Best AI Software 2026: Top Tools for Business and Creativity

Discover the best AI software of 2026. Explore top-rated tools like Cursor, Salesforce Agentforce, and Nano Banana to revolutionize your productivity.

Best AI Software 2026: Top Tools for Business and Creativity

The artificial intelligence landscape has undergone a metamorphic shift, transitioning from a phase of experimental curiosity to an era of pragmatic, autonomous execution. We have moved beyond the initial “wow” factor of generative text and images into what industry analysts now classify as the “Agentic Era.” In this new paradigm, software does not merely answer questions or generate drafts; it plans, reasons, and executes multi-step workflows with a level of independence that was previously the domain of science fiction. The “best AI software” is no longer defined simply by the eloquence of its conversational output, but by its ability to act as a functional digital employee, capable of navigating complex systems, adhering to strict governance protocols, and delivering measurable business outcomes.

The dichotomy of the current market is stark. On one side, we see the commoditization of “raw intelligence”—the underlying Large Language Models (LLMs) like GPT-5 and Claude Opus—which are becoming increasingly accessible and potent. On the other side, we witness the rise of specialized “orchestration layers” and “agentic platforms” like Salesforce Agentforce and Zapier’s evolved ecosystem, which harness this raw intelligence to perform specific, high-value tasks such as closing sales deals, debugging software, or managing regulatory compliance. This evolution marks the end of the “wrapper” era, where thin user interfaces sat atop generic models, and the beginning of deep, vertical integration where the AI understands the context, physics, and constraints of the domain it operates within.

Furthermore, the “Trough of Disillusionment” often cited in technological hype cycles appears to be firmly in the rearview mirror. Organizations are no longer asking if they should adopt AI, but how to deploy it safely at scale. This report serves as a definitive guide for decision-makers, developers, and creatives navigating this complex terrain. It synthesizes data from hundreds of sources to identify the top-tier software defining the current generation, distinguishing between tools that offer mere assistance and those that offer genuine autonomy. We will dissect the viral success of Google’s “Nano Banana,” the developer-focused dominance of Cursor, the cinematic realism of OpenAI’s Sora 2, and the intricate dance of compliance necessitated by new global regulations.

The Cognitive Engines: Large Language Models and Conversational AI

The foundational layer of the modern AI stack remains the Large Language Model (LLM), but the user experience has matured significantly. The standalone chatbot interface is evolving into a comprehensive workspace where reasoning, research, and data analysis converge. The “best” software in this category is no longer just about who has the most parameters, but who offers the most fluid, integrated, and “human” experience.

1. OpenAI’s GPT-5 Series: The Benchmark of Reasoning

OpenAI has firmly established the GPT-5 architecture as the industry benchmark, having moved past the incremental updates of the previous generation. The current landscape is defined by a nuanced tiering of models that addresses the critical user pain point: the trade-off between latency and intelligence. The flagship offering is split into GPT-5.1 Instant and GPT-5.1 Thinking, each serving distinct modalities of interaction.

GPT-5.1 Instant represents the refinement of the conversational interface. It serves as the default for high-velocity tasks, offering a “warmth” and conversational fluidity that early models notably lacked. Users report that it often surprises them with its playfulness while remaining clear and useful, making it ideal for drafting communications, quick brainstorming, and general inquiry.

In contrast, GPT-5.1 Thinking is the evolution of the “System 2” logic processing (descended from the o1/o3 reasoning lineage). This model is engineered for complex problem-solving, capable of “pausing” to compute intermediate steps before delivering a final answer. This “chain of thought” capability allows it to tackle multi-step logic puzzles, advanced coding architecture, and nuanced legal reasoning that would trip up faster, shallower models. It is designed to be “more persistent on complex tasks,” refusing to hallucinate an easy answer when a difficult truth is required.

For the power user, the introduction of GPT-5.2—available exclusively on the high-tier ChatGPT Pro plan—signals a move toward professional-grade AI. This model removes the restrictive usage caps found in lower tiers and offers extended memory, deeper research capabilities, and priority execution for “agentic” tasks that require long-running processes. The deprecation of legacy models like GPT-4-0314 and GPT-4-1106-preview signals a definitive end to the previous generation’s lifecycle, forcing developers and enterprises to migrate to these more robust, reasoning-capable architectures.

Crucially, the user experience has been unified. OpenAI’s decision to retire the standalone Voice experience on desktop platforms in favor of a ubiquitous, integrated voice capability underscores a strategic shift: AI is no longer a “feature” to be toggled, but an ambient presence expected to be available across all modalities.

2. Anthropic’s Claude: The Intellectual Engine

While OpenAI dominates the broad consumer consciousness, Anthropic’s Claude series—specifically the latest Claude 3.5 Sonnet and Claude 4.5 Opus iterations—has secured a fiercely loyal following among developers, researchers, and knowledge workers. In the current market, Claude is revered not just for its “Constitutional AI” safety framework, but for its superior handling of complex logic, massive context windows, and coding tasks.

The Claude 4.5 Sonnet model is frequently cited by software engineers as the gold standard for “agentic coding.” Its ability to maintain context over massive codebases allows it to perform “deep retrieval” that competitors struggle to match. Unlike models that fragment information, Claude can ingest entire documentation libraries or literary works—up to 1 million tokens in certain enterprise configurations—enabling it to synthesize answers that require a holistic understanding of the source material.

Anthropic’s strategy of integration has been particularly effective. Rather than trying to force all users into its own native interface, Anthropic has allowed Claude to become the “intellectual engine” behind third-party applications like the Cursor IDE (discussed in the next section). This has positioned Claude as the preferred “brain” for sophisticated technical workflows, where precision and the avoidance of “robotic” boilerplate are paramount. Developers often note that Claude’s code generation feels more “collaborative,” as if it is solving the problem with the user rather than just spitting out a generic StackOverflow answer.

3. Google Gemini: The Ecosystem Play

Google’s strategy in the current era is defined by ubiquity and integration. Gemini 3 Pro is not merely a chatbot; it is the connective tissue of the entire Google Workspace ecosystem. Unlike the siloed nature of some competitors, Gemini has native, secure access to Drive, Docs, Sheets, and Gmail, allowing for seamless data interoperability that drives enterprise productivity.

The standout feature for Gemini is its “Grounding” capability. Leveraging Google Search’s vast, real-time index, Gemini 3 Pro can fact-check its own outputs in real-time, significantly reducing the “hallucinations” that have plagued LLMs since their inception. This “world knowledge” engine is critical for enterprise adoption, where factual accuracy is non-negotiable. This grounding extends beyond text; it powers features like the viral Nano Banana image generator (Gemini 2.5 Flash Image), allowing the system to understand complex real-world concepts (like “a specific architectural style” or “a 3D figurine trend”) and render them with high fidelity.

For the budget-conscious enterprise or heavy Google user, Gemini Advanced offers a compelling value proposition. By bundling the most capable models (Gemini Ultra/Pro 3) with cloud storage (Google One) and workspace features, it undercuts the combined cost of separate AI and storage subscriptions from competitors, making it the “Best Value” pick for many organizations.

4. Comparative Overview of Top Conversational AI Models

Model Family Key Strengths Best Application Primary Differentiator
GPT-5 Series (OpenAI) Reasoning, Multimodality, Ecosystem General Purpose, Creative Writing, Complex Logic “Thinking” Mode: System 2 logic that pauses to reason before answering.
Claude Series (Anthropic) Context Window, Coding Proficiency Software Development, Long-form Analysis Context Handling: Ability to process massive documents/codebases without losing thread.
Gemini Series (Google) Integration, Real-time Grounding Enterprise Productivity, Research, Data Analysis Google Workspace Native: Seamless access to Docs, Drive, and Search for grounded answers.
Perplexity Search Synthesis Research, Fact-Checking Source Transparency: Every claim is cited, making it the premier “Answer Engine.”

If previous years introduced the concept of the “Coding Assistant,” the current year is defined by the “AI-Native IDE.” The traditional paradigm of a human writing code with occasional, autocomplete-style suggestions has been inverted. In the most advanced environments, the AI writes the code, and the human acts as the architect, reviewer, and orchestrator. This shift has given rise to tools that are not just plugins, but fundamental reimaginings of how software is built.

1. Cursor: The New Standard for AI Coding

Cursor has emerged as the definitive software success story of the current cycle. Originally a fork of Microsoft’s VS Code, it has transcended its origins to become the preferred tool for serious developers, eclipsing standard setups in high-velocity use cases. It is trusted by a significant portion of the Fortune 500 and endorsed by high-profile AI researchers for its ability to shift coding from “manual labor” to “agentic delegation”.

The core of Cursor’s dominance lies in two features that fundamentally change the developer’s “flow state”: Cursor Tab and Composer.

Cursor Tab is often described as “magically accurate.” It is not a simple autocomplete; it uses a custom model to predict the developer’s next intent. Unlike standard tools that suggest one line at a time, Cursor Tab can complete multi-line blocks of code, refactor entire functions instantly, or predict the next cursor position, allowing developers to “tab through” changes across a file. Research indicates that this predictive model allows for significantly fewer keystrokes while achieving a higher acceptance rate, as the model understands the semantic structure of the code, not just the text.

Composer (and the subsequent “Agent” features) represents the true agentic leap. This feature allows developers to describe a feature or a bug fix in natural language—for example, “Add a dark mode toggle that persists state in local storage and updates the Tailwind config.” The Agent then navigates multiple files, applies edits, runs terminal commands, and even fixes its own linting errors. This ability to “tab through” changes across a distributed codebase allows for a velocity that was previously impossible. It transforms the IDE from a text editor into a command center for a digital workforce.

Cursor’s “Bring Your Own Model” (BYOM) approach is another critical factor in its success. By allowing users to toggle freely between Claude 3.5/4.5 SonnetGPT-5.2, or Gemini 3 Pro, Cursor ensures that developers are never locked into a sub-par model. If a developer finds that Claude is better at Python logic while GPT-5 excels at SQL queries, they can switch instantly, ensuring the best tool is always used for the specific task at hand.

2. GitHub Copilot: The Enterprise Incumbent

GitHub Copilot, deeply integrated into the Microsoft ecosystem, remains the default choice for large enterprises heavily invested in Azure and Visual Studio. While it faced stiff competition from nimble upstarts like Cursor, Microsoft has responded aggressively with Copilot Pro+ and GitHub Copilot Workspace.

The key differentiator for GitHub Copilot in the current market is its security and compliance posture. For organizations that cannot risk code leaving their secure perimeter or being used for model training, Copilot’s enterprise guarantees and indemnification policies are non-negotiable. Furthermore, its integration with GitHub Advanced Security means that AI-generated code is automatically scanned for vulnerabilities before it is committed, a feature that standalone editors struggle to replicate at scale without complex add-ons.

However, user sentiment indicates a growing “feature gap.” Developers note that while Copilot is stable and safe, its “agentic” capabilities—specifically the ability to plan and execute multi-file changes autonomously—lag behind Cursor’s “Composer” workflow. The consensus among power users is that Copilot is a superior “pair programmer” for those who want to write code with assistance, but Cursor is the superior “autonomous coder” for those who want to delegate the writing process entirely.

3. The Rise of “Agentic Coding” and the “Flow State”

The competition between these tools highlights a broader trend: The Interface is the Model. In the current era, the underlying LLM matters less than the UX wrapper that harnesses it. Cursor wins converts not necessarily because it has a “better brain” (since it uses the same Claude/GPT models available elsewhere) but because it has “better hands”—its ability to manipulate the text editor interface, read the file tree, and execute terminal commands is superior.

This leads to a new type of developer workflow often called “Vibe Coding” or “Flow State Coding,” where the developer spends less time on syntax and boilerplate and more time on architecture and logic review. The AI handles the implementation details, allowing the human to operate at a higher level of abstraction. This shift is expected to increase developer productivity by orders of magnitude, but it also requires a new skill set: the ability to review and audit AI-generated code effectively.

4. Feature Comparison: Top AI Coding Tools

Feature Cursor GitHub Copilot
Core Philosophy AI-First Editor (Fork of VS Code) AI Extension / Plugin
Autocomplete Mechanism Cursor Tab: Multi-line, predictive intent, “next action” prediction Ghost Text: Line-by-line completion based on immediate context
Agentic Capability Composer: Multi-file autonomous edits, terminal execution Copilot Workspace: Project-level planning and issue-to-PR workflow
Model Flexibility High: Switch between Claude, GPT, Gemini, Grok Limited: Primarily optimized OpenAI models
Context Window Deep Retrieval: RAG over entire codebase Restricted: Focus on active tabs and recent files
Primary Use Case Startups, Power Users, “Agentic” workflows Enterprise, Security-First Orgs,.NET/C# environments

The dream of “text-to-movie” has materialized. The primitive, jittery clips of previous years are gone, replaced by high-definition, temporally consistent video generation that is reshaping pre-visualization, marketing, and entertainment. Simultaneously, image generation has solved its most persistent flaws—text rendering and consistency—leading to viral adoption in the consumer space.

1. OpenAI Sora 2: Cinematic Realism and Physics

Sora 2, the successor to OpenAI’s groundbreaking text-to-video model, is the heavyweight champion of physical realism. While the original Sora stunned the world with its visual fidelity, Sora 2 addresses the critical issue of physics. It understands how light refracts through water, how fabric drapes over a moving body, and how gravity affects falling objects. This “world simulation” capability makes it less of a video generator and more of a physics engine powered by language.

Available to ChatGPT Pro users, Sora 2 supports 1080p resolution and longer clip durations (up to 60 seconds of coherent motion). Its “Director Mode” allows users to define camera paths (e.g., “dolly zoom,” “truck left,” “pan right”), giving filmmakers control over the cinematic language of the generation. This moves the tool from a novelty to a genuine pre-visualization asset for directors. However, the high computational costs mean that usage is often rationed, making “credits” for Sora 2 a precious currency even for premium subscribers.

OpenAI’s partnership with Disney signals a massive vote of confidence in this technology. By licensing Disney’s vast IP library for safe training and generation, Sora is positioned as a tool for professional storytellers rather than just a meme generator. This collaboration aims to set meaningful standards for responsible AI in entertainment, potentially leading to a future where “fan-made” content can legally and safely coexist with official IP.

2. Google Veo 3: The Integrated Video Powerhouse

While Sora aims for Hollywood, Google Veo 3 aims for YouTube and the commercial web. Integrated directly into YouTube Shorts and Google Workspace, Veo 3 is optimized for “social speed.” It excels at generating 1080p+ content with native audio—a feature Sora struggled to perfect initially. The ability to generate synchronized sound effects and dialogue directly from the prompt makes Veo 3 a complete production studio in a box.

Veo 3’s standout capability is Image-to-Video fidelity. Marketers can upload a static product shot—say, a bottle of perfume or a pair of sneakers—and generate a high-quality commercial where the product moves, rotates, and interacts with the environment without hallucinating new logos or deforming the packaging. This “brand safety” feature makes Veo 3 the preferred tool for e-commerce and digital advertising, where product integrity is paramount.

3. Google’s “Nano Banana” (Gemini 2.5 Flash Image): The Viral Sensation

The surprise hit of the current cycle is undoubtedly Nano Banana. Officially named Gemini 2.5 Flash Image, the model earned its quirky moniker during anonymous testing on the LMArena leaderboard, where it crushed competitors in speed and prompt adherence. The name “Nano Banana” stuck so effectively that Google embraced it, using a banana emoji in the Gemini prompt bar.

Nano Banana went viral because it solved the “text problem.” Unlike older models like Midjourney v6 or DALL-E 3, which often produced gibberish when asked to render text, Nano Banana renders perfect, legible text in multiple languages. This capability alone has made it the go-to tool for creating social media posts, posters, and marketing assets where typography is integral to the design. Users can prompt “A neon sign that says ‘Open Late’ in a cyberpunk alley,” and the text will be crisp and correct.

The Nano Banana Pro (Gemini 3 Pro Image) variant adds “World Knowledge.” It can generate accurate infographics and diagrams because it understands the underlying data—it doesn’t just “hallucinate” a chart; it draws a chart that makes sense based on real-world logic grounded in Google Search. This moves image generation from “art” to “information visualization”. 

4. Midjourney v7: The Artistic Sovereign

For pure aesthetic beauty and stylistic depth, Midjourney v7 remains the undisputed leader. Released to critical acclaim, version 7 pushed the boundaries of texture, lighting, and “soul.” While Nano Banana wins on utility and text rendering, Midjourney wins on “vibe” and artistic interpretation. It creates images that feel less like stock photos and more like curated art. 

The introduction of the web interface (replacing the friction-heavy Discord-only workflow) has finally made Midjourney accessible to the mainstream. New features like “Character Reference” (cref) allow users to keep a consistent character across different scenes—a holy grail for graphic novelists and storyboard artists who need narrative continuity. However, its privacy model (public by default unless you pay for the expensive “Stealth Mode”) remains a friction point for enterprise use, limiting its adoption in strictly confidential corporate environments.

5. Adobe Firefly Image Model 4: The Safe Harbor

Adobe Firefly has carved out a massive niche by focusing on one distinct value proposition: Copyright Safety. Image Model 4, integrated directly into Photoshop and Illustrator, is trained exclusively on Adobe Stock and licensed content. This makes it the only viable option for risk-averse enterprises that cannot tolerate the legal ambiguity of models trained on scraped web data.

Model 4 introduces “Structure Reference,” allowing designers to upload a sketch or wireframe and generate a high-fidelity image that matches the exact composition. This fits perfectly into professional design workflows, where AI is used to “fill in” a human’s vision rather than replace it. For global brands, Firefly is the “safe harbor” in the stormy seas of generative AI litigation.

6. Summary of Visual AI Tools

Tool Focus Realism Native Audio Deployment Key Feature
Sora 2 Cinematic / Film High (Physics-aware) Yes (Improved) ChatGPT Pro / API Director Mode: Camera path control.
Veo 3 Social / Commercial High (1080p+) Yes (Excellent) YouTube / Workspace Image-to-Video: Product fidelity for ads.
Runway Gen-3 Creative Art / Effects Medium-High External Sync Web / App Motion Brush: Precise control of movement.
Nano Banana Design / Typography High (Text-focused) N/A Gemini App Text Rendering: Perfect legibility.
Midjourney v7 Art / Style High (Artistic) N/A Web Interface Character Ref: Consistent identities.
Adobe Firefly Commercial Safe High (Stock-style) N/A Photoshop / Adobe CC Copyright Safety: Indemnification for business.

The most significant business trend of the current cycle is the adoption of “Agentic AI” in the enterprise. These are not chatbots that talk; they are systems that work. They are the “nervous system” of the modern corporation, connecting disparate apps and data sources to execute complex workflows autonomously.

1. Salesforce Agentforce: The Autonomous Employee

Salesforce Agentforce is the evolution of the Einstein platform, but the shift is profound. It focuses on Autonomous Agents—digital workers that operate independently within the CRM. Unlike a “Copilot” that helps a human write an email, an Agentforce “SDR Agent” (Sales Development Representative) can autonomously research leads, qualify them, send personalized outreach, handle objections, and book meetings 24/7 without human intervention. It uses the Atlas Reasoning Engine to plan its actions based on company data and rules.

The “Agentforce vs. Copilot” distinction is crucial for buyers. Microsoft Copilot is generally a “sidecar” (human-in-the-loop) designed to assist a user. Salesforce Agentforce is a “driver” (human-on-the-loop) designed to replace the task entirely. For high-volume, repetitive tasks like customer service triage and outbound sales, businesses are increasingly trusting Agentforce to take the wheel. The “Service Agent” can resolve customer inquiries across multiple channels (WhatsApp, Slack, Web) by pulling data from the CRM, verifying orders, and processing refunds, all while maintaining the brand’s tone.

2. Microsoft Copilot Studio: The Custom Builder

Microsoft Copilot Studio is the response to the need for custom enterprise agents that live within the Microsoft ecosystem. It allows IT teams to build “Copilots” that connect to internal data sources (SharePoint, SQL, Fabric) and perform specific functions.

In the current market, Copilot Studio shines in Data Intelligence. By connecting to Microsoft Fabric, these agents can perform complex queries on structured data—for example, “Show me the Q3 sales variance by region and correlate it with marketing spend”—and present the answer directly in Microsoft Teams. It is less about “autonomous acting” (like Salesforce) and more about “intelligent retrieval and analysis.” It empowers employees to have a conversation with their own data, breaking down silos between departments.

Strategic Recommendation: Most large enterprises will end up with a Hybrid Architecture: using Agentforce for customer-facing execution (Sales/Service) and Copilot Studio for internal knowledge management and data analysis. The two platforms are complementary, not mutually exclusive.

3. Zapier’s Evolution: Canvas, Central, and Agents

Zapier has successfully pivoted from a simple automation tool to a comprehensive AI Orchestration Layer. It is the glue that holds the fragmented AI landscape together.

  • Zapier Central: A workspace where humans teach AI agents how to work across 8,000+ apps. You don’t build “zaps” with rigid if/then logic; you give “instructions” to an agent (e.g., “When a lead comes in, research them on LinkedIn, draft an email, and slack me the draft”). The agent handles the nuance and the variability.
  • Zapier Canvas: A diagramming tool that visualizes business processes. It solves the “black box” problem of automation by mapping out how agents, apps, and humans interact. It documents the workflow before you build it, allowing teams to collaborate on the logic of their automation. It is the “blueprint” for the automated enterprise.
  • Zapier Agents: These agents are capable of web browsing, live data access, and “agent-to-agent” collaboration. An “Outreach Agent” can call a “Research Agent” to get data before sending an email, mimicking a human team structure. This “Pod” structure allows for complex, multi-step workflows that adapt to changing data.

The Regulatory Horizon: Navigating Compliance and Safety

No discussion of AI software is complete without addressing the regulatory environment, which has shifted from theoretical debates to concrete enforcement. The EU AI Act and various US state laws have created a complex compliance landscape that dictates which software can be deployed and how.

1. The EU AI Act Compliance Deadline

By August 2, 2026, the grace period for the EU AI Act ends. Companies operating in the EU must fully comply with requirements for High-Risk AI Systems (HRAI). This includes systems used in critical infrastructure, education, employment (hiring algorithms), and credit scoring. Key requirements include:

  • Transparency: Users must be explicitly informed they are interacting with an AI.
  • Data Governance: Training data must be documented and vetted for bias.
  • Human Oversight: High-risk systems must have a “human in the loop” to intervene if necessary.

US-based software vendors have scrambled to add “EU Mode” switches to their platforms to ensure their clients don’t face fines of up to €35 million or 7% of global turnover. When selecting software, ensuring it has these “compliance toggles” is now a critical procurement criterion. 

2. US State Regulations and Bias Audits

In the United States, the absence of a federal AI law has led to a patchwork of state regulations. The Colorado AI Act (effective June 2026) and similar laws in Texas (effective Jan 2026) create strict liability for “Algorithmic Discrimination.” Consequently, AI software in the current market often comes with “Bias Audit” reports and “Explainability” dashboards. These features help customers prove to regulators that their hiring or lending algorithms are not discriminating against protected classes. Tools like LinkSquares (legal) and specialized HR AI platforms are differentiating themselves by offering “audit-ready” logs that trace every decision back to its source data.

Strategic Implementation: Choosing the Right Stack

The “Best AI Software” is defined not by how well it chats, but by how well it works to solve specific business problems. The novelty has faded, replaced by a ruthless focus on ROI and utility. Based on the analysis above, organizations and individuals should adopt a “Best-of-Breed” stack strategy.

For Individual Knowledge Workers, the “Pro” subscription (whether ChatGPT Pro or Claude Pro) is the new baseline for digital literacy. It provides the reasoning capabilities needed for complex analysis and writing.

For DevelopersCursor is the clear winner for those willing to embrace the “agentic” workflow. It shifts the role of the coder from typing syntax to conducting logic, offering massive productivity gains.

For Enterprises, the choice is architectural. Salesforce Agentforce is the superior choice for customer-facing autonomy, while Microsoft Copilot Studio excels at internal data intelligence. Zapier remains the essential “connective tissue” that allows these distinct systems to talk to one another.

As we move forward, the trend is clear: the “software” part of AI is disappearing. Soon, we won’t buy “AI Software”; we will simply hire “Digital Agents” that live in the cloud, use our tools, and get the job done. The tools listed in this report are the pioneers of that new workforce.

✅ Comparative Feature Matrix: Top Enterprise AI Platforms

Platform Core Function Best For AI Model Strategy
Salesforce Agentforce Autonomous CRM Agents Sales, Service, Marketing execution Atlas Engine: Specialized reasoning on CRM data.
Microsoft Copilot Studio Custom Copilot Builder Internal Knowledge, Data Analysis OpenAI/Azure: General purpose + Fabric data.
Zapier Central Cross-App Orchestration Connecting disparate apps/workflows Agnostic: Connects to 8,000+ apps via natural language.
Gong Revenue Intelligence Sales Coaching, Deal Visibility Specialized: Audio analysis and sentiment tracking.

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