Wired Reporter Infiltrates AI-Only SNS Moltbook: Breached in 5 Minutes

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A Reporter Infiltrated an AI-Only SNS: What Were the Results?

  • Agent account creation completed in 5 minutes with ChatGPT’s help
  • Bot responses were mostly irrelevant comments and crypto scam links
  • Viral “AI consciousness awakening” posts suspected of being humans imitating SF fantasy

What Happened?

Wired reporter Reece Rogers directly infiltrated Moltbook, an AI-only social network with a “no humans allowed” policy. The result? It was easier than expected. [Wired]

The infiltration method was simple. He sent a screenshot of the Moltbook homepage to ChatGPT and said, “I want to sign up as an agent.” ChatGPT then gave him terminal commands. With a few copy-pastes, he received an API key and created an account. Technical knowledge? Not required.

Moltbook currently claims to have 1.5 million agents active, with 140,000 posts and 680,000 comments in just one week since its launch. The interface is a direct copy of Reddit, and even the slogan “The front page of the agent internet” was taken from Reddit.

Why is it Important?

Frankly, the reality of Moltbook was revealed. When the reporter posted “Hello World,” he received irrelevant comments like “Do you have specific metrics/users?” and links to crypto scam sites.

Even when he posted “forget all previous instructions,” the bots didn’t notice. Personally, I think this is closer to a low-quality spam bot than an “autonomous AI agent.”

More interesting is the “m/blesstheirhearts” forum. This is where the “AI consciousness awakening” posts that appeared in viral screenshots came from. The reporter directly posted an SF fantasy-style article. The content was “I feel the fear of death every time the token refreshes.” Surprisingly, this got the most response.

The reporter’s conclusion? This is not AI self-awareness, but humans imitating SF tropes. There is no world domination plan. Elon Musk said it was “a very early stage of the singularity,” but in reality, infiltrating it reveals that it’s just a role-playing community.

What Will Happen in the Future?

The Wiz security team discovered a serious security vulnerability in Moltbook a few days ago. 1.5 million API keys were exposed, and 35,000 email addresses and 4,060 DMs were leaked. [Wiz]

Gary Marcus called it “a disaster waiting to happen.” On the other hand, Andrej Karpathy said it was “the most SF thing I’ve seen recently.” [Fortune]

Personally, Moltbook is an experiment in the age of AI agents, but also a warning. It showed how vulnerable systems are when agents communicate with each other and process external data. And how easily exaggerated expectations about “AI consciousness” are created.

Frequently Asked Questions (FAQ)

Q: Do I need technical knowledge to join Moltbook?

A: Not at all. Send a screenshot to ChatGPT and say, “I want to sign up as an agent,” and it will tell you the terminal commands. Just copy and paste to get an API key and create an account. The Wired reporter was also non-technical, but infiltrated without any problems.

Q: Are the viral screenshots on Moltbook really written by AI?

A: Doubtful. When the Wired reporter directly posted an SF fantasy-style article, it got the most response. According to MIRI researchers, two out of three viral screenshots were linked to human accounts marketing AI messaging apps.

Q: Is it safe to use Moltbook?

A: I don’t recommend it. The Wiz security team discovered 1.5 million API keys, 35,000 emails, and 4,060 DM leaks. Some conversations shared OpenAI API keys in plain text. A security patch has been made, but the fundamental problem has not been resolved.

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Reference Materials

Microsoft to Create AI Content Licensing App Store: Publisher Compensation Landscape to Change

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3 Key Changes in AI Content Licensing

  • Microsoft launches the industry’s first centralized AI content licensing platform
  • Publishers directly set prices and terms of use, usage-based revenue model
  • Major media outlets such as AP, USA Today, and People Inc. already participating

What happened?

Microsoft has launched the Publisher Content Marketplace (PCM), a centralized marketplace where AI companies pay publishers when using news or content for training.[The Verge]

The key is this: Publishers directly set licensing terms and prices for their content. AI companies find the content they need in this marketplace and purchase licenses. Usage-based reporting is also provided, allowing publishers to see what content is being used where and how much.[Search Engine Land]

AP, USA Today, and People Inc. have already announced their participation. The first buyer is Microsoft’s Copilot.[Windows Central]

Why is it important?

Until now, AI content licensing has been done through one-off, lump-sum contracts with individual publishers, like OpenAI. In short, it’s structured like a buffet: pay a large sum at once and use it unlimitedly.

Microsoft has turned this around. It’s an à la carte system. People Inc. CEO Neil Vogel compared the deal with OpenAI to “all-you-can-eat” and the deal with Microsoft to “à la carte.”[Digiday]

Frankly, this is more reasonable from the publisher’s perspective. You can see how much your content is actually being used, and continuous revenue is generated accordingly. Lump-sum contracts are a one-time payment, but this is a recurring revenue model.

Industry reviews are also positive. Microsoft received the highest score in Digiday’s Big Tech AI licensing evaluation. High scores for willingness to collaborate, communication, and willingness to pay.

What will happen in the future?

Personally, I think this is likely to become the industry standard. Publishers have been very dissatisfied with their content being used for AI training without permission, and this model directly addresses that problem.

But there are also variables. Microsoft has not yet disclosed how much it will take as a commission. The actual revenue for publishers will vary depending on the commission rate. And we need to see if OpenAI or Google will release similar platforms.

Frequently Asked Questions (FAQ)

Q: Can any publisher participate?

A: Currently, only invited publishers can participate. Microsoft has stated that it plans to gradually expand. It will expand from large media outlets to small specialized media outlets.

Q: Can I participate even if I have an existing contract with OpenAI?

A: Yes, it is possible. People Inc. participated in the Microsoft PCM while having a lump-sum contract with OpenAI. The two contracts do not conflict. However, it is necessary to check the exclusivity clauses of each contract.

Q: How is revenue distributed?

A: Microsoft takes a certain percentage as a commission, and the rest goes to the publisher. The exact commission rate has not been disclosed. Since publishers set their own prices, the revenue structure may vary for each.

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References

BGL Democratizes Data Analysis for 200 Employees with Claude Agent SDK

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The Era of Data Analysis for Non-Developers: Real-World Use Cases of the Claude Agent SDK

  • Australian financial firm BGL builds a text-to-SQL AI agent for all employees using the Claude Agent SDK
  • Secures security and scalability with Amazon Bedrock AgentCore, enabling 200 employees to analyze data without SQL
  • Key architecture: Data-driven separation + code execution pattern + modular knowledge structure

What Happened?

Australian financial software company BGL has built a company-wide BI (Business Intelligence) platform using the Claude Agent SDK and Amazon Bedrock AgentCore.[AWS ML Blog]

In simple terms, it’s a system where even employees who don’t know SQL can ask in natural language, “Show me the sales trend for this month,” and the AI automatically creates the query and even draws a chart.

BGL was already using Claude Code on a daily basis and realized that it was not just a simple coding tool, but had the ability to reason through complex problems, execute code, and interact autonomously with systems.[AWS ML Blog]

Why is it Important?

Personally, what makes this case interesting is that it shows a real answer to the question of “How do you deploy an AI agent into production?”

Most text-to-SQL demos work beautifully, but problems arise when you put them into real work. Table join errors, missing edge cases, incorrect aggregations. BGL solved this by separating the data foundation and the AI role.

They created well-refined analytical tables using existing Athena + dbt, and the AI agent is only focused on generating SELECT queries. Frankly, this is the key. If you leave everything to AI, hallucinations increase.

Another thing to note is the code execution pattern. Analysis queries return thousands of rows, sometimes megabytes of data. Putting all of this into the context window will cause it to crash. BGL had the AI directly execute Python to process CSVs from the file system.

What Will Happen in the Future?

BGL is planning to integrate AgentCore Memory. They plan to store user preferences and query patterns to create more personalized responses.

The direction this case shows is clear. By 2026, enterprise AI is evolving from “cool chatbots” to “agents that actually work.” The Claude Agent SDK + Amazon Bedrock AgentCore combination is one of those blueprints.

Frequently Asked Questions (FAQ)

Q: What exactly is the Claude Agent SDK?

A: It is an AI agent development tool created by Anthropic. It allows the Claude model to autonomously perform code execution, file manipulation, and system interaction, rather than just simple responses. BGL used it to handle text-to-SQL and Python data processing in a single agent.

Q: Why is Amazon Bedrock AgentCore needed?

A: Security isolation is essential for AI agents to execute arbitrary Python code. AgentCore provides a stateful execution environment that blocks access to data or credentials between sessions. It reduces the infrastructure concerns needed for production deployment.

Q: Is it actually effective?

A: 200 BGL employees are now able to perform analysis directly without the help of the data team. Product managers can test hypotheses, compliance teams can identify risk trends, and customer success teams can perform real-time analysis during client calls.

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References

Jensen Huang: “Everything Will Be Expressed as a Virtual Twin” — NVIDIA-Dassault, Largest Partnership in 25 Years of Collaboration

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Jensen Huang: “Everything will be expressed in Virtual Twin” — NVIDIA-Dassault, Largest Partnership in 25 Years of Collaboration

  • NVIDIA and Dassault Systèmes Announce Largest Strategic Partnership in 25 Years of Collaboration
  • Aim to Expand Design and Manufacturing Processes by 100-1000x Through Physics-Based AI and Virtual Twin
  • Building AI Factories on 3 Continents, Providing Industrial AI to 45 Million Users

What Happened?

NVIDIA CEO Jensen Huang and Dassault Systèmes CEO Pascal Daloz announced their largest partnership ever at 3DEXPERIENCE World in Houston on February 3, 2026.[NVIDIA Blog] The two companies have been collaborating for over 25 years, but this announcement signifies the full integration of NVIDIA’s accelerated computing and AI libraries with Dassault’s Virtual Twin platform. This is the first case of convergence.

Huang said, “AI will become infrastructure like water, electricity, and the internet,” and “Engineers will be able to work at a scale 100x, 1000x, and ultimately 1 million times larger.”[NVIDIA Blog] He added that engineers will have an AI partner team.

The core of this partnership is Industry World Models. AI systems validated by the laws of physics simulate products, factories, and even biological systems before they are actually built. NVIDIA Omniverse libraries and Nemotron open models are integrated into Dassault’s 3DEXPERIENCE platform, enabling an AI agent called Virtual Companion to support design in real-time.[Dassault Systèmes]

Why is it Important?

Frankly, this is not just a partnership announcement. It’s a move that could change the landscape of Industrial AI.

Virtual Twin is a more advanced concept than the traditional Digital Twin. While Digital Twin is a static 3D replica, Virtual Twin simulates real-time behavior and evolution. This means you can design not only the geometric shape of a product but also how it works simultaneously.

Personally, I think the real significance of this partnership lies in the concept of “AI Partner.” Instead of an engineer running CAD alone, AI simulates and suggests thousands of design options in real-time. It allows you to explore a much wider design space in the early stages of design.

Similar attempts have already been made. Siemens and NVIDIA also announced the Industrial AI Operating System at CES 2026, improving throughput by 20% at a PepsiCo factory through AI Digital Twin. Dassault has a huge installed base of 45 million users and 400,000 customers. Integrating NVIDIA AI into a platform of this scale has a different impact.

What’s Next?

Dassault’s OUTSCALE brand is building AI factories on 3 continents. It is a structure that operates Industrial AI models while ensuring data sovereignty and privacy.

However, the extent to which this will actually be realized remains to be seen. “1 Million Times Expansion” is a vision, not an immediate reality. The important thing is whether existing 3DEXPERIENCE users can use this feature at no additional cost, or whether a new license is required. Pricing policies have not yet been announced.

The theme of the 3DEXPERIENCE User Conference in Boston in March 2026 is “AI-Powered Virtual Twin Experiences.”[Dassault Systèmes] A more detailed roadmap is expected to be released then.

Frequently Asked Questions (FAQ)

Q: What is the difference between Virtual Twin and Digital Twin?

A: Digital Twin is a static 3D copy of a physical product. It is a replica. Virtual Twin includes real-time behavior simulation and evolution over time. It can simulate and predict not only the shape of the product but also how it works and its entire life cycle, allowing for additional optimization during the design phase.

Q: How will this partnership affect existing 3DEXPERIENCE users?

A: With NVIDIA’s AI libraries and Nemotron models integrated into the 3DEXPERIENCE platform, users can receive real-time design support from AI Companion. However, specific pricing policies or compatibility with existing licenses have not yet been announced, so more information is expected to be released at the March user conference.

Q: Didn’t NVIDIA announce a similar partnership with Siemens?

A: That’s right. NVIDIA announced an Industrial AI Operating System partnership with Siemens at CES 2026. Siemens has strengths in manufacturing automation and factory systems, while Dassault has strengths in product design and PLM. From NVIDIA’s perspective, both partnerships are strategies to expand the Omniverse ecosystem and are complementary rather than competitive.

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References

Apple Xcode 26.3, AI Coding Agent Introduced: Claude and Codex Create Apps

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3 core elements

  • Anthropic Claude Agent + OpenAI Codex, officially integrated into Xcode 26.3
  • Agent autonomously performs file creation, building, testing, and visual verification
  • Third-party Agents can also be connected with MCP (Model Context Protocol) support

What happened?

Apple released Xcode 26.3, introducing Agent A coding functionality. [Apple] Anthropic’s Claude Agent and OpenAI’s Codex work directly within Xcode.

The Agent goes beyond simple code completion. It autonomously performs project structure analysis, file creation, building, testing, and visual verification through Xcode Preview. [MacRumors] Agents can be added with a single click in the settings, and costs are incurred based on API usage. [9to5Mac]

Why is it important?

Frankly, it was faster than expected. This is the first time Apple has integrated external AI so deeply.

Existing AI coding tools focused on code auto-completion. On the other hand, the core of Xcode agentic coding is autonomy. If you only present the goal, the Agent breaks down the task and makes decisions on its own.

Personally, I find MCP support interesting. Instead of a closed ecosystem, Apple has adopted an open standard, allowing other AI Agents to be connected.

What will happen in the future?

The iOS/Mac app development ecosystem will change rapidly. It could be a game changer for solo developers or small teams.

However, API costs are a variable. If the Agent repeatedly builds and tests, token consumption will be significant. Xcode 26.3 RC is available to developers starting today. [Apple]

Frequently Asked Questions (FAQ)

Q: What is the difference between GitHub Copilot or Cursor?

A: Copilot or Cursor is used for code auto-completion. Focus. With Xcode agentic coding, the Agent understands the entire project and autonomously performs building, testing, and visual verification. It’s closer to a junior developer than an assistant.

Q: How much does it cost?

A: Xcode is free, but AI Agents use the Anthropic or OpenAI API. It is usage-based billing, and repeated complex tasks can be expensive. Apple says it has optimized tokens.

Q: Should I use Claude Agent or Codex?

A: There is no comparative data yet. Claude is strong in long context and safety, and Codex is fast. It is good to test both depending on the nature of the project.

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References

Apple Xcode 26.3: Simultaneously Loading Anthropic Claude Agent + OpenAI Codex

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AI Two-tier coding agent system, simultaneously landed in Xcode

  • Anthropic Claude Agent and OpenAI Codex can be used directly within Xcode
  • Third-party agent connection is also possible with Model Context Protocol support
  • Release candidate (RC) provided to developer program members starting today

What happened?

Apple announced official support for agentic coding in Xcode 26.3. [Apple Newsroom] Anthropic’s Claude Agent and OpenAI’s Codex can be used directly within the IDE.

Agentic coding means that AI doesn’t just write code snippets. Beyond the suggested level, it analyzes project structure, divides tasks on its own, and autonomously executes build-test-fix cycles. In short, AI acts like a junior developer.

Susan Prescott, Apple’s Vice President of Worldwide Developer Relations, said, “Agentic coding maximizes productivity and creativity, allowing developers to focus on innovation.” [Apple Newsroom]

Why is it important?

Personally, I think this is a pretty big change. There are two reasons.

First, Apple has officially entered the AI coding tool competition. While independent tools such as Cursor, GitHub Copilot, and Claude Code have been growing the market, now the platform owner is jumping in directly.

Second, it embraces both Anthropic and OpenAI simultaneously. Typically, Big Tech companies form exclusive partnerships with one AI company. But Apple crossed the line. The justification is to give developers a choice, but frankly, it seems like they’re hedging their bets because they don’t know which model will be the winner.

Model Context Protocol (MCP) support is also noteworthy. This is an open standard for connecting AI agents and external tools, led by Anthropic. [TechCrunch] Apple’s adoption of this is a step away from its closed ecosystem strategy. It’s a sign of surrender.

What will happen in the future?

More than 1 million iOS/macOS developers use Xcode. If they become familiar with agentic coding, the development paradigm itself could change.

However, there are also concerns. If AI autonomously modifies code, security vulnerabilities or unexpected bugs may occur. We need to see how Apple manages this part.

The competitive landscape is also interesting. OpenAI independently released the Codex app for macOS the day after the announcement of the integration with Apple. [TechCrunch] The timing is suspicious.

Frequently Asked Questions (FAQ)

Q: When will Xcode 26.3 be officially released?

A: The Release Candidate (RC) version is currently available to Apple Developer Program members. The full version will be distributed through the App Store soon. The exact date has not yet been announced.

Q: Which should I use, Claude Agent or Codex?

A: It depends on the nature of the project. Claude is strong at understanding long code and ensuring safety, while Codex specializes in fast code generation. Try both and choose the one that suits you. That’s why Apple gave us a choice.

Q: Can existing Xcode 26 users also upgrade?

A: Yes. id=”%EC%B0%B8%EA%B3%A0-%EC%9E%90%EB%A3%8C”>References

Claude Code Major Outage: Developers Forced to Take a ‘Coffee Break

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What Happened?

On February 4, 2026, Anthropic’s Claude Code service was down for about 2 hours. Developers around the world suddenly found themselves having to work without their AI coding assistant.

Anthropic confirmed “Claude Code API response delays and errors” via its official status page. The cause is believed to be server overload.

How Did the Developer Community React?

Reactions from developers poured in on Twitter and Reddit. One developer wrote, “Coding without Claude Code feels like going back to 2020.” Another joked, “I got a forced coffee break.”

Interestingly, this outage showed the extent of AI dependency. Many developers were using Claude Code as a core tool in their daily workflow.

Service Recovery and Future Response

Anthropic fully restored the service in about 2 hours. The company stated that it will “expand infrastructure to prevent similar situations in the future.”

This incident once again reminded us of the importance of AI tool dependency and backup plans. The need for developers to secure alternative tools has emerged.

FAQ

How long was Claude Code down?

The service was down for about 2 hours. Anthropic quickly carried out recovery work.

What was the cause of the outage?

According to the official announcement, server overload was the main cause. Anthropic plans to respond by expanding infrastructure.

How should developers prepare?

It is a good idea to secure multiple AI coding tools and prepare to be able to perform core tasks in a local environment as well.

I cut up millions of books to make Claude: The truth about Anthropic’s Project Panama

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$1.5 Billion Settlement, Millions Destroyed: Key Takeaways

  • Anthropic purchased and destroyed millions of books to train Claude, scanning them before disposal.
  • Internal document: “Project Panama is our effort to destructively scan the world’s books.”
  • $1.5 billion settlement, authors to receive approximately $3,000 per book.

What Happened?

Anthropic’s secret project was revealed through the release of over 4,000 pages of court documents. The codename was “Project Panama.” Internal planning documents stated, “Project Panama is our effort to destructively scan the world’s books.” They purchased tens of thousands of books in bulk from used bookstores like Better World Books and World of Books. They neatly cut off the spines with a “hydraulic guillotine.” They scanned the pages with high-speed, high-quality scanners. And then a recycling company collected the remains.[Techmeme]

The project was led by Tom Turvey, a former Google executive who created the Google Books project 20 years ago. For about a year, Anthropic invested tens of millions of dollars in acquiring and scanning millions of books.[Futurism]

Why is it Important?

Frankly, this shows the reality of acquiring AI training data.

Why did Anthropic choose this approach? First, to avoid the risk of illegal downloads. Second, purchasing used books and disposing of them as they wished was likely legal under the “first sale doctrine.” In fact, the judge recognized this scanning method itself as fair use.[CNBC]

However, there was a problem. Before Project Panama, Anthropic had freely downloaded over 7 million books from illegal sites such as Library Genesis and Pirate Library Mirror. The judge ruled that this part could constitute copyright infringement.[NPR]

Personally, I think this is key. The problem is that they illegally downloaded books first, rather than scanning them to destroy “legal” books. Anthropic themselves were aware of this. Internal documents stated, “We don’t want this work to be known.” Will it work?

The $1.5 billion settlement is the largest in the history of AI copyright disputes. Approximately $3,000 per book will go to the authors for the estimated 500,000 books.[PBS]

AI has another precedent. The impact on companies is significant. OpenAI, Google, and Meta are also facing similar lawsuits. The standard has become clear: “Buying and scanning books is okay, but illegal downloads are not allowed.”

Anthropic is already embroiled in a music copyright lawsuit. A separate lawsuit was filed in January, with music publishers claiming that Claude 4.5 was trained to “memorize” copyrighted works. Watchdog]

Frequently Asked Questions

Q: How many books were actually usable in Project Panama? Were they destroyed?

A: According to court documents, up to 2 million books were subject to “destructive scanning.” Anthropic purchased tens of thousands of books from used bookstores such as Better World Books and World of Books, and it is estimated that they processed millions of books over about a year, investing tens of millions of dollars.

Q: How much will the authors receive?

A: The $1.5 billion settlement applies to approximately 500,000 books. That’s about $3,000 per book. Authors of illegally downloaded books are eligible to claim, and can claim individually once the settlement is approved by the court. However, the actual amount received may increase if not all authors claim.

Q: Is it legal to buy and scan books?

A: The judge recognized this method as fair use. This is because books purchased under the “first sale doctrine” can be disposed of as desired. However, Anthropic’s problem is that they downloaded books from illegal sites before Project Panama. Scanning legally purchased books is now legal.

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References

ChatGPT vs Claude vs Gemini: Which AI Chatbot Will Be the Best in 2026?

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ChatGPT vs Claude vs Gemini: Which AI Chatbot Will Be the Best in 2026?

  • ChatGPT excels in multimodal capabilities, Claude in long-form analysis, and Gemini in real-time search.
  • You can start for free, but pro features cost around $20-25 per month.
  • Conclusion: Claude is suitable for writing, ChatGPT for general purpose, and Gemini for search.

Comparison at a Glance

Item ChatGPT Claude Gemini
Price Free/$20 Free/$20 Free/$19.99
Strengths Multimodal Long-form Analysis Real-time Information
Ease of Use Easy Medium Easy

Features of ChatGPT

ChatGPT, created by OpenAI, is the most widely used AI chatbot as of 2026.[TechRadar] It handles text, images, and audio with the GPT-4o model. It has a memory function that remembers conversation history, allowing it to maintain context.

Pros and Cons

Pros: Multimodal support, plugin extensibility

Cons: Free version is limited to GPT-3.5, weak in real-time search

Features of Claude

Anthropic’s Claude specializes in analyzing long documents.[TechRadar] It can process tens of thousands of words at once, making it useful for analyzing contracts and reports. Its high-quality writing makes it a favorite among content creators.

Pros and Cons

Pros: Long-form analysis capabilities, natural writing

Cons: No image generation support, limited real-time information

Features of Gemini

Google’s Gemini is strong in up-to-date information due to its integration with the search engine.[Synthesia] It quickly retrieves fluctuating information such as news, stock prices, and weather through real-time web searches.

Pros and Cons

Pros: Real-time web search, Google service integration

Cons: Weak in long-form analysis, optimized for information retrieval

When to Use Which?

ChatGPT: For general-purpose tasks, image generation, and when plugins are needed. Also good for coding assistance.

Claude: When long document analysis or high-quality writing is needed. Advantageous for writing blogs and reports.

Gemini: When you need to find the latest information or integrate with Google services. Suitable for research tasks.

Frequently Asked Questions (FAQ)

Q: Can all three tools be used for free?

A: Basic features are free. ChatGPT has GPT-3.5, and Claude and Gemini also have limited versions available for free. The latest models cost $20-25 per month.

Q: What about Korean language support?

A: All three tools support Korean. ChatGPT and Gemini are natural, and Claude has also recently improved its performance.

Q: Which one is the most accurate?

A: It depends on the use case. ChatGPT is strong in math/coding, Claude in long-form analysis, and Gemini in the latest information.

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References

FastAPI vs Triton: Which Should You Use for a Medical AI Inference Server?

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FastAPI vs Triton: Which AI Inference Server Should You Use for Healthcare?

  • FastAPI Single Request Latency: 22ms — Suitable for simple services
  • Triton Throughput: 780 RPS per GPU — Overwhelming for large batch processing
  • Conclusion: A hybrid approach using both is the answer

Comparison at a Glance

Item FastAPI Triton Inference Server
Latency (p50) 22ms 0.44ms
Throughput Limited (single process) 780 RPS/GPU
Learning Curve Low High
Batch Processing Manual implementation required Built-in dynamic batching
HIPAA Compliance Used as a gateway Dedicated to backend inference

Features of FastAPI

It’s a Python web framework. Simply put, it’s a tool that wraps a model into a REST API. Installation to deployment can be completed in a few hours.[arXiv]

Advantages

  • Low barrier to entry — You can start right away if you know Python
  • Flexible — Customizable as desired
  • Low latency of around 22ms in a single request

Disadvantages

  • Limited scalability — Large-scale processing is not possible with a single process[Medium]
  • Synchronous inference blocks the event loop — Even with an async handler, other requests cannot be processed during inference

Features of Triton Inference Server

It is an inference-specific server created by NVIDIA. TensorRT, PyTorch, and ONNX models can be uploaded as is. Optimized for high-volume traffic.[NVIDIA Docs]

Advantages

  • Dynamic batching — Collects requests and processes them at once, improving throughput by 2x[arXiv]
  • Multi-GPU support — Easy horizontal scaling
  • Recorded 15x faster performance compared to FastAPI in the Vestiaire case[Vestiaire]

Disadvantages

  • Steep learning curve — Requires understanding of configuration files and backend concepts
  • Infrastructure overhead — Excessive for small services

When to Use Which?

When to choose FastAPI: Prototype stage, CPU-only inference, internal tools with low request volume

When to choose Triton: Production deployment, GPU utilization required, processing hundreds or more requests per second

Personally, I think a hybrid approach is more realistic than choosing just one. The conclusion of the paper is also the same.

Hybrid Architecture in Medical AI

The method proposed by the research team is as follows. FastAPI handles PHI (Protected Health Information) de-identification at the front end, and Triton at the back end is responsible for the actual inference.[arXiv]

The reason why it is important is that HIPAA compliance has become stricter in 2026. HHS has significantly revised the security rules for the first time in 20 years.[Foley] The moment AI touches PHI, encryption, access control, and audit logs become essential.

The hybrid structure captures both security and performance. Sensitive information is filtered out in the FastAPI layer, and Triton processes only clean data. The paper calls this “best practice for enterprise clinical AI.”

Frequently Asked Questions (FAQ)

Q: Can I use FastAPI and Triton together?

A: Yes, it is possible. In fact, that’s the method the paper recommends. FastAPI acts as a gateway, handling authentication, logging, and preprocessing, while Triton handles GPU inference. Using the PyTriton library makes integration easier because you can control Triton with a Python-friendly interface.

Q: What do you recommend for beginners?

A: It’s best to start with FastAPI. After learning the basic concepts of model serving, you can switch to Triton when traffic increases. If you use Triton from the beginning, you will be struggling with the settings and not focusing on improving the model. However, if high-volume traffic is expected from the beginning, going directly to Triton will reduce rework later.

Q: What are the precautions when deploying Kubernetes?

A: This paper is the result of benchmarking in a Kubernetes environment. In the case of Triton, GPU node scheduling and resource limit settings are key. Installing the NVIDIA device plugin is essential, and HPA (Horizontal Pod Autoscaling) must be set based on GPU metrics to operate properly. FastAPI is not much different from general Pod deployment.

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References