called Ordered Action Tokenization (OAT) to bridge this gap. https://ift.tt/PrIf8FT The Messy Reality of Robot Actions Tokenization turns complex data into a sequence of discrete numbers (tokens). For robots, these actions are continuous signals like joint angles. Previous strategies had fatal flaws: Binning: Turns every action dimension into a ‘bin.’ While simple, it creates massive sequences that make training and inference slow. FAST (Frequency-space Action Sequence Token...
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A Coding Implementation to Establish Rigorous Prompt Versioning and Regression Testing Workflows for Large Language Models using MLflow

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In this tutorial, we show how we treat prompts as first-class, versioned artifacts and apply rigorous regression testing to large language model behavior using MLflow. We design an evaluation pipeline that logs prompt versions, prompt diffs, model outputs, and multiple quality metrics in a fully reproducible manner. By combining classical text metrics with semantic similarity and automated regression flags, we demonstrate how we can systematically detect performance drift caused by seemingly small prompt changes. Along the tutorial, we focus on building a workflow that mirrors real software engineering practices, but applied to prompt engineering and LLM evaluation. Check out the  FULL CODES here . Copy Code Copied Use a different Browser !pip -q install -U "openai>=1.0.0" mlflow rouge-score nltk sentence-transformers scikit-learn pandas import os, json, time, difflib, re from typing import List, Dict, Any, Tuple import mlflow import pandas as pd import nump...
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ByteDance Releases Protenix-v1: A New Open-Source Model Achieving AF3-Level Performance in Biomolecular Structure Prediction

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How close can an open model get to AlphaFold3-level accuracy when it matches training data, model scale and inference budget? ByteDance has introduced Protenix-v1 , a comprehensive AlphaFold3 (AF3) reproduction for biomolecular structure prediction, released with code and model parameters under Apache 2.0 . The model targets AF3-level performance across protein, DNA, RNA and ligand structures while keeping the entire stack open and extensible for research and production. The core release also ships with PXMeter v1.0.0 , an evaluation toolkit and dataset suite for transparent benchmarking on more than 6k complexes with time-split and domain-specific subsets . What is Protenix-v1? Protenix is described as ‘Protenix: Protein + X ‘, a foundation model for high-accuracy biomolecular structure prediction . It predicts all-atom 3D structures for complexes that can include: Proteins Nucleic acids (DNA and RNA) Small-molecule ligands The research team defines Protenix as a comprehen...
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How to Design Production-Grade Mock Data Pipelines Using Polyfactory with Dataclasses, Pydantic, Attrs, and Nested Models

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In this tutorial, we walk through an advanced, end-to-end exploration of Polyfactory , focusing on how we can generate rich, realistic mock data directly from Python type hints. We start by setting up the environment and progressively build factories for data classes, Pydantic models, and attrs-based classes, while demonstrating customization, overrides, calculated fields, and the generation of nested objects. As we move through each snippet, we show how we can control randomness, enforce constraints, and model real-world structures, making this tutorial directly applicable to testing, prototyping, and data-driven development workflows. Check out the  FULL CODES here . Copy Code Copied Use a different Browser import subprocess import sys def install_package(package): subprocess.check_call([sys.executable, "-m", "pip", "install", "-q", package]) packages = [ "polyfactory", "pydantic", "email-vali...
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