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Lovart subverts traditional design tools and opens a new era of AI results-oriented.
Core content:
1. How does Lovart generate full-process design works through natural language instructions
2. Lovart's advantages in vertical fields and accumulated industry experience
3. The transformation of user roles and the reconstruction of commercial value under the results-oriented model
Yang Fangxian
Founder of 53A/Most Valuable Expert of Tencent Cloud (TVP)
This week, the AI circle was almost dominated by a new vertical agent. This is the design vertical agent launched by Liblib's overseas subsidiary: Lovart.
Unlike traditional design tools or embedded AI plug-ins on the market, it can directly understand users' natural language instructions and automatically generate full-process, multimodal content from brand design to marketing materials, including images, videos and music.
For details, please watch the official video
Vertical field cultivation is better than general competitionUnlike the general AI Agent-Manus that became popular some time ago , Lovart is a design-specific Agent.
Lovert avoids direct confrontation with general agents such as Manus and focuses on the "small and deep" design vertical category.
Since its establishment in May 2023, LiblibAI has been committed to the creation and sharing of AI content. Through the "open source model ecosystem + modular tool flow" architecture, it has reduced professional-level AI capabilities to mass creation scenarios and has accumulated rich industry experience in the field of image generation.These rich industry experiences and accumulated model resources provide Lovart with sufficient materials and creative inspiration, enabling Lovart to generate diverse design works to meet the needs of different users.Lovart has encoded the accumulated professional knowledge of the design industry (such as color theory, typography specifications, and brand consistency principles) into the AI system, such as built-in tens of thousands of VI specification templates, automatic avoidance of font copyright risks and other detailed designs.This precise grasp of industry pain points enables Lovart's output to reach a quasi-professional level, rather than semi-finished products that require manual optimization.Results-oriented business value reconstruction
Traditional AI tools (such as Midjourney and Stable Diffusion) only provide single-point generation capabilities, and users need to connect the tool chain themselves to complete complex tasks.But what users really need is results, not learning how to use 20 tools.Lovart's core competitiveness lies in breaking down design requirements into full-link results guided by natural language instructions.For example, in a test conducted by the famous blogger "Digital Life Kha'Zix", when the user inputs "design a Dior lipstick advertisement", Lovart will complete tasks such as storyboard generation, video clip production, BGM adaptation, and text layer separation, and finally output a 30-second commercial advertisement.
This "problem input → finished product output" model essentially upgrades AI from a productivity tool to a creative service provider, allowing users to pay for the final results without having to pay attention to the underlying model or operational details.The user role changes from "operator" to "decision maker"In this transformation from "tool execution" to "outcome definition", the different needs of different users are met:- Non-professional users can obtain professional-level works through natural language descriptions. For example, when an ordinary user generates a "science fiction movie poster", AI automatically completes professional decisions such as composition, color tone, and element matching.
- Designers are freed from mechanical labor and can focus on creative direction. For example, after uploading a hand-drawn sketch, Lovart automatically optimizes the details and generates multiple versions of the solution, and the designer only needs to choose the best solution.
- Corporate customers can directly obtain market-proven solutions. Lovart uses big data to analyze popular trends and reduce trial and error costs.
The essence of this paradigm is to position AI as an "outcome producer" while humans become "demand definers" and "value judges."Summary: AI is not about selling tools but selling resultsThe ultimate goal of AI does not lie in the accumulation of tool functions, but in whether it can output results that can be directly commercialized.Under this model, the technical barriers will shift from a single algorithm to full-link integration capabilities (task decomposition, multi-model scheduling, industry knowledge encoding), and the commercial value will be upgraded from "tool efficiency" to "outcome premium."In the future, with the emergence of more vertical agents, "selling results" will become the core model of AI-enabled industries, and Lovart has set a benchmark for this trend.This also confirms the brilliant conclusion of the Sequoia Capital AI Summit that just ended: the next round of AI will not sell tools, but benefits.
