This article introduces Ant Group’s Ray‑based distributed agent framework Ragent, outlines its background, motivation, and design, and details the four essential modules—Profile, Memory, Planning, and Action—that power large‑language‑model agents in large‑scale AI serving.
This article explains how Context Engineering (CE) extends Prompt Engineering by integrating seven core elements—system prompts, user input, short‑term memory, long‑term memory, retrieval, tools, and structured output—using the open‑source Dify platform to build dynamic, multimodal agents that cut inference costs tenfold and raise complex‑task success rates by 40%.
This article explores Browser-Use, an AI‑driven browser automation framework that combines large language models, visual perception, and DOM analysis to enable intelligent, multi‑step web tasks such as registration, price comparison, form filling, and monitoring, while detailing its architecture, historical context, core modules, and future challenges.
This article explains how to re‑implement ByteDance's DeerFlow deep‑research framework in Go (Deer‑Go), covering the multi‑agent architecture, control‑hand‑off, interrupt & checkpoint mechanisms, integration with the Hertz SSE server, and step‑by‑step deployment instructions.
This article analyzes the memory bottleneck of massive language models, explains the mathematical modeling of memory requirements, evaluates traditional sharding limits, and details how GPT‑OSS’s MXFP4 quantization combined with Mixture‑of‑Experts reduces memory, bandwidth, and compute demands enough to fit a 1200‑billion‑parameter model onto an 80 GB GPU with minimal accuracy loss.
This article provides a comprehensive technical analysis of PPO‑based reinforcement learning methods for large language models, detailing the evolution from the original PPO algorithm through GRPO, DAPO, and GSPO, and explaining their motivations, mathematical formulations, advantages, and practical challenges such as entropy collapse and importance‑sampling variance.
Youtu-Agent, an open‑source agent framework released by Tencent Youtu Lab, combines minimalist design with high performance, delivers strong benchmark results without training or proprietary models, and offers flexible, cost‑effective, automated agent generation for researchers, developers, and AI enthusiasts.
This article explores the rise of Context Engineering as an essential discipline for large language models, comparing it to Prompt Engineering, detailing its definition, classifications, common pitfalls such as poisoning and distraction, and presenting best‑practice strategies and an LLM‑OS analogy for building robust AI agents.
The August 2025 SCALE benchmark evaluates new AI models—including the GPT‑5 family, DeepSeek‑V3.1, and the SQLShift tool—across SQL understanding, optimization, and dialect conversion, revealing distinct strengths, weaknesses, and the growing advantage of specialized tools over generic large language models.
This article explains the motivation, mathematical foundations, implementation details, advantages, experimental results, and future directions of Group Relative Policy Optimization (GRPO), a novel reinforcement‑learning algorithm that replaces PPO’s value network with efficient group‑wise relative evaluation for large language models.
This article explains the Model Context Protocol (MCP), its three‑layer architecture, its core advantages, and step‑by‑step development of an MCP server in TypeScript (with Python and C++ examples), showing how LLMs can invoke tools for tasks like Unreal Engine code analysis.
Discover how AI, especially large language models, transforms traditional web scraping by introducing semantic understanding, dynamic adaptability, and automated extraction, with in‑depth reviews of emerging tools like Crawl4AI and Browser‑use, practical code examples, best‑practice guidelines, and deployment tips for modern data collection.
Atom-Searcher introduces an atomic‑thought reinforcement‑learning framework that decomposes complex reasoning into fine‑grained units, uses a Reasoning Reward Model to assign step‑wise rewards, dynamically balances process and result incentives, and achieves state‑of‑the‑art performance on multiple LLM benchmarks.
This article chronicles the author's hands‑on journey of designing AI agents to automatically generate Helm charts for open‑source applications, exploring agent role definition, behavior paradigms like ReAct and plan‑and‑execute, prompt engineering challenges, structured workflows, multi‑agent collaboration, and practical lessons for reliable, production‑grade automation.
The article contrasts pseudo‑agent AI—such as simple LLM chatbots, RPA scripts, and RAG systems—with genuine agentic AI architectures that combine large language models, orchestrators, memory stores, tool‑calling, planning modules, and multi‑agent collaboration, highlighting key capabilities like autonomous planning, feedback loops, and dynamic tool coordination.
This article introduces Ant Group’s Ray‑based distributed agent framework Ragent, outlines its background, motivation, and design, and breaks down the four essential modules—Profile, Memory, Planning, and Action—that enable large‑language‑model agents to operate in real‑world scenarios.
This article walks through Spring AI’s three core classes—Message, Prompt, and ChatModel—explaining their roles, showing concrete code examples for constructing messages, building prompts, and invoking a large language model via a REST controller, and provides a complete demo repository.
This article explores why many enterprise AI projects fail to deliver value, analyzes the complexity of real‑world AI use cases, and presents a step‑by‑step demo that combines vector, keyword, numeric, and spatial queries using OceanBase as a unified multimodal data store.
The article analyzes Denny Zhou's Stanford CS25 lecture on large language model reasoning, explaining how intermediate token generation, chain‑of‑thought prompting, self‑consistency, reinforcement‑learning fine‑tuning, and answer aggregation together unlock powerful reasoning capabilities beyond traditional greedy decoding.
AlphaEval is a unified, parallelizable evaluation framework that assesses Alpha mining models across predictive ability, time stability, market‑perturbation robustness, financial logic, and diversity without backtesting, matching full backtest results while offering higher efficiency and open‑source reproducibility.
This article examines why context management is critical for LLM agents, compares the strategies of OpenManus, Manus, Claude Code, and Gemini-cli, and extracts practical lessons on token limits, compression techniques, and engineering trade‑offs for building efficient, cost‑effective AI systems.
This article examines the limitations of large language models' internal knowledge, explains how retrieval‑augmented generation (RAG) and tool‑augmented generation address these limits, and traces the evolution from simple retrieve‑then‑generate pipelines to autonomous, multi‑modal AI agents.
The article recounts how a risk‑control algorithm engineer transformed a generic large language model into a specialized e‑commerce fraud detector by iteratively designing prompts, injecting business rules, structuring I/O, and introducing a dual‑hypothesis decision framework to achieve accurate, automated risk analysis.
This article summarizes recent AI research on quantitative finance, covering AlphaAgent's LLM-driven alpha mining, UMI's multi‑level irrationality factors, PDU's progressive dependency learning for stock ranking, SSPT's stock‑specific pretraining transformer, and Enhancer's distribution‑aware meta‑learning framework, all of which demonstrate improved stock prediction and resistance to alpha decay.
Developers using DeepSeek V3.1's API have reported that the model intermittently inserts the Chinese character “极” (or its variants) into generated code, a bug that spreads across multiple platforms and threatens high‑precision code generation, prompting community workarounds and speculation about its root causes.
This report objectively evaluates DeepSeek‑V3.1 on professional‑grade SQL tasks, presenting its balanced strengths in understanding, optimization, and dialect conversion, highlighting its top scores in syntax error detection and Chinese database conversion while exposing weaknesses in execution‑plan analysis and large‑SQL transformations.
The article compares LangGraph with LangChain, explains why a graph‑based architecture offers greater flexibility than linear chains, outlines LangGraph’s three‑layer core architecture and its ecosystem tools—including LangSmith, LangGraph Studio, CLI, and Agent Chat UI—while noting its reliance on LangChain and the need for VPN for CLI usage.
The article explains the rapid evolution of the AI application ecosystem, outlines the challenges of end‑to‑end observability for large‑language‑model services, and details how the open‑source LoongSuite suite—through non‑intrusive instrumentation for Python and Go agents and tight integration with the Dify platform—provides comprehensive, cloud‑native monitoring, tracing, and metric collection across the entire AI stack.
This guide walks you through the fundamentals of OAuth 2.0, explains how the MCP protocol supports it, and provides a complete Python example that integrates Google OAuth 2.0 into an MCP server and client, covering configuration, code implementation, and testing procedures.
This article presents a hierarchical breakdown of Agentic AI, detailing the foundational large language models, the capabilities of AI agents, the coordination mechanisms of multi‑agent systems, and the supporting infrastructure needed for reliability, scalability, and security.
This article explains how Xiaomi designed and deployed a vertical‑domain question‑answering assistant for product and car queries, covering business background, a four‑module RAG‑plus‑LLM architecture, knowledge‑base construction, custom chunking strategies, dynamic signal handling, and the challenges overcome to achieve reliable real‑time voice interactions.
This article examines the core mechanisms of large language models, revealing asymmetric token behaviors, novel token‑compression techniques, scaling‑law theory, and mixed‑precision quantization methods that together boost inference efficiency while dramatically reducing model size.
This article presents a systematic study of extracting knowledge graphs from unstructured news articles using small quantized LLMs, exposing the brittleness of LangChain's JSON‑based pipelines, evaluating prompt‑engineering fixes, and introducing the BAML framework whose fuzzy parsing and concise schema raise extraction success from roughly 25% to over 99% on a 344‑document benchmark.
This article explains how an AIGC‑driven solution uses large language models, prompt engineering, and a layered architecture built on Flask and LangChain to automatically transform product requirement documents into structured, BDD‑style test cases, achieving 89% adoption and up to 70% time reduction.
This article distills Denny Zhou’s Stanford CS25 lecture, explaining how large language models achieve reasoning through intermediate token generation, chain‑of‑thought prompting, self‑consistency, reinforcement‑learning fine‑tuning, and answer aggregation, while highlighting theoretical foundations and practical breakthroughs.
The article analyzes why AI agent initiatives often flop compared to traditional software, explains the fundamental differences in development approaches, and introduces a three‑step Agent Capability Benchmark Testing framework with concrete evaluation criteria and a practical weekly‑report agent example.
Since last year, the debate over “Prompt Engineering” has split between practitioners who favor “Context Engineering” for building scalable agent systems and scholars who treat Prompt Engineering as a broad umbrella term, highlighting the need to dynamically construct and manage context for reliable, extensible AI applications.
This article explains why traditional load‑balancing methods fall short for large language model services and introduces Higress AI Gateway's three specialized algorithms—global minimum‑request, prefix‑matching, and GPU‑aware load balancing—detailing their design, Redis‑based implementation, deployment steps, and performance gains.
Data‑Juicer is an open‑source, one‑stop multimodal data processing system that provides fine‑grained operators, scalable pipelines, and ready‑made recipes to deliver high‑quality, diverse, and model‑friendly data for large language model pre‑training, fine‑tuning, and multimodal applications.
This article details the challenges of building an AI‑powered defect deduplication system using Retrieval‑Augmented Generation, explains why LLMs produce composite (spliced) results, diagnoses the root cause as information loss in the RAG pipeline, and presents a step‑by‑step solution that restores atomicity of records for reliable duplicate detection.
This article examines the evolution of Text-to-SQL, introduces the J‑Schema representation and chain-of-thought prompting, applies iterative DPO training and self-consistency voting, and demonstrates how these techniques raise execution accuracy on the BIRD benchmark from 56.6% to 69.2%.
This article explains how Alibaba Cloud OpenSearch LLM version evolved from RAG 1.0 to RAG 2.0, introducing the DeepSearch multi‑agent architecture that combines offline data processing, online query handling, planning, clarification, search, and summarization agents to deliver more accurate and complex AI‑driven answers.
Vibe Coding, a new AI‑centric programming paradigm introduced by Andrej Karpathy, replaces traditional code‑centric development with natural‑language‑driven interactions, enabling developers to act as product‑focused guides while large language models generate code, and discusses tools, workflows, benefits, challenges, and future trends.
This article surveys recent large‑scale corpus rewriting techniques for LLM pre‑training, covering K2’s token‑utilization strategies, domain‑specific methods like SwallowMath/Code, reStructured pretraining, the WRAP pipeline, Nemotron‑CC filtering, Pro‑X noise removal, and the MAGA multi‑style expansion, while highlighting challenges, experimental findings, and open research questions.
This article walks through designing and implementing an AI code review tool using Node.js, GitLab webhooks, and large language models, covering prompt engineering, diff augmentation, token management, response parsing, and automated comment posting to streamline the review process.
This article explores why proper document chunking is crucial for Retrieval‑Augmented Generation, explains core concepts like context windows and signal‑to‑noise, compares various chunking strategies—from simple fixed‑size splits to semantic and hybrid approaches—and provides practical Python code examples to help you build more effective RAG pipelines.
This guide explains how to reinforce system prompts for AI agents by optimizing their content and structure, using active defense, role‑based, and format constraints, providing practical examples, measurement methods, and experimental results that demonstrate up to 90% reduction in unsafe behavior.
This article examines how reinforcement learning fine‑tuning influences large language model reasoning, revealing that RL primarily amplifies pre‑trained capabilities, suffers from entropy collapse, and fails to push the model’s reasoning boundary, supported by extensive experiments on scaling laws, entropy analysis, and mitigation techniques.
This article explains the fundamentals of large language models, covering transformer self‑attention, prompt engineering, API usage with temperature and tool parameters, function calling, agent architectures, the Model Context Protocol (MCP), Agent‑to‑Agent (A2A) communication, and future AI programming roles.
The Klear‑Reasoner model, built on Qwen3‑8B‑Base and powered by the novel Gradient‑Preserving Clipping Policy Optimization (GPPO) algorithm, surpasses same‑size open‑source baselines on challenging math (AIME) and code (LiveCodeBench) benchmarks, while revealing key insights on data quality, reward design, and clipping strategies for large‑language‑model reasoning.
Large language models often generate confident but false statements—a phenomenon called hallucination—because they predict the next token based on statistical patterns rather than factual understanding, and this article explains the underlying mechanisms and practical mitigation strategies.
This article explains what tokens are in large language models, how they are counted and priced, compares tokenization methods across major models, and provides practical guidelines and code examples for optimizing token usage and selecting the appropriate model for different scenarios.
This article explains how to eliminate stuttered text output in iOS chat applications powered by local LLMs using the MNN framework, by introducing a three‑layer optimization—smart stream buffering, UI update throttling with batch processing, and a typewriter‑style animation—to achieve smooth, near‑online responsiveness.
An engineer recounts the hard‑earned lessons from moving beyond RAG to enterprise‑level AI agents, exposing three critical challenges—scheduling, memory management, and tool integration—and proposes architectural patterns that turn fragile prototypes into robust, observable, and secure AI systems.
This article systematically adapts classic deep reinforcement‑learning techniques—such as multi‑step returns, TD(λ)/GAE, V‑trace corrections, uncertainty‑aware weighting, safety constraints, distribution‑robust optimization, and value‑guided decoding—to improve large language model training and inference, providing concrete formulas, implementation tips, and empirical results.
This article systematically explains how to build reliable, high‑performance AI agents by focusing on the core components—LLM, prompts, workflows, RAG, and tools—while covering prompt engineering techniques, DSL‑based workflow design, vector‑database knowledge bases, security against prompt injection, and practical project planning.
This article systematically examines the root causes of hallucinations in large language models, evaluates their pros and cons, and presents a comprehensive set of optimization techniques—including prompt engineering, RAG, sampling tweaks, supervised fine‑tuning, LoRA, RLHF, chain‑of‑thought reasoning, and agent/workflow designs—to build more reliable and trustworthy AI applications.
The article analyzes the FilterLLM approach, which augments a frozen LLM with billions of learnable user tokens to predict a full‑user interaction probability distribution in a single forward pass, dramatically speeding up cold‑start recommendation while preserving recommendation quality across multiple benchmarks.
This article explores how large language models break the natural‑language‑to‑SQL barrier, outlines the challenges of NLP‑driven data retrieval, compares Text2SQL and Text2DSL approaches, and proposes a unified data service and metric platform to power enterprise‑grade ChatBI solutions.
This article presents a comprehensive study on improving Text-to-SQL performance by introducing J‑Schema for structured schema representation, applying iterative Direct Preference Optimization (DPO) training, and leveraging self‑consistency voting mechanisms, achieving up to a 12% accuracy gain on the BIRD benchmark.
This article surveys the evolution of Text-to-SQL, introduces the J‑Schema representation and chain-of-thought prompting, details an iterative DPO training pipeline with hyper‑parameter tuning, and demonstrates how self‑consistency voting boosts execution accuracy on the BIRD benchmark from 56.6% to 69.2%.
This article explains what an AI agent is, outlines its four core modules—perception, memory, planning, and action—describes the role of large language models, compares software development generations, discusses memory implementations, planning methods like ReAct and Plan‑and‑Solve, and covers evaluation, cost analysis, and differences between agents and workflows.
The Model Context Protocol (MCP) is an open‑source interface that standardizes how large language models interact with external data sources and tools, offering a USB‑C‑like universal connector for AI applications, with built‑in session management, security, and flexible HTTP/SSE transport for seamless real‑world integration.
Researchers from Peking University, ByteDance Seed, and Hong Kong University present SWE‑Swiss, a 32‑billion‑parameter model that, through a two‑stage training recipe and enhanced self‑consistency, achieves 60.2% accuracy on SWE‑bench Verified, matching larger models while remaining fully open‑source.
Chain‑of‑Thought (CoT) enables large language models to solve complex tasks by breaking problems into sequential reasoning steps, improving accuracy in mathematics, commonsense, code generation, business strategy, and medical diagnosis, while highlighting its principles, advantages, challenges, and future prospects.
This article explains how massive Transformer‑based large language models compress text data into mathematical representations, why scale, self‑attention, and training paradigms enable emergent general intelligence, and walks through tokenization, embedding, multi‑layer attention, architecture choices, energy costs, and hallucination mitigation.
This article explores a hybrid approach that combines traditional static application security testing (SAST) with large language models (LLM) to automatically detect business‑logic vulnerabilities, detailing the methodology, implementation stages, experimental results, and the challenges of integrating AI into code security analysis.
This article introduces four notable open‑source AI projects—Google's Gemini CLI, the voice‑interactive XiaoZhi chatbot, the comprehensive AI Engineering Hub, and the GPT‑Pilot programming companion—detailing their key features, generous free quotas, star counts, supported hardware, and providing direct GitHub repository links for each.
This article details an innovative approach that uses large‑model supervised fine‑tuning to overcome the instability of code RAG and code agents during open‑source repository upgrades, addressing domain‑specific terminology, code style differences, and improving recall, accuracy, and deployment efficiency.
This article systematically compares the architectures of recent large language models—including DeepSeek V3/R1, OLMo 2, Gemma 3, Mistral Small 3.1, Llama 4, Qwen 3, SmolLM 3 and Kimi 2—highlighting innovations such as MLA, MoE, post‑norm, sliding‑window attention, NoPE and optimizer choices, with diagrams and code examples to illustrate their impact on efficiency and performance.
This article presents a detailed design and implementation of an LLM‑based intelligent resume matching system that combines semantic vector retrieval, structured rule filtering, multi‑dimensional weighted scoring, and natural‑language interaction to create a fast, quantifiable, and explainable hiring pipeline.
Developers notice that large language models often enter an "overthinking" mode that slows down simple coding tasks, prompting calls for adjustable inference depth controls so models can switch between quick checks and deep analysis based on task risk level.
This study introduces EvoVLMA, an evolutionary vision-language model adaptation framework that automatically searches training-free VLM adaptation algorithms using a two-stage LLM-guided evolution, demonstrating superior performance—such as a 1.91 % accuracy gain on 8-shot image classification—and releasing the code publicly.
This article evaluates whether autoregressive large language models can generate several tokens in a single inference step, describing a mask‑based multi‑token prediction framework, gated LoRA adaptation, experimental results on Tulu‑3‑8B showing up to 5.2× speedup, and discusses implications for future research.
This article explains the challenges of running large language models on mobile devices, reviews recent industry efforts, and provides a step‑by‑step guide—including code snippets—for integrating a distilled GPT‑2 model with Sohu's Hybrid AI Engine using TensorFlow Lite and Keras‑NLP for on‑device inference.
This article examines how the Manus sandbox and CodeAct mechanisms inspire a GitOps‑based approach to building LLM agents, detailing the design of planner and executor components, workflow steps, advantages such as RAG and observability, and the potential for low‑cost, scalable intelligent agent development.
This article systematically compares major open‑source deep‑research agent frameworks—including DeerFlow, SmolAgents, LangChainAI, SkyworkAI, and Researcher—detailing their architectures, best practices, and commercial alternatives, to help developers and users choose the most suitable tool for automated research workflows.
This comprehensive guide explains when to use AI agents, presents core design patterns such as prompt chains, routing, parallelization, orchestrator‑worker and eval‑optimize loops, and offers concrete implementation advice and tool‑prompt engineering techniques for building reliable, high‑quality agent systems.
This article describes how the Gaode terminal team tackled large‑scale repository upgrades by building a code‑RAG and code‑Agent tool, addressing recall and stability issues, then fine‑tuning a small LLM (Qwen3‑4B) with LoRA and custom datasets to achieve reliable, low‑cost, on‑device code‑query performance.
This article explains why the Model Context Protocol (MCP) is needed for LLMs, describes its client‑server architecture, data and transport layers, and provides step‑by‑step Python examples for creating both an MCP server and a client using FastMCP and low‑level APIs.
This article explains how to combine Alibaba Cloud Milvus, a high‑performance vector database, with the low‑code workflow platform n8n to create an enterprise‑level, domain‑specific intelligent Q&A system, covering challenges, architecture, setup, workflow configuration, and verification steps.
This article explains how Retrieval‑Augmented Generation (RAG) and long‑term memory systems like MenoBase enable large language models to overcome short‑term memory limits, dynamically retrieve up‑to‑date knowledge, and personalize interactions, with practical Dify implementation steps and real‑world use cases across industries.
In the AI‑driven era, Go programmers face a dilemma about mastering Python, and this article explains when Python is essential, how to prevent naïve code translation, and provides concrete examples of writing truly Pythonic code versus direct Go‑style conversions.
This article explores how Ctrip leverages advanced AI techniques—including named entity recognition, entity linking, large language models, and vector search—to replace traditional keyword queries with semantic search, dramatically improving hotel recommendation accuracy and user experience.
This article explains how Baidu Map’s AI voice assistant converts spoken commands into precise navigation actions by detailing the speech‑to‑text pipeline, intent parsing, template and generative approaches, tool‑calling mechanisms, memory and reflection capabilities, and future directions for intelligent agents.
The mini‑SWE‑agent, a lightweight open‑source software‑engineering AI built by the original SWE‑bench team, achieves about 65% bug‑fix success on the SWE‑bench benchmark using roughly 100 lines of Python, thanks to its minimal dependencies, shell‑based execution, linear history, and support for various container environments, offering a fast, extensible alternative to the full‑featured SWE‑agent.
This article explores the importance of post‑training for large language models, explains scaling laws for pre‑ and post‑training, details common fine‑tuning methods (full, PEFT, LoRA), outlines alignment techniques such as RLHF, DPO, PPO, and presents practical workflows using Llama 3 and DeepSeek‑R1, while also discussing test‑time reasoning optimizations.
The article examines three classic paradoxes—Simpson’s paradox, the accuracy paradox, and Goodhart’s law—showing how they arise in business intelligence and large language model contexts, and offers practical guidelines to detect and mitigate their misleading effects on data‑driven decisions.
This article analyzes the prompt‑inflation bottleneck that arises when large language models (LLMs) must handle thousands of Model Context Protocol (MCP) services, and introduces the MCP‑RAG architecture—a retrieval‑augmented generation solution that builds a metadata knowledge base and intelligent retrieval layer to enable precise, efficient MCP service discovery at scale.
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 comprehensive guide explains what an AI Agent is, its core capabilities and design patterns, and walks through step‑by‑step implementations of RAG, Translation, and ReAct agents using LangGraph, complete with code samples, workflow diagrams, and practical tips for building personal ops knowledge‑base agents.
This article explains how to create a RAG‑based intelligent QA system that fetches the latest documentation (e.g., PlantUML) before querying Gemini, detailing knowledge‑base creation, embedding, vector store management, LangChain integration, and deployment tips.
This article examines the pitfalls of rule‑heavy prompt engineering, introduces a systematic four‑layer architecture for AI prompts, outlines six practical compilation principles, and demonstrates how to rewrite a tangled prompt into a clear, maintainable, and scalable system blueprint.
This article explains how Text2SQL technology converts natural language queries into executable SQL using large language models, and demonstrates how the open‑source Dify platform’s visual workflow and component‑based development dramatically lower the barrier for building, validating, and deploying secure, low‑code Text2SQL applications.
This article explains how AI agents work through perception, planning, and action, describes the four supporting systems—memory, tools, safety, and evaluation—and shows how the evolution from prompt engineering to context engineering, with strategies like selective saving, retrieval, compression, and modularization, addresses the core challenges of managing large‑scale context for reliable, efficient agent performance.
This article provides a comprehensive, hands‑on tutorial for installing Ollama, Docker, and the open‑source Coze Studio on a local machine, configuring various LLM services such as Qwen 3, DeepSeek‑V3, and OpenRouter, and running the platform via Docker Compose to create and test AI agents.
Alibaba introduces the ali‑langengine‑dflow framework, a hybrid distributed‑agent architecture that moves core intelligence to the cloud while keeping execution reachable on heterogeneous client devices, addressing data‑isolation, latency and security issues of existing cloud‑VM and local‑agent solutions for 2C internet services.
This article explains why a perfect answer from a large language model requires iterative prompt design, outlines a six‑step spiral loop for refining prompts, and offers practical tips such as starting with a minimal prompt, focusing on one improvement at a time, and preserving version history.
Vibe Coding, introduced by Andrej Karpathy in 2025, lets developers describe software goals in natural language while large language models generate the code, reshaping the developer’s role, outlining the workflow, discussing tools, risks, and future prospects of this AI‑driven programming paradigm.
Learn how to transform any PDF—including scanned documents—into well‑structured Markdown using a local LLM (Gemma 3 via Ollama), Python, PyMuPDF and Pillow, without cloud APIs or API keys, by converting pages to images, prompting the model, and saving the output.
