#agentgpt

[smartboxpro]

🚀 5 Best AI Agents for Passive Income in 2026: The Ultimate Guide

Are you ready to automate your wealth? 💸 In 2026, the secret to passive income isn't just "working hard"—it's about working smart with AI Autonomous Agents

​Unlike standard chatbots, these agents can set their own goals, manage tasks, and run your digital business 24/7 while you sleep. Whether you are in the USA or anywhere else, these tools are game-changers!

​Inside this guide, we break down:

​🤖 AutoGPT & AgentGPT: Your new digital employees.

​📈 Market Research: How to find winning niches automatically.

​🛠️ Setup Guide: Step-by-step instructions for beginners.

​The future of financial freedom is here. Don't let the AI revolution pass you by! 💡

https://smarttoolboxpro.blogspot.com/2025/12/5-best-ai-agents-for-passive-income-in.html

[jcmarchi]

FrugalGPT: A Paradigm Shift in Cost Optimization for Large Language Models

New Post has been published on https://thedigitalinsider.com/frugalgpt-a-paradigm-shift-in-cost-optimization-for-large-language-models/

FrugalGPT: A Paradigm Shift in Cost Optimization for Large Language Models

Large Language Models (LLMs) represent a significant breakthrough in Artificial Intelligence (AI). They excel in various language tasks such as understanding, generation, and manipulation. These models, trained on extensive text datasets using advanced deep learning algorithms, are applied in autocomplete suggestions, machine translation, question answering, text generation, and sentiment analysis.

However, using LLMs comes with considerable costs across their lifecycle. This includes substantial research investments, data acquisition, and high-performance computing resources like GPUs. For instance, training large-scale LLMs like BloombergGPT can incur huge costs due to resource-intensive processes.

Organizations utilizing LLM usage encounter diverse cost models, ranging from pay-by-token systems to investments in proprietary infrastructure for enhanced data privacy and control. Real-world costs vary widely, from basic tasks costing cents to hosting individual instances exceeding $20,000 on cloud platforms. The resource demands of larger LLMs, which offer exceptional accuracy, highlight the critical need to balance performance and affordability.

Given the substantial expenses associated with cloud computing centres, reducing resource requirements while improving financial efficiency and performance is imperative. For instance, deploying LLMs like GPT-4 can cost small businesses as much as $21,000 per month in the United States.

FrugalGPT introduces a cost optimization strategy known as LLM cascading to address these challenges. This approach uses a combination of LLMs in a cascading manner, starting with cost-effective models like GPT-3 and transitioning to higher-cost LLMs only when necessary. FrugalGPT achieves significant cost savings, reporting up to a 98% reduction in inference costs compared to using the best individual LLM API.

FrugalGPT,s innovative methodology offers a practical solution to mitigate the economic challenges of deploying large language models, emphasizing financial efficiency and sustainability in AI applications.

Understanding FrugalGPT

FrugalGPT is an innovative methodology developed by Stanford University researchers to address challenges associated with LLM, focusing on cost optimization and performance enhancement. It involves adaptively triaging queries to different LLMs like GPT-3, and GPT-4 based on specific tasks and datasets. By dynamically selecting the most suitable LLM for each query, FrugalGPT aims to balance accuracy and cost-effectiveness.

The main objectives of FrugalGPT are cost reduction, efficiency optimization, and resource management in LLM usage. FrugalGPT aims to reduce the financial burden of querying LLMs by using strategies such as prompt adaptation, LLM approximation, and cascading different LLMs as needed. This approach minimizes inference costs while ensuring high-quality responses and efficient query processing.

Moreover, FrugalGPT is important in democratizing access to advanced AI technologies by making them more affordable and scalable for organizations and developers. By optimizing LLM usage, FrugalGPT contributes to the sustainability of AI applications, ensuring long-term viability and accessibility across the broader AI community.

Optimizing Cost-Effective Deployment Strategies with FrugalGPT

Implementing FrugalGPT involves adopting various strategic techniques to enhance model efficiency and minimize operational costs. A few techniques are discussed below:

Model Optimization Techniques

FrugalGPT uses model optimization techniques such as pruning, quantization, and distillation. Model pruning involves removing redundant parameters and connections from the model, reducing its size and computational requirements without compromising performance. Quantization converts model weights from floating-point to fixed-point formats, leading to more efficient memory usage and faster inference times. Similarly, model distillation entails training a smaller, simpler model to mimic the behavior of a larger, more complex model, enabling streamlined deployment while preserving accuracy.

Fine-Tuning LLMs for Specific Tasks

Tailoring pre-trained models to specific tasks optimizes model performance and reduces inference time for specialized applications. This approach adapts the LLM’s capabilities to target use cases, improving resource efficiency and minimizing unnecessary computational overhead.

Deployment Strategies

FrugalGPT supports adopting resource-efficient deployment strategies such as edge computing and serverless architectures. Edge computing brings resources closer to the data source, reducing latency and infrastructure costs. Cloud-based solutions offer scalable resources with optimized pricing models. Comparing hosting providers based on cost efficiency and scalability ensures organizations select the most economical option.

Reducing Inference Costs

Crafting precise and context-aware prompts minimizes unnecessary queries and reduces token consumption. LLM approximation relies on simpler models or task-specific fine-tuning to handle queries efficiently, enhancing task-specific performance without the overhead of a full-scale LLM.

LLM Cascade: Dynamic Model Combination

FrugalGPT introduces the concept of LLM cascading, which dynamically combines LLMs based on query characteristics to achieve optimal cost savings. The cascade optimizes costs while reducing latency and maintaining accuracy by employing a tiered approach where lightweight models handle common queries and more powerful LLMs are invoked for complex requests.

By integrating these strategies, organizations can successfully implement FrugalGPT, ensuring the efficient and cost-effective deployment of LLMs in real-world applications while maintaining high-performance standards.

FrugalGPT Success Stories

HelloFresh, a prominent meal kit delivery service, used Frugal AI solutions incorporating FrugalGPT principles to streamline operations and enhance customer interactions for millions of users and employees. By deploying virtual assistants and embracing Frugal AI, HelloFresh achieved significant efficiency gains in its customer service operations. This strategic implementation highlights the practical and sustainable application of cost-effective AI strategies within a scalable business framework.

In another study utilizing a dataset of headlines, researchers demonstrated the impact of implementing Frugal GPT. The findings revealed notable accuracy and cost reduction improvements compared to GPT-4 alone. Specifically, the Frugal GPT approach achieved a remarkable cost reduction from $33 to $6 while enhancing overall accuracy by 1.5%. This compelling case study underscores the practical effectiveness of Frugal GPT in real-world applications, showcasing its ability to optimize performance and minimize operational expenses.

Ethical Considerations in FrugalGPT Implementation

Exploring the ethical dimensions of FrugalGPT reveals the importance of transparency, accountability, and bias mitigation in its implementation. Transparency is fundamental for users and organizations to understand how FrugalGPT operates, and the trade-offs involved. Accountability mechanisms must be established to address unintended consequences or biases. Developers should provide clear documentation and guidelines for usage, including privacy and data security measures.

Likewise, optimizing model complexity while managing costs requires a thoughtful selection of LLMs and fine-tuning strategies. Choosing the right LLM involves a trade-off between computational efficiency and accuracy. Fine-tuning strategies must be carefully managed to avoid overfitting or underfitting. Resource constraints demand optimized resource allocation and scalability considerations for large-scale deployment.

Addressing Biases and Fairness Issues in Optimized LLMs

Addressing biases and fairness concerns in optimized LLMs like FrugalGPT is critical for equitable outcomes. The cascading approach of Frugal GPT can accidentally amplify biases, necessitating ongoing monitoring and mitigation efforts. Therefore, defining and evaluating fairness metrics specific to the application domain is essential to mitigate disparate impacts across diverse user groups. Regular retraining with updated data helps maintain user representation and minimize biased responses.

Future Insights

The FrugalGPT research and development domains are ready for exciting advancements and emerging trends. Researchers are actively exploring new methodologies and techniques to optimize cost-effective LLM deployment further. This includes refining prompt adaptation strategies, enhancing LLM approximation models, and refining the cascading architecture for more efficient query handling.

As FrugalGPT continues demonstrating its efficacy in reducing operational costs while maintaining performance, we anticipate increased industry adoption across various sectors. The impact of FrugalGPT on the AI is significant, paving the way for more accessible and sustainable AI solutions suitable for business of all sizes. This trend towards cost-effective LLM deployment is expected to shape the future of AI applications, making them more attainable and scalable for a broader range of use cases and industries.

The Bottom Line

FrugalGPT represents a transformative approach to optimizing LLM usage by balancing accuracy with cost-effectiveness. This innovative methodology, encompassing prompt adaptation, LLM approximation, and cascading strategies, enhances accessibility to advanced AI technologies while ensuring sustainable deployment across diverse applications.

Ethical considerations, including transparency and bias mitigation, emphasize the responsible implementation of FrugalGPT. Looking ahead, continued research and development in cost-effective LLM deployment promises to drive increased adoption and scalability, shaping the future of AI applications across industries.

[jcmarchi]

The Rise of AI Software Engineers: SWE-Agent, Devin AI and the Future of Coding

New Post has been published on https://thedigitalinsider.com/the-rise-of-ai-software-engineers-swe-agent-devin-ai-and-the-future-of-coding/

The Rise of AI Software Engineers: SWE-Agent, Devin AI and the Future of Coding

The field of artificial intelligence (AI) continues to push the boundaries of what was once thought impossible. From self-driving cars to language models that can engage in human-like conversations, AI is rapidly transforming various industries, and software development is no exception. The emergence of AI-powered software engineers, such as SWE-Agent developed by Princeton University’s NLP group, Devin AI, represents a groundbreaking shift in how software is designed, developed, and maintained.

SWE-Agent, a cutting-edge AI system, promises to revolutionize the software engineering process by autonomously identifying and resolving GitHub issues with unprecedented speed and accuracy. This remarkable tool leverages state-of-the-art language models like GPT-4, streamlining the development cycle and enhancing developer productivity.

The Advent of AI Software Engineers

Traditionally, software development has been a labor-intensive process, requiring teams of skilled programmers to write, review, and test code meticulously. However, the advent of AI-powered software engineers like SWE-Agent has the potential to disrupt this age-old paradigm. By harnessing the power of large language models and machine learning algorithms, these AI systems can not only generate code but also identify and fix bugs, streamlining the entire development lifecycle.

One of the key advantages of SWE-Agent is its ability to autonomously resolve GitHub issues with remarkable efficiency. On average, it can analyze and fix problems within 93 seconds, boasting an impressive 12.29% success rate on the comprehensive SWE-bench test set. This level of speed and accuracy is unprecedented in the software engineering realm, promising to significantly accelerate development timelines and reduce the overall cost of software projects.

At the core of SWE-Agent’s success lies the innovative Agent-Computer Interface (ACI), a design paradigm that optimizes interactions between AI programmers and code repositories. By simplifying commands and feedback formats, ACI facilitates seamless communication, empowering SWE-Agent to perform tasks ranging from syntax checks to test execution with remarkable efficiency. This user-friendly interface not only enhances performance but also accelerates adoption among developers, making AI-assisted software development more accessible and approachable.

SWE agent LLM

LLM Agents: Orchestrating Task Automation

LLM agents are sophisticated software entities designed to automate the execution of complex tasks. These agents are equipped with access to a comprehensive toolkit or set of resources, enabling them to intelligently determine the best tool or method to employ based on the specific input they receive.

The operation of an LLM agent can be visualized as a dynamic sequence of steps, meticulously orchestrated to fulfill the given task. Significantly, these agents possess the capability to use the output from one tool as input for another, creating a cascading effect of interlinked operations.

BabyAGI: Task Management Powerhouse One of the most notable LLM agents is BabyAGI, an advanced task management system powered by OpenAI’s cutting-edge artificial intelligence capabilities. In tandem with vector databases like Chroma or Weaviate, BabyAGI excels in managing, prioritizing, and executing tasks with remarkable efficiency. Leveraging OpenAI’s state-of-the-art natural language processing, BabyAGI can formulate new tasks aligned with specific objectives and boasts integrated database access, enabling it to store, recall, and utilize pertinent information.

At its core, BabyAGI represents a streamlined version of the Task-Driven Autonomous Agent, incorporating notable features from platforms like GPT-4, Pinecone vector search, and the LangChain framework to independently craft and execute tasks. Its operational flow comprises four key steps: extracting the foremost task from the pending task list, relaying the task to a dedicated execution agent for processing, refining and storing the derived result, and formulating new tasks while dynamically adjusting the priority of the task list based on the overarching objective and outcomes of previously executed tasks.

AgentGPT: Autonomous AI Agent Creation and Deployment AgentGPT is a robust platform tailored for the creation and deployment of autonomous AI agents. Once a particular objective is defined for these agents, they embark on a relentless loop of task generation and execution, striving tirelessly to meet the stipulated goal. At the heart of its operation lies a chain of interconnected language models (or agents) that collaboratively brainstorm the optimal tasks to meet an objective, execute them, critically assess their performance, and iteratively devise subsequent tasks. This recursive approach ensures that AgentGPT remains adaptive, learning and refining its strategies with each loop to inch closer to the objective.

https://arxiv.org/pdf/2308.00352.pdf

Code Assistants: Enhancing Developer Productivity

Code assistants are advanced tools designed to assist developers in the code-writing process, often implemented as Integrated Development Environment (IDE) plugins, extensions, or add-ons. These assistants are capable of suggesting code completions, identifying and rectifying bugs, providing optimization recommendations, and simplifying recurring coding tasks. By incorporating generative AI models, they analyze coding patterns and furnish insights that streamline the development workflow, accelerating code generation and elevating the quality of output.

GitHub Copilot: AI-Powered Programming Companion GitHub Copilot, developed through a collaboration between GitHub and OpenAI, harnesses the capabilities of the Codex generative model, aiding developers in writing code more efficiently. Described as an AI-powered programming companion, it presents auto-complete suggestions during code development. GitHub Copilot keenly discerns the context of the active file and its related documents, proposing suggestions directly within the text editor. It boasts proficiency across all languages represented in public repositories.

Copilot X, an enhanced version of Copilot, builds upon this foundation, offering an enriched experience with chat and terminal interfaces, enhanced support for pull requests, and leveraging OpenAI’s GPT-4 model. Both Copilot and Copilot X are compatible with Visual Studio, Visual Studio Code, Neovim, and the entire JetBrains software suite.

AWS CodeWhisperer: Real-Time Coding Recommendations Amazon CodeWhisperer is a machine learning-driven code generator that offers real-time coding recommendations. As developers script, it proactively presents suggestions influenced by the ongoing code. These propositions range from concise comments to elaborately structured functions. Currently, CodeWhisperer is attuned to a multitude of programming languages, including Java, Python, JavaScript, TypeScript, and many more. The tool seamlessly integrates with platforms such as Amazon SageMaker Studio, JupyterLab, Visual Studio Code, JetBrains, AWS Cloud9, and AWS Lambda.

Bard to Code: Conversational AI for Code Generation Bard, often categorized as conversational AI or a chatbot, demonstrates an adeptness in producing human-like textual responses to a diverse spectrum of prompts, owing to its extensive training on a myriad of textual data. Moreover, it possesses the dexterity to produce code across various programming languages, including but not limited to Python, Java, C++, and JavaScript.

SWE-Agent vs. Competitors: Democratizing Access to Advanced Programming Capabilities

In a landscape dominated by proprietary solutions like Devin AI and Devika, SWE-Agent shines as an open-source alternative, democratizing access to cutting-edge AI programming capabilities. Both SWE-Agent and Devin boast impressive performance on the SWE-bench benchmark, with SWE-Agent achieving a competitive 12.29% issue resolution rate. However, SWE-Agent’s open-source nature sets it apart, aligning with the collaborative ethos of the software development community.

By making its codebase available to developers worldwide, SWE-Agent invites contributions and fosters an ecosystem of innovation and knowledge-sharing. Developers can freely integrate SWE-Agent into their workflows, harnessing its power to streamline software development processes while simultaneously contributing to its evolution. This collaborative approach empowers developers of all backgrounds and skill levels to optimize their workflows, enhance code quality, and navigate the complexities of modern software development with confidence.

Beyond its technical prowess, SWE-Agent holds the potential to catalyze a paradigm shift in software engineering education and community collaboration. As an open-source tool, SWE-Agent can be integrated into educational curricula, providing students with hands-on experience in AI-assisted software development. This exposure can help shape the next generation of software engineers, equipping them with the skills and mindset necessary to thrive in an increasingly automated and AI-driven industry.

Moreover, SWE-Agent’s collaborative nature encourages developers to share their experiences, best practices, and insights, fostering a vibrant community of knowledge exchange. Through open-source contributions, bug reports, and feature requests, developers can actively participate in shaping the future of AI-powered software engineering. This collaborative approach not only accelerates the pace of innovation but also ensures that SWE-Agent remains relevant and adaptable to the ever-evolving needs of the software development ecosystem.

The Future of Software Development

While the emergence of AI-powered software engineers like SWE-Agent presents exciting opportunities, it also raises important questions and challenges that must be addressed. One critical consideration is the potential impact on the software development workforce. As AI systems become more capable of automating various aspects of the development process, there may be concerns about job displacement and the need for reskilling and upskilling initiatives.

However, it’s important to recognize that AI is not a replacement for human developers but rather a powerful tool to augment and enhance their capabilities. By offloading repetitive and time-consuming tasks to AI systems like SWE-Agent, human developers can focus on higher-level tasks that require critical thinking, creativity, and problem-solving skills. This shift in focus could lead to more fulfilling and rewarding roles for software engineers, allowing them to tackle more complex challenges and drive innovation.

Another challenge lies in the ongoing development and refinement of AI systems like SWE-Agent. As software complexity continues to increase and new programming paradigms emerge, these AI systems must be continuously trained and updated to stay relevant and effective. This requires a concerted effort from the research community, as well as close collaboration between academia and industry, to ensure that AI-powered software engineers remain at the forefront of technological advancements.

Moreover, as AI systems become more integrated into the software development process, concerns around security, privacy, and ethical considerations must be addressed. Robust measures must be put in place to ensure the integrity and trustworthiness of the generated code, as well as to mitigate potential biases or unintended consequences. Ongoing research and dialogue within the software engineering community will be crucial in navigating these challenges and establishing best practices for the responsible development and deployment of AI-powered software engineers.

Conclusion

The rise of AI-powered software engineers like SWE-Agent represents a pivotal moment in the evolution of software development. By leveraging the power of large language models and machine learning algorithms, these AI systems have the potential to revolutionize the way software is designed, developed, and maintained. With their remarkable speed, accuracy, and ability to streamline the development lifecycle, AI software engineers promise to enhance developer productivity and accelerate the pace of innovation.

However, the true impact of AI software engineers extends beyond mere technical capabilities. As open-source solutions like SWE-Agent gain traction, they have the power to democratize access to advanced programming capabilities, fostering a collaborative ecosystem of knowledge-sharing and empowering developers of all backgrounds and skill levels.

As we embrace the era of AI-assisted software development, it is crucial to recognize the challenges and opportunities that lie ahead. While job displacement concerns and the need for reskilling exist, AI systems like SWE-Agent also present an opportunity to redefine the role of software engineers, allowing them to focus on higher-level tasks that require critical thinking and creativity.

Ultimately, the successful integration of AI-powered software engineers into the software development ecosystem will require a collective effort from researchers, developers, and industry leaders.

[thedaomarketwatch]

AgentGPT: The Evolution of AI Language Models and Their Real-World Applications

You may have heard the term “AI language models” thrown around in tech circles, but what does it actually mean? At its core, an AI language model is a machine learning algorithm trained to understand and generate human-like language. This technology has come a long way since its inception, and one of the most advanced AI language models to date is AgentGPT. AgentGPT is a neural network-based…

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[saiita]

本地安装AgentGPT

可以到AgentGPT官网体验下。 官网地址：https://agentgpt.reworkd.ai/zh GitHub：https://github.com/reworkd/AgentGPT 然后就是在本地部署AgentGpt，然后输入自己的API Key，就可以使用了，不过免费送的key，4月1日已经过期了，可信体验不了AgentGpt了。 要不得开通ChatGPT plus ，然后再使用API Key? 下面就是怎么在Ubuntu系统环境下部署AgentGpt 1、git克隆项目到Ubuntu git clone https://github.com/reworkd/AgentGPT.git 2、安装依赖项 cd AgentGPT npm install 3、创建.env，并配置相关配置项 # Deployment…

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[sharprank]

AgentGPT, BabyGPT and AutoGPT - what is the difference?

These are semi-autonomous “agents”, which can be given high level goals “make a website for selling books online”, and can figure out the high level tasks, such as front-end HTML site development, then backend database, etc. and execute each of the tasks and subtasks. They are all the same (at a high level), but use recursive mechanisms to help GPT create prompts for GPT (so meta). Which means…

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