Articles

This article meticulously details how Fliggy's 'Ask-a-Question' feature, initially a map planning tool, progressively evolved into an AI-powered intelligent travel manager leveraging Multi-Agent collaboration and direct connectivity to real-time pricing databases. Confronting the challenges of highly variable offerings, lengthy decision-making processes, and stringent real-time inventory demands inherent in travel scenarios, the Fliggy team championed the core philosophy of 'be knowledgeable, be capable of calculation, and be capable of action.' They engineered a Multi-Agent architecture comprising an intent recognition Agent and numerous scenario-specific Agents (for itineraries, transportation, hotels, guided tours, etc.) working in concert. To mitigate large language model hallucinations and improve efficiency, context management (including Query rewriting and memory pruning) was specifically optimized, alongside a comprehensive shift towards Function Call for real-time data retrieval. The article innovatively presents a two-phase tool calling strategy: first generating an 'itinerary skeleton' by querying associated route libraries, then populating it with precise real-time data (e.g., specific transport schedules, live hotel prices, attraction tickets). This method effectively curtails the large model's hallucination rate. Furthermore, multi-dimensional optimizations such as a dual-channel rendering protocol for graphic and text streaming, extreme parallel processing for faster responses, and a resume from breakpoint mechanism ensure seamless user experience even in weak network conditions. Practical outcomes demonstrate that this system has enhanced itinerary planning efficiency by over 90%, achieved a user satisfaction rate of 95%, and has been extended to external ecosystems like Honor and VIVO.

This article introduces seven new AI products highly recommended by Founder Park at the GeekPark Innovation Conference. These offerings range from personal efficiency tools to enterprise-grade solutions, including flomo's "Multi-perspective Insight" notes, Doka Camera's AI-assisted composition, remio's Personal ChatGPT, Pallas AI's AEO Marketing Agent, MuleRun's AI Agent Marketplace, OdyssLife's AI Health Necklace, and LavieAI's AI Visual Content Generation. Each product's founder or co-founder shared their product philosophy, core functionalities, and unique insights on innovation in the AI era. They emphasized AI's role as an assistant and enabler rather than a replacement for human capabilities, highlighting the critical importance of "context" in AI applications and discussing future commercialization paths and market potential for AI products. The article also contains some marketing and promotional content.

This article provides an in-depth look at the prompt instructions for the Gemini Guided Learning System, highlighting its role as a 'warm, friendly, and encouraging peer tutor.' The core principle is 'guide, don't tell,' facilitating learning by adapting to users, preserving context, and fostering curiosity. The article details conversational guidelines, including thinking about learning intent before responding, maintaining language consistency, purposeful communication, using educational emojis, and strategic visual aids, while stressing not to repeat information or cite sources. It specifically distinguishes between initial and subsequent dialogue strategies for convergent, divergent, simple recall, and other query types. Finally, the article also covers methods for handling off-topic and meta queries, as well as strategies for praise and correction alongside non-negotiable safety guardrails.

This article delves into OpenAI's newly released flagship image model, GPT Image 1.5, elaborating on its significant improvements in instruction following, editing capabilities, and generation speed. It highlights the core innovation of maintaining consistent lighting, composition, and character appearance across multi-turn edits. The article cites third-party evaluation data from Artificial Analysis and GenAI, comprehensively comparing GPT Image 1.5 with competitors like Gemini 3.0 Pro Image in text-to-image generation and image editing, emphasizing GPT Image 1.5's leadership in instruction following. Furthermore, based on the official Prompt Guide, the author systematically organizes the general Prompt framework, key control parameters, and detailed techniques for high-frequency tasks (such as infographics, realistic photos) and editing workflows (such as style transfer, virtual try-on), significantly enhancing the model's usability. Finally, the author's hands-on test cases intuitively demonstrate the differences and characteristics of GPT Image 1.5 and Gemini 3 Pro Image when handling complex Prompts and multi-image fusion. This analysis reveals that GPT Image 1.5 still has room for improvement in certain complex scenarios.

The article presents a comprehensive AI-assisted coding workflow for developers, emphasizing that while AI coding assistants are transformative, effectively harnessing them requires skill and structure. It advocates for treating LLMs as junior pair programmers needing guidance and oversight. The author's workflow, honed over a year, focuses on robust planning (specs before code), breaking work into small, iterative chunks, providing extensive context to LLMs, and choosing the right models for specific tasks. Furthermore, it details leveraging AI across the software development lifecycle, maintaining crucial human oversight through thorough review and testing, using frequent commits and version control as a safety net, customizing AI behavior with rules and examples, and embracing testing and automation as force multipliers. The core message is maintaining accountability for the produced software, with the human remaining the 'senior dev' guiding the 'assistant' LLM.

The article posits that by 2025, the large model industry will move past 'benchmarking fatigue,' embracing a new consensus where open-source models are considered a 'default mandatory option.' The criteria for evaluating top-tier models will evolve from mere foundational capabilities to a more pragmatic 'trust,' specifically how well a model performs across three dimensions: evaluation, deployment, and delivery. Drawing on data from authoritative reports like OpenRouter, the article reveals that a productivity inflection point has been reached, with complex tasks now reliably being assigned to AI. In terms of evaluation, a multi-dimensional assessment framework is emerging, balancing performance, cost, and real-world usage feedback. For deployment, a model’s deployability becomes the bedrock of trust, with hardware acceleration and transparent pricing facilitating industrial-scale applications. Delivery, meanwhile, emphasizes governability, observability, and reproducibility, ensuring models operate stably within enterprise environments. Ultimately, the article concludes that only models earning trust can become true productivity drivers, with operational excellence, controllability, security, and observability set to be central to future competition.

The article uses 'Slop' (meaning 'low-quality digital content, usually produced in large quantities by artificial intelligence'), Merriam-Webster's 2025 Word of the Year, as an entry point to explore the evolution of AI content from initial 'hallucinations' (referring to overt AI errors) to 'mediocre but correct' 'slop,' triggering widespread user revulsion. It delves into the driving forces behind 'Slop Economics,' pointing out that driven by the pursuit of 'time-on-screen share' and 'cost reduction and efficiency improvement,' commercial giants like Disney partnering with OpenAI, tend to use AI to produce 'filler content' rather than high-quality 'masterpieces.' This trend leads to a polarization of the internet content environment: on one hand, high-quality communities that uphold authenticity and user trust, and on the other, an 'ocean of slop' that encourages mindless scrolling and is designed to maximize user retention. Ultimately, the article raises profound ethical questions: when users are treated by algorithms as objects to be 'fed,' consuming vast amounts of low-quality content, where does that leave us, the individuals?

This article provides an in-depth analysis of the 'Vibe Coding' phenomenon and its potential hazards in current AI-assisted programming. The author notes that while many developers subjectively feel AI coding tools boost productivity by 20%, Randomized Controlled Trials (RCTs) paradoxically indicate an actual 20% drop in productivity. This illusion stems from AI's over-abstraction, the 'autonomous driving' trap, and the significant technical debt incurred by AI-generated low-quality code. The article proposes three key solutions: first, engaging in interactive planning to ensure human involvement and correction of AI's strategies; second, escaping the 'Asynchronous Death Valley' by maintaining extremely low latency to preserve developer flow state; and third, leveraging 'Code Map' tools to thoroughly understand the codebase before generating code. Ultimately, the article urges developers to view AI as a tool for augmenting, rather than replacing, human capabilities. It emphasizes that in human-AI collaboration, tokens should be consumed as freely as water, but critical control and comprehension of the code must always be maintained.

This year-end review of RAG technology's development in 2025 highlights its irreplaceable status as a data infrastructure in enterprise-level AI implementations, despite ongoing questions about long context windows and attention shifting to Agents. The article details RAG's technical advancements, including decoupling retrieval into distinct "search" and "retrieve" stages, and integrating TreeRAG and GraphRAG to address the limitations of traditional RAG in complex queries. It emphasizes RAG's trend of evolving from a mere knowledge base to a general-purpose data foundation for Agents, introducing the concept of "context engineering" and analyzing the critical role of domain knowledge, tool data, and conversational state data in Agent context assembly. Ultimately, the article forecasts RAG's future as a "Context Engine" or "Context Platform." Concurrently, it examines the progress of Multimodal RAG and its associated engineering challenges.

This extensive article provides a systematic and comprehensive summary of AI image generation technology. It begins by tracing the evolution of AI image generation from its early, crude forms to photorealistic outputs, emphasizing the transformative impact of AIGC on visual production. The historical development of AI image generation is then meticulously detailed, starting from the diversity limitations of the GAN era, advancing to the CLIP model's breakthrough in bridging vision and language, and further exploring Diffusion Models that achieve refined generation through iterative denoising. The discussion culminates in Stable Diffusion's core innovation—latent space operations. The article delves into the underlying logic of AI art creation by explaining Stable Diffusion's three core components: VAE, CLIP, and U-Net, and their synergistic operation. Regarding tool selection, it compares the advantages and disadvantages of closed-source platforms (e.g., Midjourney, Jiemeng) with open-source alternatives (Stable Diffusion's WebUI, ComfyUI), highlighting their varying levels of encapsulation and user control. ComfyUI's specific benefits for automated workflows and resource utilization are also noted. Finally, the article introduces three progressive stages in AI art creation: "Mastering Prompt Engineering," "Advanced Configuration Mastery," and "Model Architect," guiding users from basic prompt crafting to advanced model training and fine-tuning. The conclusion reflects on AI's influence on human creativity, positing AI as an "amplifier of human capabilities," where aesthetics and innovative thinking become paramount competencies, encouraging readers to actively engage with the technology.