Articles

The article introduces Mita AI Search's newly launched AI comic-style courseware generation feature, designed to match and potentially exceed Nano Banana 2. This feature can transform complex texts such as academic papers and industry reports into logically clear, visually rich, and explanative presentations with over 20 style options, with a single click. It can even generate "audio picture books" with voice explanations. The article demonstrates various methods to efficiently create personalized courseware, including using the "What to Learn" module, batch uploading to the knowledge base, and generating slides from search results. Furthermore, the article emphasizes Mita AI's commitment to a free, no-application, no-waiting model, providing ample free credit quotas, making it a powerful tool for assisting users in self-learning. This embodies its product philosophy: "Using technology to make knowledge accessible to everyone."

The article thoroughly analyzes the disruptive impact of AI on traditional search engines. It begins by contrasting Musk's assertion that "AI will eliminate search" with AI giants like Google Gemini 3's high-profile entry into the AI search arena, presenting the paradox of AI search being both a "battleground and on the verge of obsolescence." The main body discusses four dimensions: "strategic shift," "product form," "market landscape," and "industrial footprint." In terms of product form, the article points out that AI search is evolving from link distribution to intelligent decision-making assistants, characterized by conversational interaction, multimodal understanding, task execution and transaction brokering, and integration into general information services. In the market landscape section, the author categorizes four evolutionary paths for AI search: gradual upgrade of traditional search engines, disruptive reconstruction by AI-native search engines, ecosystem integration by super-apps (e.g., WeChat), and service-driven vertical specialization. Finally, the article analyzes AI's impact on the upstream and downstream of the industry chain, emphasizing the revaluation of credible information sources and the shift in marketing paradigms towards AEO. The conclusion calls for a return to trust and experience in an era of limited attention, while upholding technological integrity and focusing on sustainable value distribution mechanisms, and notes that AI search will unlock a trillion-level incremental market.

Google has announced major advancements to its Gemini 2.5 Flash and Pro Text-to-Speech (TTS) preview models. Key improvements focus on 'enhanced expressivity' for richer tone versatility and stricter adherence to style prompts, 'precision pacing' with smarter context-aware speed adjustments, and 'seamless dialogue' through consistent character voices in multi-speaker scenarios. These updated models, replacing previous versions, are designed to meet developers' needs for granular control over style, tone, pace, and accents in high-fidelity content generation, from audiobooks to e-learning módulos. Examples from partners like Wondercraft and Toonsutra highlight the models' capabilities in creating natural, immersive audio experiences, resulting in increased subscriptions and reduced costs for users.

IntelliJ IDEA 2025.3 focuses on resolving over 800 bug reports and usability issues, significantly enhancing the developer experience. Key updates include a unified distribution of Ultimate and Community Editions, resulting in better quality and a 30% smaller download size. The IDE now offers a better experience before indexing is complete, more detailed 'Find Usages' with relative paths, and numerous accessibility improvements. Spring support is strengthened with reliable inspections, enhanced JPA handling, and a 10x faster Spring Debugger. Language support sees 'finishing touches' for Java 25, K2 mode finalization for Kotlin, and structural search and replace for Scala. Version control integrations for GitHub and GitLab are improved with manual file review marking and better multi-line comment UX. The new terminal implementation is now available for PowerShell users, and build tools like Maven and Gradle see fixes for Micronaut and Spring projects. Web development enjoys better module resolution and monorepo support for JavaScript/TypeScript, and a unified 'JavaScript Runtime' settings page. Overall performance and UI responsiveness also received optimizations, with specific fixes for TypeScript highlighting and freezes.

This article introduces Poetiq, a startup founded by 6 former DeepMind members. Instead of developing new large language models (LLMs), they built a meta-system. This system enables existing state-of-the-art LLMs to automatically generate strategies and model combinations to solve specific tasks, effectively addressing the challenges LLMs face when tackling complex tasks individually and reducing inference costs by half. Poetiq's optimization technology has set a new SOTA on the ARC-AGI-2 leaderboard with an accuracy of 54% and a cost of $31 per task, far surpassing other models. The article elaborates on how this meta-system optimizes solutions through automatic selection of model combinations and strategies, and a recursive, self-improving loop. This allows it to quickly adapt to different models (such as Gemini, GPT series, Grok) and continuously achieve both higher accuracy and lower costs. Poetiq emphasizes that its system does not rely on specific LLMs, but rather optimizes the LLM invocation methods and inference processes through its flexible architecture and self-correction mechanism, thereby unleashing the potential of generative AI in complex reasoning.

By explaining the nature of business and wealth, the article introduces the core concept of the economic cycle – the driving role of credit and lending in economic activity. The author points out that an individual's total disposable spending equals existing money plus future credit and suggests that spending is an economic driver: your spending is someone else's income, so an initial act of borrowing by one person can trigger a multiplier effect, boosting economic growth across society. It emphasizes that economic cycles are an inevitable consequence driven by lending and divides them into four distinct phases: boom, recession, trough, and recovery. For each phase, the article provides asset allocation advice, such as investing in investable assets (e.g., real estate and commodities) during the boom phase, 'cash is king' during recession, holding bonds during the trough, and stocks during recovery. Finally, quoting Ray Dalio's three pieces of advice, it admonishes readers to pay attention to income and debt growth rates, and more importantly, to continuously improve productivity to cope with economic fluctuations.

This article, based on a survey of over 500 IT decision-makers, outlines key trends for observability in 2026. It reveals that observability has matured significantly, with 60% of organizations now considering their practices mature or expert. Simultaneously, cost management has become a critical priority, with 97% experiencing unexpected costs, driving organizations to consolidate tools and optimize spending. A major shift involves expanding observability's focus beyond operational metrics to demonstrating clear business impact, though most organizations are still in the early stages of this transition. Furthermore, observability data is increasingly being utilized by multiple internal teams beyond IT, especially cybersecurity (68%) and GRC (43%), indicating its broader value proposition. The article concludes by emphasizing the need for observability leaders to optimize costs, accelerate business impact reporting, democratize data, and evolve into strategic business partners.

The article introduces Creaibo, an AI creative tool developed by "Turing's Cat," a renowned Bilibili Top 100 Creator. Unlike conventional AI writing tools that merely generate content, Creaibo focuses on helping creators through the entire creative pipeline: from brainstorming and competitive analysis to outline construction, personalized content continuation, and even the generation of multimodal assets (images, videos, audio). Creaibo can analyze a user's writing style to produce a detailed "Creator Snapshot" and "Writing MBTI," then maintain this specific style throughout the creation process. The article thoroughly evaluates its core feature, the "Idea Canvas," showcasing its capabilities in topic analysis, outline generation, and collaborative writing with an AI assistant. It praises the tool's seamless interactive experience and its integrated multimodal asset generation, which significantly enhances creative efficiency. While noting minor bugs and areas for improvement in style imitation, the article highly commends this idealistic tool for elevating "the creative experience itself."

This article details Bilibili's engineering practices in building an AI Agent tool ecosystem. The team organized its internal AI tool capability into three evolutionary stages: starting with LangChain StructuredTool extensions within Flowise, progressing to an online JS cloud function IDE platform, and ultimately adopting the MCP protocol for cross-platform AI Agent capabilities. The core pain points identified were repetitive tool development, AI's inability to directly understand business interfaces, JSON responses being unfriendly to AI, and a lack of asset management for tools. The solution involved building a unified AI tool capability layer, providing a secure and controllable JS runtime environment through NodeVM sandboxing, and automating intranet calls and AI-friendly result transformations. Finally, to enable cross-platform, cross-model, and cross-framework tool invocation, MCP was introduced as a unified protocol, along with a proxy layer and authentication mechanisms, ensuring tool reusability and security.

This article details Reddit's process of migrating its comment backend from a traditional Python monolithic architecture to Go microservices. To address long-standing latency and scalability issues, Reddit adopted a multi-stage strategy, first validating all read endpoints of the Go service using the tap-comparison testing method to ensure consistent responses with the Python system. For write operations, the team conducted dual-write tests by deploying a replica datastore to mitigate production data risks, and addressed challenges such as serialization mismatches, database pressure, and race conditions. The migration results show that P99 latency for critical write operations was halved, with previous peaks as high as 15 seconds. Additionally, Go's concurrency features allowed the system to achieve higher throughput with fewer resources. This migration not only optimized the commenting system but also laid the foundation for Reddit's other core models (e.g., accounts, posts, subreddits) to transition to microservices.