Co-Founders of Eightfold Secure $35M for Viven, an AI Startup Creating Digital Twins for Accessing Unavailable Co-Workers

Revolutionizing Workplace Communication: Viven’s Digital Twin Technology

In today’s fast-paced work environment, effective communication is critical. However, when key team members are unavailable—whether on vacation or working across time zones—productivity suffers. Viven aims to change this dynamic.

Introducing Viven: A Game-Changer in Workforce Accessibility

Founded by Ashutosh Garg and Varun Kacholia, the minds behind the $2.1 billion AI recruiting startup Eightfold, Viven leverages the latest advances in Large Language Models (LLMs) and data privacy technologies to address these challenges. This innovative digital twin startup allows employees to access vital information from colleagues, even when they are not present.

Viven Emerges from Stealth Mode with Robust Backing

Recently launched, Viven secured $35 million in seed funding from prominent investors like Khosla Ventures, Foundation Capital, and FPV Ventures, marking a significant step in transforming workplace communication.

How Viven Creates Personalized Digital Twins

Viven builds a specialized LLM for each employee, essentially crafting a digital twin by analyzing their internal documents, including emails, Slack messages, and Google Docs. This allows other team members to query a colleague’s digital twin for immediate insights on shared projects, enhancing collaboration.

The Assurance of Privacy: Pairwise Context Technology

A critical concern is privacy, as employees often handle sensitive information. Viven addresses this through “pairwise context and privacy,” enabling LLMs to expertly manage what information can be shared and with whom, ensuring confidentiality while promoting accessibility.

Maintaining Integrity: Safeguards Against Inappropriate Queries

Viven’s system is designed to understand personal contexts, filtering out sensitive topics and protecting employee privacy. Each user has access to their digital twin’s query history, acting as a deterrent against inappropriate inquiries.

Strong Demand: Early Adoption by Major Enterprises

Viven is already in action at several enterprise-level clients including Genpact and Eightfold, demonstrating its potential to reshape workplace dynamics. Both Garg and Kacholia continue to balance their efforts between leading Eightfold and Viven.

Facing Competition: Viven’s Unique Market Position

Garg asserts that Viven stands alone in the digital twin market for enterprises. His discussions with Vinod Khosla confirmed the absence of direct competitors, which led to Khosla’s investment.

Future Landscape: Anticipating Market Changes

While there are no immediate rivals, Garg acknowledges that other tech giants like Anthropic, Google, Microsoft, and OpenAI could eventually explore similar offerings. Viven aims to maintain its edge through its innovative pairwise context technology.

Sure! Here are five FAQs based on the fundraising news about Viven, the AI digital twin startup co-founded by Eightfold’s founders:

FAQ 1: What is Viven?

Answer: Viven is an AI digital twin startup focused on creating virtual representations of co-workers, allowing users to query unavailable team members for insights, knowledge, and decisions, enhancing collaboration and productivity.

FAQ 2: How much funding did Viven raise?

Answer: Viven successfully raised $35 million in funding, which will be used to further develop its technology and expand its market reach.

FAQ 3: Who are the co-founders of Viven?

Answer: Viven was co-founded by the founders of Eightfold, a company known for its innovative approaches in AI and talent management, leveraging their expertise to drive Viven’s vision.

FAQ 4: What problem does Viven aim to solve?

Answer: Viven addresses the challenge of accessibility to knowledge and expertise when co-workers are unavailable. By creating digital twins, Viven enables teams to glean valuable insights even in the absence of key personnel.

FAQ 5: How does Viven’s technology work?

Answer: Viven’s technology utilizes AI to create digital replicas of individuals based on their knowledge, communication styles, and decision-making patterns. This allows users to interact with these digital twins to access information and insights as if they were conversing with the actual co-worker.

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While You Can’t Libel the Dead, Creating Deepfakes of Them Isn’t Right Either.

<div>
    <h2>Zelda Williams Calls Out AI Deepfakes of Her Father, Robin Williams</h2>

    <p id="speakable-summary" class="wp-block-paragraph">Zelda Williams, daughter of the late actor Robin Williams, shares a heartfelt message regarding AI-generated content featuring her father.</p>

    <h3>A Plea to Fans: Stop Sending AI Videos</h3>
    <p class="wp-block-paragraph">In a candid Instagram story, Zelda expressed her frustration: “Please, just stop sending me AI videos of Dad. It’s not something I want to see or can comprehend. If you have any decency, just cease this behavior—for him, for me, and for everyone. It’s not only pointless but also disrespectful.”</p>

    <h3>Context Behind the Outcry: New AI Technologies</h3>
    <p class="wp-block-paragraph">Zelda's emotional response comes shortly after the launch of OpenAI's Sora 2 video model and <a target="_blank" href="https://techcrunch.com/2025/10/03/openais-sora-soars-to-no-1-on-the-u-s-app-store/">Sora</a>, a social app that enables users to create highly realistic <a target="_blank" href="https://techcrunch.com/2025/10/01/openais-new-social-app-is-filled-with-terrifying-sam-altman-deepfakes/">deepfakes</a> of themselves and others, including deceased individuals.</p>

    <h3>The Ethics of Deepfakes and the Deceased</h3>
    <p class="wp-block-paragraph">Legally, creating deepfakes of deceased individuals might not be considered libel, as per the <a target="_blank" href="https://splc.org/2019/10/can-you-libel-a-dead-person/" target="_blank" rel="noreferrer noopener nofollow">Student Press Law Center</a>. However, many believe this raises significant ethical concerns.</p>

    <figure class="wp-block-image size-large">
        <img loading="lazy" decoding="async" height="546" width="680" src="https://techcrunch.com/wp-content/uploads/2025/10/zelda-williams-deepfakes.jpg?w=680" alt="Zelda Williams on the implications of deepfakes" class="wp-image-3054964"/>
    </figure>

    <h3>Deepfake Accessibility and Its Implications</h3>
    <p class="wp-block-paragraph">With the Sora app, users can create videos of historical figures and celebrities who have passed away, such as Robin Williams. However, the platform does not allow the same for living individuals without permission, raising questions about the treatment of the deceased in digital media.</p>

    <h3>OpenAI's Policies on Deepfake Content</h3>
    <p class="wp-block-paragraph">OpenAI has yet to clarify its stance on deepfake content involving deceased individuals, but there are indications that their practices may fall within legal boundaries. Critics argue that the company's approach is reckless, particularly in light of recent developments.</p>

    <h3>Preserving Legacy Amidst Digital Manipulation</h3>
    <p class="wp-block-paragraph">Zelda voiced her concerns about the integrity of people's legacies being reduced to mere digital imitations: “It’s maddening to see real individuals turned into vague caricatures for mindless entertainment.”</p>

    <h3>The Broader Debate: Copyright and Ethics in AI</h3>
    <p class="wp-block-paragraph">As AI technology continues to evolve, concerns surrounding copyright and ethical usage are at the forefront. Critics like the Motion Picture Association have called on OpenAI to implement stronger guidelines to protect creators’ rights.</p>

    <h3>The Future of AI and Responsibility</h3>
    <p class="wp-block-paragraph">With Sora leading in realistic deepfake generation, the potential for misuse is alarming. If the industry fails to establish responsible practices, we risk treating both living and deceased individuals as mere playthings.</p>
</div>

This version presents the information in a structured and engaging format while optimizing it for search engines with proper headings.

Here are five FAQs with answers based on the theme "You can’t libel the dead. But that doesn’t mean you should deepfake them."

FAQ 1: What does it mean that you can’t libel the dead?

Answer: Libel pertains to false statements that damage a person’s reputation. Since a deceased individual cannot suffer reputational harm, they cannot be libeled. However, ethical implications still arise when discussing their legacy.

FAQ 2: What are deepfakes, and how are they created?

Answer: Deepfakes are synthetic media in which a person’s likeness is altered or replaced using artificial intelligence. This technology can create realistic videos or audio but raises ethical concerns, especially when depicting deceased individuals.

FAQ 3: Why is it unethical to create deepfakes of deceased individuals?

Answer: Creating deepfakes of the deceased often disrespects their memory and can misrepresent their views or actions, potentially misleading the public and harming the reputations of living individuals associated with them.

FAQ 4: Are there legal repercussions for creating deepfakes of the dead?

Answer: While you can’t libel the dead, producing deepfakes may still lead to legal issues if they violate copyright, personality rights, or other laws, especially if used for malicious purposes or financial gain.

FAQ 5: How can society address the ethical concerns surrounding deepfakes of deceased individuals?

Answer: Societal solutions include creating clear ethical guidelines for AI technologies, promoting respectful portrayals of the deceased, and encouraging platforms to regulate deepfake content to prevent abuse and misrepresentation.

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Why is an Amazon-Backed AI Startup Creating Orson Welles Fan Fiction?

Fable’s Ambitious AI Quest to Recreate Orson Welles’ Lost Footage

On Friday, Fable, a startup dubbed the “Netflix of AI,” unveiled its bold plan to reconstruct the elusive 43 minutes of Orson Welles’ iconic film “The Magnificent Ambersons.”

Why This 1942 Classic Matters to a Modern AI Startup

Why is a company that recently secured funds from Amazon’s Alexa Fund focusing on a film from over 80 years ago? Fable has developed a platform enabling users to create animated content using AI prompts. Although they’re starting with their own intellectual property, Fable aims to expand into Hollywood IP, previously being used to create unauthorized “South Park” episodes.

Unveiling an AI Model for Long-Form Narratives

Now, Fable is rolling out a new AI model designed to weave intricate narratives. Over the next two years, filmmaker Brian Rose—who has dedicated five years to reconstructing Welles’ vision—plans to utilize this technology to remake the lost footage from “The Magnificent Ambersons.”

A Tech Demo Without Film Rights

Remarkably, Fable has yet to secure the rights to the film, rendering this endeavor a prospective tech demo unlikely to reach public viewing.

The Significance of “Ambersons” in Film History

One might wonder, why choose “Ambersons”? Even cinephiles recognize Welles’ second film often stands in the shadow of its more famous predecessor, “Citizen Kane.” While the latter is frequently hailed as the greatest film of all time, “Ambersons” is regarded as a lost masterpiece, marred by studio cuts and an incongruous happy ending.

Casualties of Artistic Vision

This sense of loss is likely what drew Fable and Rose to the project. The film’s current legacy—a reflection of Welles’ talent and the crippling interference he faced in Hollywood—underscores why “The Magnificent Ambersons” is still a topic of discussion today.

The Welles Estate’s Response

However, Fable’s oversight in not contacting Welles’ estate has sparked criticism. David Reeder, who oversees the estate for Welles’ daughter Beatrice, labeled the project an “attempt to generate publicity on the back of Welles’ creative genius,” concluding it will lack the “uniquely innovative thinking” characteristic of Welles.

Estate’s Critique and the Role of AI

Reeder expressed displeasure not solely at the project itself but at the lack of courtesy shown to the estate. While he noted that they have embraced AI technology to create a voice model for brand work, this endeavor appears different.

Artistic Integrity Versus Technological Innovation

While some might argue that consulting Welles’ heirs could legitimize the project, I stand skeptical. My interest in this “Ambersons” is minimal, much like my disinterest in witnessing a digitally recreated Welles marketing modern products.

Past Attempts to Revive Welles’ Work

This isn’t the first effort to posthumously refine or complete Welles’ films, but previous attempts utilized actual footage shot by Welles. Fable’s approach combines AI with traditional filmmaking; contemporary actors may portray original cast characters, digitally altering their faces post-production.

Rose’s Intent to Honor Welles’ Vision

Despite the questionable ethics behind this announcement, Rose seems genuinely committed to honoring Welles’ vision. Rose lamented the loss of a beautiful four-minute tracking shot, of which only 50 seconds remain in the current version.

AI Cannot Replace True Artistic Legacy

While I resonate with his sense of loss, I believe this tragedy is one that AI cannot mend. Regardless of how seamlessly Fable and Rose manage to recreate a scene, it will undeniably be their interpretation, not Welles’. The essence of Welles’ “The Magnificent Ambersons,” destroyed by RKO over 80 years ago, remains lost without a miraculous rediscovery of footage.

Sure! Here are five FAQs with answers regarding the Amazon-backed AI startup and its creation of Orson Welles fan fiction:

FAQ 1: Why is an Amazon-backed AI startup creating Orson Welles fan fiction?

Answer: The startup aims to explore the intersection of AI and creative writing by leveraging Welles’ unique storytelling style. The project illustrates how AI can generate compelling narratives inspired by classic figures, breathing new life into historical contexts while engaging contemporary audiences.

FAQ 2: What technology is the startup using for this project?

Answer: The startup utilizes advanced natural language processing and machine learning algorithms to analyze Welles’ works. This allows the AI to mimic his writing style and themes, crafting original stories that pay homage to his creative legacy.

FAQ 3: How is the fan fiction being distributed or presented?

Answer: The generated fan fiction is likely published online through various digital platforms, including the startup’s website and potentially through Amazon’s e-book services, allowing easy access for fans and readers.

FAQ 4: What are the potential implications of AI-generated literature?

Answer: AI-generated literature raises questions about authorship, creativity, and the future of storytelling. It can democratize content creation, allowing more voices to be heard, while also sparking discussions about the role of traditional writers and the authenticity of AI-generated works.

FAQ 5: Can readers interact with or influence the AI’s storytelling process?

Answer: Some interactive features may allow readers to provide input or suggestions, leading to personalized narratives. This approach would enhance engagement and make the storytelling experience more dynamic, inviting readers to participate in the creative process.

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Creating Infrastructure for Successful Vibe Coding in the Enterprise

Embracing the AI Revolution in Software Development

The transition from human-created to AI-generated code is happening at an unprecedented pace. Major players like Microsoft and Google are already producing up to 30% of their code with AI tools, while Mark Zuckerberg recently stated that Meta plans to have half of its code AI-generated within a year. In a bold projection, Anthropic’s CEO anticipates that virtually all code will be AI-generated in the upcoming year. As adoption proliferates, development teams are beginning to explore “vibe coding,” an intuitive, collaborative method allowing developers to work seamlessly with AI to quickly produce code through natural language rather than conventional programming techniques.

Vibe Coding: A Debate Between Innovation and Quality Concerns

As vibe coding gains momentum, the developer community is divided on whether this represents a groundbreaking evolution or a looming crisis for code quality. Typically, with technological advancements, the truth lies in the middle ground. AI coding assistants are reshaping how software is developed, but maximizing the potential of vibe coding and AI assistance requires solid foundational practices. Success hinges on a balanced approach involving three critical components: implementing Retrieval-Augmented Generation (RAG) systems to enhance context-awareness, designing new workflows that prioritize both speed and quality, and ensuring code integrity throughout the development lifecycle.

Leveraging RAG for Effective Vibe Coding

Retrieval-Augmented Generation (RAG) systems are pivotal for scaling vibe coding effectively. These systems transcend the limitations of a model’s training by sourcing relevant code artifacts, documentation, and contextual data from your codebase to inform code generation. While some suspect that larger context windows in language models could render retrieval systems obsolete, even the most sophisticated AI struggles with relevance when sifting through extensive codebases.

A robust RAG system retrieves code that offers essential context for the task at hand. If you’re working on a new feature, these systems can seamlessly pull in related components, security guidelines, and test cases from your codebase, ensuring that new code integrates smoothly rather than functioning in isolation. This context-driven strategy elevates vibe coding from simply generating code to producing the right code tailored for your specific environment.

The significance of effective RAG is particularly apparent in practical applications. Developers using AI tools often notice inconsistencies when applying the same vague prompt multiple times, leading to vastly different outcomes. The lack of grounded context from RAG systems transforms this inconsistency into a major hurdle. The quality of prompts and the strength of retrieval systems ultimately decide whether AI acts as a reliable collaborator aligned with your codebase or as an erratic participant.

Redefining Development Workflows for AI Integration

Conventional development workflows—design, implement, test, review—require substantial updates to accommodate vibe coding. As AI increasingly handles more implementation tasks, the entire software development lifecycle must be reimagined.

The role of developers is evolving from writing code to architecting systems that guide AI towards desired outcomes. This transformation necessitates new skills that many organizations have yet to formally introduce into their training programs.

Experienced developers are dedicating more time to crafting specifications instead of coding directly. Prioritizing detailed specifications allows for a more deliberate planning phase, often rushed in traditional development. With clear and strategic specifications, developers can collaborate with AI tools for code generation and then assess results later. This process promotes new productivity dynamics, though it requires an intuitive understanding of when to refine AI-generated code versus when to adjust the initial specifications.

For enterprises, successful AI implementation necessitates embedding AI assistance within existing development frameworks rather than circumventing them. Governance mechanisms must be established to manage how, when, and where AI support is utilized throughout the development lifecycle, ensuring compliance and consistency while still reaping productivity benefits.

Organizations attempting to adopt AI coding without workflow adjustments frequently experience initial productivity spikes, followed by a cascade of quality issues. This pattern is well known: teams celebrate initial speed gains only to grapple with substantial refactoring burdens later as technical debt mounts. Without structured refinement processes, the speed benefits of AI could culminate in slower long-term progress.

Maintaining Code Integrity Amid Speed

The principal challenge in vibe coding is not simply generating functional code, but ensuring code integrity. While AI can swiftly produce working solutions, it may neglect key aspects like maintainability, security, and compliance. Conventional code reviews are unable to keep pace when developers generate in minutes what previously took days, potentially leaving critical issues undetected. Effective vibe coding must underpin, rather than undermine, the quality standards teams have diligently established.

This challenge is magnified in complex software scenarios where the distinction between “it works” and “it’s well-constructed” becomes crucial. Implementing validation mechanisms and automated testing is essential amidst heightened development speed, as a feature could function flawlessly while lurking with duplicated logic, security vulnerabilities, or maintenance traps that appear later—leading to technical debt that eventually stalls development.

A prevalent sentiment in the development community suggests that “two engineers with AI can generate the technical debt of 50 engineers”. However, surveys indicate a more nuanced reality: while productivity may surge, technical debt typically rises at a comparatively lower rate—perhaps double that of traditional processes, but not exponentially worse. Although this viewpoint is less dire than some anticipate, it remains a considerable risk. Even a modest increase in technical debt can rapidly impede projects and negate the productivity benefits of AI-aided development. This subtle reality underscores that while AI tools may significantly ramp up code production, the absence of adequate safeguards can lead to unsustainable technical debt levels.

To thrive with vibe coding, organizations should enforce continuous integrity checks throughout the development process, rather than merely at the final review stage. Establish automated systems for immediate feedback on code quality, define clear standards that extend beyond simple functionality, and create workflows where speed and sustainability coexist.

Final Thoughts

Vibe coding signifies a remarkable evolution in software development, highlighting intuition, creativity, and rapid iteration. However, this intuitive methodology must be firmly supported by a robust infrastructure that enhances context, preserves quality, and ensures code integrity.

The path forward belongs to organizations that adeptly balance these seemingly opposing forces: harnessing AI to hasten development while simultaneously fortifying quality assurance protocols. By prioritizing effective RAG systems, reimagined workflows, and ongoing code integrity checks, teams can unlock the transformative potential of vibe coding without compromising the reliability and maintainability that quality software demands.

The technology is available; now, a deliberate approach to implementation is essential, one that embraces the “vibe” while establishing the solid framework necessary for sustainable scaling.

Certainly! Here are five frequently asked questions (FAQs) related to "Building Infrastructure for Effective Vibe Coding in the Enterprise":

FAQ 1: What is Vibe Coding?

Answer: Vibe coding is a collaborative approach to software development that emphasizes the importance of team dynamics, culture, and agile practices. It seeks to create an environment where developers can share ideas freely, foster creativity, and improve productivity.

FAQ 2: Why is infrastructure important for vibe coding?

Answer: Infrastructure is crucial for vibe coding as it provides the necessary tools, technologies, and frameworks that enable seamless collaboration and communication among team members. A robust infrastructure supports flexibility, enhances workflow efficiency, and helps build a strong team culture.

FAQ 3: What elements should be included in the infrastructure for vibe coding?

Answer: Key elements include:

  1. Collaborative Tools: Platforms like Slack, Microsoft Teams, or Jira for communication and project management.
  2. Version Control Systems: Tools such as Git to manage code changes collaboratively.
  3. Continuous Integration/Continuous Deployment (CI/CD): Systems that automate testing and launching of code.
  4. Development Environments: Accessible environments that support experimentation without disrupting the main workflow.

FAQ 4: How can enterprises foster a culture supportive of vibe coding?

Answer: Enterprises can foster a supportive culture by:

  1. Encouraging open communication and feedback.
  2. Promoting diversity and inclusion within teams.
  3. Implementing regular brainstorming sessions and hackathons.
  4. Recognizing and rewarding collaboration and innovation.

FAQ 5: What are the benefits of implementing effective vibe coding in an enterprise?

Answer: The benefits include:

  1. Increased team engagement and morale.
  2. Enhanced creativity due to a more open environment.
  3. Higher productivity through streamlined processes and collaboration.
  4. Improved quality of software due to diverse input and collective problem-solving.

Feel free to modify or expand upon these FAQs to better suit your needs!

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Creating a Cohesive Storyline for Lengthy Video Production

Unlocking the Future of Narrative Video Generation with VideoAuteur

The recent unveiling of the Hunyuan Video generative AI model has sparked discussions about the potential of vision-language models to revolutionize the film industry. However, significant challenges must be overcome before this vision becomes a reality.

Facing the Challenges of Narrative Continuity

While the idea of AI-created movies is captivating, current AI video generators struggle with maintaining consistency and narrative flow. Customization techniques like low-rank adaptation are essential to ensure seamless narrative continuity in generative video content. Without innovative approaches to address these challenges, the evolution of generative video may hit a roadblock.

VideoAuteur: A Recipe for Narrative Continuity

A groundbreaking collaboration between the US and China introduces VideoAuteur, a project that explores the use of instructional cooking videos as a blueprint for creating coherent narrative systems. With a focus on detailed narrative generation, VideoAuteur leverages cutting-edge techniques to produce captivating videos, including a mock Marvel/DC crossover trailer and other attention-grabbing content.

Dataset Curation for Cutting-Edge Video Generation

The development of CookGen, a dataset centered around cooking instructions, serves as the backbone for the VideoAuteur project. By curating a rich collection of video clips and annotations, the authors pave the way for advanced generative systems to create engaging and visually stunning content. Through meticulous dataset curation and experimentation with diverse approaches, VideoAuteur pushes the boundaries of narrative video generation.

Innovative Methods for Long Narrative Video Generation

VideoAuteur’s generative phase features a unique blend of the Long Narrative Director and visual-conditioned video generation model. By exploring different approaches to narrative guidance, the authors highlight the effectiveness of an interleaved image-text director for producing realistic and visually coherent content. The integration of state-of-the-art models like SEED-X further enhances the quality and robustness of the generated videos.

Pushing the Boundaries of Narrative Video Generation

Through rigorous testing and comparison with existing methods, VideoAuteur emerges as a frontrunner in long narrative video generation. By focusing on narrative consistency and visual realism, VideoAuteur sets a new standard for AI-generated content. Human evaluation reinforces the superiority of the interleaved approach, paving the way for future advancements in narrative video generation.

Embracing the Future of AI-Driven Content Creation

As the world of AI-driven content creation continues to evolve, projects like VideoAuteur represent the cutting-edge of narrative video generation. By combining innovative techniques with state-of-the-art models, VideoAuteur demonstrates the potential to revolutionize the entertainment industry. Stay tuned for more groundbreaking advancements in AI-generated storytelling.

  1. What is Cooking Up Narrative Consistency for Long Video Generation?
    Cooking Up Narrative Consistency for Long Video Generation is a technique used in video editing to ensure that the storyline remains cohesive and engaging throughout a long video.

  2. Why is narrative consistency important in long videos?
    Narrative consistency is important in long videos because it helps to keep viewers engaged and invested in the story being told. It also helps to prevent confusion or disinterest from viewers when watching a lengthy video.

  3. How can I use Cooking Up Narrative Consistency for Long Video Generation in my own video projects?
    To use Cooking Up Narrative Consistency for Long Video Generation in your own video projects, you can start by outlining the main storyline and key plot points before beginning the editing process. Make sure to keep continuity in mind when cutting and arranging footage to ensure a seamless flow.

  4. Are there specific techniques or tools that can help with narrative consistency in long videos?
    Yes, there are several techniques and tools that can assist with maintaining narrative consistency in long videos. These include using transitions, sound effects, and graphics to help guide the viewer through the story. Additionally, utilizing a storyboard or shot list can help keep your editing process organized and focused.

  5. How can I measure the success of narrative consistency in my long videos?
    You can measure the success of narrative consistency in your long videos by monitoring viewer engagement metrics, such as watch time and audience retention. Additionally, seeking feedback from viewers or colleagues can provide valuable insights into how well your video’s narrative was received.

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Creating LLM Agents for RAG: A Step-by-Step Guide from the Ground Up and Beyond

Unleashing the Power of RAG: Enhancing AI-Generated Content Accuracy and Reliability

When it comes to LLMs like GPT-3 and GPT-4, along with their open-source counterparts, the challenge lies in retrieving up-to-date information and avoiding the generation of inaccurate content. This often leads to hallucinations or misinformation.

Enter Retrieval-Augmented Generation (RAG), a game-changing technique that merges the capabilities of LLMs with external knowledge retrieval. By harnessing RAG, we can anchor LLM responses in factual, current information, significantly elevating the precision and trustworthiness of AI-generated content.

Dive Deeper into RAG: Crafting Cutting-Edge LLM Agents from Scratch

In this post, we delve into the intricate process of building LLM agents for RAG right from the ground up. From exploring the architecture to delving into implementation specifics and advanced methodologies, we leave no stone unturned in this comprehensive guide. Whether you’re new to RAG or aiming to craft sophisticated agents capable of intricate reasoning and task execution, we’ve got you covered.

Understanding the Importance of RAG: A Hybrid Approach for Unmatched Precision

RAG, or Retrieval-Augmented Generation, is a fusion of information retrieval and text generation. In a RAG system:

– A query fetches relevant documents from a knowledge base.
– These documents, along with the query, are fed into a language model.
– The model generates a response grounded in both the query and retrieved information.

This approach offers several key advantages, including enhanced accuracy, up-to-date information access, and improved transparency through source provision.

Laying the Foundation: The Components of LLM Agents

When confronted with intricate queries demanding sequential reasoning, LLM agents emerge as the heroes in the realm of language model applications. With their prowess in data analysis, strategic planning, data retrieval, and learning from past experiences, LLM agents are tailor-made for handling complex issues.

Unveiling LLM Agents: Powerhouses of Sequential Reasoning

LLM agents stand out as advanced AI systems crafted to tackle intricate text requiring sequential reasoning. Equipped with the ability to foresee, recall past interactions, and utilize diverse tools to tailor responses to the situation at hand, LLM agents are your go-to for multifaceted tasks.

From Legal Queries to Deep-Dive Investigations: Unleashing the Potential of LLM Agents

Consider a legal query like, “What are the potential legal outcomes of a specific contract breach in California?” A basic LLM, bolstered by a retrieval augmented generation (RAG) system, can swiftly retrieve the essential data from legal databases.

Taking the Dive into Advanced RAG Techniques: Elevating Agent Performance

While our current RAG system showcases robust performance, delving into advanced techniques can further amplify its efficacy. Techniques like semantic search with Dense Passage Retrieval (DPR), query expansion, and iterative refinement can transform the agent’s capabilities, offering superior precision and extensive knowledge retrieval.

The Road Ahead: Exploring Future Directions and Overcoming Challenges

As we gaze into the future of RAG agents, a horizon of possibilities unfolds. From multi-modal RAG to Federated RAG, continual learning, ethical considerations, and scalability optimizations, the future promises exciting avenues for innovation.

Crafting a Brighter Future: Conclusion

Embarking on the journey of constructing LLM agents for RAG from scratch is a stimulating endeavor. From understanding the fundamentals of RAG to implementing advanced techniques, exploring multi-agent systems, and honing evaluation metrics and optimization methods, this guide equips you with the tools to forge ahead in the realm of AI-driven content creation.
Q: What is RAG?
A: RAG stands for Retrieval Augmented Generation, a framework that combines retrievers and generators to improve the performance of language model based agents.

Q: Why should I use RAG in building LLM agents?
A: RAG can improve the performance of LLM agents by incorporating retrievers to provide relevant information and generators to generate responses, leading to more accurate and contextually relevant answers.

Q: Can I build LLM agents for RAG from scratch?
A: Yes, this comprehensive guide provides step-by-step instructions on how to build LLM agents for RAG from scratch, including setting up retrievers, generators, and integrating them into the RAG framework.

Q: What are the benefits of building LLM agents for RAG from scratch?
A: Building LLM agents for RAG from scratch allows you to customize and optimize each component to fit your specific needs and requirements, leading to better performance and results.

Q: What are some advanced techniques covered in this guide?
A: This guide covers advanced techniques such as fine-tuning models, improving retriever accuracy, handling multi-turn conversations, and deploying LLM agents for RAG in production environments.
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AniPortrait: Creating Photorealistic Portrait Animation with Audio-Driven Synthesis

In the realm of digital media, virtual reality, gaming, and beyond, the concept of generating lifelike and expressive portrait animations from static images and audio has garnered significant attention. Despite its vast potential, developers have faced challenges in crafting high-quality animations that are not only visually captivating but also maintain temporal consistency. The intricate coordination required between lip movements, head positions, and facial expressions has been a major stumbling block in the development of such frameworks.

Enter AniPortrait, a groundbreaking framework designed to address these challenges and generate top-tier animations driven by a reference portrait image and an audio sample. The AniPortrait framework operates in two key stages: first, extracting intermediate 3D representations from audio samples and converting them into a sequence of 2D facial landmarks; and second, utilizing a robust diffusion model coupled with a motion module to transform these landmarks into visually stunning and temporally consistent animations.

Unlike traditional methods that rely on limited capacity generators, AniPortrait leverages cutting-edge diffusion models to achieve exceptional visual quality, pose diversity, and facial naturalness in the generated animations. The framework’s flexibility and controllability make it well-suited for applications such as facial reenactment and facial motion editing, offering users an enriched and enhanced perceptual experience.

AniPortrait’s implementation involves two modules – Audio2Lmk and Lmk2Video – that work in tandem to extract landmarks from audio input and create high-quality portrait animations with temporal stability, respectively. Through a meticulous training process and the integration of state-of-the-art technologies like wav2vec2.0 and Stable Diffusion 1.5, the framework excels in generating animations with unparalleled realism and quality.

In conclusion, AniPortrait represents a significant advancement in the field of portrait animation generation, showcasing the power of modern techniques and models in creating immersive and engaging visual content. With its ability to produce animations of exceptional quality and realism, AniPortrait opens up new possibilities for a wide range of applications, marking a milestone in the evolution of animated content creation.





AniPortrait: FAQ

AniPortrait: Audio-Driven Synthesis of Photorealistic Portrait Animation

FAQs

1. What is AniPortrait?

AniPortrait is a cutting-edge technology that uses audio-driven synthesis to create photorealistic portrait animations. It can bring still images to life by animating facial expressions based on audio input.

2. How does AniPortrait work?

AniPortrait utilizes advanced AI algorithms to analyze audio input and then map the corresponding facial movements to a static image. This process creates a realistic animated portrait that mimics the expressions and emotions conveyed in the audio.

3. Can AniPortrait be used for different types of images?

Yes, AniPortrait is versatile and can be applied to various types of images, including photographs, drawings, and paintings. As long as there is a clear facial structure in the image, AniPortrait can generate a lifelike animation.

4. Is AniPortrait easy to use?

AniPortrait is designed to be user-friendly and intuitive. Users can simply upload their image and audio file, adjust settings as needed, and let the AI technology do the rest. No extensive training or expertise is required to create stunning portrait animations.

5. What are the potential applications of AniPortrait?

AniPortrait has numerous applications in various industries, including entertainment, marketing, education, and more. It can be used to create interactive avatars, personalized video messages, engaging social media content, and even assistive technologies for individuals with communication difficulties.



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