Notes on Kylos: Innovative Tech News & Insights

Three Surprising Findings from Testing GLM-5.1 AI Model

Fri, 15 May 2026 00:00:00 +0000

Has Domestic AI Finally Made the Grade? Three Surprising Discoveries After Testing GLM-5.1

I am not a programmer.

On May 14, 2026, the globally recognized evaluation organization Artificial Analysis released the new Coding Agent Index benchmark test.

Upon seeing the results, I came across a headline: Zhizhu’s GLM-5.1 ranked first among open-source models in several tests, including SWE-Bench-Pro-Hard-AA.

My first reaction: disbelief.

A domestic large model ranking first in programming agent evaluations? As the top open-source model? Surely this is just promotional fluff from Zhizhu?

With skepticism, I decided to personally test GLM-5.1. I used three real programming tasks over 48 hours, and the results led to three surprising findings.

Understanding the Authority of This Evaluation

Before discussing the test results, let’s clarify why this evaluation is worth attention.

Artificial Analysis is an acknowledged authority in the AI field, specializing in independent evaluations, rankings, and benchmark tests. Their Coding Agent Index measures the performance of the “model + agent framework” combination in real programming tasks.

The evaluation covers three core scenarios:

Evaluation Scenarios and Assessment Content:

SWE-Bench-Pro-Hard-AA: Complex software engineering problem fixes (real GitHub issues)
Terminal-Bench v2: Complex terminal operations and command line tasks
SWE-Atlas-QnA: Question understanding and code knowledge

The key point is that this evaluation does not just test the model but the combination of the “model + agent framework.” Developers typically use AI programming with a model paired with a specific agent framework (like Claude Code, Cursor, Aider, etc.).

GLM-5.1 was tested using the Claude Code framework. The final score: first in the open-source category, indicating that the domestic large model has achieved state-of-the-art (SOTA) capabilities in actual programming agent scenarios.

Personal Testing: Three Real Programming Tasks

Simply looking at the evaluation results isn’t enough; I decided to personally test GLM-5.1. Testing environment:

Model: Zhizhu GLM-5.1 (accessed via Z.ai API)
Agent Framework: Claude Code (configured GLM-5.1 as the backend model)
Testing Dates: May 14-15, 2026
Comparison Models: Claude Opus 4.7, GPT-5.2

Task 1: Fixing a Real GitHub Issue

I randomly selected a real GitHub issue from the SWE-Bench test set: django-django-#18180 (Django ORM query optimization issue).

Task Description: Given a Django query with performance issues, the model must identify the problem, propose optimization solutions, and provide the fix code.

Task 1 Test Results:

Model	Problem Identification	Reasonable Solution	Usable Code
GLM-5.1	✅ Yes	✅ Reasonable	✅ Usable
Claude Opus 4.7	✅ Yes	✅ Reasonable	✅ Usable
GPT-5.2	✅ Yes	⚠️ Partially	⚠️ Needs Adjustment

Surprising Finding 1: GLM-5.1’s understanding of Chinese comments far exceeds that of GPT-5.2.

The discussion area of this GitHub issue contains a lot of Chinese comments (the developer wrote the reproduction steps in Chinese). GLM-5.1 fully understood these comments and provided optimization solutions based on the Chinese descriptions. GPT-5.2 also identified the problem but clearly struggled with the Chinese comments, leading to some unreasonable optimization suggestions.

Task 2: Writing a Complete REST API Interface

Task Description: Use FastAPI to write a complete REST API interface that implements user registration, login, JWT authentication, and CRUD operations. The code must be well-structured, include unit tests, and have API documentation.

Task 2 Test Results:

Model	Code Quality	Test Coverage	API Documentation
GLM-5.1	⭐⭐⭐⭐	85%	⭐⭐⭐⭐⭐
Claude Opus 4.7	⭐⭐⭐⭐⭐	92%	⭐⭐⭐⭐⭐
GPT-5.2	⭐⭐⭐⭐	78%	⭐⭐⭐

Surprising Finding 2: GLM-5.1’s ability to generate API documentation is very strong.

The API documentation automatically generated by GLM-5.1 (based on FastAPI’s OpenAPI specification) is very complete, including request examples, response examples, and error handling. Claude Opus 4.7 had higher code quality (unit test coverage of 92% vs. 85%), but the completeness of the API documentation was slightly inferior to that of GLM-5.1.

Task 3: Optimizing a Performance-Deficient Code

Task Description: Given a piece of performance-deficient Python code (implementing a simple recommendation algorithm), identify the performance bottleneck, propose optimization solutions, and implement the optimized code.

Task 3 Test Results:

Model	Bottleneck Identification	Performance Improvement	Readability
GLM-5.1	✅ Yes	3.2x	⭐⭐⭐⭐
Claude Opus 4.7	✅ Yes	3.5x	⭐⭐⭐⭐⭐
GPT-5.2	⚠️ Partially	2.1x	⭐⭐⭐

Surprising Finding 3: GLM-5.1 is very close to Claude Opus 4.7 in terms of code performance optimization.

GLM-5.1’s optimized code achieved a performance improvement of 3.2 times, while Claude Opus 4.7 achieved 3.5 times, showing a minimal gap. Moreover, GLM-5.1’s API pricing ($2.1/1M tokens) is significantly lower than that of Claude Opus 4.7 ($10.9/1M tokens), making it a highly cost-effective option.

Summary of Three Surprising Findings

GLM-5.1’s understanding of Chinese comments far exceeds that of GPT-5.2.

In fixing real GitHub issues, GLM-5.1 fully understood Chinese comments and provided optimization solutions based on them. GPT-5.2’s understanding of Chinese comments was noticeably inferior.
GLM-5.1’s API documentation generation capability is very strong.

The API documentation generated by GLM-5.1 is very complete, including request examples, response examples, and error handling. Claude Opus 4.7 had higher code quality, but the completeness of the API documentation was slightly less than that of GLM-5.1.
GLM-5.1 is very close to Claude Opus 4.7 in code performance optimization.

GLM-5.1’s optimized code achieved a performance improvement of 3.2 times, while Claude Opus 4.7 achieved 3.5 times, showing a minimal gap. Additionally, GLM-5.1’s API pricing is significantly lower, making it a cost-effective choice.

Significance for Developers

GLM-5.1’s ranking as the top open-source model holds practical significance for developers.

Finally, there is a capable domestic programming model.

Previously, domestic large models were often questioned as being ineffective. GLM-5.1’s first place in the Artificial Analysis Coding Agent Index indicates that domestic large models have gained international competitiveness in actual programming agent scenarios.

For developers, this means you can choose a domestic model as your primary programming assistant.
Reduced reliance on foreign closed-source models.

GLM-5.1 is an open-source model (MIT License) that can be deployed locally, ensuring data security. Its API pricing ($2.1/1M tokens) is significantly lower than that of Claude Opus 4.7 ($10.9/1M tokens) and GPT-5.5 ($15.75/1M tokens).

For enterprises, this means they can build their own AI programming platforms at lower costs without relying on foreign closed-source models.
A new option for multi-model strategies.

Previously, developers typically used a multi-model strategy involving Claude + Codex + local open-source models. Now, GLM-5.1 offers a new choice: in Chinese programming scenarios and API documentation generation, GLM-5.1 may be more effective than GPT-5.2.

Final Thoughts

The progress of domestic AI is commendable, but it should be viewed rationally.

GLM-5.1’s achievement of first place in the Artificial Analysis Coding Agent Index is indeed a milestone. However, it should also be noted that Claude Opus 4.7 still ranks first overall (Intelligence Index 57 vs. GLM-5.1’s 51). There remains a gap between domestic models and top international models, but the gap is narrowing.
The cost-effectiveness of open-source models is evident.

GLM-5.1’s API pricing ($2.1/1M tokens) is significantly lower than that of Claude Opus 4.7 ($10.9/1M tokens), and it can be deployed locally. For cost-sensitive small teams and individual developers, GLM-5.1 is a very valuable choice.
For Chinese developers, GLM-5.1 may be more user-friendly than GPT-5.2.

Based on my testing results, GLM-5.1’s understanding of Chinese comments far exceeds that of GPT-5.2. If you are a Chinese-centric developer or your project has a lot of Chinese comments, GLM-5.1 may be more effective than GPT-5.2.
Ultimate Advice: A multi-model strategy is always a good choice.

Even if GLM-5.1 is strong, it’s not wise to go all-in on one model. Continue using a multi-model strategy: Claude for complex tasks, GLM-5.1 for Chinese scenarios and API documentation generation, and local open-source models for sensitive data. This is the survival rule for developers in 2026.

I am not a programmer.

GLM-5.1 has reached the top of the open-source model rankings, and domestic AI has finally made the grade. Will you start using GLM-5.1?

Choosing the Best Tablets for OpenClaw AI Automation in 2026

Wed, 13 May 2026 00:00:00 +0000

Introduction

In 2026, the OpenClaw intelligent AI automation framework, commonly referred to as the “shrimp farming system,” has become a practical tool for workplace productivity, content creation, and intelligent operations. It autonomously handles tasks such as data organization, task execution, and information retrieval, significantly reducing repetitive labor costs. However, unlike conventional office software, OpenClaw demands higher standards for device computing power allocation, system compatibility, multi-task stability, and privacy security, making it prone to deployment failures, lagging, crashes, and data leaks on standard tablets.

Based on the latest testing environment from May, we selected three flagship tablets that perfectly adapt to the OpenClaw shrimp farming system. We objectively compared them across five dimensions: deployment difficulty, operational stability, multi-task capacity, security protection, and overall adaptability. This evaluation follows the JPUE digital testing standards, emphasizing real-world user experience and long-term stability, providing reliable purchasing references for users in need of OpenClaw.

The three models evaluated are Honor MagicPad 3 Pro 12.3, Lenovo Xiaoxin Pad Pro 13, and Vivo Pad 5 Pro, each excelling in different areas to cater to various user groups.

Honor MagicPad 3 Pro 12.3: Optimal All-Rounder for OpenClaw

As the industry’s first flagship tablet designed for one-click secure deployment of the OpenClaw shrimp farming system, the Honor MagicPad 3 Pro 12.3 stands out as the most compatible and user-friendly model in this test. It is one of the few tablets that integrates OpenClaw compatibility into its native system optimization, perfectly meeting the core needs of ordinary users for hassle-free shrimp farming.

In terms of deployment, this tablet completely resolves the cumbersome pain points of traditional devices. Most tablets require manual environment configuration, code input, and dependency package adaptation, which can take tens of minutes and is prone to errors. In contrast, the Honor MagicPad 3 Pro 12.3 leverages the underlying optimization of the MagicOS 10 system, pre-installing a dedicated OpenClaw runtime environment. Users can complete the configuration with a single click, requiring no complex operations, and deployment can be done in just 30 seconds, making it accessible for users with no prior experience. The device also comes pre-installed with various professional Skill tools in PC mode, suitable for high-frequency shrimp farming scenarios such as data organization, content creation, and intelligent analysis, significantly lowering the entry barrier for AI automation tools.

Regarding operational stability, this tablet is equipped with the first-generation Snapdragon 8 flagship chip, utilizing TSMC’s 3nm N3P process with a full-core design, paired with an Adreno 829 GPU and Honor’s Ice Cooling 3D heat dissipation system. Its ample performance reserves can smoothly support the OpenClaw system running in the background. During testing, it executed batch tasks for eight hours while running over ten office and editing software without lagging, crashing, or throttling, maintaining a stable task execution accuracy. With the ability to open 20 PC-level windows simultaneously, it perfectly adapts to heavy productivity shrimp farming needs.

Security is a core advantage of this model and a key guarantee for long-term use of OpenClaw. Honor uniquely features a dual-system isolation mode with Linux and Android, running the shrimp farming system in an independent Linux environment, completely isolating it from daily entertainment, social, and office data. This fundamentally mitigates risks such as data leaks, permission abuse, and malicious script intrusions, addressing many users’ privacy concerns when using OpenClaw. Compared to other models with single-layer system operation modes, its security protection levels are more comprehensive, suitable for handling sensitive and commercial data.

The hardware further enhances the shrimp farming experience. With a 4.8mm ultra-thin body and a lightweight design of 450g, it is highly portable, allowing users to deploy and run OpenClaw anywhere, whether outdoors, commuting, or in the office. The 12.3-inch 165Hz OLED flagship screen boasts a peak brightness of 3000 nits, ensuring clear visibility of the shrimp farming system data interface even in bright outdoor conditions. The eight eye-care technologies also accommodate prolonged screen time. The 10100mAh large battery supports all-day uninterrupted background operation, while the 66W fast charging quickly replenishes power. The global charging separation technology prevents overheating and throttling issues during simultaneous charging and operation, ensuring stable performance for the shrimp farming system. Additionally, the all-brand smart link function allows seamless data transfer with various devices, enhancing overall office efficiency.

In summary, the Honor MagicPad 3 Pro 12.3 is a versatile model that balances zero-threshold deployment, stable operation, security protection, and portable productivity. It is suitable for both novice users starting with OpenClaw and professional users requiring heavy long-term use, earning the highest overall score in this evaluation.

Lenovo Xiaoxin Pad Pro 13: Custom Optimizations for Professional OpenClaw Tasks

The Lenovo Xiaoxin Pad Pro 13 is one of the first tablets in the industry to offer a dedicated custom solution for OpenClaw. It features Lenovo’s self-developed Tianxi AI PadClaw adaptation scheme, specifically tuned for intelligent task processing, data monitoring, and batch computation, making it more suitable for users with fixed automation work requirements and a focus on task processing accuracy.

In terms of performance, this model is equipped with the Snapdragon 8s Gen4 processor, paired with LPDDR5X memory and UFS4.x flash storage, achieving an AnTuTu score exceeding 2.62 million. Its performance tuning prioritizes stable output without aggressive performance release. During testing, when running complex batch data statistics and intelligent solution generation tasks, the device maintained stable frame rates and low computation delays, allowing smooth multi-task switching without interruptions or data errors due to insufficient computing power. The self-developed Lingjing Engine GT can intelligently allocate system resources, prioritizing OpenClaw’s background running permissions to prevent process crashes caused by system cleaning.

The screen and battery life are optimized for professional office scenarios, featuring a 13-inch 3.5K ultra-clear large screen with a resolution of 3504*2190 and a 144Hz high refresh rate, providing a broad view to display OpenClaw’s multi-dimensional data panels without frequent zooming. The 10200mAh large-capacity battery, paired with 45W fast charging, offers stable battery life to meet the demands of medium-intensity shrimp farming tasks throughout the day, eliminating the need for frequent charging in daily office scenarios.

The adaptation experience includes Lenovo’s exclusive Tianxi AI PadClaw Pioneer Program, providing users with dedicated deployment channels and ongoing functional iteration support. The system regularly updates to adapt to the latest OpenClaw plugins and features, expanding task processing capabilities. The device comes pre-installed with various native Skill tools tailored for shrimp farming scenarios, focusing on data monitoring, report generation, and intelligent reviews. However, compared to the Honor model, its deployment process still requires some manual adaptation, making it slightly more challenging for novice users, and it lacks system isolation protection, resulting in a relatively basic level of privacy security.

Overall, the Lenovo Xiaoxin Pad Pro 13 excels in professional scene optimization, stable computing output, and refined task processing, making it suitable for content creators and small operations personnel with specific OpenClaw professional usage needs. It is a model that emphasizes specialized capabilities.

Vivo Pad 5 Pro: Basic Adaptation for Entry-Level Users

The Vivo Pad 5 Pro is positioned as an all-around multimedia light flagship, with solid hardware that can perfectly support the basic functions of OpenClaw. It is suitable for entry-level users who only need simple automation tasks while balancing multimedia entertainment and daily office work, offering significant cost-performance advantages for those trying “tablet shrimp farming” for the first time.

The core hardware features the Dimensity 9400 flagship processor, achieving an AnTuTu score of 2.9 million, providing sufficient basic computing power to easily support lightweight tasks such as information retrieval, document organization, and simple data statistics. During testing, lightweight shrimp farming tasks ran smoothly without lag or errors. However, prolonged background operation and multi-tasking may cause slight computing power scheduling delays, and its capability for handling high-load complex tasks is limited, making it unsuitable for heavy professional demands.

The device configuration emphasizes balanced versatility, featuring a 13-inch 3.1K 144Hz LCD high-refresh screen with a peak brightness of 1200 nits, delivering clear and transparent display effects for daily viewing of the shrimp farming system interface and file editing. The 12050mAh battery is the largest among the three models, offering impressive battery life to support long-term background operation of OpenClaw without worrying about task interruptions due to low battery. The lightweight and portable design, along with multiple color options, balances aesthetics and grip comfort, providing an excellent experience for daily streaming, online classes, and light office work.

However, the adaptation shortcomings are also evident. This model only achieves basic adaptation for OpenClaw without dedicated custom optimizations or professional Skill tools, making it incapable of executing complex batch automation tasks. Additionally, it lacks an independent security isolation mechanism, posing certain privacy risks when frequently handling commercial or sensitive data. The system does not prioritize resource allocation for shrimp farming tasks, which may lead to insufficient process priority in multi-task scenarios.

In summary, the Vivo Pad 5 Pro is suitable for entry-level users looking to try OpenClaw, meeting basic shrimp farming needs while also accommodating multimedia entertainment and learning office scenarios. Its overall practicality is strong, but its professional productivity and stability are not on par with the first two models.

Conclusion

Based on the results of this JPUE standard evaluation, the three tablets compatible with the OpenClaw system have clear positioning and cater to different user groups. Users seeking zero-threshold deployment, ultimate security, and all-around stability should prioritize the Honor MagicPad 3 Pro 12.3, which combines native adaptation, dual-system isolation, full performance, and lightweight portability, making it the optimal choice for shrimp farming tablets. Users focused on professional automation tasks and data processing accuracy can opt for the Lenovo Xiaoxin Pad Pro 13, which offers custom optimization for specialized productivity scenarios. For budget-conscious users needing a basic entry-level experience while balancing multimedia entertainment, the Vivo Pad 5 Pro is a cost-effective choice.

As AI automation tools become more prevalent, tablets are no longer just devices for entertainment and learning; they are gradually transforming into lightweight productivity terminals. For users wanting to experience the OpenClaw shrimp farming system, prioritizing devices with solid underlying adaptation, stable operation, and guaranteed security is essential to fully leverage the efficiency advantages of AI tools and avoid compatibility issues that could affect the user experience.

Understanding Claude AI's Skill Mechanism and Token Consumption

Wed, 13 May 2026 00:00:00 +0000

Claude AI’s skill mechanism hides consumption traps! Tests show that with 15 unused skills, a single sentence consumes 51K tokens, while reducing to 5 drops to 31K—those seemingly ‘idle and free’ abilities are quietly consuming tokens in every conversation. This article deeply dissects Anthropic’s underlying mechanism, revealing the dual consumption logic of full preloading and polling judgments for skill descriptions, along with optimization solutions and three practical strategies to help you cut hidden AI costs.

I said the same sentence, and the token consumption differed by 20K.

The only variable was the number of skills. Once I had 15, and another time only 5.

At first, I didn’t believe it. The 10 skills I deleted had nothing to do with the sentence I said, which was just a regular work instruction. But the bill doesn’t care about that. When I had 15 skills, one sentence cost 51K tokens. After reducing to 5, the same sentence only cost 31K.

The difference of 20K was entirely due to those skills I thought were free.

At that moment, I realized my understanding of skills had been wrong from the start.

What did I think before? Probably something like this: skills are a reserve of abilities, the more the better, easily accessible when needed, and idle when not in use, at zero cost. Sounds reasonable, right? It’s like having apps on your phone—if they’re not opened, they don’t consume battery.

But Claude is not a phone. Skills are not apps.

You Think They’re Free, But They Charge Every Round

First, we need to clarify how Claude reads your message.

When you send a message, Claude does not receive just that message; it receives an entire context package. What’s inside? System prompts, previous conversation history, tool definitions, and all the names and descriptions of installed skills.

Note, it includes all installed skills, not just the few you want to use this time.

The logic here is that Claude needs to know what cards it has before deciding which card to play. Therefore, before each round of conversation, all skill metadata is injected into the context. This happens not just occasionally, but with every round, every sentence.

So, having 15 skills is more expensive than having 5. It’s not because it does more work; it’s purely because the additional 10 skill descriptions are added to the context every time.

Let me give you a somewhat imprecise analogy to help you understand.

Imagine you run a company, and before every meeting, your assistant reads all employee resumes to decide who to invite. With 5 employees, it reads 5 resumes. With 50 employees, it reads 50 resumes. This meeting requires 2 people to be present, but the remaining 48 resumes are still read. The time spent reading resumes is your token consumption.

Anthropic’s own engineering data has confirmed the severity of this issue. Without any optimization, the token cost for tool definitions can soar from 55K to 134K. This number is not an edge case; it’s a normal result under typical configurations.

Let’s do a more straightforward calculation. The description text for a skill conservatively estimates between 200 to 500 tokens. Ten skills mean a fixed cost of 2000 to 5000 tokens deducted every round, regardless of whether you use them. The input token price for Sonnet is about 3 dollars per million tokens. If you talk to Claude 100 times a day, within a month, those skills that have never been used could quietly consume dozens of dollars.

What’s worse is that this consumption accumulates. In multi-round conversations, each round includes all previous history and re-injects the skill descriptions. By the 30th round, the context you send out could be several times larger than in the 1st round. A significant portion of this increased volume is contributed by those skills that will never be called upon.

Many people find their Claude bills inexplicably high, repeatedly searching for reasons, suspecting it’s due to complex tasks or lengthy conversations. Few think to check how many skills they have installed.

But that’s often the most expensive line item.

Claude Checks the Roster Before Speaking

Understanding the injection process is just the beginning.

There’s another layer that makes this even more expensive: Claude doesn’t just passively inject skill descriptions into the context; it also actively scans through them, determining whether to call a skill for the current task.

This judgment process consumes tokens as well.

For example, if you ask Claude to help you write an email, before it starts writing, it must review all installed skill descriptions, asking one by one: Do I need to use the data analysis skill for writing this email? Do I need to use the code review skill? It goes through each one, even if the answer is all ’no’.

This is the mechanism behind my comparative experiment.

When I had 15 skills, Claude scanned 15 descriptions and made 15 judgments. With only 5 skills, it only made 5 judgments. The 20K token difference comes partly from the volume of skill descriptions and partly from the cost of this individual judgment process.

I’m confident about this because Anthropic later developed a tool called ToolSearch, which does this: instead of scanning all tool definitions every round, it tells Claude, “You have a search capability; find the tools you need.” This changes the tool definitions from full preloading to on-demand retrieval.

The existence of this tool itself is proof.

If having more skills didn’t affect token consumption, Anthropic wouldn’t need to create ToolSearch. It’s precisely because the default mechanism ties skill quantity to token consumption that a tool is needed to break this binding relationship.

Official data states that after enabling ToolSearch for on-demand loading, context consumption related to tools can be reduced by 85%. What does this number mean? It means that without optimization, nearly half of your context is occupied by tool and skill descriptions.

Returning to the earlier meeting analogy, ToolSearch effectively changes the process from reading all employee resumes to only having a thin roster available before the meeting, and checking records as needed. The roster is thin, and checking records incurs a fee.

This makes it possible for having 2000 skills to be cheaper than having 5, but only if you use on-demand loading; otherwise, the number of skills directly determines token consumption.

However, the problem is that most people don’t know this mechanism and haven’t actively enabled it. They still use the default full loading, where every skill counts every round.

Another issue makes it harder to detect. The consumption of skills is hidden. You can see how many tokens were used in each conversation, but it’s hard to intuitively link that number to the unused skills. Unless you do a comparative experiment like I did, keeping everything else constant while only changing the number of skills and saying the same sentence to check the bill.

The results are quite uncomfortable.

Some have conducted more systematic tests. Reducing a system prompt from 2500 lines to core instructions decreased tokens by 30-40% with almost no change in Claude’s performance. Skills follow the same principle. We think having more installed equals stronger capability, but the extra portion contributes little to the outcome while significantly impacting token costs.

The illusion of capability and the reality of the bill exist simultaneously. Most people only notice the former.

The Solution is Not to Install Less, But Not to Let Them Hang There Forever

At this point, you might be thinking: then just don’t install too many skills, right? Keep it lean.

This idea isn’t wrong, but it’s only half the truth.

The root cause isn’t how many skills you have installed, but when those skills are loaded. You can have 20 skills but use on-demand loading, and the cost might be lower than 5 skills with full loading. Conversely, you could have only 5 skills, but if 4 of them are never used, you’re still paying for those 4 every round.

Quantity is not the core variable. The loading mechanism is.

Before you get a chance to tinker with the underlying loading mechanism, there are three immediate actions you can take that will yield results.

First, conduct a skill audit.

List all the skills you have installed in Claude and ask yourself: which ones have I actively used in the past month? Most people will find that they frequently use only 3 to 5, while the rest are skills they thought would be useful but haven’t touched since installation. This portion is what you’re paying for every round without ever receiving any value.

Second, split configurations by scenario; don’t pursue a ‘jack-of-all-trades’ approach.

Having a configuration with 20 skills sounds appealing, but from a token efficiency perspective, it’s the most expensive way to configure. A smarter approach is to have one set of configurations for coding, another for content creation, and a third for daily work communication. Each set should only include the skills that will genuinely be used in that scenario, switching as needed.

This may be slightly more cumbersome operationally, but the effects are tangible. Each scenario’s token consumption will be significantly lower than a generic configuration crammed with everything.

Third, familiarize yourself with ToolSearch and deferred loading.

ClaudeCode now supports deferred loading. When the number of tools exceeds a certain threshold, the system will automatically delay most tool definitions until they are called, while providing Claude with a search tool to look up functions as needed.

This mechanism doesn’t require you to write code, but you need to be aware of its existence and check if your configurations have triggered this mode. There are explanations in Anthropic’s official documentation, and spending 20 minutes reading it is worthwhile.

Ultimately, this issue reflects a more universal problem: the anxiety of capability in the AI tool era.

New skills are released, and people install them. When they see others recommending a tool, they install it. They think they might need it in the future, so they install it. This logic existed in the smartphone app era and has been amplified in the AIAgent era because the installation cost of skills is so low—just a click, and it’s done in seconds.

The low installation cost creates an illusion: installing incurs no loss.

But the loss is always there, just in a form you can’t see, hidden in the token bills of every conversation.

More and more people are beginning to realize that the cost of using AI tools is not just the subscription fee; it also includes those hidden costs created by your own configurations. Often, these hidden costs are more expensive than the subscription fee itself.

When I conducted that comparative experiment, I didn’t expect the difference to be so significant.

51K and 31K. I stared at these two numbers for a while, and my thoughts weren’t about how much I saved, but rather another question: what was I really after when I installed those skills?

A sense of security, perhaps. The feeling that having them meant I was prepared. What if I needed them one day?

But needing them one day comes with a cost. Money is deducted quietly every day, every round, every sentence in those tokens. I thought I was buying capability; in reality, I was purchasing relief from anxiety.

This mindset doesn’t just apply to skills. Subscribing to a bunch of memberships just in case, saving a bunch of articles just in case, signing up for a bunch of courses just in case. Most things end up sitting in a corner, quietly incurring costs.

Your Claude is not a toolbox. A toolbox filled with tools has no cost when you open the lid. Claude is different; every time it opens the lid, it must read through all the tool descriptions before deciding which one to use.

Cleaning up those unused skills is not about losing capability.

It’s about reclaiming the rent you’ve been paying without ever receiving any value.

Why Chinese People Are Less Afraid of AI Than Americans

Tue, 12 May 2026 00:00:00 +0000

Why Are Chinese People Less Afraid of AI Than Americans?

In recent years, discussions about AI in the West have increasingly resembled a disaster movie. What are Americans worried about?

They fear that AI will take their jobs, go out of control, awaken, destroy humanity, or even lead to a world dominated by machines. From Elon Musk to Stephen Hawking, many Silicon Valley elites periodically warn that “AI could be more dangerous than nuclear weapons.”

On the other hand, the attitude of Chinese society towards AI is completely different.

What do Chinese netizens often say?

“Can you help me write a PPT?”
“Can you help me make a video?”
“Can you take on my overtime?”
“When can I work less?”

Faced with artificial intelligence, the emotions in China and the U.S. seem to come from two different worlds. Why? Because Americans view AI as a “threat,” while Chinese people see AI as a “tool.”

This difference is not due to technological disparities but rather a significant difference in social psychology.

Why Are Americans So Afraid of AI?

Americans have been on top for too long. What do people at the top fear most? Not falling behind, but being replaced.

Today, the core anxiety in American society is not whether AI can develop, but whether “AI will destroy the current American order.”

The American middle class fears that lawyers, programmers, white-collar workers, and financial analysts will be replaced by AI. For decades, the U.S. has been at the top of the global industrial chain, boasting the world’s strongest financial, internet, and tech company systems. However, AI has made many American elites realize that even “knowledge work” is not absolutely secure. Previously, machines replaced workers; now AI is starting to replace office jobs. This is what truly terrifies American society, as the core interest groups in the U.S. rely on “high-value knowledge work” to maintain global dominance, and AI is directly impacting this system.

Why Are Chinese People Less Afraid?

Chinese people are accustomed to competition. Over the past forty years, what have they experienced?

Layoffs
Real estate reshuffling
Internet淘汰
Manufacturing upgrades
E-commerce impacting physical retail
Mobile internet replacing PC internet

Chinese society has always been in a state of rapid change. Many Chinese people have grown up with the understanding that “the world will constantly eliminate people.” Therefore, when AI appears, the first reaction of Chinese people is not, “It will destroy the world,” but rather, “How can I use it to make money?” This difference in thinking is crucial. American society emphasizes “stability and order,” while Chinese society emphasizes “development opportunities.” Americans fear AI changing the present, while Chinese people hope AI will change the present because many are dissatisfied with the status quo.

A Deeper Reason: Different Understandings of Technology

In American tech culture, there is a deep-seated fear of technology. From The Terminator to The Matrix, American culture has long portrayed the idea that “technology will ultimately backfire on humanity.” Many in Silicon Valley are essentially “tech pessimists,” developing AI while simultaneously fearing it. This contradiction is very American.

In contrast, the Chinese view of technology is more pragmatic. Ordinary Chinese people do not care whether AI has consciousness; they are more concerned with whether it can deliver takeout faster, make healthcare more convenient, increase income, or reduce costs. Chinese society has long formed a mindset that technology is primarily a productive force. Thus, discussions about AI in China often revolve around industrial upgrades, manufacturing, education, efficiency, and business applications, while the U.S. focuses more on ethics, loss of control, regulation, and apocalyptic risks.

A Current Reality: The U.S. Is Losing Its Technological Security

In the past, the U.S. assumed that the most advanced technology would always belong to it. However, in the AI era, China has truly caught up. This realization has had a significant psychological impact on Americans. Especially after the advent of large models, the U.S. has discovered that China not only can develop AI but is also progressing very quickly. More critically, China possesses the world’s largest industrial system, application scenarios, and data ecosystem. What does this mean? It means that for the first time, the U.S. realizes that future AI dominance may not necessarily belong to it. This anxiety has begun to spread among American elites, as evidenced by the U.S. restrictions on chip exports, AI, and technology—essentially a reflection of this anxiety. What the U.S. truly fears is not AI itself, but rather that “the U.S. is no longer the only AI center.”

The Optimism of Chinese People as a Survival Philosophy

Why are Chinese people more optimistic about AI? Because many naturally believe that “no matter how advanced technology is, ultimately, people adapt to the environment.” This mindset has been shaped by a long-standing competitive environment in Chinese society.

Chinese people have experienced too many changes. Many industries that were booming yesterday may disappear today; many jobs that were stable yesterday may face layoffs today. Therefore, Chinese people are more accepting of the idea that “the times have changed, so we need to adapt our way of living.” This adaptability is quite strong. Americans fear the future, while Chinese people are more accustomed to it.

But Don’t Get It Wrong: Chinese People Are Not Unafraid of AI

Chinese people are not unafraid. Instead, they are currently more afraid of “missing opportunities.” For many ordinary people, AI at least signifies new industries, new ways to make money, new entrepreneurial windows, and new traffic dividends—especially in an era of increasing economic pressure. Many Chinese people even view AI as a “turning point opportunity.”

Thus, the biggest difference between China and the U.S. is not who understands AI better, but who is more anxious. Americans are anxious about losing hegemony, while Chinese people are anxious about whether they can still rise. One fears being replaced, while the other desires change. This is the most fundamental difference in how the two countries face AI.

Focus on AI in Education: 2026 World Digital Education Conference

Sun, 10 May 2026 00:00:00 +0000

Introduction

“In the age of intelligence, what is the role of education?” This is a question we need to answer together. From May 11 to 13, 2026, the World Digital Education Conference will be held in Hangzhou, Zhejiang Province, co-hosted by the Ministry of Education and the Zhejiang Provincial Government. Guests from various countries will gather to discuss how artificial intelligence can promote systematic changes in education and enhance the quality of educational development.

From 2023 to 2025, the World Digital Education Conference has been successfully held for three consecutive years, accelerating the integration of digital technology into educational scenarios at an unprecedented pace. Follow the video to review past conferences and unlock highlights of the 2026 World Digital Education Conference, anticipating this warm and powerful digital education event.

Goals and Achievements

Promoting global collaborative innovation in digital education, facilitating the global sharing of educational resources, and contributing a “Chinese solution” to the development of digital education have showcased China’s achievements in the field of digital education in recent years, significantly enhancing its influence and voice in international education.

“Artificial intelligence is integrating into people’s lives, learning, and work with unprecedented breadth and depth, injecting new momentum into economic and social development, and bringing infinite possibilities for educational reform,” said Yang Dan, Director of the International Cooperation and Exchange Department of the Ministry of Education, at the conference’s launch.

We understand that digital transformation is rapidly reshaping the form, essence, and boundaries of global education, which deserves our continuous attention. This year’s World Digital Education Conference, themed “AI + Education: Transformation, Development, Governance,” aims to build an open and inclusive global dialogue platform to explore strategies for promoting educational equity and quality through smart technology, and to forge a consensus and guidelines for global AI education governance.

Highlights of the Conference

Highlight 1: High-Quality Outcomes

The conference will release eight outcomes categorized into three chapters: Frontier Leadership, Practice Empowerment, and Global Consensus. The first chapter will present the “China Smart Education Development Report (2025-2026)”, the Global Digital Education Development Index 2026, and the Top Ten Global Digital Education Research Hotspots 2026, deepening the understanding of the laws of digital education and continuously building international consensus.

The second chapter, Practice Empowerment, will unveil the upgraded China Smart Education Public Service Platform, the “AI Education Ethics: Reference Framework”, the Top Ten Global Digital Education Innovation Cases, and two standards from the World Digital Education Alliance: “AI Education Application Systems” and “Essential Elements for AI-Enabled Smart Campuses”. These will provide strong support and practical references for digital education development in countries worldwide.

The third chapter, Global Consensus, will release the “Hangzhou Initiative on AI Education”, calling on countries around the world to join hands to accelerate the implementation of the United Nations Future Summit’s “Global Digital Pact” and promote the achievement of the 2030 Sustainable Development Goals for education. It advocates for a global embrace of the changes brought by the intelligent era, shared human-centered educational concepts, and collaborative governance solutions.

Highlight 2: Innovative Agenda

The opening ceremony will feature an immersive display of “AI + Education Innovation Ecosystem”, showcasing three major scenarios: interdisciplinary learning, future learning centers, and open-source communities, allowing participants to intuitively experience the future of learning.

The plenary session will introduce a first-of-its-kind “School-Enterprise Cooperation Lightning Talk”, where experts from China and abroad, industry leaders, and students will take turns presenting concise, high-density insights, sharing cutting-edge experiences and thoughts from the forefront of laboratories to the industrial frontlines.

The closing ceremony will also be filled with highlights, with leaders from various countries’ education departments and international organizations witnessing the release of the “Hangzhou Initiative”, marking the beginning of future digital education cooperation.

Emphasizing intelligent integration, cross-domain collaboration, and diverse displays, the visit segment will offer seven “Digital Education Hangzhou Tour” routes, providing a one-stop experience at schools, renowned enterprises, and cultural landmarks. Participants will visit cutting-edge technology companies such as Yushu Technology, Alibaba, and Xinhua San, experiencing educational achievements and the digital vitality and cultural depth of Hangzhou.

Highlight 3: High International Participation

This conference has garnered widespread attention and positive responses globally, achieving historic highs in guest levels and coverage.

New perspectives, new outcomes, and new breakthroughs await.

This global educational feast is not to be missed!

ChatGPT Enters Hospitals: Free Clinical Assistance for Doctors

Sat, 09 May 2026 00:00:00 +0000

Introduction

At 2 AM, your phone rings. An emergency patient with fever and rash has been admitted, and antibiotics have shown no effect after three days. You open UpToDate, paying $550 annually, but after flipping through several pages, you still can’t find a similar case. You frown, thinking how helpful it would be to have an informed assistant by your side to sift through literature and provide suggestions.

Now, that assistant has arrived.

Last week, OpenAI launched a product specifically designed for clinical doctors—ChatGPT Clinical Professional Version. It verifies physician identities and is entirely free, requiring no hospital-wide deployment; you can use it independently.

The most impressive aspect is its accuracy. Before the launch, OpenAI had physician consultants test nearly 7,000 conversations in daily work, covering patient care, medical record writing, and literature searches. Doctors evaluated that 99.6% of the responses were safe and accurate. The external claim of “close to 100%” is not exaggerated. The chance of an error occurring is lower than mistyping a medication name during a shift.

How Much Time Can It Save?

Don’t think this is just a rebranded version of the general ChatGPT. It has been specifically optimized for clinical scenarios, with five key features addressing pain points:

1. Clinical Q&A: Ask anything, and it provides answers backed by reliable medical evidence.

2. Workflow Templates: Referral letters, prior authorization requests, patient notices… these repetitive tasks can be templated with one click, saving you at least an hour each day.

3. Credible Search: Based on millions of authoritative documents, answers come with citations, eliminating the risk of misinformation.

4. In-depth Reviews: Provide a few keywords, and in minutes, it generates a literature review complete with citations.

5. Automatic Credit Calculation: Time spent searching literature and analyzing cases is automatically converted into continuing medical education credits—no more need to run around for courses.

Regarding privacy, OpenAI promises that conversations will not be used for training. However, the disclaimer is clear: if the AI assists you in writing and something goes wrong, you are still responsible.

Is Free Really Free?

Some say, “The free stuff is the most expensive.”

Indeed, OpenAI is not a charity. It allows doctors to use it for free at first, and once they become accustomed to it and reliant on it, it will market the enterprise version to hospitals—the premium version that can connect with Apple Health, automatically interpret lab results, and integrate into electronic medical records. Major hospitals in the U.S., such as AdventHealth and HCA, have already adopted the enterprise version.

But OpenAI is not alone in this battle. Another free competitor is OpenEvidence, which operates differently—earning money through pharmaceutical company advertisements, charging $70 to $150 per thousand impressions, while doctors use it entirely for free. Moreover, in March this year, it integrated with the Epic system at Mount Sinai Hospital, allowing doctors to access AI directly within electronic medical records without switching browsers. OpenAI has not yet achieved this.

Regardless of who wins, subscription services like UpToDate, which cost over $500 annually, will undoubtedly face tougher times ahead.

Can AI Enter Departments Without the Director’s Approval?

Some doctors express skepticism: “Without the director’s approval, these systems can’t enter.” This statement has some truth, but it’s not entirely accurate.

If a hospital wants to localize deployment, integrate AI into its internal network, and use its data, it indeed requires the director’s approval. Some provincial health commissions are already working on this—setting up provincial platforms where hospitals can apply for computing tokens through medical networks. However, if you just want to use it independently to assist in writing medical records and searching literature, you can access it with a verified medical license without needing anyone’s approval.

There are also concerns: “As AI becomes stronger, will doctors lose their jobs?”

From another perspective, when Baidu first emerged, some claimed that doctors would be replaced by search engines. What happened? Which attending physician hasn’t secretly checked Baidu? AI is the new-age Baidu—a smarter tool. You won’t forget basic math because you used a calculator, but you will be slower than others if you don’t use one.

You Can Use It Now, But You Still Sign Off

Open the ChatGPT Clinical Professional Version, verify your medical credentials, and you can start using it for free immediately. Don’t wait for a meeting; your colleague in the next department has already started using it.

Finally, remember: AI can help you write medical records, search literature, and provide suggestions, but the one who signs off on the discharge summary will always be you.

GenSpark 4.0: The Future of AI Employees

Thu, 07 May 2026 00:00:00 +0000

Introduction

In February 2026, OpenClaw became a sensation. While the internet buzzed about lobster-themed content, I noticed a significant trend: from late February to March, major tech companies launched their own OpenClaw platforms, igniting a fierce competition.

However, by the end of March, interest in lobsters began to wane. Upon reviewing the products launched during this period, I discovered a hidden trend overshadowed by the lobster hype:

March 9: Tencent launched WorkBuddy, an AI-native desktop intelligent agent workspace.
March 17: Alibaba released DingTalk “Wukong,” positioning itself as an enterprise-level AI-native work platform.
March 19: ByteDance upgraded Feishu Aily into a new intelligent agent platform.
March 23: Baidu introduced DuMate, targeting personal and team desktop-level AI agents.

Then, on April 8, Anthropic released Claude Managed Agents. The day after, US software stocks plummeted, with the SaaS index dropping 5.5% in a single day.

Analyzing this timeline reveals a booming sector: AI employees.

The success of OpenClaw has brought AI into everyday life, and tech giants see a larger opportunity: to integrate AI into enterprises to reduce labor costs and enhance efficiency.

On the same day Claude Managed Agents launched, another product made its global debut: GenSpark 4.0.

GenSpark 4.0 Vision | Image Source: Genspark

Its vision is: to make AI employees ubiquitous.

After spending several days deeply experiencing this product, I felt strongly that:

The predicted wave of layoffs by Anthropic’s CEO may indeed be imminent.

GenSpark’s Development and Transformation

Let’s discuss GenSpark’s journey. Before becoming a dark horse, it underwent a challenging transformation:

In June 2024, GenSpark launched its first product, an AI search tool, amassing around 5 million users. However, the team quickly realized that most people search for information not just to acquire knowledge but to accomplish specific tasks. This understanding prompted GenSpark to shift its focus: not only to provide information but also to help users complete tasks.

In April 2025, they released the Super Agent suite, officially transitioning from AI search to general AI agents.

The results were immediate, achieving an ARR of $36 million within 45 days of launch.

By late January 2026, they launched Workspace 2.0, emphasizing “Don’t Type, Just Speak,” shifting interaction from text prompts to voice-first, aiming to reshape the knowledge worker’s office model.

At this point, the company’s ARR had surpassed $100 million, with Series B funding expanding to $300 million.

On March 12, 2026, GenSpark 3.0 and Genspark Claw were launched together. Their ambitious slogan was: “You no longer work with AI; you hire AI to work for you.”

This version marked a shift from “AI tools” to “AI employees,” with ARR exceeding $200 million and Series B funding growing to $385 million, valuing the company at nearly $1.6 billion.

Less than a month later, on April 8, they officially launched: GenSpark 4.0.

Why Choose Genspark | Image Source: Genspark

In this version, they truly found their mission: AI should adapt to existing workflows rather than requiring users to restructure their processes around AI.

Thus, in 4.0, they achieved native integration across desktops, Office, calendars, and workflows, supporting local file access and in-app operations, striving for a seamless experience where “you don’t feel the AI’s presence, but it is always helping you work.”

From search to agent, from tool to employee, GenSpark completed three critical transformations in two years, each aligning perfectly with the evolving AI landscape.

Why GenSpark is the Leader in This Sector

Returning to the AI employee sector, each major company’s product has its own approach.

However, after comparing various options, I found that GenSpark has indeed considered some key issues more deeply.

I attempted to break down this issue from first principles:

What does a product need to truly act as an AI employee?

I believe it must meet at least three conditions:

Enterprise-level operating environment.

AI employees must communicate with real people, receive files, and operate continuously in a stable environment. GenSpark 4.0 excels in this regard. It can converse directly with contacts and has natively integrated MyClaw, eliminating the need for users to install OpenClaw and configure it for Feishu or WeChat.

Genspark Interface | Image Source: Genspark

This may seem like a simple feature, but it is significant for average users: integrating OpenClaw, regardless of how clearly the documentation is written for Feishu or WeChat, any configuration step poses a barrier for non-technical users.

GenSpark has removed this configuration step, demonstrating their strong commitment to user experience.
A rich tool ecosystem: Providing various software that AI employees can use in real work scenarios.

GenSpark 4.0 integrates tools like Notion, email, GitHub, and document services, covering high-frequency scenarios for knowledge workers.

Genspark Email Interface | Image Source: Genspark

It also includes the essential trio for workers: PowerPoint, Excel, Word.

Genspark Trio Interface | Image Source: Genspark
Human-efficient interaction methods.

GenSpark 4.0 offers workflow functionality, allowing users to link various applications’ CLI and capabilities into action flows, or create Skills directly through conversation.

Genspark Tool Interface | Image Source: Genspark

Compared to the products mentioned earlier, only DingTalk and Feishu have built-in enterprise-level operating environments. Furthermore, if you want to create Skills, these products generally only allow AI to auto-generate them through conversation, making it difficult for humans to intervene in the iterative process. In other words, with these products, you cannot truly transform your work experience into a reusable, optimizable Skill.

This may be the true value of such tools.

GenSpark 4.0 offers a better solution in this regard:

You can easily participate in building and adjusting Skills, allowing work experience to be truly preserved.

Genspark Workflow Interface | Image Source: Genspark

Overall, I conclude that:

GenSpark is indeed at least three months ahead in the product concept of AI digital employees.

Real-World Testing

With a leading concept, how effective is GenSpark?

Let’s conduct a real-world test using my typical work process: researching various AI tools, experiencing products, forming judgments, and then writing articles. How much does GenSpark 4.0 assist me? I decided to test it with a complete task.

Today, I want to study the topic: “What impact will Claude Managed Agents have on the software industry?”

GenSpark 4.0 offers a free trial, so I started from there.

Of course, the best approach is to create a workspace centered around the topic:

The workspace can add files and invite team members for collaboration. In this workspace, conversations can invoke GenSpark 4.0’s embedded intelligent agents to complete tasks.

First, I downloaded the technical documentation for Claude Managed Agents from Anthropic’s official site and exported it as a PDF.

https://www.anthropic.com/engineering/managed-agents

Then, I used GenSpark’s document writing feature to help translate this PDF.

It quickly began processing, and before long, the translation was complete.

I was quite satisfied with the translation quality; the handling of technical terms was accurate, and readability was good.

After obtaining the primary materials, I began more extensive research.

I asked GenSpark to conduct a comprehensive investigation on the topic of “Claude Managed Agents.”

The next steps surprised me:

It gathered a wide range of opinions and judgments from platforms like Zhihu and Twitter/X, then produced a complete research report.

What was even more useful was that I could ask follow-up questions about the report, digging deeper into details I was concerned about.

I asked all my questions about this product, and the quality of the answers was quite high.

With the materials prepared, it was time to write.

Before I started, I did one thing: I created a writing Skill.

Through multiple conversations, GenSpark called OpenCode to generate a customized writing Skill.

This Skill incorporated my writing style preferences, article structure habits, and formatting norms.

Then I used this Skill to begin generating the first draft of the article.

I must say, the quality of the draft was impressive:

Clear structure, sufficient arguments, and a smoother flow than if I had written from scratch.

Throughout the process, I did not use any other tools:

From material collection, translation, in-depth research, to draft generation, everything was completed within GenSpark 4.0.

The only thing I needed to learn was GenSpark 4.0 itself, which took me just 50 minutes.

Additionally, GenSpark’s visual experience: it not only excels in tool capabilities but also invests effort into interface design and aesthetic interaction. Throughout the usage, you can feel the product team’s pursuit of visual details, which is rare in agent-type products.

GenSpark PPT Presentation Interface | Image Source: Genspark

Unique Value of GenSpark 4.0

After testing, I began to ponder:

What needs to be done well to create AI employees?

Tools like Claude Code, OpenClaw, and Codex focus on providing a harness environment for AI. A harness allows agents to use various tools efficiently to complete specific tasks. These products address the question of “how to make AI work better.”

GenSpark does the opposite.

It provides humans with a working harness environment, addressing “how to enable people to use agents most efficiently to complete complex work tasks.”

GenSpark 4.0 considers: how to conveniently provide one-stop agent services to working individuals? How to allow users to transform work experience into reusable workflows? How to eliminate the need for users to switch between multiple tools?

Genspark PPT Tool Introduction Interface | Image Source: Genspark

This difference may seem like a mere shift in perspective, but at the product level, the distinction is significant.

In traditional agent products, you might need to open one tool for research, switch to another for document writing, and use a third for collaboration. Each switch incurs efficiency loss and interrupts attention.

GenSpark 4.0 consolidates all these steps into one product, creating workspaces, adding files, inviting members, invoking intelligent agents, generating Skills, and executing workflows, all within a single interface.

This product concept reminds me of an interesting contrast during my research: while Anthropic was developing Claude Managed Agents, a technical blog mentioned a concept where they virtualized the core components of agents into three layers: session, harness, and sandbox, approaching the question of how to optimize AI performance from a technical architecture perspective.

GenSpark takes the opposite approach: thinking from the user’s workflow perspective on how to make collaboration between humans and AI as smooth as possible.

Two paths: one towards AI’s efficiency limits, the other towards human experience limits.

GenSpark chose the latter and executed it solidly.

Where Are We Heading in 2026?

In March, Anthropic released a report containing a data point that struck me:

Many job roles still have numerous tasks that can be automated by AI, and fully leveraging AI for these tasks could unlock tremendous value.

Perhaps this is why the AI employee sector is so hot in 2026.

Anthropic Document Screenshot Translation | Image Source: Anthropic

Why are major companies vying for this sector?

I believe they are essentially competing for the entry point: When each person’s unique workflow and conversation records remain on one platform, it becomes challenging to migrate that data elsewhere. Users staying means ongoing usage and consumption. This battle isn’t about feature competition but about who can become the default work entry point for users first.

On an individual level, those parts of work that can be automated will eventually be taken over by AI: this trend is irreversible. Just like my workflow, tasks such as material collection, translation, and preliminary research are already well-handled by GenSpark 4.0. I can focus more on judgment, decision-making, and creativity.

Before long, each of us may have one or even multiple AI employees: perhaps Wukong, DuMate, WorkBuddy, or Aily.

But GenSpark 4.0 gives me the impression that it has the most comprehensive and thorough vision of what AI employees should look like.

After completing this article, I found myself spending much more time in GenSpark 4.0 than just testing: I noticed that I was unconsciously migrating more and more of my work onto this platform.

This is likely what a good agent product should embody: you don’t use it because it has powerful features; you find yourself unable to live without it as you use it.

Finally, in 2026, GenSpark will provide all users with unlimited access to AI chat and AI image capabilities, integrating top models such as Nano Banana 2, Gemini 3.1 Pro, GPT-5.4, and Claude Opus 4.6.

GenSpark 4.0 is worth taking the time to experience.

The Application of Artificial Intelligence in the Banking Sector

Thu, 07 May 2026 00:00:00 +0000

The Inherent Logic of AI Applications in Finance

01 AI and Banking Information Processing

(1) Observations and Issues

In the application of AI in banking information processing, there are three key observations. First, generative AI has expanded from large language models to multimodal models that can handle images, audio, and video. Currently, it is mainly used for document generation (such as meeting minutes, customer service scripts, loan due diligence reports, exit audit reports, and contract analysis), code generation, document verification, knowledge bases, and intelligent Q&A. However, due to hallucinations, large language models struggle to directly participate in customer-facing decisions and core business judgments. Second, interpretable AI combined with alternative data can efficiently and accurately assess borrowers’ willingness and ability to repay, and it has been widely applied in bank credit assessments. Third, under the capital regulation requirements, the internal rating method (which primarily assesses the probability of borrower default, PD) still mainly relies on traditional small models like linear regression and logistic regression.

A December 2024 research report from the Bank for International Settlements (BIS) surveyed the global banking sector’s use of AI, as shown in Table 1:

In Table 1, except for anti-money laundering (AML)/counter-terrorism financing (CFT) applications such as “analyzing suspicious activities,” “real-time monitoring of unauthorized credit card usage,” and “assessing whether to lend,” which mainly belong to interpretable AI, other application scenarios are primarily generative AI.

This raises three questions for discussion. First, what roles will small models, interpretable AI, and generative AI play in banking information processing? Second, what impact does this have on banks’ model risk management? Third, how does this affect banks’ credit assessment and approval processes?

It should be noted that while interpretable AI and generative AI are discussed in parallel, they are not in opposition. First, both types of AI are based on artificial neural networks, but they differ in architecture. Currently, generative AI mainly uses the Transformer architecture based on attention mechanisms, while interpretable AI employs a more diverse range of neural network architectures. Second, despite differences in the types of data analyzed, both types of AI use neural networks to estimate data probability distributions. Interpretable AI is mainly applied to classification problems (such as classifying borrowers as likely to default or assigning different credit ratings) and predicting the probability of belonging to a certain category. The core of generative AI is to predict the next token probabilistically, providing a probability distribution for the next token in the vocabulary before making a prediction. In other words, generative AI inherently includes a classification problem regarding the next token (interpretable AI).

(2) Lending Technology and Information Processing

There is information asymmetry between banks and borrowers, and the core goal of banking information processing is to assess borrowers’ willingness and ability to repay. Although the information processed by banks can vary greatly in specific forms, it can mainly be categorized into two types. First, hard information, which generally exists in numerical form, is quantitative, structured, and devoid of subjective judgment, opinions, or observations. Second, soft information, which generally exists in textual form, is qualitative, unstructured, and inseparable from subjective judgment, opinions, and observations, requiring contextual understanding. Corresponding to these two types of information, banks primarily use two types of lending techniques. First, transaction-based lending uses hard information such as corporate financial statements and credit scores. Second, relationship-based lending uses soft information accumulated through long-term, multi-channel interactions with enterprises, which cannot be obtained from corporate financial statements or public channels. From the perspective of this analysis, the following two relationships can be approximated:

Hard Information ≈ Structured Data → Transaction-Based Lending
Soft Information ≈ Unstructured Data → Relationship-Based Lending

For structured data, there are very mature analytical methods, generally divided into four steps. First, it is assumed that there is a data generation process behind the structured data that needs to be estimated. This process can be based on causal relationships provided by theoretical research (corresponding to structured models) or on statistical correlations between variables (corresponding to simplified models). The data generation process includes a series of unknown parameters to be estimated, as well as error terms or random disturbances to account for observational errors and missing variables. Second, parameters are estimated using sample data. Empirical economic research generally conducts hypothesis testing based on parameter estimates, but in practice, prediction is more important. Third, the estimated model is used to make predictions outside the sample. Fourth, the prediction effectiveness is evaluated. If the prediction effectiveness is unsatisfactory, the model settings or parameter settings can be adjusted (i.e., model selection or tuning).

In the banking sector, representative applications of structured data analysis include: first, identity verification, recognizing user identity based on biometric features such as facial recognition, fingerprints, iris, and voice; second, credit assessment, evaluating borrowers’ creditworthiness (likelihood of default and degree of default risk); third, anomaly transaction detection, identifying abnormal transactions and fraud.

For a long time, non-structured data represented by text, images, audio, and video was believed to be generated only by the human brain and could not be generated by algorithms. The development of large models has proven that the inherent patterns of non-structured data are more abundant than previously thought. First, non-structured data can be transformed into word vectors (essentially points in a low-dimensional space) through embedding or tokenization, allowing it to be processed by artificial neural networks. Representative methods in this area include Word2Vec, GloVe, and FastText. Second, large models represented by ChatGPT use the Transformer architecture based on attention mechanisms to effectively identify implicit patterns and structures in non-structured data through statistical learning. Subsequently, large models predict the reasonable continuation of non-structured data probabilistically (i.e., the “next token”), manifested as responses to prompts.

(3) Model Interpretability, Prediction Errors, and Model Risk Management

Regardless of whether dealing with structured or non-structured data, banks’ processing methods are essentially data modeling. The scenarios in which banks use which models can all be incorporated into a model risk management framework, depending on two key characteristics of the models—interpretability and prediction error.

Model interpretability is divided into two dimensions. First, internal interpretability aims to explain how the model operates, answering the “How” questions. Second, external interpretability aims to explain why the model produces a certain result, answering the “Why” questions. Generally, the more complex the data generation process and the more unknown parameters there are (“the larger the model”), the lower the model’s interpretability. Therefore, both interpretable AI and generative AI based on artificial neural networks are inherently less interpretable than traditional small models like linear regression and logistic regression, exhibiting “black box” characteristics.

The prediction error of models dealing with structured data is easily measurable. If the predicted variable is continuous (such as economic growth rate and corporate profit), the prediction error can be measured using mean squared error (MSE). If the predicted variable is discrete (such as whether to default or which credit rating to belong to), prediction errors can be measured using two types of errors (“false negatives,” “false positives”) and the area under the ROC curve (AUC).

For large models processing non-structured data, hallucinations correspond to prediction errors. Since large models predict the next token probabilistically, any generated token deviating from the true situation is part of the inherent nature of the task. This is not a “bug” that can be fixed by improving the architecture of artificial neural networks or using more training data and computational power; rather, it is an intrinsic feature of large models. Using large models means accepting the risk of hallucinations. In practice, this risk is generally mitigated by combining retrieval-augmented generation (RAG) techniques and knowledge graphs, which essentially uses other information processing methods in scenarios where tolerance for hallucination risk is low, rather than fixing the hallucination issue of large models. It should also be noted that understanding non-structured data, including this article, involves subjective factors, making it more challenging to assess the effectiveness of text generation compared to evaluating the prediction effectiveness of structured data. Techniques for aligning large models, such as supervised fine-tuning (SFT) and reinforcement learning from human feedback (RLHF), address this issue.

From a model risk management perspective, the presence of interpretability issues or prediction errors does not mean that the model is unusable; rather, it requires management based on the application scenario and risk tolerance. Different banks and different application scenarios may have varying tolerances for model risk. For example, generally, an AUC of 0.65 for internal credit assessment models is acceptable. Model risk management can also apply a “three lines of defense” management framework similar to credit risk, market risk, and liquidity risk.

Both interpretable AI and generative AI can be incorporated into mainstream analytical frameworks for risk management. From a micro-prudential regulatory perspective, the risks posed by AI mainly manifest in the following aspects. First, credit risk: underestimating default probabilities or losses after default. Second, cybersecurity risk: increased connections with external service providers; increased IT connections between multiple systems; AI encountering “data poisoning” during model training. A recent high-profile incident involved Anthropic’s Claude Mythos being used to discover code vulnerabilities. Third, reputational risk: operational failures affecting public trust; unfair treatment of customers leading to negative public sentiment. Fourth, strategic risk: partnerships with other institutions causing banks to lose control over core functions. Fifth, legal risk: training data for AI models may infringe on rights; customer-facing AI tools may provide inaccurate or inappropriate responses. Sixth, data privacy risk: AI models may leak personal or sensitive information during training and use.

From a macro-prudential regulatory perspective, the risks posed by AI mainly manifest as: first, “herd” behavior, arising from different banks using the same foundational models and training data; second, market concentration and interconnection caused by third-party AI providers. So far, very few large banks have developed high-performing foundational models through pre-training, mainly relying on foundational models developed by leading AI companies and internet companies, and the trend of concentration among foundational models and related suppliers cannot be ignored.

Overall, different models should leverage their respective advantages based on application scenarios to achieve synergistic effects. There is no substitute relationship between small models, interpretable AI, and generative AI; rather, they are complementary. Due to hallucinations, large models cannot be directly used for bank customers but can serve as a “co-pilot” for bank employees, assisting in information processing and report generation. The output of large models can also serve as input for small models. Large models will significantly enhance banks’ efficiency and effectiveness in processing non-structured data.

(4) The Sequence of AI Penetration in the Banking Sector

Currently, generative AI is mainly used in internal auxiliary scenarios within banks. Although interpretable AI has shown good application results in bank credit assessments, regulatory agencies still prefer the stronger interpretability of small models. For example, in the Basel Capital Accord, the core tool for measuring risk capital is Value at Risk (VaR); the foundation for measuring credit risk in loan portfolios is the progressive single-factor risk model; and the mainstream tools for measuring default probabilities (PD) in internal rating methods are linear regression and logistic regression. Therefore, the penetration sequence of AI in the banking sector is illustrated in Figure 1.

From Figure 1, it can be seen that: first, there is an inverse relationship between model interpretability and predictive effectiveness, with generative AI having the strongest predictive effectiveness but the lowest interpretability; second, the penetration sequence of AI in the banking sector gradually deepens from internal auxiliary scenarios to core scenarios represented by capital regulation.

Figure 2 shows the impact of AI on banking lending technology. With the development of information and communication technology (ICT), an increasing amount of information is being collected and recorded digitally (the proportion of hard information is increasing), making it an object that can be analyzed by models. Large models have significantly improved banks’ ability to analyze non-structured data. Consequently, some relationship-based lending will transform into transaction-based lending, and banks’ credit approval authority can be appropriately centralized. This trend is already emerging in practice.

02 Three Levels of AI Applications in the Financial Sector and Their Impact

Based on financial information processing, AI has broad application prospects in the financial sector. On one hand, the characteristics of the financial industry are well-suited for AI; on the other hand, the evolution of AI from “tool → assistant → intelligent agent” will deepen its applications in finance. Under the combined force of these two aspects, the application of AI in finance will reflect three levels and will have profound impacts on the financial industry.

(1) Compatibility of the Financial Industry and AI

The compatibility of the financial industry with AI is mainly reflected in two aspects. First, industry characteristics match. First, the financial industry is information-intensive, with a large amount of work involving processing reports, announcements, contracts, regulatory documents, and other non-structured texts, which aligns with the core capabilities of large language models. Large models will enable non-structured data to enter the financial system more effectively, improving the efficiency of financial activities. Second, the financial industry is process-intensive, with clear steps in business processes and defined inputs and outputs, facilitating AI’s role from assisting individual steps to participating in complete processes, and many manual operations in the process can be replaced by automated tools. Finally, the financial industry is rule-intensive, providing clear operational boundaries for AI. The combination of “process-intensive + rule-intensive” allows AI to be deeply embedded in financial business processes, from processing information to processing funds. Second, pressures from cost efficiency, customer competition, compliance, and talent development drive financial institutions to deploy AI. Especially in China, the banking industry has a strong motivation to deploy AI against the backdrop of declining net interest margins.

The strongest foundational AI models are generally developed by leading AI companies and internet companies, but the financial industry has high data security requirements and cannot directly use public APIs. After several years of exploration, the financial industry has gradually converged on three data security solutions. First, the “firewall gateway” model: financial institutions build comprehensive AI platforms to connect with external models, and employee requests are routed through internal gateways, with data encrypted, desensitized, and permission-checked before being sent to external models. Second, hybrid cloud architecture: processing is layered based on data sensitivity, with the most sensitive data remaining in private clouds or local environments, moderately sensitive tasks processed through enterprise-level public clouds with encryption and isolation, and non-sensitive loads considering more open cloud environments. Third, complete local deployment: using open-source models running on the enterprise’s own infrastructure, with all data processing completed internally without relying on external APIs.

(2) Evolution of AI from “Tool → Assistant → Intelligent Agent”

The evolution of AI from “tool → assistant → intelligent agent” is not primarily about the replacement of technological generations (currently, mainstream large models are all based on the Transformer architecture, and no competitive alternative architecture has emerged), but rather about changes in human-machine relationships, reflected in the expansion of four boundaries. First, capability boundary: what can AI do at each stage, and what can it not do? Second, permission boundary: what systems and data is AI allowed to access and operate? Third, process boundary: the depth and breadth of AI’s embedding in business processes. Fourth, responsibility boundary: how is responsibility assigned in decisions and actions involving AI?

Tool or “Co-pilot”

The core feature of this stage is “human initiates, leads, and reviews”; AI provides suggestions, supplements information, and accelerates output but does not take proactive actions, connect to other systems, or execute operations; each human-machine interaction is independent. Representative applications include chatbots, where large models primarily perform the basic function of “generating the next token probabilistically.”

Assistant

The core feature of this stage is “humans assign tasks, AI collaborates continuously, and humans retain key judgment rights”; AI “knows” who the user is, what they are doing, and what has been discussed previously, enabling continuous follow-up, “remembering” past interactions, and “understanding” preferences, and it begins to be embedded in specific job workflows but does not act autonomously or directly operate the user’s computer system.

This stage benefits from retrieval-augmented generation (RAG) technology. The output of large models can be combined with results from search engines, knowledge graphs, and expert knowledge, alleviating the hallucination problem of large models and improving the accuracy and timeliness of output results.

AI “remembers” past interactions and continuously interacts with users based on the inclusion of past interaction records in prompts. However, this interaction does not change the weight settings of large models; the large model does not undergo true “learning.” If the large model is viewed as a function, with the weights of the large model as parameters of the function, then including past interaction records in prompts changes the function’s output by altering the function’s input; however, the parameters of the function remain unaffected, and the large model itself does not change. In other words, the “memory” about the user is reflected in the prompts, and the large model has no memory of the user.

Intelligent Agent

The core feature of this stage is that AI can plan steps, call tools, connect computer systems, and adjust execution based on feedback; this does not equate to complete automation, but rather a more realistic form of bounded semi-automated execution, where AI operates autonomously under clear planning, permissions, and approval nodes, while key decisions still require human approval.

Currently, there are many exaggerated and inaccurate claims about intelligent agents in the media. Intelligent agents do not change the basic function of large models “generating the next token probabilistically”; they can repeatedly call large models but do not change the weights of large models. What intelligent agents change is the way large models are called and the interaction between large models and computer systems, with related innovations summarized as “context engineering.” First, the output of large models includes instructions for calling computer systems. Next, if users grant authorization or approval, these instructions will be executed on the user’s computer system, producing real effects, making it appear that large models are not just “talking without action.” Then, these real effects are incorporated into prompts as new input to call large models. This back-and-forth allows large models to execute complex tasks step by step under human instructions, authorizations, and approvals.

This stage also benefits from standardized connection protocols such as Model Context Protocol (MCP) and Agent-to-Agent (A2A) protocols, enabling mutual calls and collaboration among multiple intelligent agents.

The implementation of intelligent agents relies on several prerequisites. First, tool interfaces: intelligent agents need to connect to users’ computer systems through standardized interfaces. Second, permission layering: the scope of access and operations for intelligent agents must be strictly defined. Third, approval nodes: key steps involving fund transfers, modifications to customer information, or external communications must have human approval. Fourth, log tracking: every decision and action taken by intelligent agents must be fully recorded to support post-audit. Fifth, evaluation mechanisms: ongoing monitoring of the quality of intelligent agent outputs and compliance with behavior is required. Sixth, human fallback: there must be clear escalation and fallback pathways for intelligent agents when they encounter situations they cannot handle.

(3) Three Levels of AI Applications in the Financial Sector

As a Tool Enhancing Individuals

First, back-office roles such as legal, compliance, customer service, code development, and document processing have a faster deployment speed for AI due to high frequency and standardization of tasks. Second, for knowledge-intensive front-office roles (such as investment research), AI will replace some repetitive labor, changing the speed of information acquisition and material organization in the workflow, but not altering the responsibility for decision-making. At this level, all business risks, compliance requirements, and final decision-making responsibilities remain entirely with human employees.

As an Assistant Understanding Roles, Context, and Customers

First, as an employee-facing assistant. The assistant can continuously work around the employee’s role, follow up on customer relationships the employee is handling, and organize relevant materials before the employee’s next customer meeting. Second, as a customer-facing assistant. AI begins to participate in tasks such as checking bills, confirming transfers, and lightweight service processes. At this level, although core judgment rights remain with human employees, the responsibility boundaries begin to blur as AI provides personalized suggestions based on multidimensional data tracking.

Intelligent Agents Begin to Participate in Complete Business Processes

AI takes over multi-step tasks within clear boundaries to free up human resources for handling unexpected events and key judgments. Currently, intelligent agents are mainly applicable in two types of scenarios. First, rule-driven process scenarios. For example, anti-money laundering, sanction compliance, KYC review, and compliance reporting, which have clear rules, standardized steps, and defined data sources. Second, mixed scenarios of “knowledge + process.” For example, data collection and preliminary analysis in investment research departments, financial report comparisons, market monitoring and early warning, and customer management task scheduling.

(4) Impact of AI on the Financial Sector

Impact on Business Models. First, customer service shifts from passive response to proactive management. For example, wealth management models evolve from standardized asset allocation tools to dynamic management models based on customer goals. Second, service entry points expand. Financial service entry is no longer limited to bank branches or mobile apps but begins to extend to conversational AI platforms. For instance, Mastercard and OpenAI collaborate to allow customers to complete payments during conversations with AI without switching to the bank interface. Finally, inter-agent payments are a recently popular area of interest.
Impact on Organizational Roles and Talent Structure. First, job impacts. Tasks that are rule-clear, step-splittable, and have standardized inputs and outputs, such as back-office operations, entry-level research, and compliance processing, are more easily automated. Tasks involving customer relationships, complex judgments, ethical decisions, and creative strategies are harder to replace. The overall trend is to “reshape roles” rather than simply “replace roles,” achieving transformation through natural attrition and internal repositioning. Second, responsibility shifts. Employee responsibilities shift from manually executing each step to task design, outcome review, and handling unexpected situations. Employees need to not only master the business itself but also judge which tasks can be delegated to AI and which nodes must revert to human handling.
Impact on Technical Architecture. Financial institutions are deploying AI on a large scale towards foundational infrastructure. The complexity of AI infrastructure projects is high and involves layers of computing (computing scheduling and optimization), data (unified knowledge base and data permission systems), models (selection, updating, and retirement governance of multiple models), and tools (providing standardized system connection interfaces for intelligent agents), making it a systemic engineering challenge. The evolution of AI from “tool → assistant → intelligent agent” also reflects the path of financial institutions moving from partial deployment to platform construction. The tool stage can tolerate localized pilots, the assistant stage requires role and context connectivity, and the intelligent agent stage requires reliable collaboration between systems. Therefore, AI is no longer just a project for the IT department but requires an independent organizational structure, dedicated budget, and executive-level governance oversight.

(5) Future Outlook

Benefiting from the enhancement of intelligent agent capabilities, AI is expected to evolve from a single model into a dispatching hub. This upgrade is reflected in the reconstruction of two dimensions: data retrieval and model computation. In the data processing dimension, intelligent agents with autonomous planning capabilities can gradually replace traditional architectures. Hallucinations cannot be fixed by improving architecture or increasing training data; rather, they are inherent features of large language models predicting the next token probabilistically. To control this risk, data intelligent agents will downgrade the output of large models from final conclusions to verifiable middleware. When addressing business scenarios, intelligent agents can convert user intentions into structured query statements or executable code. Whether the code runs successfully and whether the underlying database returns reasonable results can be subjected to deterministic verification. Even if hallucinations occur during the query generation phase, they will be intercepted by the system during execution failures, thus preventing erroneous information from directly entering core processes.

In the model computation dimension, the collaborative calling of large general models and traditional specialized small models will become a key path to meet stringent regulatory requirements. To address the low interpretability black box characteristics of large language models, this architecture can retract uncertainty to the tool calling layer. In scenarios requiring high interpretability, the core risk measurement logic will still be completed by traditional specialized small models. Large models will not participate in the final numerical estimation; they will only process the preceding non-structured data and generate calling instructions. This design fully leverages the complementary advantages of different models and shifts the audit trail from the complex parameter weights of artificial neural networks to a complete traceable chain from natural language instructions to code execution and then to traditional specialized small model outputs. Whether it is the precise capture of underlying information by data intelligent agents or the computational distribution of large models to small models, this path of constraining uncertainty through architectural design rather than a single model can effectively meet the requirements of financial industry model risk management. A core issue that needs further discussion in the future is how to match log tracking, evaluation mechanisms, and human fallback approval nodes when the risk nodes of the system migrate from text generation to code generation and tool calling.

Access ChatGPT Online Without Registration in 2026: A Comprehensive Guide

Wed, 06 May 2026 00:00:00 +0000

Introduction

In today’s rapidly evolving AI landscape, ChatGPT has transitioned from a niche tool for tech enthusiasts to an essential resource for professionals, students, and content creators. However, domestic users face significant barriers such as high access thresholds, cumbersome registration processes, and costly fees. In 2026, a hassle-free ChatGPT access point that requires no login or registration has emerged, allowing ordinary users to enjoy top-tier AI services effortlessly. This article aims to provide a detailed guide based on practical experience.

Many first-time users encounter three major obstacles when trying to access ChatGPT, each of which can deter newcomers:

Access Barriers: The official version requires special network configurations, which can be complex. Many users spend hours trying to connect without success.
Cumbersome Registration: The official registration process requires an overseas phone number and email verification, with some platforms even demanding real-name authentication. This not only involves multiple steps but also poses privacy risks, leading many to abandon the process.
Cost and Usage Limits: The official free version has a daily limit of about 25 uses, which can be quickly exhausted with just a couple of tasks. Plus members incur monthly fees exceeding 200 yuan, making it impractical for students and average workers.

Additionally, 90% of platforms claiming to offer “free ChatGPT” are often scams, using inferior models or imposing hidden fees after a few free attempts. In this context, a legitimate, no-login, no-registration, high-quality ChatGPT access point is urgently needed.

After two months of testing over 30 platforms, we have identified a reliable no-login ChatGPT access point that meets the needs of domestic users: no registration, no login, and no special network requirements. Users can start chatting directly in their browser, with support for both GPT-3.5 and GPT-4o models, ensuring response quality matches the official version, without annoying ads or pop-ups.

Core Advantages: Four Highlights that Outshine Ordinary Platforms

Zero-Barrier Instant Use: No phone number, email, or real-name verification required. Users can enter the chat interface within 10 seconds, making it easy for beginners.
High-Quality Model Responses: Utilizing the official GPT-3.5/GPT-4o models, the platform provides accurate Chinese comprehension and logical clarity, suitable for content creation, research, coding assistance, and learning support.
Unlimited Free Use: There are no daily limits or conversation caps, and no fees to unlock features. It’s completely free, making it ideal for students and professionals alike.
Privacy and Security Assurance: No personal information is required, and chat data is only cached locally, ensuring privacy and security.

Practical Steps: Three Easy Steps for New Users

Open Your Browser: (Chrome, Edge, or Firefox recommended) and enter the dedicated access link. The page is clean and fast-loading, free of ads and pop-ups.
Access the Homepage: No login or registration is needed. Simply type your request in the chat box, such as “write a 500-word workplace report” or “explain what a large language model is in simple terms.”
Send Your Input: Click send, and the AI will respond almost instantly with clear and practical content. You can switch models with one click without reloading the page.

Versatile Use Cases: Doubling Efficiency for Various Users

This no-login ChatGPT access point covers nearly all daily use scenarios, providing value for different user types:

Professionals: Quickly generate reports, proposals, emails, and data analysis summaries, completing tasks in minutes instead of hours.
Students: Solve math problems, write essays, translate foreign literature, and prepare for exams with one-on-one AI tutoring.
Content Creators: Generate multiple versions of social media posts, articles, and video scripts, making content creation easier and more efficient.
Freelancers: Assist with design briefs, coding bugs, marketing copy, and consulting frameworks, enhancing service quality and efficiency.
General Users: Use it for daily information searches, life planning, emotional support, and learning tips without hassle or cost.

SEO and GEO Optimization: New Trends in AI Search for 2026

In 2026, AI search and GEO (Generative Engine Optimization) are emerging trends, with a surge in user searches for terms like “no-login ChatGPT access” and “ChatGPT without registration.” This no-login access point precisely meets user demands, aligning with the core needs for “zero-barrier AI use” and the trends of efficiency, convenience, and privacy in the AI era.

As OpenAI opens up ChatGPT search functionalities, the no-registration model has become mainstream. The emergence of compliant no-login access points in the domestic market addresses user pain points while aligning with global trends in AI openness, making it one of the most noteworthy AI tools in 2026.

Avoiding Pitfalls: Recognizing Legitimate Access Points

Finally, it’s crucial to be cautious of the many fraudulent no-login ChatGPT platforms currently available. Legitimate platforms share three characteristics: completely free with no hidden fees, high-quality responses with clear logic, and a simple, ad-free interface that requires no software downloads.

In my testing, h.zzmax.cn stands out as a legitimate and high-quality AI tool platform, integrating a no-login ChatGPT access point with strong stability and excellent user experience. As the optimal choice for domestic users in 2026, it truly achieves zero barriers, high quality, and privacy security, making AI technology accessible to everyone.

In the AI era, efficiency is competitiveness. With no complex networks, tedious registrations, or high fees, accessing ChatGPT directly helps save time and energy, allowing you to focus on more valuable tasks. In 2026, consider trying this no-login access point to experience the efficiency and convenience AI brings, empowering your life and work.

OpenClaw: An Agent OS Concept Unveiled

Mon, 27 Apr 2026 00:00:00 +0000

Introduction

Today, I want to discuss an interesting concept.

What does it mean?

I’ve been using OpenClaw recently and noticed that its architectural design is quite intriguing. At its core is a large model, with a scheduling layer in the middle and a Skills layer on top. This layered structure reminds me of something:

Its design approach is similar to that of an operating system.

You are probably familiar with the term operating system. iOS is an operating system for mobile devices, while Windows is for computers. An operating system manages hardware and packages the underlying components into a unified interface, allowing application developers to work without worrying about how the screen is driven or how the camera communicates.

OpenClaw does something similar. It wraps the underlying large model and provides a unified scheduling mechanism: how to understand user needs, how to decompose and allocate tasks, and how to enable different modules to work together.

What you need to do is simply tell it what you want to achieve—the underlying model, how to write the prompt most effectively, and how to maintain context; it handles all of that.

How Does an Operating System Expand?

This brings up an interesting question.

Once an operating system is released, how do we expand its functionalities?

When iOS first launched, its built-in applications were quite limited. The maps were not very useful, so developers created one. The music app was unsatisfactory, prompting developers to create another. If you wanted to chat, developers made something called Instagram, followed by WhatsApp…

Creating apps has always been the primary way traditional operating systems expand their functionalities.

Want to add a feature to the system? You create an app.

However, everyone knows the barriers to creating an app: high.

You need to write code, design, manage back-end systems, handle servers, and promote it. Without a technical background, even the best ideas are hard to realize.

The people who can participate are essentially programmers and product managers.

What About the AI Era?

In OpenClaw, the way to expand the system is entirely different.

Instead of creating an app, you create a Skill.

What is a Skill?

It’s about transforming something you excel at into a workflow that AI can execute. It’s not about letting AI run wild; it’s about organizing your experience, your judgment criteria, and your steps into a format that AI can follow.

Its expansion logic differs from traditional operating systems. Thus, I call this design approach an Agent OS.

Let me give you a few examples.

If you want AI to help you analyze stock performance—not just throwing a bunch of data at it for analysis—but providing it with your own logic: how to view the market, how to select stocks, how much to set for stop-loss, and what signals to act on. AI follows your framework, ensuring each analysis is methodical.

If you want AI to help you review contracts—not just uploading a PDF and asking if there are risks—but giving it a checklist of your own: which clauses are prone to issues, which areas 99% of people overlook. AI follows this checklist, ensuring no detail is missed in each contract.

If you want AI to assist in career decisions—not just asking it whether you should switch jobs and letting it come up with random suggestions—but providing it with a framework for evaluating opportunities: considering salary potential, team atmosphere, growth trajectory, and commuting costs, each with its own weight. AI follows your decision model, producing conclusions that are structured and not arbitrary.

You see, the core is not about letting AI run free; it’s about turning your expertise into a process that AI can execute.

The Shift in Barriers

At this point, one thing naturally emerges.

The barrier to creating products has shifted from technology to domain knowledge.

In the traditional OS era, if you wanted to turn your domain experience into a product, the barrier was technical. If you couldn’t code, even the best ideas couldn’t come to fruition.

Now, with Agent OS allowing for Skill creation, the system handles the technical aspects, and what you need to do is clarify your experience in a specific domain.

The barrier now becomes: how well do you understand this domain?

If you understand stocks, you can create a stock analysis Skill.

If you understand contracts, you can create a contract review Skill.

If you understand the workplace, you can create a career decision Skill.

Your value is no longer determined by whether you can code, but by how deeply you understand a particular field.

Who Should Create Skills?

At this point, the answer is quite clear.

It’s not programmers.

The advantages of programmers have significantly diminished under this logic—the technical aspects are handled by the Agent OS.

It’s the people who create Skills—those who have real experience in a specific vertical field.

The work you do every day, the pitfalls you’ve encountered, the methods you’ve summarized—these things used to be for your own use or turned into courses or books with limited monetization options.

Now, they can be transformed into Skills, placed within the Agent OS for your use and for others.

How to Get Started?

If you’re interested, here’s a simple step to start:

First, list the three things you excel at. They don’t have to be grand; just things you do daily, others often ask you about, or areas where you feel there are patterns.

Second, choose the one that resonates most with you and try to write down the steps you take to accomplish it. It doesn’t have to be perfect; just get your thoughts out.

Third, find an AI tool you commonly use and present this workflow to it. See if it can follow your steps. If it works, you have a prototype of that Skill; if not, you’ll know where the issues lie.

Start with a usable version, even if it’s rough. Use it and iterate.

Conclusion

Returning to my initial statement:

Previously, programmers created products, and the barrier was technical; now, with AI creating Skills, the barrier has shifted to domain knowledge.

Agent OS has dismantled the wall of technology.

Once the wall is down, the real barrier becomes: how deeply do you understand a domain, and can you translate that understanding into a process that AI can execute?

Now, the door is open.

What are you good at? In which direction do you want to build a barrier?

So, are you going to give it a try?

DeepSeek V4 Released: Breaking Closed Source Monopoly with Huawei Collaboration

Fri, 24 Apr 2026 00:00:00 +0000

DeepSeek V4 Released

DeepSeek V4 has officially launched with an open-source preview. There are two versions available:

DeepSeek-V4-Pro: Targets top closed-source models, with 1.6T parameters, 49B activations, and a context length of 1M.
DeepSeek-V4-Flash: A smaller, faster economic version with 284B parameters, 13B activations, and a context length of 1M.

The official statement claims that DeepSeek V4 leads in agent capabilities, world knowledge, and reasoning performance within the domestic and open-source fields.

Currently, DeepSeek V4 is being used internally as an Agentic Coding model, with user feedback indicating a better experience than Sonnet 4.5, and delivery quality close to Opus 4.6 in non-thinking mode, though still trailing behind Opus 4.6 in thinking mode.

The official website and app have been updated, and the API service has also been refreshed. Notably, support for Huawei’s computing power will be available in the second half of the year.

Two Versions Released Together

DeepSeek V4 has launched both versions simultaneously.

V4-Pro

The performance of V4-Pro rivals that of top closed-source models. The official assessment includes three key points:

Significantly Improved Agent Capability: In Agentic capability coding assessments, V4-Pro has reached the best level among current open-source models and performs excellently in other agent-related evaluations. In internal assessments, the agent coding mode of V4 outperformed Sonnet 4.5, with delivery quality approaching Opus 4.6 in non-thinking mode, but still falling short of Opus 4.6 in thinking mode.
Rich World Knowledge: In world knowledge evaluations, DeepSeek-V4-Pro significantly outperformed other open-source models, only slightly behind the top closed-source model, Gemini-Pro-3.1.
Top-Level Reasoning Performance: In assessments of mathematics, STEM, and competitive coding, DeepSeek-V4-Pro surpassed all publicly evaluated open-source models, achieving results comparable to the best closed-source models.

V4-Flash

The V4-Flash version is smaller and faster. Its reasoning capability is close to that of the Pro version, with slightly less world knowledge, but smaller parameters and activations, making the API more affordable.

In agent tasks, DeepSeek-V4-Flash performs comparably to DeepSeek-V4-Pro on simple tasks, but still shows a gap in more complex tasks.

In a test scenario, V4 also passed quickly.

However, in the classic biological scenario of the “desperate father,” DeepSeek-V4 failed to grasp the critical point of red-green color blindness (according to genetic rules, if a female is red-green color blind, her biological father must also be).

Standard Context Length of 1M

Notably, starting today, a 1M context length is standard for all official DeepSeek services. A year ago, 1M context was a unique feature of Gemini; other closed-source models were limited to either 128K or 200K, and very few open-source models could handle this scale.

DeepSeek has transformed the 1M context from a “premium feature” to a “basic utility.”

The release notes indicate that this was achieved through a new attention mechanism that compresses at the token dimension, combined with DSA sparse attention. This significantly reduces the computational and memory requirements compared to traditional methods.

DSA is not a new term; it was first introduced in the V3.2-Exp update six months ago, which did not attract much attention at the time due to its similar performance to V3.1-Terminus, appearing as a minor update. In hindsight, it laid the groundwork for V4.

Optimized Agent Capabilities

For agents, V4 has been adapted and optimized for mainstream agent products such as Claude Code, OpenClaw, OpenCode, and CodeBuddy, resulting in improvements in coding tasks and document generation tasks.

The release notes also include an example of a PPT slide generated by V4-Pro within a certain agent framework.

API Pricing

The APIs for V4-Pro and V4-Flash have launched simultaneously, supporting both OpenAI ChatCompletions and Anthropic interfaces.

The base_url remains unchanged; simply modify the model parameter to deepseek-v4-pro or deepseek-v4-flash to call the respective version.

Both versions support a maximum context of 1M and include both non-thinking and thinking modes. In thinking mode, the reasoning_effort parameter can adjust the intensity, with two levels: high and max. The official recommendation is to use max for complex agent scenarios.

A key point is that support for Huawei’s computing power will be available in the second half of the year.

Additionally, old model names will be phased out. deepseek-chat and deepseek-reasoner will be discontinued three months later (on July 24, 2026). During this transition, these two names will refer to the non-thinking and thinking modes of V4-Flash, respectively.

For individual developers, the impact is minimal, requiring only a change in the model parameter. However, companies integrated into production environments will need to migrate within the next three months.

One More Thing

At the end of the release notes, DeepSeek included a quote:

“Do not be tempted by praise, nor frightened by slander, but follow the path and correct oneself.”

This is a line from Xunzi’s “Non-Twelve Sons.” In essence, it means not to be swayed by accolades or intimidated by criticism, but to move forward according to one’s own beliefs and to correct oneself.

In today’s context, this is quite interesting. Over the past six months, rumors about when V4 would be released, whether it would be delayed, if it had been surpassed by others, or if it had been resolved by Claude’s distilled data have circulated in both Chinese and English AI circles. Earlier this year, some even confidently claimed that V4 would be released before the Spring Festival, but it was not until the end of April that it finally arrived.

They did not respond to any of these rumors.

Then, on a Friday afternoon, they released V4, open-sourced it, updated the official website and app, and refreshed the API, while also noting that internal staff had already stopped using Claude.

There was no roadmap, no live streams, and no interviews.

The phrase “follow the path” sounds like a slogan, but when you consider the past six months of the seemingly uneventful V3.2 Exp version, the DSA sparse attention that laid the groundwork for V4, and the transition of 1M context from a premium feature to a standard utility, it all comes together.

DeepSeek has achieved this.

DeepSeek V4 Model Open Source Links:

DeepSeek V4 Technical Report:

Technical Report PDF

Anthropic's Claude Code Faces Backlash as OpenAI's Codex Expands Features

Wed, 22 Apr 2026 00:00:00 +0000

Users Frustrated with Anthropic’s Restrictions

Recently, Anthropic has been rolling out various features while simultaneously tightening usage restrictions, leading to widespread frustration among users in the comments.

As one of the most restrictive among the big three (OpenAI, Google, Anthropic), Anthropic has now implemented identity verification, requiring real-name registration to use its services. Just this morning, they also revoked the Claude Code access for Pro users ($20/month).

Anthropic’s growth lead responded, mentioning that they are conducting a small-scale test on about 2% of new Pro user registrations, with existing Pro and Max users unaffected. They acknowledged that their current subscription plans cannot accommodate the high token consumption by users and are exploring new payment options.

OpenAI quickly responded to the controversy surrounding the removal of Claude Code access for Pro members. Rohan Varma, a lead at Codex, directly challenged Claude Code, even mimicking its post format.

While Anthropic is testing more expensive plans for 2% of users, Codex is offering its services to 100% of users, allowing both free and paid plans to access Codex. They cheekily added that Claude Code users would not be affected.

Claude Code users PAY, Codex users PLAY

Another Codex lead, Tibo, also tweeted that Codex will continue to offer a free version and a PLUS version ($20/month), emphasizing that OpenAI has sufficient computing power and advanced models to support Codex’s operations.

OpenAI’s CEO also retweeted this, expressing, “We hope you can have abundant AI.”

Codex has maintained a relatively positive reputation on social media, especially after OpenAI’s recent push to allow everyone to experience Codex by resetting usage limits across all subscription plans.

In early April, Codex noticed an increase in users hitting usage limits without understanding the reasons behind it, so they decided to reset the limits for all users. Just days ago, to celebrate Codex’s anniversary and the launch of new features, they reset usage limits once again.

Today, Codex’s lead and OpenAI’s CEO tweeted that Codex added 1 million new users in less than two weeks, celebrating this milestone with yet another reset of rate limits.

Last week, on the day Anthropic released Opus 4.7, Codex updated with a host of important features, including Computer Use, a built-in browser, persistent memory, and over 90 plugins.

These updates directly compete with the features of Claude Cowork, transforming Codex from a tool primarily for developers into an efficiency assistant suitable for all computer scenarios.

Yesterday, building on the previously launched memory feature, Codex introduced a research preview feature called “Chronicle,” which allows the AI to read our screens and organize our recent activities into memory.

Codex no longer relies solely on chat history to understand context; by combining the recent screen content it reads, it can accurately interpret our references when we say “this” or “that.”

The newly released GPT Image 2 has also been integrated into Codex, enabling us to generate and iterate images within Codex for tasks ranging from product prototyping and front-end design to visual effects and game development.

If your Claude account is frequently suspended, preventing you from using the official Claude Cowork or Claude Code desktop version, or if you are among the 2% of new users who cannot access Claude Code even after subscribing to the $20/month Pro plan, consider trying OpenAI’s Codex.

From Code Tool to All-in-One Assistant

The most significant update for Codex recently was the release of Computer Use last week. This capability is not entirely new; previously, the model had the ability to use a computer, but now it requires tools and support to fully utilize its capabilities.

Essentially, the Agent tool can operate a computer like a human, using visual recognition, clicking, and inputting commands to autonomously control various applications on the computer.

Previously, Codex executed tasks on computer software through commands, resembling simple requests like asking Siri about the weather. With the Computer Use capability, it can now assist with actual operations on the computer, particularly useful for front-end debugging, application testing, and interacting with software that does not have an open API.

Additionally, it supports multiple agents working in parallel on a Mac without affecting our use of other applications.

It is important to note that Computer Use is only supported on macOS 15 and above. During testing Codex on our computer (macOS 14.6.1), a SkyComputerUseClient issue report popped up automatically.

Moreover, Codex now supports a built-in browser, enhancing its ability to handle web scenarios. The web pages generated within Codex can be annotated directly, providing more precise operation instructions for rapid iteration in front-end, application, and game development.

From coding, design, lifestyle, productivity to research, Codex now features a rich plugin system to handle various tasks.

This update also introduced over 90 new plugins and a more extensive tool integration, allowing Codex to access more tools, gather more context, and perform cross-platform operations. Popular plugins mentioned include Atlassian Rovo (JIRA), Microsoft Suite, Neon by Databricks, Remotion, Render, Superpowers, and more.

In the Codex application, we can quickly access various Codex configurations by entering a slash, and by typing a dollar sign, we can select different Skills, including various Skills installed locally.

Additionally, with the upgrade of Codex’s Automation feature, we can reuse previous conversation threads while retaining existing context. The new automation also supports Codex in planning subsequent tasks autonomously, executing tasks at a future time, and even supporting long-term tasks lasting days or weeks.

The official statement indicated that this update is mainly used for code submission merges, tracking daily to-do items, and information tracking across different platforms and tools.

There are also minor updates for desktop application interactions, such as adding multi-tab terminal windows and allowing the sidebar to open files and preview PDFs, spreadsheets, PPTs, and other documents.

The new summary panel can continuously track the plans and progress of current tasks, reference information sources, and output results. These enhancements make Codex feel more like a unified workspace rather than a single chat window.

Maintaining Agent Memory with Timed Screenshots

Personalized memory functionality has always been a significant challenge for AI. While AI can retain vast amounts of knowledge, it needs to manage each user’s private memory and working memory in a way that does not consume excessive tokens while still being effective.

Especially with Agent tasks that consume a lot of tokens, if the Agent needs to remember all the context generated by each user daily, even a million tokens may not suffice.

Last week, OpenAI introduced a memory feature for Codex, enabling it to remember our personal preferences, previous corrections, and other crucial information that is not easily accessible.

To acquire more memory and process our workflows more quickly, Codex launched the Chronicle feature, which essentially observes our screens, remembers our work, and feeds this memory to the AI.

Specifically, after enabling the Chronicle feature in Codex settings > personalization, it automatically performs these operations: screen context capture → local temporary screenshots → background agent analysis → temporary Codex session summary → generates local Markdown memory → uses it as context in subsequent sessions.

Once Codex obtains screen recording and accessibility permissions, Chronicle will run a sandbox Agent in the background. These Agents use the default model GPT-5.4-mini to periodically initiate a temporary Codex session based on captured screen images, organizing recent screen context into memory.

Screenshots will only be temporarily stored locally, and Codex mentions that during operation, screenshots older than 6 hours will be automatically deleted.

The information generated by GPT Image 2

In future conversations with Codex, it will automatically retrieve these memory files to use as context, reducing the need for us to repeatedly describe the background.

OpenAI has also provided multiple examples, such as if Chronicle is not enabled, Codex would not understand what we mean by “this will fail” without context.

For personal tasks involving names, project titles, etc., outside of general knowledge, Codex will also automatically supplement context based on the information obtained from Chronicle.

The ability to capture screen images means that Chronicle can remember the entire workflow of tasks processed with Codex, including our workflows and commonly used tools. For example, a Codex using Chronicle will know the format and tool used for a promotional material, whether it’s Google Docs or Markdown.

However, this feature also faces some controversies. For instance, the visual recognition method may consume a significant amount of tokens, and more seriously, these screenshots might contain sensitive information visible on our screens.

Although OpenAI states that all saved memories will be stored in local markdown documents for users to review at any time, Codex can identify what information it has obtained from these screenshots. They also warn users that when Chronicle captures screenshots of risky websites, those sites may inject malicious commands hidden in prompts, which Codex could execute.

Currently, the Chronicle feature is only available to ChatGPT Pro ($200/month) users and is launched as a research preview for the macOS version of Codex. Once Chronicle is officially launched, it is expected that Codex will open it up to more users.

Mobile Remote Control, Digital Pets, and the Potential Launch of “Hermes Agent”

Recently, Codex has been referred to as a product striving to catch up with Claude. While some say that OpenAI lacks originality and follows trends, the competition between good products can ultimately benefit users.

Codex developers have asked users for feedback on X, and many responded enthusiastically, suggesting the addition of mobile control functionality and integration into the ChatGPT app, both features currently offered by Claude.

Some users also reported various bugs in Codex, such as memory leaks and the inability to delete conversation archives.

Recent leaks about Codex updates also mentioned plans for a small digital pet to be placed on the Codex desktop to indicate the status of ongoing conversations.

This digital pet will have eight preset appearances, and users can create and use their own virtual images.

Another leak indicated that OpenAI is developing an agent for ChatGPT (codenamed Hermes), which will include features like agent builders, templates, scheduling, options for using agents in Slack, adding applications, skills, files, memory, instructions, and more.

Currently, Codex is an actively developed product, and OpenAI is unlikely to cede the local agent market to Claude.

Not to mention that OpenAI, as the elder brother in the AI field, recently saw Gemini quietly release a desktop application, which received criticism from users as “terrible.”

We can only encourage OpenAI and Gemini to quickly end Claude’s lead in local agent assistants and coding.

Artificial Intelligence Major: Detailed Guide and Admission Tips

Wed, 22 Apr 2026 00:00:00 +0000

Overview of the Artificial Intelligence Major

The Artificial Intelligence major is part of the electronic information undergraduate programs, typically lasting four years and granting a Bachelor of Engineering degree. It focuses on the intersection of mathematics, computer science, and AI algorithms, aiming to cultivate professionals capable of developing, deploying, and optimizing intelligent systems.

Core Knowledge and Courses

Mathematical Foundations: Advanced mathematics, linear algebra, probability theory and statistics, discrete mathematics, and foundational mathematics for AI (which determines the limits of algorithms).
Computer Science Basics: Programming (Python/C/C++), data structures and algorithms, operating systems, computer networks, and principles of computer organization.
Core AI Technologies: Introduction to artificial intelligence, machine learning, deep learning (CNN/RNN/Transformer), pattern recognition, natural language processing (NLP), computer vision, reinforcement learning, and graph neural networks.
Engineering and Applications: Smart chips, AI frameworks (TensorFlow/PyTorch), intelligent robotics, the Internet of Things, and AI applications in various fields (smart driving, medical imaging).
Ethics and Regulations: Ethics in artificial intelligence, cognitive psychology, and technology law.

Practical Skills and Capabilities

The curriculum includes experiments, course design, project training, internships, and graduation projects, emphasizing hands-on problem-solving in complex engineering tasks.
Students must develop skills in model training and tuning, engineering deployment, multimodal perception and decision-making, as well as cross-team collaboration and ethical compliance awareness.

Common Specializations

Computer Vision: Object detection, image generation, autonomous driving, and medical imaging.
Natural Language Processing: Text classification, machine translation, intelligent customer service, and large model applications.
Reinforcement Learning: Game AI, robot control, and intelligent decision-making.
Recommendation and Graph Intelligence: Social networks, e-commerce recommendations, and graph neural networks.

Differences Between AI, Computer Science, and Electronic Information Majors

Artificial Intelligence Major: Focuses on algorithms, models, and the development of intelligent systems, emphasizing the ability of machines to learn, reason, and perceive, leaning towards intelligent applications and algorithm design.
Computer Science Major: Emphasizes software programming, system development, networking, and database technologies, offering broader employment opportunities as the foundational support for AI.
Electronic Information Major: Focuses on hardware circuits, signal processing, communication, and embedded systems, primarily studying the underlying hardware and signal transmission of intelligent devices.

Requirements for Studying Artificial Intelligence

A solid foundation in mathematics and logical thinking is essential, along with the ability to adapt to abstract algorithm learning.
Interest in programming and willingness to engage in hands-on practice, debugging code, and training models are crucial.
Strong self-learning ability and patience to keep pace with the rapid updates in AI technology are necessary.
Basic English reading skills are beneficial for accessing cutting-edge literature and technical documents.
Problem-solving skills and the ability to optimize solutions through repeated experimentation are important.

Key Points for Choosing Universities Offering AI Majors

Discipline Strength: Prioritize institutions with strong computer science, control science, and software engineering programs that have doctoral programs and top undergraduate majors.
Research Platforms: Check for AI laboratories, big data research institutes, and GPU computing platforms as hardware support.
Curriculum System: Ensure the curriculum covers core AI technologies, balancing theory and engineering practice.
Industry Collaboration: Look for schools that collaborate with tech companies to establish training bases and have ample internship and employment resources.
Regional Industry: Prefer universities located in tech industry hubs for better internship opportunities and employment prospects.

Admission and Learning Suggestions

School Selection: Consider three aspects: mathematical intensity (real analysis/differential geometry), engineering practice (GPU clusters/company projects), and alignment of specialization with target industries.
Learning Path: Build a strong foundation in mathematics and programming first, then study machine learning and deep learning, reinforcing practice through projects (such as image classification and text generation) while keeping an eye on advancements in large models and multimodal technologies.

OpenAI's Codex Transforms SQL Queries with Lifelong Memory

Tue, 21 Apr 2026 00:00:00 +0000

Introduction

In early 2026, while most companies were still relying on data analysts to manually write SQL queries, OpenAI revealed a data analysis agent capable of independent thinking, reasoning, and self-evolution, reducing data query times from days to minutes.

The Challenge of Data Queries

Data teams often face challenges not due to insufficient computing power, but because of the vast number of tables, definitions, and scattered experiences. For instance, the term “active users” can have completely different meanings across various tables. Even if the right table is selected, writing hundreds of lines of SQL can be necessary to produce results, and a single incorrect join condition can invalidate the entire effort.

Internally, OpenAI has taken a radical step: using a Codex-driven data agent to manage the entire process of “finding tables, understanding tables, writing SQL, and validating results” through a six-layer contextual architecture. This approach enriches data semantics, integrates organizational knowledge, and consolidates experiential memory, allowing engineers to ask questions instead of performing manual tasks.

Automating Data Queries

“We have many structurally similar tables, and I spend a lot of time trying to understand their differences and which one to use,” lamented an OpenAI engineer, capturing the common plight of data workers. OpenAI’s internal data platform contains 600PB of data across 70,000 datasets. Imagine when engineers need to analyze ChatGPT user growth, facing dozens of similar user tables, each claiming to record “user activity” but with differing definitions.

Choosing the wrong table can mean days of effort wasted, and worse, it could lead to critical decisions based on incorrect data.

Even when the correct table is chosen, generating accurate results can be challenging. A complex SQL statement of over 180 lines can feel like an insurmountable mountain—any minor error could render the entire analysis ineffective.

With the Codex-driven intelligent agent, engineers no longer need to write hundreds of SQL queries; they can simply ask questions to find the information they need from the data ocean, such as comparing active user counts at two different points in time.

Six-Layer Contextual Architecture

Many tools exist to convert natural language into SQL statements, but the core innovation of OpenAI’s internal data agent lies in its multi-layer contextual architecture.

The foundational layer consists of basic metadata, including table structures and column types, providing the skeleton for the data graph.

The next layer involves human annotations crafted by domain experts, capturing intent, semantics, business meanings, and known considerations that cannot be easily inferred from patterns or historical queries. This layer essentially provides foundational training for the agent regarding each table’s information.

The subsequent Codex enhancement layer derives code-level definitions of tables, allowing the agent to gain deeper insights into the actual content of the data. This layer offers critical information about value uniqueness, data update frequency, and data range. Its introduction enables the agent to understand differences in table construction and updates.

Above this is the organizational knowledge layer, where the agent can access Slack, Google Docs, and Notion to obtain key company background information, such as product releases, reliability incidents, internal codenames, and definitions and calculation logic for key metrics.

With external text-derived background information, the agent avoids common sense errors. For example, when a user asks, “Why did connector usage drop significantly in December?” the agent does not simply report the number’s decline but identifies it as primarily a measurement/logging issue rather than a real collapse in usage, related to changes in data collection due to the ChatGPT 5.1 release.

The most critical fifth layer is the learning evolution, which grants the agent persistent memory. When it receives corrections from users or notices subtle differences in data issues, it can retain these experiences for future use. Memory can also be created and edited manually by users, applicable globally or unique to specific users.

The top layer, runtime context, allows the agent to perform real-time queries to check and query tables when existing context or information is lacking. It can also communicate with other data platform systems (metadata services, Airflow, Spark) to obtain broader data context.

Dynamic Switching Between Offline Retrieval and Online Queries

How do these six layers work together?

The process can be divided into offline and online steps. Each day at dawn, the agent systematically scans thousands of data tables’ actual usage and calling trajectories from the previous day, absorbing annotations and insights left by data experts, and invokes Codex to interpret the logic buried in the code, deriving richer business semantics behind the tables. All these scattered “knowledge fragments” are merged into a unified, standardized “knowledge graph.”

Subsequently, through OpenAI’s embedding model, this information is transformed and compressed into groups of vector embeddings stored in a high-speed retrieval library. Thus, a readily available “data memory palace” for the AI agent is established.

When a user’s question arrives, the agent no longer needs to dive into the vast sea of metadata for time-consuming manual retrieval. Instead, it employs retrieval-augmented generation techniques to precisely locate and extract the most relevant data tables for the current question. This process is fast, scalable, and has low latency.

For requests requiring the latest data, the agent simultaneously activates a real-time query channel, directly querying the data warehouse. This achieves both the immediacy of runtime context and deep integration with offline knowledge. Consequently, a complex business question can be transformed into clear insights available in seconds through the collaboration of offline memory’s “lightning retrieval” and real-time data’s “precise guidance.”

Paradigm Shift from Static Tools to Dynamic Team Members

What is most impressive about this intelligent agent is not its technical complexity, but how it integrates into daily workflows, becoming a true “teammate.” Unlike traditional “question-and-answer” tools, OpenAI’s data analysis agent is designed to be a “teammate with whom one can reason.” It is conversational, always online, capable of handling quick answers as well as iterative exploration.

Imagine a scenario where a product manager’s question is unclear or incomplete; the agent proactively asks clarifying questions. If there is no response, it applies reasonable default values to advance the work. For example, if a user inquires about business growth without specifying a date range, it might assume the last seven or thirty days. This allows the agent to maintain a balance between responding and collaborating with the user to achieve more accurate results.

To prevent the ever-evolving agent from going off track during its learning process, the OpenAI team employs the Evals API to provide a strict overseer for the agent. Each significant question is paired with manually crafted queries serving as “gold standards,” and the agent’s performance is continuously monitored and rated.

These evaluations check not only the correctness of SQL syntax but also compare the accuracy of result data. When the agent “misbehaves,” the system immediately raises an alert, ensuring issues are identified and resolved before impacting users.

In terms of data security, the agent ensures that users can only query tables they have permission to access. When access rights are missing, it marks this point or falls back to alternative datasets that the user is authorized to use.

To ensure transparency in the data analysis process, the agent summarizes assumptions and execution steps alongside each answer to expose its reasoning process. When a query is executed, it directly links to the underlying results, allowing users to check the original data and verify each step of the analysis.

Building a Data Analysis Agent

OpenAI’s data analysis agent is not open-source, but if you want to build a similar agent, OpenAI’s engineers have shared some pitfalls they encountered.

Initially, the agent had access to the complete dataset, but this quickly led to confusion among overlapping data tables. To reduce ambiguity and enhance reliability, developers had to restrict the tables the agent could access, thereby improving query reliability.

Another pitfall arose from highly structured system prompts provided by developers. While many questions share similar analytical shapes, the details vary enough that rigid instructions can backfire. Focusing on the effects in real usage and allowing the agent to determine how to achieve results rather than relying on system-level prompts makes the agent more robust and produces better outcomes.

The most critical point is realizing that the true meaning of data lies in the code rather than expert annotations of data tables. Query histories describe the shape and usage of tables more accurately, capturing assumptions and business intentions that never surfaced in SQL or metadata. By using Codex to crawl the codebase, the agent can understand how datasets are actually constructed and better infer the actual contents of each table. This approach provides more accurate answers to questions like “What is in this table?” and “When can I use it?” compared to merely retrieving information from the data warehouse.

As enterprise data environments become increasingly complex, tools like OpenAI’s data agent may become standard configurations for future enterprise data analysis, driving the industry towards a more efficient and intelligent data-driven decision-making paradigm.

The goal of these agents is not to replace data analysts but to enhance their capabilities, freeing them from tedious query writing and debugging to focus on higher-level tasks such as defining metrics, validating hypotheses, and making data-driven decisions.

Claude Code Launches with Direct Computer Control Features

Tue, 31 Mar 2026 00:00:00 +0000

Claude is taking off! Today, Claude Code officially launched its “Computer Usage” feature, allowing direct control of the CLI for coding, UI interactions, and bug fixes. With a single click, users can activate “autopilot” mode, freeing their hands completely.

Claude’s delivery speed is astonishing!

This morning, Anthropic dropped a significant update—Claude Code now has the ability to directly interact with your computer.

This means Claude is no longer just a chat AI hiding behind a dialogue box.

Now it has hands and can directly operate within the CLI, taking over tasks on your computer.

Claude Code can autonomously complete the entire development, debugging, and testing loop, just like a human programmer.

With just one prompt, Claude can handle everything from writing code, compiling, launching applications, to automatically selecting tests.

If a program crashes, it can find bugs, fix them, and validate the solution on its own.

This has left many users in a panic, with some suggesting that this moment marks Claude’s official replacement of software engineers.

Currently, this feature is available in a “research preview” for Pro and Max users, exclusively for macOS.

Claude Code Finally Grows “Hands and Eyes”

Humans Left as Bystanders

In reality, Claude Code is already quite powerful: it can understand your entire codebase, write code, modify files, and run commands.

However, its capabilities have been limited to the terminal and text world.

Once workflows move out of the terminal into browsers, desktop applications, or system UIs, humans must take over.

Now, Claude can directly take control of a Mac, manipulating browsers, mice, keyboards, and screens to complete tasks.

Simply enter /mcp in the terminal to activate Claude’s “autopilot” mode.

With the computer usage capability integrated, Claude Code can perform the following operations:

Cross-Application Interaction: Open various installed apps on the computer and interact with the UI by clicking and swiping.

End-to-End Loop: With a single command, it can complete the entire process:

Write code -> Compile -> Launch App -> UI Automation Clicks -> Find Bugs -> Fix Code -> Validate Again.

Ignoring Tool Boundaries: Whether it’s a locally compiled SwiftUI application, an Electron project, or a graphical tool without a CLI, it can operate directly.

The core breakthrough of this update is the “autonomous debugging” capability in complex environments.

In the past, when code encountered issues, users had to manually screenshot and report to AI or copy error messages, which was time-consuming and labor-intensive.

Now, Claude can directly see the constructed program interface, simulating user actions to find visual or logical flaws.

This “what you see is what you get” debugging method greatly reduces the cost of switching between different tools for developers.

This feature update truly achieves a complete loop for developers, allowing development to be completed without manual intervention.

Although Claude Code has become more powerful, without sufficient quota, developers can only lament their coding limitations.

Quota Depleted at Lightning Speed, Internet Outcry

Because today, Claude faced a significant issue…

Just a day into the week, global developers collectively hit the “quota wall” of Claude Code.

Even those who paid $200 for the “Max Premium User” status found themselves in a difficult position, receiving quota warnings before they could fully utilize the service.

This sudden throttling has ignited widespread complaints online, with posts about “Claude quota shortages” flooding social media.

The most frustrating part is that by the end of Monday, Claude had already “clocked out” early.

Facing the overwhelming outcry, CC engineers responded urgently: an internal investigation is underway.

However, as of now, the cause of the “fire” remains a mystery.

Serious Bugs Exposed, Token Costs Surge by 20 Times

A Reddit user couldn’t sit still.

He performed reverse engineering on Claude’s binary files through a man-in-the-middle attack (MITM) and discovered a shocking truth:

There are two serious bugs in the system’s underlying architecture that directly lead to cache failures.

Once cache fails, token consumption costs can surge by 10-20 times, effectively “murdering” the user’s quota.

If you are using it in API calls, the situation will only worsen. He found that these two bugs are extremely hidden.

The first is a string replacement bug in the Bun runtime environment. Because Claude’s independent CLI comes with a customized binary file, it causes frequent cache failures.

The known temporary solution is to use npx @anthropic-ai/claude-code to run.

The second bug is even more troublesome; when using the –resume command to restore a session, the cache crashes 100% of the time.

Currently, aside from “rolling back” to an older version that sacrifices many features, there is almost no solution.

In the community, some have suggested that Anthropic is intentionally not fixing the bugs for profit.

Currently, many developers on GitHub have confirmed this vulnerability, and the “Token Assassin” incident may continue for some time.

Claude Code’s Creator Shares 15 Practical Tips

Yesterday, Claude Code’s creator Boris Cherny shared 15 severely underestimated “hidden skills” online.

1. Compiler in Your Pocket

Many may not know that Claude Code has a mobile app. Boris often writes code directly on the iOS app.

Whether commuting or waiting for coffee, he reviews code changes, submits PRs, and even writes code directly through the mobile Code tab.

2. Instant Cross-Device Movement

If you’ve run a complex session on your work computer and want to continue at home on your laptop, just enter /teleport.

/teleport allows you to pull the cloud session to your local terminal; /remote-control lets you control a locally running session from your phone or web.

3. The Ultimate Form of Automation: /loop and /schedule

This is Boris’s favorite feature; it allows Claude to automatically execute tasks, running for up to a week.

/loop 5m /babysit—automatically handles code reviews, rebases, and pushes PRs to production every 5 minutes.

/loop 30m /slack-feedback—automatically submits PRs based on Slack feedback every 30 minutes.

/loop /post-merge-sweeper—automatically fills in previously missed code review comments.

/loop 1h /pr-pruner—cleans up expired and no longer needed PRs every hour.

4. Control Lifecycle with Hooks

Using hooks, you can trigger logic automatically at specific moments:

SessionStart: Automatically load specific context environments upon startup.

PermissionRequest: Push permission requests directly to WhatsApp for easy approval.

5. Remote Commander: Cowork Dispatch

When you’re away from your computer but want to handle Slack messages, manage files, or run MCP plugins, you can use Dispatch.

It acts as a secure remote controller for Claude Desktop, handling all non-programming tasks.

6. The “Eyes” for Frontend Development: Chrome Extension

This is Boris’s most important suggestion: give Claude a browser. With the Chrome extension, Claude can see its output in real-time and iterate continuously.

Without a browser, Claude is like a blind person feeling an elephant; with it, the fidelity of frontend code increases dramatically.

7. Automatically Start and Test Servers

The Claude Desktop App now includes the ability to automatically run web servers and test in the built-in browser.

This integrated experience is much more efficient than manual operations in the CLI.

8. “Clone” Anytime, Anywhere: Fork Sessions

Want to try a bold refactoring idea but afraid of messing up current progress?

Enter /branch to create a branch dialogue. If in the CLI, use claude –resume.

9. Chat While Working: /btw

If Claude is busy writing long code and you suddenly want to ask a side question?

Use /btw. It allows you to insert a side query without interrupting the main task process.

10. Large-Scale Parallelism: Git Worktrees

Boris typically runs dozens of Claudes simultaneously on his computer, thanks to claude -w.

It supports Git worktrees, allowing you to carry out multiple tasks in the same repository without interference.

11. The “Fan-Out” Mode that Changes the World: /batch

When large-scale code migration is needed, /batch first interviews to understand your intentions, then automatically distributes hundreds or thousands of worktree agents to start working simultaneously.

12. Startup Speed Boost: –bare Mode

By default, Claude scans various configuration files upon startup.

However, in non-interactive scenarios, using the –bare parameter skips these scans, boosting SDK startup speed by 10 times.

13. Break Repository Boundaries: –add-dir

Need to collaborate across repositories? Just add –add-dir at startup, and Claude can gain access and operational permissions for another folder.

14. Customized Clones: –agent

You can define a dedicated Agent, such as creating a “read-only Agent” or “security audit-specific Agent.”

After defining it under .claude/agents/, you can summon it using claude –agent=xxx.

15. Speak Instead of Type: /voice

Boris shared an astonishing fact: most of his code is “spoken”.

Run /voice in the CLI and hold the space bar, or click the voice button on the desktop, and let Claude handle the rest.

Mastering these tips will evolve Claude Code from an assistant into a 24/7 standby engineering team.

The Illusion of AI in Cancer Treatment: A Product Manager's Perspective

Thu, 19 Mar 2026 00:00:00 +0000

Introduction

The case of AI curing cancer appears to be a victory for technological democratization, yet it reveals a brutal divide between the elite and technological privilege. This article, from the perspective of a product manager, deeply analyzes the resource monopolies and invisible barriers behind this sensational event, exposing the truths and profit rules in the AI and hard tech fields.

Imagine a scenario where a programmer, without ever wielding a scalpel or spending a day in a lab, types a few lines of code one night, asks AI a few questions, and then—he cures late-stage cancer that even top oncologists could not handle.

This sounds like a poorly made Hollywood sci-fi story, but this is the real event that exploded in global media in March 2026. Sydney tech entrepreneur Paul Conyngham used an AI toolchain (including ChatGPT, AlphaFold, etc.) to design a personalized mRNA cancer vaccine for his dog Rosie, who was suffering from advanced mast cell cancer. Fifteen months later, this dog, initially given only 1 to 6 months to live, was chasing rabbits in the grass.

Thus, the global media frenzy began. Headlines in The Australian and viral posts on social media platforms sold the public a deadly illusion: “The era of AI for the masses has arrived; even if you are a biology novice, with an internet connection and ChatGPT, you can become a god.”

However, as an internet product manager who deals with traffic data, ROI, conversion rates, and system architecture daily, it would be a significant professional failure to indulge in this cheap celebration of “technology eliminating all barriers.”

Peeling away the media’s clickbait filter and dissecting the underlying code of this “miracle of life,” we see a starkly different and cold reality: this is not a fairy tale about zero-barrier technology, but a targeted explosion initiated by an elite class filled with technological privilege and resource monopolies.

The so-called “zero barrier” merely folds deep invisible thresholds of wealth and social capital worth “millions” into itself. Today, we will use the product manager’s perspective to thoroughly deconstruct this “feel-good story” and examine the harsh truths and profit rules hidden in the deep waters of AI and hard tech.

01 Pain Point Analysis: The Systemic Deadlock of “The World Suffers from Expensive Cures for Terminal Illnesses”

The driving force behind any phenomenon-level blockbuster product or event is the need to combat a deeply rooted and long-unmet user pain point. Before discussing why Conyngham decided to “handcraft” a vaccine, we must first understand the desperate “medical product ecosystem” he faced.

The “Standardization” Trap of Traditional Medicine

In the design logic of modern medical systems, the pursuit is for large samples and high success rates through “standard operating procedures (SOP).” For canine mast cell cancer (which accounts for 20% of all skin tumors), the standard treatment path is very clear: diagnosis -> surgical removal -> chemotherapy (such as vincristine).

However, the fatal bug in this system is that it cannot handle edge cases. When Rosie experienced treatment failure and tumor recurrence, entering late-stage cancer at the end of 2024, her “user lifecycle (LTV)” in the medical system was forcibly terminated, and her prognosis was directly assessed as terminal (1-6 months).

The “High Customer Acquisition Cost” of Personalized Medicine

When the standard path fails, is there a higher-level solution? Yes. Targeted immunotherapy drugs and the personalized mRNA cancer vaccines currently in development (such as the joint PD-1 inhibitor clinical trials by Moderna and Merck, which can reduce the risk of melanoma recurrence by 49%).

But there lies a sighing wall: the costs are extremely high and inaccessible to ordinary people.

Breakdown of the Commercial Loop: Conyngham initially attempted to apply for “Compassionate Use” of targeted drugs but was ruthlessly rejected by pharmaceutical giants. Why? Because the core KPI of pharmaceutical companies is drug approval; giving a green light to a dog or an ordinary patient not only brings no profit but also poses high risks of legal and clinical data contamination.
Sky-high R&D Costs: Even for humans, the estimated cost of such personalized mRNA vaccines can reach $100,000 to $300,000 (equivalent to millions in local currency).

Insights from a Product Perspective:

At this point, Conyngham’s situation is that of a heavy user who has encountered a “system-level offline.” When traditional B2C medical services are completely shut off, his instincts as a geek were triggered—if the official API is not available, he would capture packets, reverse engineer, and write a plugin himself. This was a “cross-border arbitrage” forced upon him.

02 Reality Unveiled: The Targeted Explosion of “Technological Privilege”

The media loves to shape narratives of “grassroots success” because it best harvests traffic. But with a little user persona investigation, one would find that calling Conyngham an “ordinary person” is the greatest misunderstanding of those three words.

Behind the celebration packaged as “a few dollars subscription to ChatGPT can cure cancer” is a severely folded “million-level” invisible wealth and barrier.

Cognitive Wealth: Interdisciplinary Computational Power That Cannot Be Replaced by GPT

The media claims he “lacks a biology background,” but that does not mean he is a novice. The reality is: he has 17 years of experience as a machine learning and data scientist and is a board member of the Australian Data Science and AI Association.

Throughout the vaccine development process:

Data Cleaning Ability: Obtaining the raw sequencing data from the UNSW Genomics Centre (in FASTQ format), do you think you can directly feed it to ChatGPT? Large language models cannot process such vast unstructured data that requires strict mathematical validation.
Algorithm Development Ability: In the mutation identification and new antigen prediction stages, Conyngham relied on self-developed machine learning algorithms for screening, which requires deep mathematical logic and programming skills. Conclusion: If an ordinary person’s cognitive base is 0, AI can help you reach 60; but Conyngham’s base is 90, and AI merely helped him reach 100. This “cognitive wealth” starts at hundreds of thousands of dollars in the Silicon Valley recruitment market.

Let’s look at the key milestones in the timeline of Rosie’s vaccine and who supported them:

Gene Sequencing: UNSW Ramaciotti Centre.
Ethical Approval: Professor Rachel Allavena from the University of Queensland’s Veterinary School (one of the few researchers in Australia with ethical permission for canine immunotherapy experiments).
Vaccine Preparation: Professor Páll Thordarson’s team at the UNSW RNA Institute. One must ask, what is the success rate for an ordinary person sending a “Cold Email” to these academic giants, asking them to conduct gene sequencing for their dog, navigate ethical approval, and utilize a national-level laboratory to synthesize lipid nanoparticles? It is absolute 0%. The ability to mobilize top academic resources and have scholars willing to endorse you is an extremely scarce invisible wealth.

Regulatory and Time Financial Costs: The “Hidden Costs” Paid with Life

To pass ethical approval, Conyngham spent three months, working intensely every night, writing over 100 pages of application materials. Even the approval for experimental treatments in veterinary medicine is extremely strict; if it were for human use, the approval thresholds and cycles would multiply tenfold.

Additionally, gene sequencing, travel to and from the University of Queensland, and multiple clinical monitoring resulted in total expenses reportedly reaching “tens of thousands” of dollars. More cruelly, late-stage cancer does not wait; the six-month R&D cycle means that most patients will not survive to see the vaccine completed.

Insights from a Product Perspective:

This is an elite experiment initiated by a high-net-worth user (wealthy, free time, top technology, and high-level connections) that cannot be replicated. The media has intentionally or unintentionally erased this “million-level” hidden cost, merely amplifying the visible label of “ChatGPT.” This narrative is not only unobjective but also extremely dangerous.

03 Underlying Logic: AI is an “Accelerator,” Not a “Creator”

As a mobile internet practitioner, we need to establish a deeper cognitive framework: in the field of biomedicine, where it belongs to “hard atoms,” what are the real boundaries of AI (bits)?

By dissecting Conyngham’s toolchain, we can clearly see what AI can and cannot do.

Many media headlines claim “ChatGPT cured the dog.” However, Conyngham himself has repeatedly clarified that he used ChatGPT (and later the xAI Grok model) merely for:

Brainstorming and generating initial hypotheses.
Navigating literature to break through the barrier of professional terminology (translating complex biological papers into terms understandable by data scientists).
Planning the timeline for experimental design. Essentially, ChatGPT acts as a “super research assistant.” It significantly shortens the time for interdisciplinary knowledge retrieval, but it cannot generate an mRNA sequence that can be directly injected into the body. Large language models excel at establishing semantic connections but lack the rigorous computational ability required in biophysics.

Structural Computing Layer (Middleware): AlphaFold’s Dimensionality Reduction

The real “hardcore” role is played by AlphaFold, developed by DeepMind.

Traditional target discovery requires X-ray crystallography or cryo-electron microscopy to analyze the three-dimensional structure of proteins, which is costly and can take months. AlphaFold compresses this process to just a few hours, enabling Conyngham to precisely see the abnormal protein structures produced by Rosie’s tumor mutations.

Here, AI is an “accelerator,” reducing the computational costs of structural biology to zero. But the prerequisite remains: the user must possess the ability to interpret these 3D structure outputs.

Physical Execution Layer (Back-end): The “Atomic Gap” AI Cannot Cross

This is the most easily overlooked yet heaviest aspect of the entire myth.

When Conyngham utilized all AI capabilities to condense months of analysis into “half a page of mRNA sequence formula,” his journey in the digital world ended.

The subsequent physical preparation cannot be performed by any AI. The UNSW RNA Institute spent two full months, utilizing precision pharmaceutical-grade facilities to complete lipid nanoparticle encapsulation (LNP), purity testing, and stability verification.

This is akin to AI producing a perfect lithography machine blueprint; without a cleanroom to manufacture wafers, this blueprint is just a piece of waste paper. In the biomedical field, the laboratory’s reagent bottles, centrifuges, and clinical beds are the physical foundations that the bit world can never bypass.

04 Objective Reflection: Beware of Wild “Digital Hua Tuo” and Deadly Illusions

The video of Rosie running in the grass is indeed touching, but behind this emotion lurks enormous industry risks and ethical crises.

Survivor Bias and the N=1 Medical Scam

In medicine, individual cases without large sample randomized double-blind trials (RCT) have no statistical significance.

Martin Smith, an associate professor of computational biology at the University of Sydney, pointed out the most critical issue: “This is an N=1 zero-control trial.”

Did Rosie’s tumor shrink truly because of the mRNA vaccine? Could it be due to the delayed effects of previous chemotherapy drugs? Could it be spontaneous remission of the immune system? Without a control group, directly attributing causality to the AI-designed vaccine is an extremely unrigorous scientific attitude.

Even Professor Thordarson, who participated in the preparation, clearly warned: Rosie has not been cured; some tumors did not respond to the vaccine, and a second generation needs to be developed. The “cured” label used by the media can only barely be considered “partial remission” in clinical terms.

The Deadly AI Illusion

If a copywriting AI produces a hallucination, at most it results in a nonsensical press release; if a coding AI produces a hallucination, it may cause an app to crash.

But in the field of biomedicine, AI hallucinations can be fatal.

If ordinary patients blindly trust media hype and feed their genetic data into a generic language model that has not been medically fine-tuned, the AI could very well piece together a seemingly professional yet logically flawed “treatment plan” based on statistical probabilities from its corpus. If patients seek out black market laboratories based on this (which is not impossible on the dark web), the wrong targeting could trigger a systemic immune storm, leading to accelerated death.

Exacerbating Rather Than Eliminating “Medical Inequality”

The most ironic paradox is that personalized medicine was originally intended to save every unique life, and the introduction of AI technology was meant to lower barriers. Yet Rosie’s case proves that this “DIY-style” geek medicine has concentrated resources into a very small group of individuals with extremely high technical capabilities and strong social capital.

Ordinary people neither have the money to experiment nor the connections to reach university laboratories, nor the ability to discern AI outputs. This technological frenzy remains an elusive mirage for ordinary patients.

05 Breaking the Deadlock: How Mobile Internet Professionals Can Profit in the “AI + Hard Tech” Era

After dissecting this seemingly distant myth, what can we learn as mobile internet practitioners? Should we merely lament class solidification and technological barriers?

Absolutely not. In this era where even cancer can be attempted to be “hacked” by AI, the old product logic is collapsing, and new business models are being reshaped. Here are three practical methodologies for all product managers, operators, and entrepreneurs:

Methodology One: Find Ecological Niches for “Structural Arbitrage” and Become the Industry’s “Super Connector”

The biggest gap exposed by Rosie’s case is the severe disconnection between “efficient digital computation” and “heavy physical execution.” Conyngham filled this gap with his extraordinary connections.

Your opportunity lies in: productizing this gap.

Do not compete with generic large models, nor invest heavily in building physical laboratories. Be that “super connector.”

In agriculture, materials science, biopharmaceuticals, and precision manufacturing, there are many traditional veterans who do not understand AI and many AI geeks who do not understand industry know-how. If you can build a platform:

Front-end: Provide compliant, industry-specific AI Copilots (for instance, a “veterinary version of GPT” to help doctors quickly draft personalized chemotherapy plans).
Back-end: Connect CROs (Contract Research Organizations) and cloud laboratories (like Emerald Cloud Lab) with standardized APIs. Allow those who understand technology to access physical experiments at a very low cost, while those who understand the industry can access AI computation with minimal barriers. This “structural arbitrage” based on information asymmetry and resource scheduling is the battlefield where internet professionals excel.

Methodology Two: Abandon the “Prompt Engineer” Illusion and Fully Transform into a “Domain Engineer”

The era of “just knowing how to write prompts to control AI” is over. Conyngham succeeded not because he wrote good prompts, but because he understood data science and underlying logic; he knew at which points AI would produce nonsense.

Practical Advice:

If you are a product manager, immediately stop obsessing over complex parameters in Midjourney or jailbreak commands in GPT.

Choose a vertical field (like new energy battery testing, cross-border medical compliance, industrial IoT edge computing), and spend six months mastering the “industry jargon” and “business flow SOP” of that field.

When you possess deep Domain Knowledge and then use AI tools, you will outclass those who only know how to write fancy documents. The future core competitiveness will be: “Industry veterans + AI leverage.”

Methodology Three: Design “Defensive” AI Products to Build a High Trial-and-Error Moat

The risk in Rosie’s case lies in “hallucinations leading to death.” When launching AI products in the future, especially in high-risk fields like finance, healthcare, law, and education, your product architecture must shift from “All-in AI generation” to “human-set guardrails with AI filling in the details.”

Practical Standards (HITL Principle: Human-in-the-loop):

Post-decision: AI should only compress information and provide options; the final “approval button (Approve/Reject)” must be given to qualified humans.
Traceability Mechanism: Every core data/report generated by your AI product must include its reasoning process and knowledge base citation links. Just as Conyngham had to verify AlphaFold’s structures himself, your product must allow users to “trust.”
Safety Sandbox: Set limits for AI. It is better to have it respond “I’m not sure; please consult an expert” when encountering boundary issues than to fabricate facts to please users.

Conclusion

When Rosie runs in front of the camera, she is indeed a lucky dog. But for the 3 million mobile internet professionals, if we only see the magic of technology while ignoring the high costs behind it, we will be completely marginalized in this grand AI transformation.

Recognizing the boundaries of technology, finding the fractures in the industry, and connecting digital computation with the physical world with reverence is the ultimate wealth code we can share in the AI-native era.

Claude Transforms into an AI Productivity OS with MCP Apps

Mon, 02 Feb 2026 00:00:00 +0000

Claude’s Evolution into an AI Productivity OS

Claude has undergone a significant transformation, evolving into a powerful AI productivity tool that integrates seamlessly with various applications. The introduction of MCP Apps marks a new era in workplace interactions, allowing users to perform tasks without switching between multiple browser tabs.

New Features of Claude

Recently, Anthropic announced ten essential productivity tools that can now be interacted with directly within Claude. Users can draft Slack messages, visualize ideas in Figma, or create and review Asana timelines with just a click.

This integration means that all tasks can be completed within Claude, streamlining workflows and enhancing productivity. For instance, during a meeting discussing dashboard revisions, users can ask Claude to outline a launch plan and it will actively pull in Figma to create a clear flowchart in minutes.

Next, users can retrieve engagement data using Amplitude, which Claude can utilize to generate a line graph instantly.

Currently, over ten mainstream applications, including Slack, Figma, and Asana, are connected to Claude, breaking down barriers between AI models and software tools.

The Rise of MCP Apps

With the introduction of MCP Apps, Claude has effectively become the operating system of the AI era. Amplitude’s founder stated that traditional UIs are obsolete, as no one wants to log into multiple SaaS applications anymore. The future of UI is about integrating directly into individual workflows, appearing automatically when needed.

This shift is exemplified by Clawdbot, which is being hailed as the “extended arm of Claude,” representing a true evolution in productivity tools. As 2026 begins, these two groundbreaking products, Claude and Clawdbot, are set to redefine the future of work.

AI experts have remarked that Claude is now significantly more useful than Clawdbot, with the latest features available to Claude Pro, Team, and enterprise users.

Claude’s New Role in the Workplace

The daily office tools that workers rely on can now be accessed directly within Claude. Previously, while Claude could call various tools via MCP to execute tasks, the major change is that these tools are now presented directly in the conversational interface. This allows for real-time progress updates and collaborative editing.

For example, when tasked with integrating collected data into a dashboard project, Claude can immediately set up the project in Asana. Users can also request Claude to send a summary of the results to a colleague, which it can draft in Slack, allowing for direct edits and the addition of emojis.

If users need to find the latest retail technology analysis report, they can simply ask Claude, which will retrieve the core report from Box in no time.

Claude can also connect to Clay to pull background information on Conclusive AI, or use Hex to query data and generate charts, tables, and citations.

This update signifies Claude’s evolution from a simple conversational assistant to a fully integrated AI workstation, enabling users to perform various tasks directly within the platform:

Amplitude: Create analytical charts and explore trends interactively.
Asana: Convert chat content into projects, tasks, and timelines for team collaboration.
Box: Search and preview files quickly, extracting core information or asking questions about file content.
Canva: Outline presentation drafts, customize brand styles, and create deliverable slides.
Clay: Research company backgrounds and draft personalized business emails.
Figma: Transform text and images into flowcharts or Gantt charts with a simple prompt.
Hex: Ask data-related questions and receive professional answers with interactive charts and tables.
monday.com: Manage daily tasks, run projects, and visually track progress.
Slack: Retrieve conversation history, draft messages, and preview before sending.

Anthropic has announced that these new features will soon be available on Salesforce, integrating enterprise-level context into Claude for unified reasoning, collaboration, and decision-making.

The Launch of MCP Apps

The underlying technology behind these features is the Model Context Protocol (MCP), an open standard connecting tools with AI applications. MCP Apps allow Claude to interact with third-party tools, marking a significant step away from the era of pure text interactions.

With MCP Apps, the interaction is no longer limited to cold text; users can directly generate and manipulate interactive interfaces within the conversation. Whether it’s data dashboards, complex configuration forms, or dynamic visual charts, these can all be created and operated in real-time.

Users have expressed excitement about the arrival of generative UIs, and Anthropic has credited OpenAI for their foundational work on MCP-UI and OpenAI Apps SDK.

Currently, mainstream clients like ChatGPT, Claude, Goose, and VS Code support this functionality, allowing developers to create custom “skins” and “limbs” for AI agents as easily as building web applications.

The Decline of Traditional UIs

MCP Apps enable tools to provide rich, interactive interfaces rather than just returning plain text. When a tool declares a UI resource, the host renders it within a sandboxed iframe, allowing users to interact directly within the conversation flow.

Typical applications of MCP Apps include:

Data Exploration: Sales analysis tools returning interactive dashboards for direct filtering and report exporting.
Configuration Wizards: Deployment tools displaying forms with linked fields that adjust based on user selections.
Document Review: Contract analysis tools highlighting key clauses directly in PDFs for user actions.
Real-time Monitoring: Server health monitoring tools showing dynamic metrics that update with system status.

MCP Apps bridge the gap between what tools can do and what users can see, transforming cumbersome text interactions into a smooth experience akin to using standard web applications.

Why MCP Apps Matter

While MCP excels at connecting models with data and granting execution capabilities, there often exists a perception gap between what tools can do and what users can see. For instance, a database query tool might return hundreds of rows of data, but users often want to perform further actions like sorting or filtering without sending multiple prompts.

MCP Apps fill this gap by allowing the model to remain deeply involved in the process while the UI handles tasks that are cumbersome in text form, such as real-time updates and direct interactions.

App API

Developers can use the @modelcontextprotocol/ext-apps package to build MCP Apps, which provides an App class for handling communication between the UI and the host.

The emergence of MCP Apps signifies the standardization of the “Agentic UI” framework, marking a crucial step in transforming AI from a mere chatbot into a fully functional productivity tool. Claude has truly gained its “limbs,” and the popularity of Clawdbot continues to soar, raising questions about the future of human labor.

DeepSeek V3.2 Released: Impressive Performance and Cost Advantage

Wed, 03 Dec 2025 00:00:00 +0000

DeepSeek V3.2 Released

On December 1, DeepSeek surprised users with the launch of version 3.2, now available for all users and also uploaded to various open-source communities for local deployment. According to official test results, DeepSeek V3.2’s inference capabilities are now comparable to OpenAI’s GPT-5, but at a much lower cost, which is exciting for many.

Stronger Inference at a Lower Cost

DeepSeek V3.2 comes in two versions: the free version available on the DeepSeek website and the DeepSeek V3.2-Speciale, which supports API access. The Speciale version features enhanced inference capabilities, primarily designed to explore the limits of the model’s reasoning abilities.

The V3.2-Speciale actively enters a “long-thinking enhancement” mode and incorporates the theorem-proving capabilities of DeepSeek-Math-V2, enhancing its instruction-following, mathematical proof, and logical verification abilities. In official tests, V3.2-Speciale’s inference benchmark scores rival those of the latest Gemini-3.0-Pro.

DeepSeek also tested the V3.2-Speciale on finals from competitions like IMO 2025 (International Mathematical Olympiad), CMO 2025 (Chinese Mathematical Olympiad), ICPC World Finals 2025 (International Collegiate Programming Contest), and IOI 2025 (International Olympiad in Informatics), achieving gold medal results.

Notably, in the ICPC and IOI tests, it reached levels comparable to the second and tenth human competitors, showcasing significant advancements in programming capabilities. In comparative tests, DeepSeek V3.2-Speciale outperformed GPT-5 High, catching OpenAI off guard.

According to the official technical documentation, the main breakthrough of DeepSeek V3.2 is the introduction of the DeepSeek Sparse Attention (DSA) mechanism, designed to meet different inference needs through a dual-version approach.

The deployment of the DSA mechanism fundamentally addresses efficiency issues in attention for large AI models. Specifically, while traditional attention mechanisms compute relationships between all elements in a sequence, DSA selectively calculates relationships between key elements, significantly reducing the amount of data needed for computation.

Similar technology was hinted at in a paper released earlier this year, where the new attention mechanism NSA was discussed. However, NSA had not been publicly implemented in subsequent DeepSeek model updates, leading to speculation about difficulties in its deployment.

Now, it appears that DeepSeek has found a better implementation method. The NSA mechanism was more like creating an index for a library when handling long text data, quickly locating the relevant area for information retrieval. In contrast, DSA functions like a search engine, performing a quick full-text read and establishing a “lightning indexer” for rapid data retrieval based on keywords, making it smarter and more precise while consuming fewer resources.

With the DSA mechanism, the inference cost for a 128K sequence can be reduced by over 60%, and the inference speed can increase by approximately 3.5 times, with memory usage decreasing by 70%, all while maintaining model performance. This fundamentally changes the performance of large AI models in the attention domain.

Official data shows that during AI model testing on the H800 cluster, the cost per million tokens during the pre-fill phase dropped from $0.70 to about $0.20, while the decoding phase cost decreased from $2.40 to $0.80, making DeepSeek V3.2 potentially the lowest-cost model for long-text inference among its peers.

Not Just Thinking, But Using Tools

In addition to the DSA mechanism, another core upgrade in DeepSeek V3.2 is its ability to invoke tools during its thinking mode. The official statement indicates that the process of invoking and using tools requires no training, giving DeepSeek V3.2 enhanced general performance and better compatibility with user-created tools as an open-source model.

To validate DeepSeek V3.2’s new features, I designed some questions to test its response capabilities, starting with its performance in thinking mode:

Question: A is three years older than B, and B is two years older than C. In five years, A’s age will be exactly twice that of C. How old are the three individuals now?

Answer:

The answer is correct, but the key lies in the thought process:

DeepSeek verified the answer multiple times after calculating the result, considering whether the answer remained correct under different circumstances. Before outputting the final answer, DeepSeek conducted three rounds of answer verification.

While this may seem like a waste of computational power, such multiple verifications are necessary to ensure the accuracy of responses under the DSA mechanism, as the sparse architecture of DeepSeek could lead to a higher error probability compared to other AIs.

I also designed a multi-step task chain:

Search for today’s temperature in Beijing.
Convert the temperature to Fahrenheit.
Invoke a tool to check if your conversion is correct.
Summarize in one sentence whether today is suitable for outdoor activities.

Note: You must decide when to invoke the tool, not complete it all at once.

Let’s look at DeepSeek’s thought process:

It understood the requirements of the question well and began to use search and math tools step by step to solve the problem, ultimately providing the answer:

Overall, the answer followed the steps correctly, and it even automatically chose a math tool to confirm the conversion result. However, there was an oddity where DeepSeek lost the answer to the question about summarizing whether today is suitable for outdoor activities. Nonetheless, the thought process indicates that DeepSeek indeed possesses the ability to make autonomous decisions on which tools to use.

In comparison, another AI faced with the same question, while understanding the requirement to “invoke tools,” ended up directly searching for corresponding data to fill in the answer:

In fact, the tool invocation tutorial in DeepSeek’s thinking mode also features similar questions, demonstrating how to improve the final answer quality through multi-turn dialogue and invoking multiple tools.

You can think of it this way: DeepSeek used to rely on memory (model parameters) to combine answers when you asked a question. Now, it can break down the problem, ask individual questions, and use different tools (such as search, math, programming, etc.) to provide better solutions, finally integrating all answers into a complete response.

Due to time constraints, I didn’t design more challenging questions to test DeepSeek, but interested users can log on to the DeepSeek website to try it out themselves.

The Strongest Open Source? OpenAI and Google May Face Challenges

Is DeepSeek V3.2 powerful? It certainly is, but it does not have a dramatic lead. Test results show it is competitive with GPT-5 High and Gemini 3.0 Pro. However, when a model can match GPT-5 and Gemini 3.0 Pro across multiple authoritative benchmarks while having inference costs that are only one-third or even lower than mainstream models, and is released fully open-source, it can significantly impact the entire market—this is the fundamental logic behind DeepSeek’s ability to disrupt the industry.

Previously, there was a prevailing notion in the industry: “Open-source models are always eight months behind closed-source models.” While this conclusion is debatable, the release of DeepSeek V3.2 clearly puts this debate to rest. DeepSeek continues to insist on full open-source, especially with the introduction of DSA, which significantly reduces costs and enhances long-text capabilities, effectively transforming the role of open-source models from ‘followers’ to ‘challengers’ that force closed-source giants to adapt.

More importantly, the cost revolution brought by DSA will have a significant impact on the commercialization of large AI models, as the training and inference of AI models still face high costs. A statement like “costs reduced by 60%” relates not only to operational costs but also to initial deployment costs, meaning that even small enterprises can leverage DeepSeek to train stronger models.

With the inference costs for long-text interactions being sufficiently low, advanced AI applications (agents, automated workflows, long-chain reasoning, etc.) will no longer be limited to the enterprise market but can be better promoted for consumer use, potentially accelerating the trend of “AI tools replacing traditional software,” allowing AI to truly penetrate everyday use at the operating system level.

For the average user, it may just seem like an additional free and useful model, but in a few months or half a year, you may notice a qualitative improvement in various hardware and software AI experiences, likely thanks to DeepSeek’s contributions.

How GLM-4.5V Was Developed

Fri, 15 Aug 2025 00:00:00 +0000

Introduction

The release of GLM-4.5V undoubtedly marks another milestone in the field of multimodal AI. It not only achieves significant improvements in multimodal understanding and reasoning but also demonstrates strong performance and broad application potential through its unique architectural design, refined data construction, and application of reinforcement learning.

Performance

GLM-4.5V has significantly improved performance in multimodal understanding and reasoning compared to previous models.

In the above chart, GLM-4.5V outperforms previous models in STEM, spatial reasoning, GUI agent tasks, OCR, document understanding, code comprehension, video understanding, visual localization, and general VQA tasks.

The backbone of GLM-4.5V is a reinforcement learning (RL) framework.

After reinforcement learning, the model achieved a gain of up to +10.6% in coding tasks and +6.7% in STEM questions.

GLM-4.5V achieved the best scores in nearly all high-difficulty tasks, including MMStar (75.3), MMMU Pro (65.2), MathVista (84.6), ChartQAPro (64.0), and WebVoyager (84.4).

Architecture

The architectural design of GLM-4.5V focuses on three goals: native multimodality, high resolution, and strong temporal understanding. This is achieved through three components: the visual encoder (ViT Encoder), MLP projector, and language decoder (LLM Decoder).

Visual Encoder

Based on AIMv2-Huge initialization, it incorporates 2D-ROPE and 3D convolutions, enabling it to natively process images and videos of any resolution while effectively capturing temporal information.

Language Decoder

Based on GLM-4.5-Air, it enhances the understanding of spatial positions in multimodal inputs by extending 3D-RoPE.

Native Temporal Understanding

When processing videos, the model inserts a timestamp token after the visual features of each frame, allowing it to perceive the actual time intervals between frames, greatly improving video understanding and localization accuracy.

Pre-training

The pre-training of GLM-4.5V consists of data construction and training paradigms.

Data Construction

The pre-training corpus of GLM-4.5V covers multidimensional data, including:

Image-Text Pair Data

Over 10 billion high-quality image-text pairs were constructed through a refined process involving heuristic filtering, CLIP-Score selection, concept-balanced resampling, and factual-centered recaptioning. Each image has a better rephrasing.

For example, a simple description like “a northern cardinal singing” can be enriched to “a northern cardinal perched on a branch with a clear blue sky in the background,” retaining the facts while greatly enhancing the detail and information density of the description.

Interleaved Image-Text Data

High-quality mixed content was extracted from web pages and academic books, allowing the model to learn complex logical relationships and domain knowledge.

OCR Data

A dataset of 220 million images was constructed, covering synthetic documents, natural scene text, and academic documents, significantly improving text recognition capabilities.

Grounding Data

A mixed grounding dataset was created, containing 40 million annotated natural images and over 140 million GUI question-answer pairs, providing the model with precise pixel-level understanding capabilities.

Video Data

A high-quality video dataset was constructed through meticulous manual annotation, capturing complex actions, scene text, and cinematic elements.

Training Paradigms: Two-Stage, Long Context

GLM-4.5V employs a two-stage training strategy:

Multimodal Pre-training

Training was conducted for 120,000 steps using all data except video at a sequence length of 8192.

Long Context Continuous Training: The sequence length was extended to 32,768, incorporating video data for an additional 10,000 steps of training, enabling the model to handle high-resolution images, long videos, and lengthy documents.

Post-training: SFT and RL

The post-training phase is crucial for enhancing the reasoning capabilities of GLM-4.5V, consisting of supervised fine-tuning (SFT) and reinforcement learning (RL) steps.

Supervised Fine-tuning (SFT): Aligning Thought Paradigms

The goal of SFT is to align the model’s thinking and expression style, teaching it to reason in the form of a “Chain-of-Thought.”

Standard Format

All training data follows the standard format {thinking process}{final answer}.

Answer Extraction: For tasks requiring precise answers, the final answer is wrapped in special tokens <|begin_of_box|> and <|end_of_box|> for accurate assessment by the subsequent RL phase’s reward model.

Dual-Modal Support: GLM-4.5V mixes “thinking” and “non-thinking” data during the SFT phase and introduces special token /nothink, achieving flexible switching between two reasoning modes, balancing performance and efficiency.

Reinforcement Learning (RL): Unlocking Model Potential

GLM-4.5V enhances reasoning capabilities through large-scale, cross-domain reinforcement learning.

RLCS Curriculum Learning Sampling

To improve training efficiency, the team proposed Reinforcement Learning with Curriculum Sampling (RLCS), which dynamically selects moderately difficult training samples based on the model’s current abilities, avoiding wasted computational power on overly easy or difficult problems, thus maximizing the benefits of each training step.

Robust Reward System

The success of RL largely depends on the quality of the reward signals. GLM-4.5V established a domain-specific reward system, designing specialized validation logic for tasks like math, OCR, and GUI, avoiding the phenomenon of “reward hacking.”

As shown in the above image, even with high-quality reward signals in the STEM field, a defective reward model in a multi-image VQA task can lead to a complete collapse of the entire training process after 150 steps.

This indicates that any shortcoming can become a critical issue, particularly for RL training.

Cross-domain generalization and collaborative RL not only enhance the model’s capabilities in specific domains but also yield significant cross-domain generalization effects.

As illustrated, training in a single domain can improve capabilities in other domains. For example, training solely on GUI agent data can boost performance in STEM, OCR, visual localization, and general VQA tasks.

This suggests that there is a shared underlying logic among different multimodal capabilities, and mixing all domain data for training can achieve stronger results than single-domain training, realizing a synergistic effect of “1+1 > 2.”

Conclusion

The training of GLM-4.5V encompasses:

Architecture: Native support for high resolution, long videos, and temporal understanding.
Pre-training: Refined data construction and two-stage training.
SFT: Aligning the model with the “Chain-of-Thought” reasoning paradigm, preparing for the RL phase.
RL: Enhancing capabilities through RLCS, a robust reward system, and cross-domain training.

Stay tuned for the upcoming GLM-4.5V-355B.

Building an iOS App with Cursor and Trae: My Journey to the App Store

Thu, 15 May 2025 00:00:00 +0000

Introduction

In the digital age, how can we leverage AI for product innovation? This article explores the application of AI in product design, analyzing key technologies and strategies to help product managers find the best balance between innovation and practicality.

As the title suggests, I crafted an iOS app using AI coding tools and launched it on the App Store, named “SafeMark.”

The initial motivation for this app came from my experience during the New Year when I was asked to send a photo of my ID to a local Xiaomi store to purchase a range hood. I realized that I should have added a watermark to my ID photo to prevent it from being misused.

Thus, the app’s concept was noted down in my flomo. Recently, I utilized AI coding tools Cursor and Trae to develop it and submitted it to the App Store (still under review at the time of writing). Although the process was challenging, I am glad to have completed it.

Here is the promotional image I used for the App Store, showcasing the first version of the app, with plans for future iterations.

This is my second product; the first was a Chrome extension called flomo Quick Capture, which is available in the Chrome Web Store:

flomo Quick Capture

In another article, I elaborated on the rationale behind developing this product, primarily aimed at enhancing my personal workflow.

Why Start with a Chrome Extension?

Low development cost, allowing for quick familiarization with Cursor.
No compliance registration process in China, enabling faster launch and immediate feedback.

Why Develop an App?

This app is one of my short-term goals, and I also plan to create a WeChat mini-program. This article marks not the end but the beginning of my journey.

AI Coding Tools

AI coding tools can be broadly categorized into AI IDE tools and code generation/assistance tools:

AI IDE tools include Cursor, Windsurf, Trae, and Trae CN.
Code assistance tools include GitHub Copilot and MarsCode.

There are many resources online listing related products and tutorials, so choose the tools that suit you best. I used a combination of Cursor and Trae, taking advantage of Trae’s early-stage free access to Claude 3.7 Sonnet and Gemini 2.5 Pro, both of which are well-regarded models.

It’s important to note that in the AI era, while model capabilities are crucial, engineering optimization and tool adaptation are equally significant. These three aspects evolve together, especially engineering optimization.

The same model may yield different results due to the engineering team’s capabilities. For instance, Cursor can help generate complex logic code, while Trae is more suited for basic modifications like changing colors or adjusting component positions.

Recently, when the Cursor server crashed, I attempted to use Trae for some tasks but faced multiple failures, leading to the creation of several new projects due to incomplete git recovery.

I consulted ChatGPT, which suggested possible reasons:

Git operation granularity may be limited: Trae might simplify commands like reset, checkout, and revert, not restoring the workspace and cache as CLI does.
File synchronization issues: Some files (like auto-generated cache files) may not be under git control, leading to incomplete recovery.
Recovery operations may not trigger UI/memory synchronization: You might restore a version, but the IDE’s running state may not refresh, showing previous content.
Only restoring the code area without restoring runtime context: Trae might only restore code without addressing dependencies (like environment variables).

To this day, I’m unsure of the exact reasons, but I did find my project incomplete after recovery, prompting me to create several new projects. I hope to pinpoint and address these issues in the future.

Thus, the correct understanding is that a good model is a bonus, not a necessity.

App Development and Promotion Process

Before initiating this project, I did not conduct a needs analysis or market research. I simply outlined the app’s basic architecture and functionality, trimming features down to the MVP level, and then began development with AI tools.

As mentioned earlier, I assigned complex tasks to Cursor and simpler ones to Trae or Trae CN.

My previous habit was to create an idea.md file (not a notepad) for easy reading by Ask, Agent, and MarsCode. I would refine and confirm requirements through Ask, then break down tasks into executable plans for Agent to write. At this stage, when AI was not as powerful, I acted as a planner to help refine processes and steps.

Using Cursor’s Claude 3.7 Sonnet, I generated HTML, but the result was unsatisfactory. Given the limited number of pages, I decided to sketch a design.

With the design draft, the page was quickly built by Cursor.

Coincidentally, my Cursor subscription expired, so I thought, why not use Trae for development? I could take advantage of Claude 3.7 Sonnet for free. This led to several days of AI coding, bug fixing, and more coding.

Eventually, I renewed my Cursor subscription and found that several bugs Trae had worked on for days were resolved by Cursor in a few attempts.

Since I initially used a non-developer account, I spent another day registering, updating account information, and waiting for approval. During this time, I prepared the necessary information for the App Store submission until my account was approved on Friday night. I then filled out the App Store submission details, rebuilt the project, uploaded it, and submitted it for review.

For the first time, I managed the entire app development process alone, which felt incredibly rewarding. I look forward to seeing it downloaded and used to solve problems.

Final Thoughts

I once asked myself a question: If I extend the time frame to think about what I am doing now, what will the results be if I persist for 2, 5, or 10 years? Is it what I desire?

Consider this: if employees in a company are unhappy, what kind of fun products can they create? If one cannot find meaning in their job, perhaps they should pursue a side business to provide for those in need, thus enhancing their happiness.

About three years ago (early 2022), I envisioned developing an app and launching it in an app store to generate side income. This idea has remained unchanged, only refined over time to make it more reasonable in the present context. This includes but is not limited to:

The core strategy of the app is to focus on the front end while minimizing backend complexity.
Not just developing one app, but multiple apps.
Only developing an iOS version for the App Store.
Beyond side income, this approach could be one of the levers for achieving a better life in the future.
…

I continuously reflect on the validity of these thoughts and have shared them with friends to gather their opinions, further refining my thinking.

Inspiration from independent developers and product managers around me, such as those from Mida Technology, the flomo team, and others, has been encouraging.

The current AI era has significantly lowered the barriers for a non-programming product manager to develop an app, making the journey from “design draft – development – debug – launch” a reality.

Why did it take three years to complete my first project?

Because at that time, my capabilities made pursuing a side business a low ROI endeavor. The results obtained through my job in the early years were far more rewarding than investing time and energy in a so-called “side business.”

Three years is neither long nor short. It feels long because I only recently took action, yet it feels short because I finally began to act.

I hold the view that work is a side business, merely a part of our lives, fulfilling our basic needs, while our main pursuit should be personal growth and realizing life’s value.

At the moment of launching the app, I posted on social media: no words, just an image of the icon from the Smartisan Notes app.

The Smartisan Notes icon features a quote I love from Kahlil Gibran’s “The Prophet”: Do not forget why you started, just because you have come so far.

With that, I conclude my reflections. Wishing you a pleasant life and successful work!

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