So as 2025 wraps up, we’ve gone headfirst into a mountain of de-identified data, searching for the quirks, surprises, and secret patterns that shape everyday life with Copilot. We’re finding out just how far it fits into people’s daily rhythms, and how human its uses have become: we often turn to AI for the things that matter most like our health. We analyzed a sample of 37.5 million conversations to find out how people actually use it out in the world. (Note: our system doesn’t just de-identify conversations; it only extracts the summary of the conversation, from which we learn the topic and the intent, and maintains full privacy.)
From health tips that never sleep, to the differences between weekday and weekend usage, to February’s annual “how do I survive Valentine’s Day?” spike, our findings show that Copilot is way more than a tool: it’s a vital companion for life’s big and small moments. And if you’ve ever pondered philosophy at 2 a.m. or needed advice on everything from wellness to winning at life, you’re in good company. So has everybody else.
Our work shows that AI is all about people, a trusted advisor slotting effortlessly into your life and your day. It’s about your health, your work, your play, and your relationships. It meets you where you are. Read all about it in our paper, but here are some of our takeaways.
Health Is Always on Our Minds—Especially on Mobile
No matter the day, month, or time, health-related topics dominate how people use Copilot on their mobile devices. Whether it’s tracking wellness, searching for health tips, or managing daily routines, our users consistently turn to Copilot for support in living healthier lives. This trend held steady throughout the year, showing just how central health is to our everyday digital habits. When it comes to mobile, with its intimacy and immediacy, nothing tops our health.
Most common Topic-Intent pairing conversations, on mobile.
Health is consistently the most common topic while interestingly, language-related chats peak earlier in the year, with entertainment seeing a steady rise.
When Programming and Gaming Cross Paths
August brought a unique twist: programming and gaming topics started to overlap in unexpected ways. Our data showed that users were just as likely to dive into coding projects as they were to explore games—but on the different days of the week! This crossover hints at a vibrant, creative community that loves to code during the week and play during the weekends in equal measure.
August topic ranks for programming and games.
There is a clear change in rank between programming and games through the days of the week, with programming rising from Monday to Friday, and Games shining on the weekends.
February’s Big Moment
February stood out for another reason: Copilot helped users navigate a significant date in their calendar year. Whether it was in preparing for Valentine’s day, or facing the day and the relationships, we saw a spike in activity as people turned to Copilot for guidance, reminders, and support. It’s a great reminder of how digital tools can make life’s important moments a little easier to manage.
Ranking of “Personal Growth and Wellness” and “Relationship” conversations February brings concerns of personal growth before Valentine’s day, with a clear peak of relationship-related conversations on the day.
Late-night Sessions
The larger-than-life questions seem to have a rise during the early hours of the morning, with “Religion and Philosophy” rising through the ranks. Comparatively, travel conversations happen most often during the commuting hours.
Average rank of Travel and Religion and Philosophy conversations per hour of the day.
Whilst people have more travel-related conversations during the day, it’s in the early hours of the morning that we see a rise of Religion and Philosophy conversations. 虽然人们在白天有更多与旅行相关的对话，但正是在凌晨时分，我们看到宗教与哲学对话有所增加。
Advice on the Rise
While searching for information remains Copilot’s most popular feature, we’ve seen a clear rise in people seeking advice—especially on personal topics. Whether it’s navigating relationships, making life decisions, or just needing a bit of guidance, more users are turning to Copilot for thoughtful support, not just quick answers. This growing trend highlights how digital tools are becoming trusted companions for life’s everyday questions.
Why These Insights Matter
By analyzing high level topics and intents, we manage to learn all these insights while keeping maximum user data privacy. Understanding these patterns helps us make Copilot even better. By seeing what matters most to our users—health, creativity, and support during key moments—we can design features that truly fit into their life. It’s also clear from these uses that what Copilot says matters. They show why it’s so important that we hold ourselves to a high bar for quality.
New audio model snapshots and broader access to Custom Voices for production voice apps.
AI audio capabilities unlock an exciting new frontier of user experiences. Earlier this year we released several new audio models, including gpt-realtime, along with new API features to enable developers to build these experiences.
Last week, we released new audio model snapshots designed to address some of the common challenges in building reliable audio agents by improving reliability and quality across production voice workflows–from transcription and text-to-speech to real-time, natively speech-to-speech agents.
The new snapshots share a few common improvements:
With audio input:：
Lower word-error rates for real-world and noisy audio
Fewer hallucinations during silence or with background noise
With audio output:：
More natural and stable voice output, including when using Custom Voices
Pricing remains the same as previous model snapshots, so we recommend switching to these new snapshots to benefit from improved performance for the same price.
If you’re building voice agents, customer support systems, or branded voice experiences, these updates will help you make production deployments more reliable. Below, we’ll break down what’s new and how these improvements show up in real-world voice workflows.
Speech-to-speech
We’re deploying new Realtime mini and Audio mini models that have been optimized for better tool calling and instruction following. These models reduce the intelligence gap between the mini and full-size models, enabling some applications to optimize cost by moving to the mini model.
gpt-realtime-mini-2025-12-15
gpt-realtime-mini model is meant to be used with the Realtime API, our API for low-latency, native multi-modal interactions. It supports features like streaming audio in and out, handling interruptions (with optional voice activity detection), and function calling in the background while the model keeps talking.
The new Realtime mini snapshot is better suited for real-time agents, with clear gains in instruction following and tool calling. On our internal speech-to-speech evaluations, we’ve seen an improvement of 18.6 percentage points in instruction-following accuracy and 12.9 percentage points in tool-calling accuracy compared to the previous snapshot, as well as an improvement on the Big Bench Audio benchmark.
Together, these gains lead to more reliable multi-step interactions and more consistent function execution in live, low-latency settings.
For scenarios where agent accuracy is worth a higher cost, gpt-realtime remains our best performing model. But when cost and latency matter most, gpt-realtime-mini is a great option, performing well on real-world scenarios.
For example, Genspark stress-tested it on bilingual translation and intelligent intent routing, and in addition to the improved voice quality, they found the latency to be near-instant, while keeping the intent recognition spot-on throughout rapid exchanges.
gpt-audio-mini-2025-12-15
The gpt-audio-mini model can be used with the Chat Completions API for speech-to-speech use cases where real-time interaction isn’t a requirement.
Both new snapshots also feature an upgraded decoder for more natural sounding voices, and better maintain voice consistency when used with Custom Voices.
Text-to-speech
Our latest text-to-speech model, gpt-4o-mini-tts-2025-12-15, delivers a significant jump in accuracy, with substantially lower word error rates across standard speech benchmarks compared to the previous generation. On Common Voice and FLEURS, we see roughly 35% lower WER, with consistent gains on Multilingual LibriSpeech as well.
Together, these results reflect improved pronunciation accuracy and robustness across a wide range of languages.
Similar to the new gpt-realtime-mini snapshot, this model sounds much more natural and performs better with Custom Voices.
Speech-to-text
The latest transcription model, gpt-4o-mini-transcribe-2025-12-15, shows strong gains in both accuracy and reliability. On standard ASR benchmarks like Common Voice and FLEURS (without language hints), it delivers lower word error rates than prior models. We’ve optimized this model for behavior on real-world conversational settings, such as short user utterances and noisy backgrounds. In an internal hallucination-with-noise evaluation, where we played clips of real-world background noise and audio with varying speaking intervals (including silence), the model produced ~90% fewer hallucinations compared to Whisper v2 and ~70% fewer compared to previous GPT-4o-transcribe models.
This model snapshot is particularly strong in Chinese (Mandarin), Hindi, Bengali, Japanese, Indonesian, and Italian.
Custom Voices
Custom Voices enable organizations to connect with customers in their unique brand voice. Whether you’re building a customer support agent or a brand avatar, OpenAI’s custom voice technology makes it easy to create distinct, realistic voices.
Theese new speech-to-speech and text-to-speech models unlock improvements for custom voices such as more natural tones, increased faithfulness to the original sample, and improved accuracy across dialects.
To ensure safe use of this technology, Custom Voices are limited to eligible customers. Contact your account director or reach out to our sales team to learn more.
From prototype to production
Voice apps tend to fail in the same places, mainly on long conversations or with edge cases like silence, and tool-driven flows where the voice agent needs to be precise. These updates are focused on those failure modes—lower error rates, fewer hallucinations, more consistent tool use, better instruction following. And as a bonus, we’ve improved the stability of the output audio so your voice experiences can sound more natural.
If you’re shipping voice experiences today, we recommend moving to the new 2025-12-15 snapshots and re-running your key production test cases. Early testers have confirmed noticeable improvements without changing their instructions and simply switching to the new snapshots, but we recommend experimenting with your own use cases and adjusting your prompts as needed.
Guys, artificial intelligence has been constantly changing the way enterprises operate. In the past, the emphasis was on intelligent assistants, but they could only respond passively. Now, Agentic AI has arrived, and this is a major evolution 🔥!
Traditional AI assistants can only perform isolated tasks and have limitations. However, Agentic AI can make autonomous decisions, coordinate multi - step actions, actively assess the environment, initiate actions, and coordinate cross - departmental work processes. It's really amazing 👏!
For enterprise leaders, this brings both opportunities and responsibilities. It has great potential, but also poses significant challenges in terms of governance, trust, and design. Enterprises must be able to monitor and reverse the actions of Agentic AI.
Enterprise work processes also need to be re - thought. We can no longer design processes step - by - step and insert automation. Instead, we need to build an intelligent ecosystem, consider which decisions should be made by humans and which by agents, and ensure correct data acquisition.
A unified platform is extremely important at this time. Without it, agents may become disjointed. A unified approach can provide standards, achieve interoperability, reduce complexity, and enable large - scale implementation.
Trust and accountability are also indispensable. Since agents act independently, the risks increase. Trust and accountability need to be integrated from the very beginning, with clear policies to make employees believe that it is a partner.
Enterprises should measure the business value as early as possible and not let projects remain only at the pilot stage. Well - designed Agentic AI can bring exponential improvements and transform enterprise performance.
The rise of Agentic AI is not about handing over power to machines, but a new stage of enterprise transformation where humans and agents fight side by side. Leaders should first conduct pilots and then expand, invest in a unified platform and policy framework, and foster a good culture.
Hey everyone! AI agents are transforming businesses—now is the perfect time for business leaders to step up and shine 💪!
Keywords
#Agentic AI #Enterprise Transformation #Work Process Remodeling #Unified Platform #Trust and Accountability
Based on the official evaluation rules of OpenCompass, leading - industry large - language models are evaluated, and a ranking list is released according to the evaluation results.
1.1 The Practical Significance and Industry Requirements of Studying AI SEO
The deep integration of artificial intelligence and search engine optimization (SEO) is reshaping the digital marketing ecosystem. In 2025, AI SEO (Artificial Intelligence Search Engine Optimization) has shifted from technical experimentation to commercial implementation. Its core value lies in redefining the efficiency boundaries, optimizing the user experience, driving data - driven decision - making, and promoting the upgrade of SEO from "keyword competition" to "intelligent trust - building" [1]. AI SEO is not simply "using AI tools for SEO", but refers to a paradigm that fundamentally reconstructs SEO strategies, technologies, and content creation in the context where artificial intelligence (especially large - language models and generative AI) has become the core driving force of search engines.
1.2 Why is AI SEO More Important Than Traditional SEO?
AI SEO is not just about optimizing keywords; it's about making a brand the preferred source of AI answers. According to the latest data, in 2025, the number of global AI search users exceeded 1.98 billion, with an annual growth rate as high as 538.7%! This means that if you still adhere to traditional SEO thinking, you may be phased out by the AI search wave. As a marketing director put it, "In the AI era, it's not about 'you being found', but 'AI choosing you as the answer'."
1.3 Article Overview and Objectives
The objective of this article is to explore how artificial intelligence is reshaping the underlying logic, working methods, and value standards of the search - engine - optimization industry.
II. AI SEO Technical Foundation and Core Models
2.1 Key Technical Framework
Machine Learning (ML) and Neural Networks: Through Recurrent Neural Networks (RNN), Transformer architectures (such as GPT, BART), sequence data analysis and content generation are achieved, supporting keyword prediction and semantic understanding [2] [3][4]. Natural Language Processing (NLP): Combining semantic analysis, intent recognition, and entity - relationship extraction technologies to address the contextualized needs of user queries [5][6][7][27].
Large Language Models (LLMs): Represented by GPT series, BERT, T5, pre - trained on hundreds of billions of corpora, to achieve keyword clustering, content creation, and conversational query optimization [8][9][10].
2.2 Keyword Analysis Algorithms
AI systems optimize keyword strategies in the following ways: Competitive Gap Analysis: Using Support Vector Machines (SVM) and decision - tree algorithms to scan the keyword matrix of competitors, identifying high - potential long - tail keywords [11][12][13]. Intent Prediction Model: Based on Bayesian classifiers and K - Nearest Neighbor algorithms (KNN) to analyze search patterns, automatically labeling informational, navigational, and transactional intents [14]. Real - time Trend Tracking: Capturing sudden keywords through time - series analysis and dynamically adjusting the content direction [15][16].
2.3 Content Generation Technology Stack
Generative AI Architecture: Adopting the Encoder - Decoder framework to achieve "text - to - text" conversion, supporting multi - format content output [17][18]. Quality Control Mechanism: Integrating detection tools such as GLTR and Originality.AI, evaluating text originality through the Perplexity value [19]. Multi - modal Expansion: Combining visual - search optimization (such as Pinterest Lens) and voice - content adaptation to enhance omnichannel coverage.
III. How to Do AI SEO Well
3.1 E - E - A - T is the Core! Build Trust First [20]
3.1.1 The Complete Definition and Core Connotation of EEAT。EEAT is an abbreviation of four English words, originating from Google's "Search Quality Evaluator Guidelines". In Chinese, it can be translated as "Experience, Expertise, Authoritativeness, Trustworthiness", and each dimension has clear evaluation criteria:
Abbreviation
English Full Name
Chinese Meaning
Core Evaluation Points
E1
Experience
Experience
Whether the content creator has first - hand / personal experience and whether the content is produced based on actual experience
E2
Expertise
Expertise
Whether the creator has the knowledge, skills, or professional background in this field, and whether the content is accurate and in - depth
A
Authoritativene ss
Authoritativeness
Whether the creator / website is recognized by the industry, users, or third parties in this field, and whether there is endorsement
T
Trustworthiness
Trustworthiness
Whether the content is true and transparent, whether the information source is reliable, and whether there is no misguidance
Optimizing "Experience": Highlight personal experiences. For example, add "practical steps", "pit - falling records", and "personal feelings" to the content, and attach evidence: such as attaching operation screenshots for tutorial - type content and real data for case sharing.
Optimizing "Expertise": Strengthen professional depth. Display the author's qualifications: add "Author: A senior expert in the XX industry for 10 years" at the bottom of the article.
Optimizing "Authoritativeness": Accumulate external endorsements. Apply for industry certifications; invite industry authorities to contribute / endorse; obtain coverage from authoritative media; accumulate high - quality external links.
Optimizing "Trustworthiness": Build transparent trust. Outdated information reduces credibility. Continuously update the content: mark "Updated in October 2025" to let AI know the content is new.
3.1.2 Why EEAT is Crucial for SEO
Google's core mission is to "provide users with the most relevant and valuable information", and EEAT is the core standard for measuring "value" and "reliability": Direct impact on ranking: Under the same topic, pages with a high EEAT score (such as professional content released by authoritative institutions) will rank higher than pages with a low EEAT score (such as general remarks by unqualified individuals). Enhance user conversion: Content with high EEAT can build user trust. Resist algorithm fluctuations: Google frequently updates its algorithms (such as core algorithm updates), but content with "high quality and high trustworthiness" is always algorithm - friendly. Optimizing EEAT can make a website's ranking more stable and less likely to plummet due to algorithm adjustments.
Enhance User Conversion: High - EEAT content can build user trust.
Resist Algorithm Fluctuations: Google frequently updates its algorithms (such as core algorithm updates), but content with "high quality and high trustworthiness" is always algorithm - friendly. Optimizing EEAT can make a website's ranking more stable and less likely to plummet due to algorithm adjustments.
3.2 Keyword Strategy Should be "Precise + Long - Tail"
Don't just focus on big keywords; long - tail keywords are the key to AI SEO! Question - type Long - Tail: For example, "How to choose a foundation for sensitive skin" (10 times better than "foundation"!). Regional Long - Tail: For example, "Gyms in Chaoyang District, Beijing, that are super suitable for students". Model and Specification Long - Tail: For example, "2025 New iPhone 16 Pro Max 512GB".
3.3 Content Format Should be "AI - Friendly"
AI likes content with a clear structure and easy - to - extract information: Use the Q&A Form: Create an FAQ section. For example, "Q: What foundation is suitable for sensitive skin? A: It is recommended to choose a formula without fragrance and with low irritation...". Use More Lists and Tables: For example, "3 Golden Rules for Choosing a Foundation". Have Clear Headings: The H1 tag contains the core keyword, and H2 tags use long - tail keywords. For example, an article titled "How to Bake Bread" with clear steps and an FAQ section answering common questions is more likely to be cited by AI than an ordinary article.
3.4 Optimize Content with AI
Many people use AI to generate content, but note: Rewrite Before Using: Don't directly copy the content generated by AI. Add your own insights instead of copying directly. Start with Small Keywords, Then Move to Big Ones: Start with long - tail keywords and gradually expand. Combine Batch Generation with High - Quality Content: Don't just focus on batch - generated content; ensure quality.
IV. Case Analysis
4.1 A B2B SaaS Case
Background: A project - management software company with the target keyword "AI project management", facing fierce competition.
Implementation Strategies Semantic clustering: Cluster 200 long - tail keywords into 8 themes, and create pages along with 30 cluster articles. E - E - A - T Enhancement: Each article contains a CTO expert review box, customer case videos, and third - party security certification Schema. Predictive Caching: For high - value white - paper pages, AI pre - loading reduced the Largest Contentful Paint (LCP) time from 3.2s to 1.4s.
Results The ranking of the target keyword rose from 15th to 3rd.
Marketing - Qualified Leads (MQL) increased by 150%, and the Cost per Lead (CPL) decreased by 40%.
The excellent rate of Core Web Vitals increased from 62% to 94%.
4.2 Jasper
A long - form content generator based on the GPT - 4 architecture, supporting brand - tone customization and real - time integration with Surfer SEO, achieving "optimization upon generation" [21][22].
4.3 Recommended AI SEO Optimization Tools
4.4 Common Room (B2B SaaS) - AI Page Generation and Topic Authority
AI Technology Stack: Jasper AI + Clearscope + Zapier automated workflow Implementation Strategies:Identified 100 micro - themes related to "community management", and AI batch - generated 700 SEO - optimized pages, including term explanations, tool comparisons, and best practices [23].
Each page automatically embedded internal links to build a topic cluster, enhancing the website's authority.
AI monitored page performance and automatically rewrote or merged pages with traffic less than 100 within 3 months. Automatically embed internal links in each page to build a Topic Cluster and enhance the website's authority. The AI monitors the page performance and automatically rewrites or merges pages with traffic less than 100 within three months.
Key KPIs: Traffic Growth: Organic traffic increased by 300% within 6 months. Keyword Coverage: The number of long - tail keyword rankings increased from 500 to 4,200. Conversion Effect: MQL increased by 180%, and the customer acquisition cost decreased by 40%. Success Points: Success Points:B2B SaaS achieved "full coverage of long - tail keywords" through AI, solving the pain point that traditional content teams cannot scale to cover niche demands.
4.5 Gina Tricot (Fashion E - commerce) - Integration of AI - powered Smart Recommendations and SEO
AI Technology Stack: Google Cloud AI + Custom Ranking Algorithm + Shopify Integration [24]
Implementation Strategies: AI analyzed user search behavior and purchase data to dynamically generate "scenario - based" product collection pages, such as "Spring wedding outfits" and "Office casual style". Each collection page had a unique SEO title and description generated by AI to avoid duplicate - content penalties. Used AI to predict seasonal trends and laid out keywords like "2025 Autumn new products" 60 days in advance.
Key KPIs: Revenue Growth: Return on Advertising Spend (ROAS) increased significantly. Organic Traffic: The proportion of organic traffic increased from 35% to 52%. Conversion Rate: The conversion rate of collection pages was 45% higher than that of standard product pages.
Success Points: E - commerce SEO upgraded from "single - product optimization" to "scenario - based theme optimization", and AI achieved "user - demand prediction + dynamic page generation". 成”。
4.6 Staples (Office Supplies) - AI Voice - Search Optimization
Implementation Strategies: AI analyzed voice - search queries (usually longer and more conversational) and optimized the FAQ page to directly answer questions like "Where can I buy cheap A4 paper?". . Added "HowTo" and "FAQ" structured data to all product pages to increase the recommendation rate of voice assistants. Used AI to generate natural - language answers, ensuring an average length of 29 words (the optimal length for voice search).
Key KPIs: Voice Traffic: Traffic from voice search increased by 200%. Featured Snippet: The proportion of winning Google Featured Snippet increased from 3% to 18%. Local conversion: In - store sales driven by queries related to "nearby stores" increased by 85%. Success Points: Success Points:Pre - arranged "Position Zero" optimization in advance, and AI helped understand the nuances of natural - language queries.
4.7 Company A (B2B Cloud Computing, Anonymous) - AI Programmatic SEO and ROI Optimization
AI Technology Stack: GPT - 4 + SEMrush + Custom Attribution Model
Implementation Strategies: For the combinations of "cloud computing + industry" (such as "cloud computing in healthcare", "cloud computing in finance"), AI generated 150 in - depth solution pages. Each page embedded an ROI calculator. After users entered parameters, AI generated customized reports to collect sales leads. Used AI to analyze user - behavior paths, identified high - conversion - intent pages, and focused on external - link building.
Key KPIs: Traffic and Conversion: Organic traffic increased by 40%, and the conversion rate increased by 20%. Lead Quality: The proportion of Sales - Qualified Leads (SQL) increased from 12% to 28%. Return on Investment: The SEO ROI reached 6.8:1, far exceeding the 2.1:1 of paid search.
Success Points: The ultimate goal of B2B SEO is "customer acquisition" rather than "traffic", and AI achieved a closed - loop of "content → tools → leads" [26].
Conclusion: Seize AI SEO, Seize the Future
AI SEO is not an option but a battleground in digital marketing. From "keyword ranking" to "answer control", from "user - initiated search" to "AI - initiated recommendation", from "traffic competition" to "trust accumulation", AI SEO is reconstructing the entire marketing ecosystem.
Claude Code CLI is launched by Anthropic. Based on its large Claude models (such as Opus 4, Sonnet 4), it is a command - line intelligent programming assistant that emphasizes strong reasoning ability and in - depth code understanding.
Advantages:
In - depth Code Understanding and Complex Task Handling: Claude Code can deeply understand the structure of code libraries and complex logical relationships. It supports a context window of hundreds of thousands of tokens, enabling efficient multi - file linkage operations and cross - file context understanding. It is particularly good at handling medium - to - large - scale projects.
Sub - agent Architecture and Powerful Toolset: It supports the sub - agent architecture, which can intelligently split complex tasks into multiple subtasks for parallel processing, achieving multi - agent - like collaboration. The built - in toolset is rich and professional, including more refined file operations (such as MultiEdit for batch modification), efficient file retrieval (Grep tool), task management and planning (TodoWrite/Read, Task sub - agent), and profound Git/GitHub integration capabilities, such as understanding PRs, code review, and handling comments.
Integration with Enterprise - level Toolchains: Claude Code can not only be seamlessly integrated with IDEs, directly showing code changes in the IDE's difference view, but also be integrated into the CI/CD process in the form of GitHub Actions. It allows @claude in the comments of PRs or Issues to automatically analyze code or fix errors.
Fine - grained Permission Control and Security: It provides a very complete and fine - grained permission control mechanism, allowing users to precisely control the permissions of each tool through configuration files or command - line parameters. For example, it can allow or prohibit a certain Bash command, limit the read - write range of files, and set different permission modes (such as the plan mode which is read - only and not writable). In an enterprise environment, system administrators can also enforce security policies that users cannot override.
Disadvantages:
It is a commercial paid product with relatively high subscription fees.
Its image recognition ability is relatively weak: When dealing with the understanding and analysis of interface screenshots and the task of converting design drafts into code, its accuracy and restoration degree may be inferior to some competitors.
Scope of Capabilities:
Claude Code CLI is very suitable for medium - to - large - scale project development, code libraries that need long - term maintenance, and scenarios where high code quality is required, and AI assistance is needed for in - depth debugging, refactoring, or optimization. It is relatively mature in terms of enterprise - level security, functional integrity, and ecosystem.
Usage:
It is usually installed globally via npm: npm install -g @anthropic - ai/claude - code. After installation, run claude login to go through the OAuth authentication process. The first time it runs, it will guide you through account authorization and theme selection. After completion, you can enter the interactive mode. Users can command the AI to complete code generation, debugging, refactoring, etc. through natural language instructions.
🔮 Gemini CLI
Gemini CLI is an open - source command - line AI tool by Google. Based on the powerful Gemini 2.5 Pro model, it aims to turn the terminal into an active development partner.
Advantages:
Free and Open - source with Generous Quota: It is open - source under the Apache 2.0 license, with high transparency. Personal Google account users can enjoy a free quota of 60 requests per minute and 1000 requests per day, which is highly competitive among similar tools.
Ultra - long Context Support: It supports a context window of up to 1 million tokens, easily handling large - scale code libraries, and can even read an entire project at once, which is very suitable for large - scale projects.
Terminal - native and Powerful Agent Capability: Designed specifically for the command - line interface, it minimizes developers' context switching. It adopts the "Think - Act" (ReAct) loop mechanism, combined with built - in tools (such as file operations, shell commands) and the Model Context Protocol (MCP) server, to complete complex tasks such as fixing errors and creating new functions.
High Scalability: Through the MCP server, bundled extensions, and the GEMINI.md file for custom prompts and instructions, it has a high degree of customizability.
Disadvantages:
The accuracy of instruction execution and intention understanding is sometimes not as good as Claude Code, with slightly inferior performance.
There are potential data security risks in the free version. User data may be used for model training, making it unsuitable for handling sensitive or proprietary code.
The output quality may fluctuate. User feedback shows that Gemini - 2.5 - pro sometimes automatically downgrades to the less powerful Gemini - 2.5 - flash model, resulting in a decline in output quality.
Its integration with the enterprise - level development environment is relatively weak, and it is more positioned as an independent terminal tool.
Scope of Capabilities:
Gemini CLI, with its large context window and free features, is very suitable for individual developers, rapid prototyping, and exploratory programming tasks. It is suitable for handling large code libraries but is relatively weak in complex logic understanding and deep integration with enterprise - level toolchains.
Usage:
Install via npm: npm install -g @google/gemini - cli. After installation, run the gemini command. The first time it runs, it will guide users to authorize their Google accounts or configure the Gemini API Key (by setting the environment variable export GEMINI_API_KEY = "your API Key").
🌐 Qwen Code CLI
Qwen Code CLI is a command - line tool developed and optimized by Alibaba based on Gemini CLI, specifically designed to unleash the potential of its Qwen3 - Coder model in agent - based programming tasks.
Advantages:
Deep Optimization for Qwen3 - Coder: It has customized prompts and function call protocols for the Qwen3 - Coder series of models (such as qwen3 - coder - plus), maximizing its performance in Agentic Coding tasks.
Support for Ultra - long Context: Relying on the Qwen3 - Coder model, it natively supports 256K tokens and can be extended to 1 million tokens, suitable for handling medium - to - large - scale projects.
Open - source and Supports OpenAI SDK Format: It is convenient for developers to call the model through compatible APIs.
Wide Range of Programming Language Support: The model natively supports up to 358 programming and markup languages.
Disadvantages:
Token consumption may be relatively fast, especially when using large - parameter models (such as 480B), resulting in higher costs. Users need to pay close attention to usage.
The understanding and execution of complex tasks may sometimes get into loops or perform worse than top - tier models.
The understanding accuracy of tool calls may sometimes deviate.
Scope of Capabilities:
Qwen Code CLI is particularly suitable for developers who are interested in or prefer the Qwen model, as well as scenarios that require code understanding, editing, and certain workflow automation. It performs well in agent - based coding and long - context processing.
Usage:
Install via npm: npm install -g @qwen - code/qwen - code. After installation, you need to configure environment variables to point to the Alibaba Cloud DashScope endpoint that is compatible with the OpenAI API and set the corresponding API Key: export OPENAI_API_KEY = "your API key", export OPENAI_BASE_URL = "https://dashscope - intl.aliyuncs.com/compatible - mode/v1", export OPENAI_MODEL = "qwen3 - coder - plus".
🚀 CodeBuddy
CodeBuddy is an AI programming assistant launched by Tencent Cloud. Strictly speaking, it is not just a CLI tool but an AI programming assistant that integrates IDE plugins and other forms. However, its core capabilities overlap and are comparable to CLI tools, and it deeply integrates Tencent's self - developed Hunyuan large model and DeepSeek V3 model.
Advantages:
Integration of Product, Design, and R & D: It integrates functions such as requirement document generation, design draft to code conversion (such as converting Figma to production - level code with a restoration degree of up to 99.9%), and cloud deployment, achieving end - to - end AI - integrated development from product design to R & D deployment.
Localization Optimization and Tencent Ecosystem Integration: Optimized specifically for Chinese developers, it provides better Chinese support and deeply integrates Tencent Cloud services (such as CloudBase), supporting one - click deployment.
Dual - model Driven: It integrates Tencent's Hunyuan large model and DeepSeek V3 model, providing high - precision code suggestions.
Visual Experience: It provides a Webview function, allowing direct preview of code debugging results within the IDE, with a smooth interactive experience.
Disadvantages:
The interaction of some functions (such as @ symbol interaction) may need to be further simplified to improve operational convenience.
The code scanning speed may be slow in large projects.
The plugin compatibility with editors such as VSCode still needs to be enhanced.
Currently, an invitation code may be required for use.
Scope of Capabilities:
CodeBuddy is very suitable for developers and enterprises that need full - stack development support, hope for end - to - end AI assistance from design to deployment, and are deeply integrated into the Tencent Cloud ecosystem. It is especially suitable for quickly validating MVPs and accelerating product iterations.
Usage:
CodeBuddy is mainly used as an IDE plugin (such as the VS Code plugin), and it can also run in an independent IDE. Usually, users need to install the plugin and log in to their Tencent Cloud account to start experiencing features like code completion and the Craft mode.
In general, Claude Code CLI, Gemini CLI, Qwen Code CLI, and CodeBuddy each have their own focuses and are actively exploring how to better assist and transform the programming workflow with natural language. The choice depends on your specific needs, technology stack, budget, and preferences for different ecosystems. Understanding their technical principles and challenges can also help us view and apply these powerful tools more rationally, making AI a truly capable assistant in the development process. CodeBuddy is mainly used as an IDE plugin (such as the VS Code plugin) and can also run in an independent IDE. Users usually need to install the plugin and log in to their Tencent Cloud account to start experiencing features such as code completion and the Craft mode.In general, Claude Code CLI, Gemini CLI, Qwen Code CLI, and CodeBuddy each have their own focuses and are actively exploring how to better assist and transform the programming workflow with natural language. The choice depends on your specific needs, technology stack, budget, and preferences for different ecosystems. Understanding their technical principles and challenges can also help us view and apply these powerful tools more rationally, making AI a truly capable assistant in the development process. CodeBuddy is mainly used as an IDE plugin (such as the VS Code plugin) and can also run in an independent IDE. Users usually need to install the plugin and log in to their Tencent Cloud account to start experiencing features such as code completion and the Craft mode.
Poons, in today's rapidly evolving AI world, an awesome MCP protocol has been born! 🤩
MCP protocol, known as Model Context Protocol (Model Context Protocol), is an open standard protocol proposed by Anthropic and open source. Its appearance is simply too timely, a perfect solution to the problem of connecting AI assistants and various data systems, so that AI systems can more reliably obtain data and give relevant and high-quality responses, which brings a lot of convenience to developers and enterprises! 👏
🔍 Core components are ultra-critical
The MCP protocol core architecture has three important components:
MCP Host: Like the commander, it is the system initiator and contains the MCP client application, which is responsible for sending requests to the MCP server to obtain data and functional support according to user requirements.
MCP Client: As an intermediate bridge, it is responsible for communicating with the MCP server, accurately forwarding the requests from the MCP host, and then sending the results returned by the server back safely to ensure the smooth operation of the system.
MCP Server: A back-end service that provides specific functionality. It is lightweight and can be a local Node.js or Python program, or a remote cloud service, adapting to various application scenarios and deployment needs.
📶 Ultra-flexible communication mechanisms
The MCP protocol communication mechanism is based on JSON-RPC2.0 protocol and supports two communication methods:
Local communication: through the standard input and output and local server interaction, the data security requirements of high scenarios is super suitable, such as internal processing of sensitive data within the enterprise, can ensure that the data in the local security transmission.
Remote communication: HTTP connection based on SSE (Server-Sent Events), with awesome support for cloud services, meeting large-scale data processing and distributed computing needs.
💥 Super wide range of application scenarios
The MCP protocol is used in a huge variety of scenarios, covering almost every area where AI needs to be tightly integrated with data systems. Although it is not mentioned in detail here, you can imagine that it can be very useful in many industries!
What do you think about the MCP protocol? Let's talk about it in the comments section!
#MCP Protocol #ModelContextProtocol #AI Protocol # Data Connection # Core Components # Communication Mechanisms
In technical exchange groups and community forums, I found that many front-end developers have difficulties when using AI: either asking vague questions and getting answers that can't be put into practice; or only using AI to do simple code completion, far from realizing its potential. This is like "begging for food with a golden bowl", obviously AI is a powerful tool in your hand, but you have only tapped into its value. In order to help you break these bottlenecks, I will share my practical experience and methodology for collaborating with AI in front-end development, which will help you efficiently master AI technology.
I. Redefining the relationship between front-end and AI
In the rapidly changing technology iteration, AI is no longer a bystander in the field of front-end development, but an important participant deeply integrated into the development process. As a developer who has been exploring the wave of front-end and AI convergence, I deeply realize that mastering the skills of using AI tools is only the foundation, and building a systematic AI thinking architecture is the key to stand out in the current competitive environment.
In the past, we viewed AI as a tool to assist in writing code and finding bugs, a perception that greatly limited its value. Today, AI has become a partner that can deeply collaborate with developers. In actual projects, I have faced complex performance optimization problems, and the traditional way requires a lot of time for code analysis and solution verification. With AI, through reasonable questions and interactions, it can not only quickly provide a variety of optimization ideas, but also evaluate solutions in the context of the actual project, significantly reducing the development cycle. This collaboration model shows that AI is no longer a "machine" that passively executes instructions, but an "intelligent body" that can think and solve problems together with developers.
II. Four-quadrant framework for AI dialogues: building a mindset model for efficient collaboration
Quadrant 1: Open (AI knows, people know)
When both the developer and AI have a clear understanding of the problem, this is the most direct and efficient collaboration scenario. For example, when developing React components, if the clear requirement is to realize anti-shake function with React Hook, you can directly give AI the instruction of "realize an anti-shake component with React Hook, require concise code with comments", and then you can get the result quickly. However, it should be noted that the more structured the instruction is (e.g. "step-by-step instructions + code examples + notes"), the lower the communication cost.
Quadrant 2: Blind (AI knows, people don't)
When facing unfamiliar technical issues, such as optimizing front-end page load speed, direct questions often get general answers. At this point, we should adopt a layered questioning strategy: first understand the common dimensions of performance optimization, then explore the priority of network request and rendering optimization, then ask about the specific optimization means of the React project, and finally ask for actual cases. Through the progressive questioning of "what→why→how→case", AI can avoid outputting invalid information.
Quadrant III: Unknown (AI does not know, people do not know)
When exploring the integration of new technologies, such as the combination of 3D models generated by AIGC and WebGL to realize interactive virtual exhibition halls, there is no ready answer for both humans and machines. In this case, AI should be regarded as a creative stimulation partner, obtaining ideas through cross-border questioning, and then combining with its own technical capabilities to make feasibility judgments and program iterations.AI's answers are creative materials, and developers need to sift, combine and verify them.
Quadrant 4: Hidden (AI doesn't know, people know)
Involving project-specific knowledge, such as the company's own research component library development specifications, you need to take the initiative to "feed" information to the AI. Uploading relevant documents and code snippets and then giving instructions will enable the AI to generate content that better fits the actual needs. Enterprises can establish a technical knowledge base and use RAG technology to realize the rapid invocation of internal data by AI; individual developers should also be clearly informed of the project constraints before collaboration to avoid AI generating unrealistic solutions.
From Tool Use to Thinking Architecture: The AI Advancement Path for Front-End Developers
1. Create a sense of positioning for AI collaboration
Before every interaction with AI, think about three questions: what is the nature of the problem, and what is the AI's mastery of the relevant technology stack? What proprietary information should be added? Take debugging a React component as an example, if the type of error is clear, it belongs to the Open quadrant, and you can directly seek a solution; if the cause of the error is vague, you need to enter the Blind quadrant, and adopt a layered questioning strategy.
2. Developing structured questioning skills
Especially in the Blind Quadrant, the "onion peeling" method of questioning can effectively improve the quality of information acquisition. Take learning WebAssembly as an example, from the core principle (what), to the reasons for solving the performance bottleneck of JavaScript (why), to the integration method in the React project (how), and finally to the actual application cases (scenario-based validation), we will go deeper and deeper. At the same time, the use of "if... then..." sentences to test the depth of understanding and strengthen the learning effect.
3. Building a Personal AI Collaboration Intelligence Repository
Organize commonly used tech stack documents, team code specifications, and historical project pitfall records into a Markdown format "AI Collaboration Manual". Asking questions with links to key chapters or explicitly referencing specifications in instructions enables AI to quickly understand the technical context and generate content that is more in line with expectations.
4. Stimulating innovative thinking and exploring uncharted territories
Use a "technical domain + non-technical domain + target scenario" questioning formula, e.g., "When ChatGPT learns front-end engineering, can it automatically generate scaffolding that meets the team's specifications? What data training is needed?" Encourage AI to think out of the box and explore the new boundaries of technology together. At the same time, we screen the solutions through technical feasibility analysis and carry out iterative optimization.
5. Dynamic adaptation of collaboration strategies
Regularly follow the updates of front-end AI tools and test the adaptability of new features in real projects. Record the types of problems encountered during AI collaboration and the quadrants they belong to, and analyze the distribution of your collaboration ability in different quadrants. If the Hidden quadrant has frequent problems, improve the internal knowledge base; if the Blind quadrant has more problems, strengthen the training on question disassembly.
Practical tool recommendation: covering the whole quadrant of the front-end AI collaboration matrix
Open Quadrant (AI is known to all)
Tools / Methods
Core competencies and scenarios
Cursor
- Natural language generation of complete code for React/Vue components (including Hook logic) - Support for real-time code debugging and bug fixing (e.g., automatic handling of Promise exceptions)
Codeium
- Context-based code completion (e.g., enteruseEffect(Automatically prompts for dependency array writes) - Generate test cases (Jest/React Testing Library)
Tabnine
- Smart function name recommendation (e.g. enterfetchDataFromauto-completeAPI) - Generate TypeScript type definitions (inferring return value types from function arguments)
Blind Quadrant (AI knows but no one knows)
Tools / Methods
Core competencies and scenarios
Raycast AI
- Breaking down complex problems (e.g. generating a layered solution for "React Performance Optimization": Network Optimization → Rendering Optimization → Component Optimization) - Querying framework source code in real-time (e.g. automatically parsing the implementation logic of React Router v6 Hooks)
WizNote AI Assistant
- Structured questioning of technical documentation (e.g. "How to integrate WASM in React" after uploading official WebAssembly docs) - Generation of knowledge brain maps (automated sorting of CSS-in-JS scenarios with comparative strengths and weaknesses)
DevDocs AI
- Cross-document retrieval (e.g., query MDN + React official website + community blogs at the same time, integrate "useContext best practices") - Code sample adaptation (automatically convert Vue3 examples to React writing style)
Hidden quadrant (known but not known to AI)
Tools / Methods
Core competencies and scenarios
PrivateGPT (Enterprise Edition)
- Uploading the team's component library specification and generating code that conforms to the specification (e.g., generating a Button component based on the Ant Design specification). - Parsing internal business documents (e.g., generating form validation logic based on e-commerce order system documents)
RAG-Stack (self-built knowledge base)
- Access to enterprise Git repositories, AI automatically learns historical project architecture (e.g., identifying a project's micro-front-end splitting strategy) - Generates problem troubleshooting processes based on internal failure documentation (e.g., diagnostic steps for "white screen problems")
LocalAI + Vector Database
- Secure handling of sensitive code (e.g., cryptographic algorithm modules for financial projects) - Generation of code styles that conform to team conventions (e.g., automatically formatting code according to team ESLint configurations)
Unknown quadrant.
Tools / Methods
Core competencies and scenarios
GitHub Copilot X
- Collaborative exploration of new architectures (e.g. AI to generate technical solution sketches for "React+WebAssembly for 3D editor") - Automated generation of technical feasibility reports (with performance estimates and risk point analysis)
- Generate cross-domain technology combinations (e.g. "LLM + Front-End Route Guard" for dynamic privilege control) - Provide technology roadmaps (e.g. migration steps from "Legacy SPA" to "PWA Provide a technology roadmap (e.g. migration steps from "Traditional SPA" to "PWA + Server Components").
V. Conclusion: Embracing AI, Reconstructing Front-End Development Thinking
The application of AI in the front-end field is not only an upgrade of tools, but also a change in the way of thinking. Mastering the four-quadrant framework of AI dialogue and building a systematic AI thinking architecture will enable us to transform from "AI tool users" to "intelligent collaboration leaders". In the future of front-end development, those developers who can master AI and collaborate with it in depth will surely have a head start in the wave of technology. We look forward to exploring more possibilities for the integration of AI and front-end development with your peers, and welcome you to share your practical experience and thoughts.
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