The article analyzes 2026 hiring trends that show traditional front‑end demand falling while AI‑enabled front‑end engineers earn 40% more, outlines a four‑layer capability pyramid, recommends a modern tech stack, and provides a concrete roadmap from junior to senior AI‑focused front‑end roles.
The open‑source AgentScope Java framework now ships with version 2.0, introducing HarnessAgent for long‑running tasks, a Workspace‑based persistence layer, enterprise‑grade multi‑tenant isolation, streaming events, and a refactored middleware model, all illustrated with runnable Java examples and a concise feature table.
In the AI‑driven era, enterprises need a data foundation that supports both real‑time consumption and long‑term historical analysis, and the emerging "zero‑ETL" trend moves generic ingestion capabilities from external Flink/Spark jobs into a streamlined Kafka‑to‑Iceberg pipeline, reducing complexity while preserving low latency, consistency, schema evolution, CDC semantics and open‑ecosystem compatibility.
This tutorial walks Java backend developers through the mature Java LLM ecosystem, comparing frameworks like Spring AI and LangChain4j, and demonstrates step‑by‑step how to create a Spring Boot application with a chat endpoint, streaming responses, and dynamic model switching among OpenAI, Tongyi Qwen, and Ollama.
In the AI era, enterprises need a data foundation that supports both low‑latency streaming and long‑term analytics, and the combination of Kafka, Iceberg and object storage is emerging as a preferred solution; by moving ingestion capabilities closer to the message layer and eliminating external ETL jobs, a "zero‑ETL" approach reduces architectural complexity, improves consistency, and streamlines schema evolution and small‑file management.
This tutorial walks you through preparing the environment, initializing a Node.js project, writing the first Pi‑AI agent code, using both simple and streaming calls, swapping providers with a single parameter change, and building a continuous‑conversation CLI—all in less than ten minutes.
This guide walks through setting up a Spring Boot 3.5 environment, configuring Flink 1.20 and Flink CDC 3.5, preparing MySQL tables, and using both the Flink CDC CLI and SQL client to achieve near‑millisecond synchronization of data from MySQL to Elasticsearch, including custom sink programming and real‑time monitoring via the Flink Web UI.
The article shows that Polars, a query‑compiling DataFrame library, can accelerate ten‑million‑row GroupBy workloads by 6‑10× compared with Pandas, explains the underlying optimizer, Arrow columnar engine and Rust parallelism, provides a 20‑item syntax map, three real migration scenarios, streaming for out‑of‑memory data, and AI‑pipeline use cases, and offers a step‑by‑step migration guide.
The article explains why streaming output is essential for responsive LLM agents, compares batch and streaming latency, details the five LangGraph streamMode options with code examples, shows how to combine them, and lists common pitfalls to avoid runtime errors and poor user experience.
This article explains three Spring Boot 3.5.0 techniques—ResponseBodyEmitter, SseEmitter, and StreamingResponseBody—for delivering multiple asynchronous values as a stream, complete with code samples, curl demonstrations, and a front‑end EventSource example.
The article explains Flink Agents' current 0.3 preview, detailing its layered architecture—from Agent definition to execution plan and runtime operators—while outlining the roadmap for Skills integration, Mem0 long‑term memory, durable execution, and observability enhancements aimed at production readiness.
LinkedIn, the original home of Apache Kafka, abandoned the platform for a home‑grown system called Northguard, redesigning log storage, decentralizing metadata, and adding a virtualized Xinfra layer to handle trillions of daily events, while still acknowledging Kafka’s relevance for most companies.
This article analyzes JD's end‑to‑end upgrade of its retail coupon search infrastructure, detailing the business drivers, data‑skew challenges, the shift from dual KV and batch pipelines to a unified stream‑batch model built on Apache Doris, and the resulting performance, resource and stability gains across multiple scenarios.
This tutorial shows Java engineers how to set up a Spring Boot 4 project, configure Spring AI for the MiniMax large‑language model, call it via simple and streaming endpoints, use prompt templates with dynamic parameters, add metadata and advisors, and switch between different LLM providers with minimal code changes.
This tutorial walks through creating a fully functional Tool-Calling Agent using LangChain.js, covering tool definition, model binding, manual execution loops, the high‑level createReactAgent API, streaming responses, state management with thread IDs, common pitfalls, and a complete runnable example.
This comprehensive guide walks you through Claude Code model selection, API authentication, request construction, multi‑turn conversation handling, system prompts, temperature tuning, streaming responses, and clean JSON extraction, providing practical Python examples for building robust AI‑powered applications.
This guide walks you through Claude Code model choices, secure API key handling, Python SDK setup, request construction, multi‑turn conversation management, system prompts, temperature tuning, response streaming, and extracting clean structured data such as JSON, all with practical code examples and diagrams.
This article presents ten concrete techniques—including in‑memory and Redis caching, semantic caching, parallel execution, batch processing, prompt compression, model routing, streaming output, and connection‑pool reuse—to dramatically reduce latency and token costs in production LangChain applications.
The article breaks down MiMo-Audio, a next‑token‑prediction‑style large‑scale audio model built on Qwen2, detailing its acoustic front‑end, RVQ tokenizer, patch‑based transformer architecture, streaming capabilities, performance advantages, engineering constraints, and recommended application scenarios.
This step‑by‑step tutorial shows how to configure gRPC, define protobuf service contracts, and implement streaming upload and download in C++, covering environment setup, code generation, server and client logic, testing, and performance tuning for efficient file transfer.
When a Spring Boot application reaches millions of rows, it often suffers from OutOfMemoryErrors, slow queries, and high CPU, but by applying five proven strategies—pagination, streaming, batch processing, indexing, and asynchronous execution—you can halve memory usage and achieve up to ten‑fold speed gains.
This tutorial walks you through creating a lightweight, single‑file AI command‑line interface powered by Anthropic's Claude API, covering prerequisites, repository setup, core components, streaming responses, built‑in commands, and the underlying stateless architecture.
This article explains Spark Structured Streaming’s new Real-Time Mode introduced in Spark 4.1, detailing how it reduces latency to the millisecond level by redesigning micro‑batch processing, concurrent stage scheduling, streaming shuffle, and checkpointing, and compares it with Flink’s native streaming.
This article shows Java developers how to add generative AI to enterprise applications using the open‑source LangChain4j library and Quarkus, covering a chatbot example, streaming responses, chat memory management, and structured output without learning a new programming language.
FastAPI feels lightning‑fast on small datasets, but returning millions of rows can exhaust memory, block the event loop, and cripple the database; this article explains why that happens and provides concrete design rules—selective fields, pagination, cursor‑based queries, streaming, and chunked processing—to keep APIs stable at scale.
This guide enumerates ten frequent traps encountered when streaming JSON in Node.js—such as assuming one chunk per object, UTF‑8 split issues, missing newline delimiters, back‑pressure overload, and handling of large numbers—and presents reliable patterns like using NDJSON framing, StringDecoder, pipeline, and proper error handling to avoid data loss and memory spikes.
Learn five practical Python techniques—streaming large files, using generator pipelines, leveraging __slots__, avoiding temporary objects in loops, and reusing buffers—that together can reduce memory consumption by up to 70% and dramatically improve performance when processing gigabyte‑scale datasets.
This article reveals that gRPC should be seen not merely as a high‑performance RPC framework but as a novel connection primitive that treats network links as living, stateful entities, reshaping how developers design, monitor, and reason about distributed systems.
This article presents a production‑grade Flink ClickHouse sink that solves common pain points such as lack of size‑based batching, static table schemas, and distributed‑table latency by introducing data‑size batching, dynamic table routing, local‑table writes, load‑balanced node discovery, back‑pressure queues, dual‑trigger flush, and recursive retry with node exclusion, all integrated with Flink checkpoint semantics for at‑least‑once guarantees.
The article walks through five concrete Python techniques—streaming file reads, generator expressions, __slots__, avoiding temporary objects in loops, and reusing buffers—showing code examples and measured memory reductions that together lowered overall RAM consumption by about 70%.
The article explains how Kuaishou modernized its data lake by partnering with Apache Hudi to address latency, storage cost, and consistency issues in both AI and BI pipelines, detailing architectural changes, new ingestion tools, partitioning strategies, compaction mechanisms, performance gains and future plans.
This article explains Apache Flink’s stream processing fundamentals, introduces the open‑source Flink Agents framework for building event‑driven AI agents, details Alibaba Cloud’s Flink AI Function for real‑time LLM inference, and showcases demos, architecture, integration patterns, and practical use cases such as VOC analysis, live‑stream analytics, and intelligent operations.
In under a month the author migrated more than 100 heterogeneous tables from MySQL and MongoDB to PostgreSQL across isolated networks by automating Navicat's import/export logic, streaming data, handling special characters, configuring flexible sync strategies, and using OSS as a bridge to avoid OOM and lock‑contention.
LinkedIn, facing planetary‑scale data volumes, found Kafka’s architecture hitting fundamental limits and built Northguard—a decentralized, log‑striped streaming platform with Raft‑based metadata and an Xinfra migration layer—to gradually replace Kafka’s core responsibilities while maintaining compatibility.
This tutorial explains the OpenAI‑style request and response schema, demonstrates low‑level API calls with the requests library, compares them to the high‑level openai package, and walks through building a streaming multi‑turn chatbot that interacts with a locally hosted large language model.
This article examines the growing demand for real‑time data processing, outlines the high development, operational, and scalability challenges of traditional streaming systems, and presents a Kubernetes‑based cloud‑native PaaS solution that automates resource management, provides configuration‑driven development, and delivers observable, elastic, and service‑oriented streaming capabilities.
This article explains Kafka’s core mechanisms for preventing data loss, covering replica architecture, leader‑follower roles, ACK policies with ISR, log persistence and flush strategies, as well as producer idempotence and transactional support that together ensure reliable, exactly‑once delivery.
This article explains how to handle long‑running Spring endpoints by using three asynchronous streaming tools—ResponseBodyEmitter, SseEmitter, and StreamingResponseBody—showing their appropriate scenarios, configuration details, and complete code examples that keep the servlet thread free and avoid timeout problems.
TanStack AI (Alpha) introduces a framework‑agnostic, type‑safe, and visual‑debuggable AI SDK that works across Vue, React, Solid, and vanilla JavaScript, supporting multiple model providers and languages, with step‑by‑step guides that let developers quickly build streaming chat, actions, and RAG applications.
This article explains how to import a hundred‑million‑row Excel dataset into MySQL by using CSV format, streaming parsers like EasyExcel, batch inserts, asynchronous processing, and partial‑transaction strategies to ensure feasibility, data integrity, and high performance.
The 2025 Apache Paimon release brings major performance boosts, AI‑centric multimodal storage, deeper streaming‑batch integration, and broader engine compatibility, detailing query and write optimizations, memory management tweaks, and a unified lake format for structured and unstructured data.
This article compiles a comprehensive set of Kafka interview questions covering basic concepts, advanced mechanisms, and expert-level topics to help candidates prepare effectively for technical interviews.
This guide outlines a step‑by‑step learning path for Apache Spark, covering core concepts, environment setup, hands‑on WordCount code, API mastery, ecosystem extensions like Structured Streaming and MLlib, deployment options, performance tuning, and practical project advice.
This article reviews Spring AI's evolution, architecture, and key features—including unified APIs, prompt engineering, streaming, RAG, multi‑modal support, and agent workflows—showing how Java back‑ends can efficiently expose AI capabilities without handling low‑level model logic.
Learn how to design and deploy a high‑performance, production‑grade LLM API using FastAPI, covering async routing, type‑safe Pydantic models, streaming via SSE/WebSockets, middleware, caching, rate limiting, observability, retries, and cost‑control strategies for robust AI services.
Apache Kafka 4.1.0 introduces native queue semantics, a new Streams rebalancing protocol, multi‑version Connect plugins, a revamped consumer‑group protocol, enhanced transaction safety, and numerous client, monitoring, and security improvements, offering a comprehensive upgrade over the 4.0 release.
This guide presents concrete techniques for improving Kafka consumer and producer performance, scaling clusters, tuning broker settings, and designing asynchronous buffering layers to prevent message accumulation and boost overall throughput.
This article explains Kafka’s core concepts—including topics, partitions, log segmentation, indexing, and acknowledgment mechanisms—then provides a step‑by‑step guide to deploy a Zookeeper‑Kafka cluster integrated with Filebeat, Logstash, and the ELK stack for reliable log collection and analysis.
This article breaks down Kafka’s architecture, explaining how sequential I/O, zero‑copy, batching with compression, and partition‑based horizontal scaling combine to deliver ultra‑high throughput, low latency, and strong reliability for handling billions of messages.
This tutorial explains how to implement sequential streaming for multipart uploads and downloads in Spring MVC and reactive streaming in Spring WebFlux, enabling large files to be processed without excessive memory usage and improving performance for high‑load web applications.
This article introduces the open‑source Go AI Agent framework Blades, showing how to quickly set up a chat with a large model, enable streaming responses, and implement multi‑turn conversations using prompts, memory, and custom middleware, with complete code examples.
This article explains how the Model Context Protocol (MCP) bridges large language models and external services by converting OpenAPI specifications into callable tools, covering generation with openapi‑generator, mapping rules, three runtime modes (stdio, streamable, SSE), and implementation details in Java, Python, and Go.
This article explains Spark Streaming's rate control mechanisms, covering static limits, the dynamic back‑pressure feature introduced in Spark 1.5, the PID‑based estimator, RPC communication, and how Guava's token‑bucket RateLimiter enforces the calculated thresholds to ensure stability and optimal throughput.
This article examines the challenges of a legacy Lambda‑based membership data warehouse, introduces Apache Paimon’s lakehouse architecture and its key features, and showcases three real‑world implementations—partial‑update order wide tables, Bitmap‑based UV counting, and branch‑based data correction—while discussing benefits, remaining challenges, and future directions.
Lazada transformed its e‑commerce product selection by building a unified, real‑time platform on Alibaba Cloud Flink and Hologres, overcoming data silos, freshness delays, and high‑throughput challenges to enable millisecond‑level decisions across six Southeast Asian markets.
This article explains the essential components of a Kafka cluster—including producers, consumers, brokers, Zookeeper, and the KRaft controller—detailing how topics, partitions, and replication enable high‑throughput, scalable, and fault‑tolerant distributed messaging.
This article explains Kafka’s core architecture—producer, broker storage, and consumer groups—detailing how messages are partitioned, buffered, replicated, and consumed to achieve high throughput, low latency, and scalable real‑time data processing.
This guide explains Apache Flink 2.1's new AI model DDL and ML_PREDICT function, showing step‑by‑step how to create, manage, and invoke AI models in streaming SQL, configure resources with Little's Law, and process risk‑assessment results in real time.
This guide shows how to install the asynchronous webman/openai client, make streaming and non‑streaming calls to the Tongyi Qianwen API, and classify user feedback in PHP with high concurrency using the Webman framework.
This article details Bilibili's third‑generation streaming transcoding system, explaining its architecture, core modules, testing strategies, quality‑assurance plans, and code examples that together dramatically reduce processing time and support millions of daily video uploads.
This article explains the fundamentals of financial reconciliation, outlines the two‑step reconciliation process, and details a streaming reconciliation architecture capable of handling 30 billion USD per hour (≈27 777 TPS) with high concurrency, distributed parsing, and efficient result aggregation.
This article details how a real‑time data warehouse built on Flink, Kafka, HBase and MySQL was redesigned using Paimon to eliminate costly deduplication, handle out‑of‑order events, enable streaming reads, simplify aggregation, replace multiple lookup sources, and achieve faster, more reliable batch repairs, resulting in major resource and operational gains.
This guide explains why the official Coze SDK lacks PHP support, shows how to install the community‑made pfinalclub/coze_sdk package via Composer, and demonstrates chat, bot management, and both basic and advanced streaming features with complete code examples and future improvement plans.
LangChain4j 1.2.0 introduces a suite of stable modules, advanced inference and thinking capabilities, streaming tool calls, and extensive AI service enhancements, offering developers finer control, lower latency, and richer debugging for LLM‑driven applications.
This article explores how Apache SeaTunnel addresses modern data integration challenges by providing a high‑performance, distributed, plugin‑based platform that decouples connectors from execution engines, enabling seamless batch and streaming synchronization across heterogeneous sources such as databases, message queues, and data lakes.
In the era of rapid AI growth, Fluss offers a unified lake‑stream architecture that tackles data quality, timeliness, scale, and multimodal challenges by tightly integrating Flink streaming with a high‑performance data lake, enabling seamless real‑time and batch analytics for AI workloads.
The article reviews key topics from the FFA2025 Singapore conference, highlighting Flink’s new AI‑focused Agents framework, the breakthrough Flink 2.0 disaggregated state architecture, emerging lake storage solutions like Paimon, and the Fluss streaming table store, illustrating how big‑data platforms are evolving for AI workloads.
This article explains Kafka’s high‑performance distributed streaming architecture, key components such as topics, partitions, producers, consumers, brokers, offsets, and ZooKeeper, and provides step‑by‑step workflows for producers and consumers along with performance‑testing tips and Maven setup.
This guide presents a step‑by‑step Kafka learning roadmap covering core concepts, architecture, configuration, monitoring tools, practical project ideas, advanced components like Streams and KSQL, plus code samples and resource recommendations to help beginners become proficient in real‑time data streaming.
Learn how to create and use non‑blocking (asynchronous) and streaming gRPC clients in Java, covering stub creation, request handling with ListenableFuture, blocking vs listener approaches, and stream observer implementation, enabling high‑concurrency performance testing and real‑time data processing scenarios.
This article provides a comprehensive overview of Kafka, covering its high‑throughput distributed messaging model, key components such as producers, consumers, brokers, topics, partitions, and metadata management, as well as the end‑to‑end workflow from production to consumption.
This article explains how to efficiently handle massive file uploads and downloads in Spring Boot 3 using traditional blocking I/O, reactive WebFlux streams, and proper configuration to keep memory usage low while maintaining high performance.
Eino is an open‑source Golang framework for building large‑model applications, offering reusable components, robust orchestration, clean APIs, best‑practice templates, and full‑cycle DevOps tools, with code examples for both Ollama and OpenAI modes, plus streaming and normal output options.
This article explains why Kafka is essential for modern data engineering, highlighting its widespread adoption, high throughput, scalability, durability, integration with streaming ecosystems, and common real‑time use cases, while also providing a concise list of interview topics for aspiring engineers.
Learn how FastAPI handles various response types—including default JSON, custom status codes and headers, HTMLResponse, FileResponse, StreamingResponse, and user‑defined response classes—while covering best practices, common pitfalls, and code examples for building robust APIs.
Lalamove’s international logistics platform transformed its real‑time data warehouse by leveraging Apache Flink and the Paimon lakehouse, addressing challenges of multi‑region data centers, time‑zone diversity, frequent upstream changes, and high costs, while improving scalability, latency, and operational efficiency across global markets.
Kafka 4.0 replaces ZooKeeper with the native KRaft (Kafka Raft) consensus layer, simplifying deployment, boosting scalability, cutting latency, and introducing a new incremental consumer rebalance protocol and share‑group model that together reshape how distributed streaming applications are built and operated.
This article explains how Huawei's terminal cloud adopts the Apache Iceberg table format to efficiently manage large-scale datasets, detailing its architecture, feature engineering, merge operations, LSM-based storage, schema versioning, AB testing support, catalog enhancements, and future roadmap for full lifecycle data governance.
Learn how to overcome memory and speed bottlenecks when exporting massive datasets to Excel by using streaming APIs like SXSSFWorkbook and EasyExcel, optimizing database pagination, tuning JVM and connection pools, and applying asynchronous shard processing to achieve stable sub‑200 MB memory usage for millions of rows.
This article introduces Spring Framework's ResponseBodyEmitter, explains its role in asynchronous HTTP responses, outlines typical use cases such as long polling and real‑time log streaming, provides a complete Spring Boot example with code, and compares it with SSE and raw OutputStream approaches.
The Kuaishou‑hosted Apache Hudi Asia technical salon gathered over 230 attendees and featured seven experts from Kuaishou, Meituan, TikTok, Huawei, JD and others, who shared best practices, architecture designs, and performance optimizations for large‑scale data lake applications across AI, BI, and real‑time workloads.
This article explains the purpose, core methods, and practical scenarios of Spring Framework’s ResponseBodyEmitter, compares it with SSE and raw streaming, and provides a complete Spring Boot controller example that demonstrates how to implement real‑time log streaming and other asynchronous HTTP responses.
The Apache Hudi Asia technical salon held in Beijing on March 29 gathered over 230 on‑site participants and 16,000 online viewers, featuring expert talks from leading Chinese tech companies that showcased real‑world Hudi implementations, performance optimizations, and future roadmap for data‑lake technologies.
This article provides a comprehensive overview of Flink CDC 3.3, detailing its CDC fundamentals, new connectors, Transform module enhancements, asynchronous snapshot splitting, community adoption, and upcoming roadmap for broader ecosystem support and batch‑mode execution.
iQIYI transformed its real‑time data warehouse by replacing a costly Kafka‑based Lambda stack with a unified stream‑batch Iceberg lake, cutting storage expenses by 90%, halving compute costs, extending data retention, and delivering minute‑level freshness for 90% of use cases while preserving second‑level processing where needed.
Apache Kafka 4.0 eliminates Zookeeper with the KRaft consensus protocol, upgrades Java requirements to 17, introduces a next‑generation consumer rebalance protocol, adds Share Group queue semantics, and bundles numerous performance, security, and API improvements for modern streaming workloads.
Chronon is an open‑source ML feature platform that lets engineers declaratively define, compute, and serve both batch and real‑time features with built‑in observability, data‑quality checks, and a low‑latency retrieval API, ensuring online‑offline consistency while simplifying pipeline management and enabling future automation.
This article explains how Spring Framework's ResponseBodyEmitter enables real‑time, chunked HTTP responses for use cases such as log streaming, progress updates, chat, and AI output, detailing its advantages over SSE, usage scenarios, core methods, a complete controller example, and best‑practice considerations.
Apache Kafka 4.0 introduces a groundbreaking KRaft architecture that removes ZooKeeper, a revamped consumer‑group protocol that dramatically speeds up rebalancing, a new queue mode for point‑to‑point messaging, upgraded Java requirements, streamlined APIs, and numerous performance and security enhancements, reshaping both development and operations for large‑scale streaming workloads.
Apache Kafka 4.0 has been officially released as the first major version that runs entirely without Apache ZooKeeper, introducing KRaft mode, a new consumer group protocol (KIP‑848), early‑access queue support (KIP‑932), updated Java requirements, and other enhancements aimed at improving scalability, operability, and messaging versatility.
This article presents a comprehensive overview of Flink CDC + Hologres high‑performance data synchronization, detailing write and consumption optimizations, architectural principles, and future directions to achieve low latency and high throughput in real‑time data pipelines.
Alibaba Mama’s advertising platform migrated from a monolithic Flink‑Kafka pipeline to a layered Paimon lakehouse, adding DWS upsert support and multi‑layer storage, which delivers minute‑level data freshness, cuts latency by 2.5 hours, reduces resource use over 40 %, halves development effort and achieves ≥99.9 % availability.
gRPC offers superior performance, efficient Protobuf encoding, strong API contracts, seamless streaming, cross‑language support, and HTTP/2 advantages, making it a compelling alternative to REST for modern web services, while tools like gRPC‑Gateway, ConnectRPC, and Buf streamline migration and ecosystem integration.
The article introduces DeepSeek4j 1.4, a Java Spring Boot library that overcomes existing framework limitations by preserving DeepSeek's chain‑of‑thought capabilities, adding full reactive streaming, and providing a simple one‑line API along with quick‑start instructions and code examples.
This article introduces Spring Framework's ResponseBodyEmitter, explains its advantages over SSE for asynchronous HTTP responses, demonstrates practical usage with a real‑time log‑streaming example, outlines core methods, working principles, best‑practice considerations, and compares it with traditional Streaming and Server‑Sent Events.
This article examines how Apache Iceberg’s snapshot‑based ACID transactions, logical‑physical partition evolution, and COW/MOR update modes enable efficient real‑time data lake ingestion, and demonstrates AutoMQ’s Kafka‑to‑Iceberg Table Topic solution that simplifies schema management, reduces latency, and cuts operational costs.
Processing large files efficiently in Python requires techniques such as line-by-line iteration, chunked reads, generators, buffered I/O, and streaming, which help avoid memory errors, improve speed, and optimize resources for tasks like log analysis, data scraping, and real-time API handling.
This article introduces Alibaba's LangEngine, a pure Java AI application framework, detailing its high‑availability gateway architecture, communication protocols, streaming and non‑streaming output, multi‑level metadata caching, asynchronous and serverless designs, and future open‑source roadmap, offering practical guidance for building robust AI services.
This guide explains how to install, configure, and use the Grok‑PHP client to call Grok AI models from PHP, covering basic usage, advanced options, available model enums, and streaming responses with code examples.
The article introduces DeepSeek4j 1.4, a Java‑based framework that overcomes Spring AI’s limitations by fully preserving DeepSeek’s chain‑of‑thought and billing features, adding reactive streaming, providing Spring Boot starter integration, and offering quick‑start code samples and configuration guidance.
