Claude - gettectonic.com
Google Prepares AI-Powered Jarvis Agent

Google Prepares AI-Powered Jarvis Agent

Google Prepares AI-Powered Jarvis Agent for Automated Browser Tasks in Chrome Google is reportedly gearing up to launch “Project Jarvis,” an AI-powered browser agent designed to automate tasks directly within the Chrome ecosystem. According to The Information, the tool is expected to roll out in December to select users and will leverage Google’s advanced Gemini 2.0 AI model. Jarvis aims to simplify repetitive online tasks, such as organizing information or booking reservations, offering a seamless and efficient digital assistant embedded within Chrome. This initiative reflects Google’s broader vision to enhance user experiences by automating web-based routines, making its browser a central hub for task automation. Anthropic Expands Desktop Automation with Claude 3.5 Sonnet Anthropic, a key player in the AI landscape, has advanced its Claude 3.5 model with a new “Computer Use” feature, enabling direct interaction with a user’s desktop. This update allows Claude to perform tasks such as typing, clicking, and managing multiple applications, making it a powerful tool for automating workflows like data entry, document management, and customer service. Available through APIs and platforms like Amazon Bedrock and Google Cloud’s Vertex AI, Claude’s new capabilities position it as a versatile solution for businesses seeking desktop-level automation, contrasting Google Jarvis’s browser-specific approach. By interpreting screen elements, Claude’s “Computer Use” mode supports broader applications beyond web tasks, offering businesses an edge in efficiency and scalability. How Google Jarvis Stands Out Unlike Anthropic’s desktop-oriented Claude Sonnet, Google Jarvis focuses on automating tasks within Chrome. Jarvis analyzes screenshots of web pages, interprets user commands, and executes actions like clicks or data entry. While still in development, Jarvis’s design suggests a future where mundane web-based tasks are seamlessly handled by AI. Powered by Google’s Gemini 2.0 language model, Jarvis is tailored for users who prioritize web-specific functions, creating a user-friendly assistant that requires no external software. This aligns with Google’s strategy to deepen integration within its ecosystem, making Chrome a more intuitive and productive environment. Microsoft’s Copilot Agents Lead Business Automation Microsoft, meanwhile, continues to enhance its Copilot AI agents, particularly within Dynamics 365. These specialized agents are designed to automate industry-specific workflows, from lead qualification in sales to financial data reconciliation. Unlike Google Jarvis or Anthropic Claude, Microsoft’s Copilot agents target enterprise users, embedding automation within business applications like Teams, Outlook, and SharePoint. With tools like Copilot Studio, organizations can customize workflows to meet specific needs, offering a level of flexibility that resonates with enterprise clients. Early adopters, including Vodafone and Cognizant, have reported significant productivity gains through these integrations. Microsoft’s efforts position Copilot as a robust partner for day-to-day operations, transforming tasks like analysis, project coordination, and document management into automated, efficient processes. Competing Visions for AI Agents As Google, Anthropic, and Microsoft refine their AI strategies, they’re carving out distinct niches in the AI agent landscape: These approaches highlight the diverse applications of AI agents, from enhancing individual user experiences to transforming business operations. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

Read More
Shift From AI Agents to AI Agent Tool Use

Shift From AI Agents to AI Agent Tool Use

The focus of AI development is evolving—from creating autonomous AI Agents to expanding the tools they use, significantly boosting their capabilities and flexibility. Tool access, described and utilized through natural language, is now a critical factor in the functionality and reach of these agents, enabling them to tackle increasingly complex tasks. The Role of Tools in AI Agent Effectiveness AI Agents thrive in user-specific environments like desktops, where rich context enables them to perform tasks more effectively. Instead of just scaling model power, leading AI companies such as OpenAI and Anthropic are pivoting toward tool-enabled frameworks, allowing agents to interact directly with computer GUI navigation for multi-step workflows. This shift positions tools as essential components of AI ecosystems, bridging the gap between raw computational power and actionable user outcomes. OpenAI’s “Operator” and the Future of Autonomous Agents OpenAI is set to release Operator, an AI Agent designed to autonomously perform tasks such as coding and travel booking on a user’s computer. Available as a research preview in January, Operator is part of a broader industry trend toward Agentic Tools that enable seamless, multi-step task execution with minimal user oversight. This approach reflects a shift toward real-time AI capabilities, moving beyond model-centric enhancements to unlock practical, task-driven use cases for AI Agents. Anthropic’s Desktop AI Agent Anthropic is also advancing the field with a reference implementation for computer use, enabling rapid deployment of AI-powered desktop agents. This implementation allows users to leverage Claude, Anthropic’s AI model, in a virtual machine environment with powerful tools for GUI interaction, command-line operations, and file management. Key Features This system provides a controlled yet versatile environment for AI Agents to operate in a safe, flexible, and efficient manner. Technical Implementation To deploy Anthropic’s computer-use demo: bashCopy codeexport ANTHROPIC_API_KEY=%your_api_key% docker run \ -e ANTHROPIC_API_KEY=<Your Anthropic API Key Goes Here> \ -v $HOME/.anthropic:/home/computeruse/.anthropic \ -p 5900:5900 \ -p 8501:8501 \ -p 6080:6080 \ -p 8080:8080 \ -it ghcr.io/anthropics/anthropic-quickstarts:computer-use-demo-latest Tools Overview Each session starts fresh but maintains state within the session, enabling smooth task execution. The Bigger Picture AI Agents are no longer defined solely by their autonomous capabilities. Instead, their success now hinges on how effectively they utilize tools to extend their reach and flexibility. Whether it’s through GUI navigation, command-line interactions, or file management, tool access is transforming the way AI Agents deliver value to users. By focusing on tools rather than just AI model power, companies like OpenAI and Anthropic are building the foundation for a new era of AI-driven productivity. Expect to see more advancements in Agentic Tool design, as the emphasis shifts from autonomy to capability. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

Read More
Consider AI Agents Personas

Consider AI Agents Personas

Treating AI Agents as Personas: Introducing the Era of Agent-Computer Interaction The UX landscape is evolving. While the design community has quickly adopted Large Language Models (LLMs) as tools, we’ve yet to fully grasp their transformative potential. With AI agents now deeply embedded in digital products, they are shifting from tools to active participants in our digital ecosystems. This change demands a new design paradigm—one that views AI agents not just as extensions of human users but as independent personas in their own right. The Rise of Agent-Computer Interaction AI agents represent a new class of users capable of navigating interfaces autonomously and completing complex tasks. This marks the dawn of Agent-Computer Interaction (ACI)—a paradigm where user experience design encompasses the needs of both human users and AI agents. Humans still play a critical role in guiding and supervising these systems, but AI agents must now be treated as distinct personas with unique goals, abilities, and requirements. This shift challenges UX designers to consider how these agents interact with interfaces and perform their tasks, ensuring they are equipped with the information and resources necessary to operate effectively. Understanding AI Agents AI agents are intelligent systems designed to reason, plan, and work across platforms with minimal human intervention. As defined during Google I/O, these agents retain context, anticipate needs, and execute multi-step processes. Advances in AI, such as Anthropic’s Claude and its ability to interact with graphical interfaces, have unlocked new levels of agency. Unlike earlier agents that relied solely on APIs, modern agents can manipulate graphical user interfaces much like human users, enabling seamless interaction with browser-based applications. This capability creates opportunities for new forms of interaction but also demands thoughtful design choices. Two Interaction Approaches for AI Agents Design teams must evaluate these methods based on the task’s complexity and transparency requirements, striking the right balance between efficiency and oversight. Designing Experiences Considering AI Agents Personas As AI agents transition into active users, UX design must expand to accommodate their specific needs. Much like human personas, AI agents require a deep understanding of their capabilities, limitations, and workflows. Creating AI Agent Personas Developing personas for AI agents involves identifying their unique characteristics: These personas inform interface designs that optimize agent workflows, ensuring both agents and humans can collaborate effectively. New UX Research Methodologies UX teams should embrace innovative research techniques, such as A/B testing interfaces for agent performance and monitoring their interaction patterns. While AI agents lack sentience, they exhibit behaviors—reasoning, planning, and adapting—that require careful study and design consideration. Shaping the AI Mind AI agents derive their reasoning capabilities from Large Language Models (LLMs), but their behavior and effectiveness are shaped by UX design. Designers have a unique role in crafting system prompts and developing feedback loops that refine LLM behavior over time. Key Areas for Designer Involvement: This work positions UX professionals as co-creators of AI intelligence, shaping not just interfaces but the underlying behaviors that drive agent interactions. Keeping Humans in the Loop Despite the rise of AI agents, human oversight and control remain essential. UX practitioners must prioritize transparency and trust in agent-driven systems. Key Considerations: Using tools like agentic experience maps—blueprints that visualize the interactions between humans, agents, and products—designers can ensure AI systems remain human-centered. A New Frontier for UX The emergence of AI agents heralds a shift as significant as the transition from desktop to mobile. Just as mobile devices unlocked new opportunities for interaction, AI agents are poised to redefine digital experiences in ways we can’t yet fully predict. By embracing Agent-Computer Interaction, UX designers have an unprecedented opportunity to shape the future of human-AI collaboration. Those who develop expertise in designing for these intelligent agents will lead the way in creating systems that are not only powerful but also deeply human-centered. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

Read More
Scaling Generative AI

Scaling Generative AI

Many organizations follow a hybrid approach to AI infrastructure, combining public clouds, colocation facilities, and on-prem solutions. Specialized GPU-as-a-service vendors, for instance, are becoming popular for handling high-demand AI computations, helping businesses manage costs without compromising performance. Business process outsourcing company TaskUs, for example, focuses on optimizing compute and data flows as it scales its gen AI deployments, while Cognizant advises that companies distinguish between training and inference needs, each with different latency requirements.

Read More
AI Agents and Digital Transformation

Ready for AI Agents

Brands that can effectively integrate agentic AI into their operations stand to gain a significant competitive edge. But as with any innovation, success will depend on balancing the promise of automation with the complexities of trust, privacy, and user experience.

Read More
AI in Programming

AI in Programming

Since the launch of ChatGPT in 2022, developers have been split into two camps: those who ban AI in coding and those who embrace it. Many seasoned programmers not only avoid AI-generated code but also prohibit their teams from using it. Their reasoning is simple: “AI-generated code is unreliable.” Even if one doesn’t agree with this anti-AI stance, they’ve likely faced challenges, hurdles, or frustrations when using AI for programming. The key is finding the right strategies to use AI to your advantage. Many are still using outdated AI strategies from two years ago, likened to cutting down a tree with kitchen knives. Two Major Issues with AI for Developers The Wrong Way to Use AI… …can be broken down into two parts: When ChatGPT first launched, the typical way to work with AI was to visit the website and chat with GPT-3.5 in a browser. The process was straightforward: copy code from the IDE, paste it into ChatGPT with a basic prompt like “add comments,” get the revised code, check for errors, and paste it back into the IDE. Many developers, especially beginners and students, are still using this same method. However, the AI landscape has changed significantly over the last two years, and many have not adjusted their approach to fully leverage AI’s potential. Another common pitfall is how developers use AI. They ask the LLM to generate code, test it, and go back and forth to fix any issues. Often, they fall into an endless loop of AI hallucinations when trying to get the LLM to understand what’s wrong. This can be frustrating and unproductive. Four Tools to Boost Programming Productivity with AI 1. Cursor: AI-First IDE Cursor is an AI-first IDE built on VScode but enhanced with AI features. It allows developers to integrate a chatbot API and use AI as an assistant. Some of Cursor’s standout features include: Cursor integrates seamlessly with VScode, making it easy for existing users to transition. It supports various models, including GPT-4, Claude 3.5 Sonnet, and its built-in free cursor-small model. The combination of Cursor and Sonnet 3.5 has been particularly praised for producing reliable coding results. This tool is a significant improvement over copy-pasting code between ChatGPT and an IDE. 2. Micro Agent: Code + Test Case Micro Agent takes a different approach to AI-generated code by focusing on test cases. Instead of generating large chunks of code, it begins by creating test cases based on the prompt, then writes code that passes those tests. This method results in more grounded and reliable output, especially for functions that are tricky but not overly complex. 3. SWE-agent: AI for GitHub Issues Developed by Princeton Language and Intelligence, SWE-agent specializes in resolving real-world GitHub repository issues and submitting pull requests. It’s a powerful tool for managing large repositories, as it reviews codebases, identifies issues, and makes necessary changes. SWE-agent is open-source and has gained considerable popularity on GitHub. 4. AI Commits: git commit -m AI Commits generates meaningful commit messages based on your git diff. This simple tool eliminates the need for vague or repetitive commit messages like “minor changes.” It’s easy to install and uses GPT-3.5 for efficient, AI-generated commit messages. The Path Forward To stay productive and achieve goals in the rapidly evolving AI landscape, developers need the right tools. The limitations of AI, such as hallucinations, can’t be eliminated, but using the appropriate tools can help mitigate them. Simple, manual interactions like generating code or comments through ChatGPT can be frustrating. By adopting the right strategies and tools, developers can avoid these pitfalls and confidently enhance their coding practices. AI is evolving fast, and keeping up with its changes is crucial. The right tools can make all the difference in your programming workflow. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

Read More
AI Prompts to Accelerate Academic Reading

AI Prompts to Accelerate Academic Reading

10 AI Prompts to Accelerate Academic Reading with ChatGPT and Claude AI In the era of information overload, keeping pace with academic research can feel daunting. Tools like ChatGPT and Claude AI can streamline your reading and help you extract valuable insights from research papers quickly and efficiently. These AI assistants, when used ethically and responsibly, support your critical analysis by summarizing complex studies, highlighting key findings, and breaking down methodologies. While these prompts enhance efficiency, they should complement—never replace—your own critical thinking and thorough reading. AI Prompts for Academic Reading 1. Elevator Pitch Summary Prompt: “Summarize this paper in 3–5 sentences as if explaining it to a colleague during an elevator ride.”This prompt distills the essence of a paper, helping you quickly grasp the core idea and decide its relevance. 2. Key Findings Extraction Prompt: “List the top 5 key findings or conclusions from this paper, with a brief explanation of each.”Cut through jargon to access the research’s core contributions in seconds. 3. Methodology Breakdown Prompt: “Explain the study’s methodology in simple terms. What are its strengths and potential limitations?”Understand the foundation of the research and critically evaluate its validity. 4. Literature Review Assistant Prompt: “Identify the key papers cited in the literature review and summarize each in one sentence, explaining its connection to the study.”A game-changer for understanding the context and building your own literature review. 5. Jargon Buster Prompt: “List specialized terms or acronyms in this paper with definitions in plain language.”Create a personalized glossary to simplify dense academic language. 6. Visual Aid Interpreter Prompt: “Explain the key takeaways from Figure X (or Table Y) and its significance to the study.”Unlock insights from charts and tables, ensuring no critical information is missed. 7. Implications Explorer Prompt: “What are the potential real-world implications or applications of this research? Suggest 3–5 possible impacts.”Connect theory to practice by exploring broader outcomes and significance. 8. Cross-Disciplinary Connections Prompt: “How might this paper’s findings or methods apply to [insert your field]? Suggest potential connections or applications.”Encourage interdisciplinary thinking by finding links between research areas. 9. Future Research Generator Prompt: “Based on the limitations and unanswered questions, suggest 3–5 potential directions for future research.”Spark new ideas and identify gaps for exploration in your field. 10. The Devil’s Advocate Prompt: “Play devil’s advocate: What criticisms or counterarguments could be made against the paper’s main claims? How might the authors respond?”Refine your critical thinking and prepare for discussions or reviews. Additional Resources Generative AI Prompts with Retrieval Augmented GenerationAI Agents and Tabular DataAI Evolves With Agentforce and Atlas Conclusion Incorporating these prompts into your routine can help you process information faster, understand complex concepts, and uncover new insights. Remember, AI is here to assist—not replace—your research skills. Stay critical, adapt prompts to your needs, and maximize your academic productivity. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more Alphabet Soup of Cloud Terminology As with any technology, the cloud brings its own alphabet soup of terms. This insight will hopefully help you navigate Read more

Read More
AI-Driven Chatbots in Education

AI-Driven Chatbots in Education

As AI-driven chatbots enter college courses, the potential to offer students 24/7 support is game-changing. However, there’s a critical caveat: when we customize chatbots by uploading documents, we don’t just add knowledge — we introduce biases. The documents we choose influence chatbot responses, subtly shaping how students interact with course material and, ultimately, how they think. So, how can we ensure that AI chatbots promote critical thinking rather than merely serving to reinforce our own viewpoints? How Course Chatbots Differ from Administrative Chatbots Chatbot teaching assistants have been around for some time in education, but low-cost access to large language models (LLMs) and accessible tools now make it easy for instructors to create customized course chatbots. Unlike chatbots used in administrative settings that rely on a defined “ground truth” (e.g., policy), educational chatbots often cover nuanced and debated topics. While instructors typically bring specific theories or perspectives to the table, a chatbot trained with tailored content can either reinforce a single view or introduce a range of academic perspectives. With tools like ChatGPT, Claude, Gemini, or Copilot, instructors can upload specific documents to fine-tune chatbot responses. This customization allows a chatbot to provide nuanced responses, often aligned with course-specific materials. But, unlike administrative chatbots that reference well-defined facts, course chatbots require ethical responsibility due to the subjective nature of academic content. Curating Content for Classroom Chatbots Having a 24/7 teaching assistant can be a powerful resource, and today’s tools make it easy to upload course documents and adapt LLMs to specific curricula. Options like OpenAI’s GPT Assistant, IBL’s AI Mentor, and Druid’s Conversational AI allow instructors to shape the knowledge base of course-specific chatbots. However, curating documents goes beyond technical ease — the content chosen affects not only what students learn but also how they think. The documents you select will significantly shape, though not dictate, chatbot responses. Combined with the LLM’s base model, chatbot instructions, and the conversation context, the curated content influences chatbot output — for better or worse — depending on your instructional goals. Curating for Critical Thinking vs. Reinforcing Bias A key educational principle is teaching students “how to think, not what to think.” However, some educators may, even inadvertently, lean toward dictating specific viewpoints when curating content. It’s critical to recognize the potential for biases that could influence students’ engagement with the material. Here are some common biases to be mindful of when curating chatbot content: While this list isn’t exhaustive, it highlights the complexities of curating content for educational chatbots. It’s important to recognize that adding data shifts — not erases — inherent biases in the LLM’s responses. Few academic disciplines offer a single, undisputed “truth.” AI-Driven Chatbots in Education. Tips for Ethical and Thoughtful Chatbot Curation Here are some practical tips to help you create an ethically balanced course chatbot: This approach helps prevent a chatbot from merely reflecting a single perspective, instead guiding students toward a broader understanding of the material. Ethical Obligations As educators, our ethical obligations extend to ensuring transparency about curated materials and explaining our selection choices. If some documents represent what you consider “ground truth” (e.g., on climate change), it’s still crucial to include alternative views and equip students to evaluate the chatbot’s outputs critically. Equity Customizing chatbots for educational use is powerful but requires deliberate consideration of potential biases. By curating diverse perspectives, being transparent in choices, and refining chatbot content, instructors can foster critical thinking and more meaningful student engagement. AI-Driven Chatbots in Education AI-powered chatbots are interactive tools that can help educational institutions streamline communication and improve the learning experience. They can be used for a variety of purposes, including: Some examples of AI chatbots in education include: While AI chatbots can be a strategic move for educational institutions, it’s important to balance innovation with the privacy and security of student data.  Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more Top Ten Reasons Why Tectonic Loves the Cloud The Cloud is Good for Everyone – Why Tectonic loves the cloud You don’t need to worry about tracking licenses. Read more

Read More
gettectonic.com