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Fluent API – DEV Community

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Configurando o Entity Framework Core com Fluent API

Ao trabalhar com o Entity Framework Core, existem convenções que são feitas no EF. No entanto, às vezes você pode desejar não seguir essas convenções e fazer algo diferente. As duas maneiras de fazer isso são usando Annotations e FluentAPI.

Exemplos de convenções

Algumas das convenções feitas pelo EF Core incluem:

  • Chave Primária: Propriedades chamadas ID ou <nomeclasse>Id</nomeclasse> são automaticamente configuradas como chaves primárias.
  • Colunas: O EF Core cria colunas para todas as propriedades de uma classe de entidade com o mesmo nome da propriedade por padrão.

Configurando o Model

A configuração do modelo é feita usando a classe ModelBuilder, que é uma Fluent API. Com essa classe, podemos configurar o mapeamento do modelo dos bancos de dados. Isso inclui o schema padrão, funções do banco de dados, atributos adicionais de Data Annotation e entidades a serem excluídas do mapeamento.

Configurando a Entidade

A configuração da entidade é feita usando a classe Entity. Essa classe é responsável por configurar as tabelas da entidade e mapear os relacionamentos.

Configurando a Propriedade

A configuração da propriedade é feita usando a classe Property. Essa classe é responsável por configurar as propriedades do mapeamento da coluna, incluindo nome, valor padrão, nullabilidade, tipo de dados, coluna de concorrência, entre outros.

Exemplos de aplicações

O Fluent API é particularmente útil quando precisamos personalizar configurações que vão além das anotações de atributos nas classes, como por exemplo:

  • Configurações avançadas de relacionamentos
  • Configurações não suportadas por Data Annotation
  • Configurações globais e reutilizáveis
  • Definição de tipos de propriedade
  • Validações complexas

Reflexo no Banco de Dados SQL

A seguir, é apresentado o exemplo de como o código C# é refletido no banco de dados SQL:

CREATE TABLE Telefone (
  Id INT PRIMARY KEY,
  DDD VARCHAR(3),
  Numero VARCHAR(9),
  TipoTelefoneID INT,
  RestauranteID INT
);

Conclusão

O Fluent API é um recurso mais avançado que o Data Annotation e é útil quando precisamos personalizar configurações que vão além das anotações de atributos nas classes. Isso permite maior flexibilidade e controle sobre o mapeamento entre as entidades e o banco de dados.

Perguntas Frequentes (FAQs)

Q: O que é o Fluent API?
A: O Fluent API é uma classe que é usada para configurar o mapeamento do modelo dos bancos de dados no Entity Framework Core.

Q: Qual é a diferença entre Fluent API e Data Annotation?
A: A Data Annotation é um recurso mais simples que é usado para decorar classes com anotações para configurar o mapeamento, enquanto o Fluent API é um recurso mais avançado que é usado para configurar o mapeamento de forma programática.

Q: Quando usar Fluent API?
A: Você deve usar o Fluent API quando precisar personalizar configurações que vão além das anotações de atributos nas classes, como configurações avançadas de relacionamentos, configurações não suportadas por Data Annotation, configurações globais e reutilizáveis, definição de tipos de propriedade e validações complexas.

Revolutionizing Customer Experience with AI

How AI Agents are Revolutionizing Customer Experience

Have you ever chatted with a customer service bot and felt like it truly understood your needs? AI agents are no longer just basic chatbots; they are evolving into intelligent assistants that can understand emotions, predict issues, and provide instant solutions. In 2025, businesses leverage AI agents for customer service to deliver faster, more personalized, and highly efficient customer support. From handling complex queries to offering real-time recommendations, these AI-powered systems are redefining customer interactions.

What Are AI Agents?

AI agents are smart computer programs that can think and act on their own. They help businesses talk to customers, answer questions, and solve problems without needing a human every time. AI agents use advanced machine learning and natural language processing (NLP) to understand and respond to customer queries more effectively. Unlike traditional chatbots, AI agents for customer service can handle complex conversations, detect emotions, and provide highly personalized responses. To know in detail how to build an AI agent, read our blog.

How AI Has Grown in Customer Service

Before: Simple chatbots that could only answer basic questions.
Now (2025): AI agents that understand feelings, remember past chats, and give solutions based on what the customer actually wants.

Why Are Businesses Increasingly Adopting AI Agents?

  • Rapid Response Times – Customers expect swift assistance, and AI eliminates the need to wait on hold.
  • Advanced Language Comprehension – With advancements in NLP, AI can now understand and interpret human language more effectively.
  • Cost Efficiency – AI customer service agents operate 24/7 without requiring breaks, reducing operational costs.
  • Scalability – AI can efficiently manage thousands of customer inquiries simultaneously.
  • Consistency and Accuracy – Unlike human agents, AI ensures uniform and precise responses, enhancing customer satisfaction.

The Role of AI in Reducing Customer Service Costs

One of the most significant advantages of AI in customer service is its ability to reduce operational costs while improving efficiency. AI agents can automate many tasks that would traditionally require human intervention, leading to considerable savings for businesses.

Building Rapport

  • AI uses empathetic language to calm frustrated customers.
  • Personalizes interactions by celebrating customer milestones, like birthdays or anniversaries.
  • AI adapts conversation styles based on customer preferences.

Challenges and Ethical Considerations

  • Data Privacy and Security – AI must ensure customer data is handled responsibly and securely.
  • Bias and Fairness – AI algorithms should be regularly audited to prevent discrimination.
  • Human Oversight – While AI automates tasks, human intervention is needed for complex scenarios.

Why Businesses Must Invest in AI

  • AI enhances efficiency, reduces costs, and improves customer satisfaction.
  • Companies looking to build AI-powered solutions should partner with experts like LITSLINK to stay ahead of the competition.
  • Investing in AI today ensures a competitive edge in the evolving market landscape.

Wrapping Up

AI agents are transforming the customer experience by offering immediate support, providing predictive insights, and fostering emotionally intelligent interactions. These advancements help businesses stay competitive by improving operational efficiency, reducing costs, and creating personalized customer service. However, ethical considerations such as data privacy, fairness, and transparency are crucial to maintaining consumer trust. As AI evolves, its integration with emerging technologies like AR, VR, and digital humans will further revolutionize customer interactions. Now is the ideal time to explore how AI can enhance your customer experience. Partner with LITSLINK to integrate AI solutions tailored to your specific business needs.

FAQs

Q: What is an AI agent?
A: An AI agent is a smart computer program that can think and act on its own, helping businesses talk to customers, answer questions, and solve problems.

Q: How has AI evolved in customer service?
A: AI has grown from simple chatbots to intelligent assistants that can understand emotions, predict issues, and provide instant solutions.

Q: Why are businesses adopting AI agents?
A: AI agents reduce operational costs, improve efficiency, and enhance customer satisfaction.

Q: What are the challenges of AI in customer service?
A: AI agents must ensure data privacy, prevent bias, and provide transparent decision-making processes.

Q: Why should businesses invest in AI?
A: AI enhances efficiency, reduces costs, and improves customer satisfaction, giving businesses a competitive edge in the evolving market landscape.

Configurable Graph-Based Task Solving with Marco Multi-AI Agent Framework for Chip Design

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Marco Framework for Configurable Graph-Based Task Solving and Multi-AI Agents

Introduction

Chip and hardware design present numerous challenges due to their complexity and advancing technologies. These challenges result in longer turn-around time (TAT) for optimizing performance, power, area, and cost (PPAC) during synthesis, verification, physical design, and reliability loops.

Marco Framework Overview

The Marco framework is a configurable graph-based task solving and multi-AI agents framework that encompasses graph-based task solving, agent configurations for sub-tasks, and skill/tool configurations for each AI agent in real-time. The framework is designed to flexibly integrate chip-design knowledge (e.g., circuits, timing, etc.) with dynamic and static configurable graph-based task solving.

Task Graph and Agent Configurations

The task graph represents each node as a sub-task, and each edge represents the execution or knowledge relationship between nodes. To solve each sub-task, the Marco framework configures single-AI or multi-AI agents with a knowledge database, tools, and memory. Table 1 summarizes the task graph, agent, and LLM configurations for various agents.

Automated Hardware Description Languages Code Generation

One key area where autonomous agents are making an impact is in the generation of hardware description languages (HDLs), such as Verilog. LLMs can generate Verilog code from natural language descriptions, but often struggle to produce code that is both syntactically and functionally correct. The Marco framework addresses this challenge through the use of a combination of retrieval-augmented generation (RAG) and ReAct prompting to enable LLMs to iteratively debug and fix syntax errors.

DRC Layout Code Generation

DRC-Coder uses multiple autonomous agents with vision capabilities and specialized DRC and Layout DRV analysis tools to generate DRC code. The system interprets design rules from textual descriptions, visual illustrations, and layout representations.

Standard Cell Layout Optimization

LLM agents can also be used for standard cell layout optimization by generating high-quality cluster constraints incrementally to optimize cell layout PPA and debug routability with ReAct prompting.

Multi-Corner Multi-Mode Timing Report Debug and Analysis

The multi-corner multi-mode (MCMM) timing analysis agent uses dynamic task graphs to complete the specification-to-RTL and extract key takeaways of timing reports, respectively.

Conclusion

The proposed Marco framework enables more flexible and domain-specialized methods for real-time hardware design tasks solving. By using task graph and flexible single-AI and multi-AI agent configurations with domain-specific tools and knowledge, we developed various agents for tasks such as cell layout optimization, Verilog syntax error fixing, Verilog and DRC code generation, and timing debugging on problematic blocks, nets, and wires.

FAQs

Q: What is the Marco framework?
A: The Marco framework is a configurable graph-based task solving and multi-AI agents framework that enables more flexible and domain-specialized methods for real-time hardware design tasks solving.

Q: What are the key benefits of the Marco framework?
A: The key benefits of the Marco framework include faster product cycles, lower costs, improved design reliability, and reduced risk of costly errors.

Q: What are the future directions for agent research on hardware design?
A: The future directions for agent research on hardware design include training LLMs with high-quality hardware design data, improving LLM-based agents’ ability for hardware signal and waveform debugging, incorporating PPA metrics into the design flow, and developing more efficient self-learning techniques and memory systems for LLM agents for solving more complex hardware tasks.

Samsung’s First Pro Series Gen 5 PCIe SSD Arrives in March

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Samsung’s First PCIe Gen 5 SSD: 9100 Pro Series

Introduction

The first PCIe Gen 5 SSDs from Seagate and Crucial have been available on the market for nearly two years, but Samsung has been absent from this category until now. This will change in March with the release of the Samsung 9100 Pro series, a consumer-ready PCIe Gen 5 SSD built with NVMe 2.0. It will initially be available in 1TB, 2TB, and 4TB capacities in an M.2 form factor, with or without heatsinks. An 8TB configuration is planned for the second half of 2025.

Performance

By the numbers, the 9100 Pro’s theoretical maximum random read and write speeds – 2,200K and 2,600K input-output operations per second (IOPS) – are at least twice as fast as the last-gen Samsung 980 Pro, a PCIe Gen4 SSD. Our comparison of the 980 Pro with Seagate’s Firecuda 540 and Crucial’s T700 showed no noticeable benefits for PC gaming; however, the difference may be more evident for heavier computing workflows.

Large-Volume File Transfers

The gains might be more apparent if you’re talking about large-volume file transfers, which videographers or software engineers working with large datasets might appreciate. Samsung claims the 9100 Pro can reach sequential read and write speeds of up to 14.8GBps and 13.4GBps, respectively. This is roughly double the last-gen 980 Pro and about 2-3GBps per second faster than the earliest PCIe 5 SSDs can manage.

Conclusion

The Samsung 9100 Pro series offers improved performance and speed, making it a great option for those who require high-performance storage solutions. With its faster speeds and increased storage capacity, it is an excellent choice for professionals and individuals who work with large files and datasets.

Frequently Asked Questions

Q: What are the available capacities of the Samsung 9100 Pro series?
A: The initial launch will feature 1TB, 2TB, and 4TB capacities in an M.2 form factor, with or without heatsinks. An 8TB configuration is planned for the second half of 2025.

Q: What is the theoretical maximum random read and write speed of the 9100 Pro?
A: The theoretical maximum random read and write speeds are 2,200K and 2,600K input-output operations per second (IOPS), respectively.

Q: How does the 9100 Pro compare to the last-gen Samsung 980 Pro?
A: The 9100 Pro’s performance is at least twice as fast as the last-gen 980 Pro, a PCIe Gen4 SSD.

OpenAI’s Deep Research

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Deep Research: OpenAI’s New AI Agent for Conducting Multi-Step Research

What’s better than an AI chatbot that can assist you with tasks? One that can do them for you. OpenAI continues to build out its AI agents in ChatGPT with the launch of Deep Research.

Deep Research

Earlier this month, OpenAI unveiled Deep Research, an AI agent that can conduct multi-step research for you by pulling a robust amount of information from the web and synthesizing those sources for you in a comprehensive report. Once prompted, Deep Research can work entirely independently; it’s like having a research analyst at your command.

Access to Deep Research

At launch, it was only accessible to ChatGPT Pro users, meaning you would have to pay $200 per month. Now, Deep Research is rolling out to more paid subscribers, including ChatGPT Plus, Team, Edu, and Enterprise users. However, the added users will have 10 deep research queries per month, compared to the 120 deep research queries per month Pro users have access to.

Powering Deep Research

Powering Deep Research is a version of the OpenAI o3 model optimized for web browsing and data analysis. By leveraging o3’s advanced reasoning capabilities, it can search and interpret massive amounts of content from the web, including texts, images, and more, and then output it in a report targeted to your needs.

How it Works

Each report is generated in five to 30 minutes, depending on the task at hand. However, you can work on other tasks during that time, optimizing your workflow productivity. The finished report is output in the chat. Along with the update that expanded access to the feature, OpenAI announced Deep Research now includes embedded images with citations in the output and is better at understanding uploaded files.

Target Audience

The target audience for Deep Research includes those who do intensive knowledge work in finance, science, policy, and engineering — and who need reliable, thorough research, according to OpenAI. Every report includes clear citations and a summary of the agent’s thinking so that users can double-check the information for themselves.

Performance Compared

In the blog post announcing the feature, OpenAI includes the same side-by-side results of GPT-4o versus Deep Research to showcase how the same prompt generates very different results. The ones generated with Deep Research were much more robust and better organized.

Conclusion

Deep Research is a game-changer in the world of AI research, allowing users to conduct multi-step research independently and efficiently. With its advanced reasoning capabilities and ability to search and interpret massive amounts of content, Deep Research is a valuable tool for those who need reliable, thorough research.

FAQs

Q: What is Deep Research?
A: Deep Research is an AI agent that can conduct multi-step research for you by pulling a robust amount of information from the web and synthesizing those sources for you in a comprehensive report.

Q: How does Deep Research work?
A: Deep Research uses a version of the OpenAI o3 model optimized for web browsing and data analysis to search and interpret massive amounts of content from the web, including texts, images, and more, and then output it in a report targeted to your needs.

Q: Who is the target audience for Deep Research?
A: The target audience for Deep Research includes those who do intensive knowledge work in finance, science, policy, and engineering — and who need reliable, thorough research, according to OpenAI.

Q: How much does Deep Research cost?
A: Deep Research is available to ChatGPT Plus, Team, Edu, and Enterprise users, with 10 deep research queries per month, compared to the 120 deep research queries per month Pro users have access to.

iPhone Dictation Feature Transcribes the Word ‘Racist’ as ‘Trump’

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Apple’s AI System Spots "Racist" as "Trump" in Text Messages, Raising Questions about Bias

Issue Appears to be Phonetic Overlap, but Experts Suggest Software Code May be at Fault

Apple’s automatic dictation feature on some iPhones has been experiencing a peculiar bug, causing the word "racist" to temporarily appear as "Trump" in text messages. The issue has sparked controversy and raised questions about Apple’s artificial intelligence capabilities.

The Bug: What Happened?

The problem started after an update to Apple’s servers, according to John Burkey, the founder of Wonderrush.ai, an artificial intelligence start-up, and a former member of Apple’s Siri team. The issue was first reported by The New York Times and later replicated on TikTok, a popular social media platform.

The Fix: Apple’s Response

An Apple spokeswoman attributed the issue to phonetic overlap between the two words. The company is working on a fix, but experts are skeptical. Burkey suggests that the problem is not just a technical issue, but rather a software code that causes the iPhone to write the word "Trump" when someone says "racist".

The Controversy: Bias and Political Implications

The issue has sparked concerns about bias and political implications, given Apple’s close ties to President Trump. Apple’s CEO, Tim Cook, has met with President Trump on several occasions, and the company has made significant investments in the United States.

A History of Controversies: Apple’s AI System

This is not the first time Apple’s AI system has been at the center of a controversy. In 2018, Siri displayed a nude image in response to the question "Who is Donald Trump?" The bug was linked to rogue Wikipedia editors who had changed the source of Siri’s information. Last month, Apple disabled one of its AI system’s signature capabilities, aggregating and summarizing news notifications, after it inaccurately summarized news headlines from several media outlets.

Conclusion

The latest issue with Apple’s AI system highlights the need for greater transparency and accountability in the development and deployment of artificial intelligence. While phonetic overlap may be a factor in this case, experts suggest that software code may be at fault. As AI becomes increasingly integrated into our daily lives, it is essential to address these concerns and ensure that these systems are fair, unbiased, and transparent.

Frequently Asked Questions

Q: What caused the issue with Apple’s AI system?
A: The issue was caused by phonetic overlap between the words "racist" and "Trump", according to Apple’s spokeswoman.

Q: Is this a technical issue or a software problem?
A: Experts suggest that it may be a software code that causes the issue, rather than just a technical glitch.

Q: Is this the first time Apple’s AI system has been at the center of a controversy?
A: No, Apple’s AI system has been involved in several controversies in the past, including a bug that displayed a nude image in response to a question about Donald Trump and another issue with inaccurate news summaries.

Unlock Exclusive Features

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Free and Unlimited Access to Microsoft’s Copilot AI Features

Free and Unlimited Access to All Copilot Users

On Tuesday, Microsoft announced that all Copilot users will have unlimited access to the Think Deeper and Voice features at no additional cost. This is a major advantage over ChatGPT Plus, which costs $20 per month, and grants access to ChatGPT Advanced Voice and o1, the OpenAI equivalent to these two features.

Think Deeper

The o1 model underlying the Think Deeper feature was trained to "think before it speaks," and as a result, it takes a bit longer to process your query — around 30 seconds, according to Microsoft. This is especially useful for STEM-related tasks such as coding, analysis, and advanced math problems. Other use cases include in-depth advice and planning.

Accessing Think Deeper

To access the feature, sign into Copilot using your Microsoft account. Once you do, next to the text box, you will see the "Think Deeper" button, which you can click to activate the model when answering your prompt.

Copilot Voice

Copilot Voice is Microsoft’s take on AI-powered voice assistants that mimic human conversations. Copilot Voice has four voices — Wave, Meadow, Grove, and Canyon — and is available across all the same platforms that you can access Copilot, including the app, standalone site, and Windows.

What You Need to Know

  • All Copilot users will have unlimited access to Think Deeper and Voice features at no additional cost.
  • The o1 model takes around 30 seconds to process your query, making it suitable for STEM-related tasks and in-depth advice and planning.
  • To access Think Deeper, sign into Copilot using your Microsoft account and click the "Think Deeper" button next to the text box.
  • Copilot Voice has four voices and is available across all platforms, including the app, standalone site, and Windows.

Frequently Asked Questions

Q: What are the benefits of unlimited access to Think Deeper and Voice features?
A: All Copilot users will have access to these features at no additional cost, making it a major advantage over ChatGPT Plus.

Q: What is the o1 model used for in Think Deeper?
A: The o1 model is used to provide in-depth advice and planning, and is especially useful for STEM-related tasks such as coding, analysis, and advanced math problems.

Q: How do I access Think Deeper in Copilot?
A: Sign into Copilot using your Microsoft account and click the "Think Deeper" button next to the text box.

Q: What is Copilot Voice?
A: Copilot Voice is a voice assistant that mimics human conversations, available across all platforms, including the app, standalone site, and Windows.

A Team of Female Founders Is Launching Cloud Security Tech That Could Overhaul AI Protection

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The Rise of Edera: A New Era in Cloud Infrastructure Security

A Personal Discovery

While working on internet-of-things security in the mid-2010s, Alex Zenla realized something troubling. Unlike PCs and servers that touted the latest, greatest processors, the puny chips in IoT devices couldn’t support the cloud protections other computers were using to keep them siloed and protected. As a result, most embedded devices were attached directly to the local network, potentially leaving them more vulnerable to attack.

The Birth of Edera

After puzzling over the problem for a few years, Zenla started working on a technology to make it possible for nearly any device to run in its own isolated cloud space, known as a “container.” Now, a decade later, she’s one of three female cofounders of a security company that’s trying to change how cloud infrastructure shares resources.

Edera’s Mission

Edera makes cloud workload isolation tech that may sound like a niche tool, but it aims to address a universal security problem when many applications or even multiple customers are using shared cloud infrastructure. Ever-growing AI workloads, for example, rely on GPUs for raw processing power instead of standard CPUs, but these chips have been designed for maximum efficiency and capacity rather than with guardrails to separate and protect different processes.

Challenges and Solutions

"These problems are very hard, both on the GPU and the container isolation, but I think people were too willing to accept trade-offs that were not actually acceptable," Zenla says.

The company’s technology addresses these challenges by providing a way to separate and protect different workloads and applications within a shared cloud infrastructure. This is achieved through the use of containers, which are isolated and secure environments that can be deployed on a shared infrastructure.

Funding and Milestones

In October, Edera announced a $5 million seed round and has now raised a total of $20 million in funding. The company’s latest funding round was led by Microsoft’s venture fund, M12.

Breaking the Glass Ceiling

Edera’s all-female founding team, including CEO and cofounder Emily Long, and cofounder Ariadne Conill, is a rare sight in the tech industry. Female founders who do get initial backing have a more difficult time raising subsequent rounds than men and face much steeper odds founding another company after one fails.

"We can’t ignore the fact that we are a small minority in our industry, and that a lot of the changes that are happening around us are not lifting us up," says Edera CEO and cofounder Emily Long. "We take great pride and responsibility in continuing to be in the front on this. Since our founding, I can’t tell you how many incredibly technical, talented women have proactively asked us to hire them from large institutions. So you start to see that just by existing and being different, you are showing what’s possible."

Conclusion

Edera’s technology has the potential to revolutionize the way cloud infrastructure shares resources and provides a secure and isolated environment for different workloads and applications. With its all-female founding team, Edera is breaking the mold and paving the way for more women to join the tech industry.

FAQs

Q: What is Edera’s technology?
A: Edera’s technology provides cloud workload isolation, allowing for the separation and protection of different workloads and applications within a shared cloud infrastructure.

Q: How does Edera’s technology work?
A: Edera’s technology uses containers, which are isolated and secure environments that can be deployed on a shared infrastructure.

Q: Who are the founders of Edera?
A: Edera was founded by three women: Alex Zenla, Emily Long, and Ariadne Conill.

Q: How much funding has Edera received?
A: Edera has received a total of $20 million in funding, with a $5 million seed round and a $15 million series A round led by Microsoft’s venture fund, M12.

Fetch.ai Launches First Web3 Agentic AI Model

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Fetch.ai has launched ASI-1 Mini, a native Web3 large language model designed to support complex agentic AI workflows. This game-changer for AI accessibility and performance delivers results on par with leading LLMs at significantly reduced hardware costs, making it enterprise-ready.

Democratising AI with Web3: Decentralised ownership and shared value

Fetch.ai’s vision is to democratise foundational AI models, enabling the Web3 community to not just use, but also train and own proprietary LLMs like ASI-1 Mini. This decentralisation unlocks opportunities for individuals to directly benefit from the economic growth of cutting-edge AI models, which could achieve multi-billion-dollar valuations. Through Fetch.ai’s platform, users can invest in curated AI model collections, contribute to their development, and share in generated revenues. For the first time, decentralisation is driving AI model ownership, ensuring financial benefits are more equitably distributed.

Advanced reasoning and tailored performance

ASI-1 Mini introduces adaptability in decision-making with four dynamic reasoning modes: Multi-Step, Complete, Optimised, and Short Reasoning. This flexibility allows it to balance depth and precision based on the specific task at hand. Whether performing intricate, multi-layered problem-solving or delivering concise, actionable insights, ASI-1 Mini adapts dynamically for maximum efficiency. Its Mixture of Models (MoM) and Mixture of Agents (MoA) frameworks further enhance this versatility.

Mixture of Models (MoM)

ASI-1 Mini selects relevant models dynamically from a suite of specialized AI models, which are optimized for specific tasks or datasets. This ensures high efficiency and scalability, especially for multi-modal AI and federated learning.

Mixture of Agents (MoA)

Independent agents with unique knowledge and reasoning capabilities work collaboratively to solve complex tasks. The system’s coordination mechanism ensures efficient task distribution, paving the way for decentralized AI models that thrive in dynamic, multi-agent systems.

Transforming AI efficiency and accessibility

Unlike traditional LLMs, which come with high computational overheads, ASI-1 Mini is optimized for enterprise-grade performance on just two GPUs, reducing hardware costs by a remarkable eightfold. For businesses, this means reduced infrastructure costs and increased scalability, breaking down financial barriers to high-performance AI integration. On benchmark tests like Massive Multitask Language Understanding (MMLU), ASI-1 Mini matches or surpasses leading LLMs in specialized domains such as medicine, history, business, and logical reasoning.

Tackling the "black-box" problem

The AI industry has long faced the challenge of addressing the black-box problem, where deep learning models reach conclusions without clear explanations.

AgentVerse integration: Building the agentic AI economy

ASI-1 Mini is set to connect with AgentVerse, Fetch.ai’s agent marketplace, providing users with the tools to build and deploy autonomous agents capable of real-world task execution via simple language commands. This ecosystem enables open-source AI customization and monetization, creating an "agentic economy" where developers and businesses thrive symbiotically. Developers can monetize micro-agents, while users gain seamless access to tailored AI solutions.

Conclusion

Fetch.ai’s ASI-1 Mini is a groundbreaking AI model that democratizes AI ownership and enables the Web3 community to invest, train, and own proprietary LLMs. With its advanced reasoning capabilities, Mixture of Models and Mixture of Agents frameworks, and integration with AgentVerse, ASI-1 Mini is poised to revolutionize the AI industry, making high-performance AI more accessible and affordable for businesses and individuals alike.

Frequently Asked Questions

Q: What is ASI-1 Mini?
A: ASI-1 Mini is a native Web3 large language model designed to support complex agentic AI workflows.

Q: What are the key features of ASI-1 Mini?
A: ASI-1 Mini offers advanced reasoning capabilities, Mixture of Models and Mixture of Agents frameworks, and integration with AgentVerse.

Q: How does ASI-1 Mini differ from traditional LLMs?
A: ASI-1 Mini is optimized for enterprise-grade performance on just two GPUs, reducing hardware costs by a remarkable eightfold.

Q: What is the potential impact of ASI-1 Mini on the AI industry?
A: ASI-1 Mini has the potential to democratize AI ownership, enabling the Web3 community to invest, train, and own proprietary LLMs, and revolutionizing the AI industry.

Quora’s Poe

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Poe Launches New Capabilities for Building Visual Interfaces with AI Models

Poe Apps

Quora’s platform, Poe, has launched a new capability that allows users to build visual interfaces, or apps, on top of any combination of AI models. This feature is called Poe Apps, and it enables users to describe the app they want to create in the new App Creator tool.

App Creator Tool

The App Creator tool, powered by Anthropic’s recently released Claude 3.7 Sonnet, translates the description into code for the app interface along with custom logic expressed in JavaScript. Poe Apps can run side-by-side with Poe’s chatbot window or be entirely visual, and their underlying code is exposed for manual adjusting and fine-tuning.

Example Apps

Quora created a few example apps, including one that transforms photos into 3D anime-style art using OpenAI’s GPT-4o and Black Forest Labs’ Flux-Pro-1.1. Another example app removes unwanted objects from images, leveraging Bria’s Bria-Eraser model.

Sharing and Monetization

Poe Apps can be shared with other Poe users, currently only on the web, with iOS and Android support on the way. Each time an app uses an AI model, it will draw from a user’s point balance with Poe. Free users receive a daily point allotment, while users subscribed to Poe’s $9.99 per month premium tier get flexible daily or monthly point packages.

Future Roadmap

In a blog post, Quora hinted at possible app monetization options, stating that "This is an early launch and we have a long roadmap ahead to give creators even more power, including the ability to earn money directly from their apps."

Conclusion

Poe Apps expand on the web apps feature Poe launched last July, and they demonstrate how far models’ programming capabilities have come. With the ability to build visual interfaces with AI models, users have a new way to create and share innovative applications.

FAQs

Q: What is Poe Apps?
A: Poe Apps is a new feature that allows users to build visual interfaces, or apps, on top of any combination of AI models.

Q: How do I use the App Creator tool?
A: Users can describe the app they want to create in the App Creator tool, and the tool will translate the description into code for the app interface along with custom logic expressed in JavaScript.

Q: Can I share my Poe Apps?
A: Yes, Poe Apps can be shared with other Poe users, currently only on the web, with iOS and Android support on the way.

Q: How do I monetize my Poe Apps?
A: Quora hints at possible app monetization options, including the ability to earn money directly from their apps, but no details have been provided yet.