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360 Zhihui Cloud Developer
360 Zhihui Cloud Developer
Apr 22, 2026 · Databases

Unlock PostgreSQL’s Multi‑Model Power: Graph, Vector, Full‑Text, JSONB & GIS in Practice

PostgreSQL’s extensible architecture lets it evolve from a relational DB into a true multi‑model system supporting graphs, vectors, full‑text, time‑series, GIS and key‑value data, with detailed architecture explanations, practical code demos, cross‑model queries, extension selection, and production‑grade best‑practice tips.

Apache AGEFull‑Text SearchJSONB
0 likes · 39 min read
Unlock PostgreSQL’s Multi‑Model Power: Graph, Vector, Full‑Text, JSONB & GIS in Practice
AI Explorer
AI Explorer
Mar 27, 2026 · Artificial Intelligence

Why Tsinghua’s Multi‑Intelligence DeepSeek‑R1 Shifts AI from Depth to Width

Tsinghua University and WuWen XinQiong unveil DeepSeek‑R1, a multi‑model AI architecture that prioritizes width over depth, enabling parallel expert models to tackle complex, multi‑format data, addressing single‑model limitations while attracting significant industry investment and posing new engineering challenges.

AI ArchitectureDeepSeek-R1Tsinghua
0 likes · 7 min read
Why Tsinghua’s Multi‑Intelligence DeepSeek‑R1 Shifts AI from Depth to Width
Frontend AI Walk
Frontend AI Walk
Mar 11, 2026 · Artificial Intelligence

OpenClaw Full‑Domestic Model Stack: 6 Role‑Based Selections and Strategies

This guide outlines a role‑based selection strategy for building a fully domestic OpenClaw model stack, explains common pitfalls when replacing foreign models, details why specific Chinese models fit each role, presents three balanced configurations, and offers a step‑by‑step migration plan.

BGE‑M3DeepSeekGLM-5
0 likes · 15 min read
OpenClaw Full‑Domestic Model Stack: 6 Role‑Based Selections and Strategies
Alibaba Cloud Native
Alibaba Cloud Native
Dec 19, 2025 · Artificial Intelligence

What Enterprises Are Learning from the State of Agent Engineering Report

The recent LangChain "State of Agent Engineering" report, combined with data from the AI‑Native Application Architecture whitepaper, reveals rapid production adoption of AI agents, persistent quality challenges, widespread observability, multi‑model strategies, and evolving evaluation practices across organizations of all sizes.

AI agentsLLMObservability
0 likes · 10 min read
What Enterprises Are Learning from the State of Agent Engineering Report
Wuming AI
Wuming AI
Sep 20, 2025 · Artificial Intelligence

How to Use Cherry Studio for Simultaneous Multi‑Model AI Calls

This guide shows how to install Cherry Studio, configure multiple AI model providers, and send a single prompt that triggers several models at once, with step‑by‑step screenshots, layout tips, and cost considerations for free and paid services.

AI toolsCherry StudioModel Aggregation
0 likes · 5 min read
How to Use Cherry Studio for Simultaneous Multi‑Model AI Calls
JavaEdge
JavaEdge
Jun 5, 2025 · Artificial Intelligence

How Amazon’s Strands Agents SDK Simplifies Building AI Agents

Amazon’s newly open‑source Strands Agents SDK lets developers create AI agents with minimal code by defining prompts, tools, and models, offering a lightweight, production‑ready framework that supports multiple model providers, observability, multi‑agent collaboration, and extensible tooling via dedicated packages.

AI agentsAmazonLLM
0 likes · 7 min read
How Amazon’s Strands Agents SDK Simplifies Building AI Agents
DevOps
DevOps
Mar 6, 2025 · Artificial Intelligence

Building Multi-Model Chat Agents with Dify: Integrating DeepSeek‑R1 and Gemini

This article explains how to create a high‑performance multi‑model chat agent on the Dify platform by combining DeepSeek‑R1 for reasoning and Gemini for answer generation, covering the underlying principles, configuration steps, API integration, performance benchmarks, and practical deployment guidance.

ChatbotDeepSeekDify
0 likes · 12 min read
Building Multi-Model Chat Agents with Dify: Integrating DeepSeek‑R1 and Gemini
Tencent Architect
Tencent Architect
Sep 4, 2024 · Databases

X‑Stor: A Cloud‑Native Multi‑Model NoSQL Database Service – Design, Architecture, and Evaluation

The article introduces X‑Stor, a cloud‑native NoSQL database service that supports multiple data models and process‑level multi‑tenant isolation, describes its plugin‑based architecture and Request Unit metric, and presents experimental results showing superior performance, effective tenant isolation, and load‑balancing benefits compared with traditional single‑model NoSQL systems.

Cloud-nativeNoSQLRequest Unit
0 likes · 11 min read
X‑Stor: A Cloud‑Native Multi‑Model NoSQL Database Service – Design, Architecture, and Evaluation
Tencent Cloud Developer
Tencent Cloud Developer
Aug 30, 2024 · Databases

How X‑Stor Achieves Multi‑Model, Multi‑Tenant Cloud‑Native NoSQL Performance

This article introduces X‑Stor, a cloud‑native NoSQL service that supports multiple data models and process‑level multi‑tenant isolation, explains its architecture and design choices, and presents extensive experimental results that compare its performance and resource management against single‑model databases.

Database ArchitectureNoSQLPerformance Evaluation
0 likes · 12 min read
How X‑Stor Achieves Multi‑Model, Multi‑Tenant Cloud‑Native NoSQL Performance
dbaplus Community
dbaplus Community
Sep 24, 2023 · Databases

How Oracle 23c’s Converged Database Simplifies Multi‑Model Development

Oracle 23c turns the converged database vision into reality by unifying relational, document, and graph data under a single SQL engine, offering built‑in microservice support, lock‑free reservations, immutable tables, and an integrated SQL firewall to reduce architecture complexity and development cost.

23cConverged DatabaseDatabase Features
0 likes · 8 min read
How Oracle 23c’s Converged Database Simplifies Multi‑Model Development
IT Architects Alliance
IT Architects Alliance
Nov 2, 2022 · Databases

The Importance, Evolution, and Future Trends of Distributed Databases

This article examines why databases are foundational to modern IT, traces the historical development of distributed database technologies, compares various architectural approaches such as sharding middleware, shared‑storage and shared‑nothing designs, and discusses emerging trends like multi‑model, HTAP, cloud‑native, and open‑source ecosystems.

Cloud NativeDistributed SystemsHTAP
0 likes · 11 min read
The Importance, Evolution, and Future Trends of Distributed Databases
Efficient Ops
Efficient Ops
Sep 27, 2018 · Databases

How SequoiaDB’s Multi-Model Architecture Redefines Cloud‑Native Distributed Databases

SequoiaDB, a financial‑grade open‑source distributed database, combines a multi‑model engine with a compute‑storage separation architecture to deliver full MySQL, PostgreSQL and SparkSQL compatibility, elastic scaling, HTAP capabilities, and robust multi‑site disaster recovery for cloud‑native enterprise workloads.

Compute-Storage SeparationHTAPcloud-native
0 likes · 12 min read
How SequoiaDB’s Multi-Model Architecture Redefines Cloud‑Native Distributed Databases
dbaplus Community
dbaplus Community
Aug 25, 2018 · Databases

Why Multi-Model Databases Are the Future of Cloud Data Management

The article explains how cloud-driven demands and diverse data types have spurred the rise of multi-model databases, detailing their architecture, storage structures, compression techniques, and access methods using SequoiaDB as a concrete example.

BSONCloud DatabasesDatabase Architecture
0 likes · 14 min read
Why Multi-Model Databases Are the Future of Cloud Data Management
Architects' Tech Alliance
Architects' Tech Alliance
Jul 28, 2018 · Databases

Technical Requirements and Architectural Directions for Cloud Databases

The article explains the key technical requirements of cloud databases, such as elastic scaling, compute‑storage separation, multi‑model support and self‑management, and discusses emerging architectural trends like storage‑SQL separation, multi‑model engines, and disaster‑recovery/multi‑active designs for various enterprise scenarios.

cloud databasedbPaaSelastic scaling
0 likes · 16 min read
Technical Requirements and Architectural Directions for Cloud Databases