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Claude 3.7 AI: A New Frontier in Context, Reasoning, and Applications

Claude 3.7


Claude 3.7 Sonnet is Anthropic’s latest AI model, unveiled in February 2025 as the company’s most advanced model to date (Claude 3.7 Sonnet \ Anthropic). It introduces “hybrid reasoning” abilities, a massive context window, and significant improvements in coding and analytical prowess. In this post, we’ll explore Claude 3.7’s context size, pricing, usability, key strengths (with comparisons to OpenAI’s GPT-4 and Google’s Gemini), technical improvements over its predecessors, and the industries where it can shine.

 Claude 3.7 Context Size: Massive 200K Token Window

One of Claude 3.7’s headline features is its enormous context window. It can handle up to 200,000 tokens of input context (Claude 3.7 Sonnet \ Anthropic) – roughly equivalent to hundreds of pages of text or an entire book. This means Claude 3.7 can ingest and reason about very large documents or extensive conversation history without losing track. In practical terms, users can provide Claude with lengthy reports, codebases, or knowledge bases and still get coherent, context-aware responses.

On top of that, Claude 3.7 can output extremely long responses when needed. In its new “extended thinking” mode, it supports generating up to 128K tokens in a single response (currently in beta), which is 15× longer than before (Claude 3.7 Sonnet \ Anthropic). This extended output is useful for tasks like lengthy document drafting, coding large programs, or step-by-step reasoning through complex problems.

Comparisons: Claude 3.7’s context window outpaces many other models. For instance, OpenAI’s latest GPT-4 variants feature up to 128K tokens of context (GPT-4o vs Claude 3.7 Sonnet - Detailed Performance & Feature Comparison), which is substantial but still smaller than Claude’s 200K (GPT-4o vs Claude 3.7 Sonnet - Detailed Performance & Feature Comparison). Google’s Gemini models are pushing context limits even further – Gemini 2.0 Flash supports a 1 million token context, and an experimental Gemini 2.0 Pro boasts an astonishing 2 million token context window (Gemini 2.0 model updates: 2.0 Flash, Flash-Lite, Pro Experimental). However, those massive contexts are very new and not yet standard. Claude 3.7 stands out as a widely available model that comfortably handles very large contexts, enabling it to digest entire knowledge bases or code repositories in one go.

Claude 3.7 Pricing: Free Chats, Pro Tiers, and API Costs

Anthropic has structured Claude 3.7’s pricing to cater to different users – from casual individuals to developers and enterprises:

  • Consumer Access (Claude.ai): Anyone can chat with Claude 3.7 for free on the Claude.ai website or mobile apps. The free tier provides basic access (with some usage limits) to the model’s core capabilities (Claude AI Pricing: How Much Does Anthropic's AI Cost?). Notably, Business Insider reports that Claude 3.7 Sonnet is “free to use” for the general public (Anthropic's Claude 3.7 Sonnet: 'Extended Thinking' Beats Grok, ChatGPT - Business Insider). This lets individuals try out Claude’s powerful AI without cost. However, advanced features like the extended thinking mode may be limited on free accounts – Anthropic offers a Claude Pro subscription (about $20/month) that unlocks higher usage (5× the free limit) and priority access (Anthropic's Claude 3.7 Sonnet: 'Extended Thinking' Beats Grok, ChatGPT - Business Insider) (Claude AI Pricing: How Much Does Anthropic's AI Cost?). Pro users can also leverage Claude’s extended reasoning mode in the chat interface, allowing the model to spend more time on complex queries.

  • Team and Enterprise Plans: For organizations, Anthropic provides a Team plan (~$25 per user/month) and Enterprise plans with custom pricing (Claude AI Pricing: How Much Does Anthropic's AI Cost?). These plans come with centralized administration, increased usage quotas, and enterprise features. Notably, enterprise customers may get expanded context or integration options (Anthropic hinted at “expanded context window” for enterprise, likely leveraging the model’s full capabilities) (Claude AI Pricing: How Much Does Anthropic's AI Cost?). Businesses also benefit from dedicated support and stronger security (SSO, data integration) on these plans.

  • API Usage: Developers can integrate Claude 3.7 via API, paying per token. The API pricing for Claude 3.7 Sonnet starts at $3.00 per million input tokens and $15.00 per million output tokens (Claude 3.7 Sonnet \ Anthropic). This translates to roughly $0.003 per 1K input tokens and $0.015 per 1K output tokens (Claude 3.7 Sonnet Model Card). These costs include any “thinking” tokens consumed during extended reasoning. Anthropic also offers up to 90% cost savings with prompt caching and 50% savings with batch requests (Claude 3.7 Sonnet \ Anthropic), which can dramatically lower expenses for high-volume or repeated queries. Comparatively, OpenAI’s GPT-4 (128K context version) has slightly lower token rates (around $2.50 per million input and $10 per million output) – meaning Claude 3.7’s API is about 1.4× more expensive per token than GPT-4 for similar usage (GPT-4o vs Claude 3.7 Sonnet - Detailed Performance & Feature Comparison). Nonetheless, Claude’s larger context and features may justify the cost for certain applications.

Overall, individual users can start with Claude 3.7 for free, while power users and developers can opt for paid tiers or pay-as-you-go tokens depending on their needs. This tiered model makes the powerful AI accessible at entry-level and scalable for enterprise use.

Usability and Access: How to Use Claude 3.7

Claude 3.7 is designed to be accessible on multiple platforms, ensuring users can interact with it in various ways:

  • Claude.ai Web & Mobile: The simplest way to use Claude 3.7 is via the Claude.ai chat interface, available on the web and as iOS/Android apps (Claude 3.7 Sonnet \ Anthropic). This works similarly to other AI chatbots – users can start a conversation, ask questions or give instructions, and get responses in a friendly chat format. The interface supports both quick answers and the new extended reasoning (for Pro users) mode with step-by-step solutions. The chat platform requires no technical setup, making Claude’s capabilities available to non-developers. (As noted, free users have some daily message limits and may not access every feature, but basic usage is open to all.)

  • API and Developer Platforms: For developers who want to integrate Claude 3.7 into their own applications or workflows, Anthropic provides an API. With an API key, developers can programmatically send prompts to Claude and retrieve its outputs. In addition to Anthropic’s own API endpoint, Claude 3.7 is also offered through major cloud AI services – Amazon Bedrock and Google Cloud Vertex AI both host Claude 3.7 Sonnet (Claude 3.7 Sonnet \ Anthropic). This means companies already using AWS or Google Cloud can plug Claude into their existing cloud infrastructure easily. Integration via API allows for customizing prompts, fine-tuning conversation flows, and even toggling the model’s reasoning mode programmatically (e.g. enabling extended thinking when needed). Anthropic has also released a “Claude Code” companion (in research preview) for coding-specific use cases (Claude 3.7 Sonnet \ Anthropic), highlighting the focus on software development integration.

  • Third-Party Integrations: Claude’s presence is expanding across tools. It can be used in productivity platforms (for example, some users connect Claude to messaging apps like Slack or Notion via bots or plugins). Through its function calling abilities, Claude 3.7 can be directed to use external tools or perform actions such as web browsing or running code, if set up by a developer. This makes it possible to incorporate Claude as an AI assistant in custom workflows – from summarizing documents in a knowledge management system to powering a customer support chatbot on a website.

Using Claude 3.7 is as straightforward as chatting for end-users, and as flexible as a typical large language model API for developers. Anthropic’s multi-platform availability ensures that whether you’re a student on a mobile phone or a developer building an enterprise app, you can access Claude 3.7’s capabilities with relative ease.

Strengths of Claude 3.7 (vs. GPT-4 and Gemini)

Claude 3.7 Sonnet brings a host of strengths that make it stand out in the current AI landscape:

1. Hybrid Reasoning (“Extended Thinking”): Claude 3.7 is described as the first hybrid reasoning model, meaning it has two distinct modes of operation (Anthropic's Claude 3.7 Sonnet: 'Extended Thinking' Beats Grok, ChatGPT - Business Insider). In standard mode, it generates answers almost instantly, which is great for straightforward queries and chatty interactions. In extended thinking mode, Claude can take more time to reason step-by-step, breaking down complex problems and even showing its chain-of-thought to the user (Claude 3.7 Sonnet \ Anthropic). This is particularly useful for complex logical, mathematical, or multi-step tasks where a quick answer might be wrong. Anthropic notes that extended thinking provides a significant boost in math, physics, and science problems, as the model systematically works through the problem (Claude 3.7 Sonnet debuts with “extended thinking” to tackle ...) (Claude 3.7 Sonnet \ Anthropic). In testing, Claude’s extended mode has shown improvements in creativity and problem-solving – one report found it brainstormed and self-corrected effectively on challenging prompts (Anthropic's Claude 3.7 Sonnet: 'Extended Thinking' Beats Grok, ChatGPT - Business Insider). Neither OpenAI’s GPT-4 nor Google’s Gemini has an explicit user-toggleable reasoning mode like this. GPT-4 of course can also perform step-by-step reasoning, but it does so behind the scenes or when prompted, whereas Claude 3.7 gives developers control over how long it “thinks” and even lets users watch the thought process. This hybrid approach is a unique strength of Claude 3.7, blending speed and depth as needed.

2. Large Context and Low Hallucination: With its 200K token memory, Claude 3.7 can keep track of extremely long conversations or documents. This lends itself to better understanding of context and less tendency to go off-track. Anthropic claims Claude 3.7 has “low rates of hallucination”, making it reliable when asked detailed questions about provided material (Claude 3.7 Sonnet \ Anthropic). In scenarios like Q&A over large knowledge bases or codebases, Claude can use its entire context window to find relevant details and provide grounded answers (Claude 3.7 Sonnet \ Anthropic). GPT-4 is also known for strong factuality, but when contexts become very large, Claude’s expanded window can reduce the need to summarize or omit details. Google’s Gemini, particularly in its advanced versions, also emphasizes tool use and factual grounding with retrieved information, but Claude 3.7’s performance in knowledge tasks is highly competitive. Its ability to ingest hundreds of pages means domains like law or research (where source documents are long) benefit from fewer context breaks. In practice, users have found Claude 3.7 to be excellent at summarizing and analyzing lengthy texts without making things up, as long as the information is in the prompt.

3. State-of-the-Art Coding Skills: Coding is a standout strength of Claude 3.7. Anthropic built on the coding prowess of earlier Claude models to make 3.7 a top-tier coding assistant. It is “state-of-the-art for agentic coding” – able to plan, write, debug, and even refactor code across an entire software project (Claude 3.7 Sonnet \ Anthropic). The model was trained and evaluated on real-world software engineering tasks, and it can handle everything from generating simple scripts to managing large codebases with multiple files. In fact, Claude 3.7 can use its long context to keep an entire project in mind, making it especially useful for large-scale refactoring or tracking complex dependencies in code. Compared to GPT-4, which is also extremely good at coding (often used in GitHub Copilot or ChatGPT’s Code Interpreter), Claude 3.7 matches or exceeds that level on many coding benchmarks (Claude 3.7 Sonnet \ Anthropic) (Claude 3.7 Sonnet \ Anthropic). Early benchmarks and tests indicate Claude 3.7 may have an edge in multi-step coding tasks where it needs to use tools or external resources (since Claude can simulate computer use and tool interaction more explicitly) (Claude 3.7 Sonnet \ Anthropic). Google’s Gemini 2.0 Pro is also positioned as excellent at coding with an agentic focus, but Claude 3.7’s release has set a high bar, with some developers favoring Claude for its reliable code completion and inline reasoning about code bugs. In short, if you need an AI pair-programmer or code analyst, Claude 3.7 is one of the best options currently available.

4. Warm, Advanced Conversational Abilities: Beyond hardcore technical tasks, Claude 3.7 excels at general natural language generation and understanding. It has an approachable, human-like tone and is skilled at following nuanced instructions. The model can adapt its writing style to produce anything from a casual explanation to a formal report. Anthropic notes it “excels at writing” with an understanding of nuance and tone, allowing it to generate more compelling content and also analyze text on a deeper level (Claude 3.7 Sonnet \ Anthropic). This makes Claude great for creative tasks like storytelling, marketing copy, or brainstorming, where tone and style matter. When comparing to GPT-4, many users find Claude’s responses to be more verbose and friendly (sometimes even too wordy), whereas GPT-4 can be terse. With Claude 3.7’s improvements, it remains very good at maintaining a consistent and friendly persona in chat, which can be a plus for customer-facing chatbot scenarios. Google’s Gemini, being multimodal, might be used for image descriptions or visual context dialogs that Claude can’t do yet, but for pure text conversation Claude 3.7 is on par with the best, often demonstrating a strong grasp of context and intent over long dialogues.

5. Tool Use and “Agentic” Behavior: A newer strength in Claude 3.7 is its ability to interface with tools and simulate actions. Anthropic enabled a feature where Claude can control a virtual computer interface – clicking buttons, typing text, and so on – via API (an experimental feature) (Claude 3.7 Sonnet \ Anthropic). This is a step toward AI agents that can perform tasks like a human user would. For example, Claude could read an email and then actually draft a reply in a webmail client if given the right permissions, or it could navigate a spreadsheet. While this is still in beta, Claude 3.7 is more reliable at tool use than its predecessor (Claude 3.5) which first introduced this ability (Claude 3.7 Sonnet \ Anthropic). In comparison, OpenAI’s GPT-4 can use tools via the plugin system or function calling, but it doesn’t have a built-in “computer use” simulation out-of-the-box. Google’s Gemini is designed with agentic tasks in mind (it can call Google Search or execute code as noted in Google’s announcements (Gemini 2.0 model updates: 2.0 Flash, Flash-Lite, Pro Experimental)), so Claude is not alone in this direction. However, Claude 3.7 being available on AWS and API means developers today can experiment with AI-driven task automation in a straightforward way. This strength is especially relevant for complex workflows and integrations, where an AI can not only reason but also act on that reasoning in external applications.

In summary, Claude 3.7’s strengths lie in its extreme context capacity, innovative reasoning mode, coding excellence, and well-rounded conversational skills. It matches or exceeds GPT-4 in areas like context size and coding, while offering a different approach to reasoning. Against Google’s Gemini family, Claude holds its own in text-based tasks, though Gemini’s newer multimodal and ultra-long-context capabilities are emerging. It’s clear that Claude 3.7 has positioned itself as a top-tier model for 2025, pushing the boundaries on how AI can think and how much it can remember in one go.

Technical Improvements Over Previous Claude Versions

Claude 3.7 didn’t appear out of thin air – it’s the successor to Anthropic’s Claude 3.5 (and before that, Claude 2). With this iteration, Anthropic introduced several important technical enhancements:

  • Hybrid Reasoning Architecture: The marquee change in Claude 3.7 is its hybrid reasoning system. Previous Claude models would generate answers in a single-pass manner (like most GPT models), but Claude 3.7 can internally use a two-phase approach: a fast “standard” pass or an “extended thinking” pass where it deliberates. This required tuning the model to manage longer chains-of-thought without drifting off-topic. It’s a novel architecture choice that blends techniques from straightforward language modeling and more complex step-by-step planning. Anthropic calls Claude 3.7 “the first hybrid reasoning model” available (Anthropic's Claude 3.7 Sonnet: 'Extended Thinking' Beats Grok, ChatGPT - Business Insider), highlighting how this is a leap from Claude 3.5 which, while powerful, did not have a dedicated extended reasoning mode.

  • Expanded Context Window: Claude 3.5 already had a large context (Claude 2 introduced the 100K token window), but Claude 3.7 doubled down to 200K tokens for input. This expansion demanded optimizations in the model’s attention mechanisms to handle so much information efficiently. Anthropic likely improved how Claude “forgets” or prioritizes information in very long prompts to use the context effectively (such details were hinted in their research publications). Additionally, the output limit jumped from about 8K tokens to 128K in 3.7’s extended mode (Claude 3.7 Sonnet \ Anthropic). Achieving such long outputs meant overcoming challenges in model stability and memory – ensuring the model doesn’t lose coherence or crash when writing extremely long responses. This is a significant technical improvement enabling new use cases (like writing an entire chapter of a book in one go).

  • Coding and Tool Use Enhancements: Claude 3.5 Sonnet had introduced “agentic capabilities” and the ability to use a computer interface in beta (Claude 3.7 Sonnet \ Anthropic). Claude 3.7 refined these capabilities. It has more robust coding knowledge (trained on larger datasets of code and documentation, one can assume) and better integration with the Claude Code system. Its ability to use tools (via function calling or API directions) is more reliable – Anthropic notes Claude 3.7 is “our most accurate model to reliably use computers [to complete tasks]” so far (Claude 3.7 Sonnet \ Anthropic). Under the hood, this might involve improved action planning and result verification by the model. These changes make Claude 3.7 far more effective for tasks like executing code, querying databases, or controlling apps as part of its response, compared to earlier versions.

  • Knowledge Update: Each Claude version comes with an updated training knowledge cutoff. Claude 3.7’s training data goes up to around April 2024 (GPT-4o vs Claude 3.7 Sonnet - Detailed Performance & Feature Comparison), which is a few months more recent than the data used for Claude 3.5 and significantly newer than the original Claude 2. This means Claude 3.7 is better aware of late-2023 events, newer libraries and tools in coding, and more contemporary language usage. (It still won’t know 2025 events in detail, as the cutoff is 2024.) Keeping the model updated was a technical challenge due to the enormous dataset and fine-tuning with safety rules, but it results in a more knowledgeable AI. For context, GPT-4’s knowledge cutoff was around October 2023, so Claude 3.7 has a slight edge in knowing newer information (GPT-4o vs Claude 3.7 Sonnet - Detailed Performance & Feature Comparison).

  • Safety and Alignment Tweaks: Although not as flashy, Anthropic put substantial work into making Claude 3.7 safer and more aligned with user intentions. According to Anthropic’s safety card, they conducted extensive testing with external experts for this release (Claude 3.7 Sonnet \ Anthropic). Improvements include better refusal of disallowed requests, reduced toxic or biased outputs, and controls to prevent the AI from misusing its tool-use abilities. Technically, this likely involved reinforcement learning from human feedback (RLHF) updates and new training data focused on ethical and accurate responses. So, compared to earlier versions, Claude 3.7 should be less likely to produce harmful content or make critical errors when given tricky instructions.

In summary, Claude 3.7 is a big step up from Claude 3.5 in terms of how it thinks (hybrid reasoning), how much it can remember (context), what it can do (coding & tool use), and how reliably it behaves. These improvements solidify Claude’s position as a cutting-edge model and set a foundation for future Claude versions (perhaps Claude 4.0 and beyond) to continue evolving in capability.

Applications: Where Claude 3.7 Shines the Most

With its blend of strengths, Claude 3.7 opens up opportunities across numerous industries and fields. Here are some of the domains that can benefit most from this model:

  •  Software Development & IT: Perhaps the biggest winner is the software industry. Claude 3.7 can function as an AI pair programmer – generating code, explaining codebases, finding bugs, and even orchestrating multi-step coding tasks. Development teams can use it to automate parts of the software lifecycle, from drafting design docs to writing unit tests. Its large context allows feeding entire project files or API documentation into the prompt, so it can make sense of complex frameworks in one go. With state-of-the-art coding abilitie (Claude 3.7 Sonnet \ Anthropic)】, companies can leverage Claude for faster development, code review, and helping onboard developers by answering questions about the code. It’s like having an expert engineer who has read your entire code repository and all the docs.

  • Business Intelligence & Data Analysis: Claude 3.7’s capacity to analyze large datasets and even interpret visual data makes it a powerful assistant for data analysts. For example, an analyst could feed in a massive CSV or a report (converted to text) and ask Claude to derive insights, trends, or summaries. Anthropic notes Claude can extract information from charts, graphs, and complex diagrams with ea (Claude 3.7 Sonnet \ Anthropic)8】, which suggests it can work with textual descriptions of visuals or structured data. This is useful in finance (analyzing market data or earnings reports), marketing (understanding survey results), or operations (finding patterns in logs). Instead of manually combing through hundreds of pages of figures, a business user can let Claude do the heavy lifting and return an analysis. Its extended reasoning means it can perform multi-step calculations or simulations if needed. While Gemini’s multimodal might directly accept an image of a chart, Claude can still handle the underlying data effectively.

  • Customer Service and Chatbots: Companies aiming to build advanced chatbots or virtual assistants will find Claude 3.7 extremely useful. It has a friendly and coherent conversational style, and with the improved reasoning, it can handle complex customer queries that involve multiple steps or pulling info from various sources. For instance, a customer support bot powered by Claude could take a long customer history, troubleshoot an issue step-by-step, and provide a resolution all within one interaction. Claude’s “superior instruction following” and ability to correct its own mistakes on the fly make it ideal for such customer-facing age (Claude 3.7 Sonnet \ Anthropic)28】. Additionally, the tool-use capability means a Claude-powered agent could potentially look up a customer’s order in a database or create a support ticket during the chat (if integrated via API). Compared to previous models, Claude 3.7’s reliability and polite tone can lead to higher customer satisfaction with AI-driven service.

  •  Content Creation and Editing: Writers, marketers, and content creators can harness Claude 3.7 for generating and refining content. The model can produce well-structured articles, blog posts, marketing copy, or even fiction with appropriate style and tone. Its large memory lets it maintain context over a long narrative or reference points from earlier in a document, which is great for consistency in long-form writing. You could provide Claude with an outline and see it flesh out each section or give it a rough draft and ask for improvements and proofreading. Because Claude understands nuanced instructions and t (Claude 3.7 Sonnet \ Anthropic)34】, you can specify the voice or audience (e.g., “make this sound professional yet friendly”) and it will adapt the content accordingly. It can also analyze content – for example, checking if a piece of writing aligns with a brand’s guidelines or summarizing and extracting key points from meeting transcripts. While GPT-4 is also popular for content generation, Claude 3.7’s longer output limit means it might handle writing an entire e-book chapter or long report in one go better than others.

  •  Research and Education: In academia and research fields, Claude 3.7 can serve as an intelligent assistant to sift through large volumes of literature. Researchers can feed multiple papers or a large dataset into Claude and ask it to summarize findings, compare results, or even generate hypotheses. Its high token limit shines here – you could literally input a full thesis or hundreds of study abstracts, and Claude could help identify common themes or conflicting evidence. This capability is also beneficial in legal and compliance industries, where professionals need to analyze long contracts or regulations. Claude can read a 500-page legal document and answer specific questions about it, saving countless hours. Educators and students might use Claude to explain complex concepts (it can break down a tough physics problem step-by-step), generate quiz questions, or translate materials into more digestible summaries.

  •  Automation & Operations (RPA): With Claude’s improved instruction following and ability to integrate with tools, it’s well-suited for Robotic Process Automation. Businesses can use Claude 3.7 to automate routine tasks that involve unstructured data or decision-making. For example, processing and responding to emails, updating entries in multiple systems based on some rules, or monitoring and reporting on logs can be handled by Claude as an AI agent. Anthropic specifically mentions using Claude 3.7 for automating complex processes and operations given its strong instruction-foll (Claude 3.7 Sonnet \ Anthropic)L135】. Essentially, tasks that previously required a human to read and interpret some content and then take action can potentially be handed off to Claude (with the AI reading the content and using an API to perform the action). This opens up possibilities in HR (resume screening), finance (invoice processing), IT (ticket triage), and more.

These are just a few areas – the versatility of Claude 3.7 means it can add value in almost any field that uses language or data. From creative arts to scientific analysis, any scenario where understanding context and generating coherent responses is needed could benefit from this model. Its combination of deep reasoning and broad knowledge makes it a generalist with the potential to be an expert helper in specific domains once it’s given the right information.

Conclusion

Claude 3.7 AI (Sonnet) represents a significant leap forward for Anthropic and large language models in general. With its huge 200K token context window, cost-effective access (including a free tier for experimentation), and cutting-edge features like extended thinking mode, it offers a blend of power and flexibility that few models can match. Users can interact with Claude 3.7 on a simple chat app or integrate it into complex pipelines on the cloud – in either case unlocking AI capabilities that handle tasks previously considered too lengthy or complicated for AI.

In the ongoing rivalry of AI models, Claude 3.7 holds its own against giants like GPT-4 and Google’s Gemini. It may not generate as much buzz as a multimodal model that can see or a model claiming million-token contexts, but Claude 3.7 delivers real-world usability: it’s here now, widely available, and excels at what it does. Its strengths in reasoning, coding, and reliable content generation make it a formidable tool for developers and businesses. Just as importantly, the technical improvements show Anthropic’s commitment to addressing prior limitations (like context length and reasoning depth), which bodes well for future iterations.

As organizations and individuals explore Claude 3.7, we’re likely to see new innovative applications emerge – from AI assistants that truly understand whole project archives to educational tutors that can parse entire textbooks. Claude 3.7 is a reminder that the AI race isn’t just about who’s smartest in a single exchange, but who can contextualize, reason, and help across the long haul. And with Claude 3.7, that long haul just got a lot longer (200K tokens, to be exact) and a lot more interesting for the AI community.

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