I am a Ph.D. and digital product business owner who uses AI (Claude, ChatGPT, and other AI tools) every day to build, market, and scale my business. My 12-year-old son and I are looking for an experienced AI tutor who can teach us how to work with AI effectively—not just how to ask questions, but how to think, build, create, and solve problems with AI. This is an ongoing coaching relationship, not a one-time class. I already use AI daily and want to become significantly more advanced in prompt engineering, AI workflows, automation, and business applications. My son is curious, creative, and highly motivated. We want someone who can grow with him over the coming years as AI continues to evolve. WHAT WE ARE LOOKING FOR • Weekly one-on-one coaching sessions (one for me, one for my son) • Hands-on learning using real projects—not lectures or slide presentations • Practical skills that can be used immediately • A structured curriculum that builds over time • Someone who enjoys teaching and can explain complex ideas clearly • Experience with Claude, ChatGPT, and current AI tools MY LEARNING GOALS I use AI every day and want to continue improving how I work with it. Topics include: • Advanced prompt engineering • AI workflow design • Prompt refinement and iteration • Research and fact-checking • Marketing copy • Product descriptions • Sales pages • Email sequences • Business automation • AI-assisted content creation • Website content • Productivity systems • Emerging AI tools and best practices JORDAN'S LEARNING GOALS Jordan is 12 years old. While we'll certainly use AI for school projects and writing, our larger goal is to help him develop future-ready skills that will grow with him through middle school, high school, college, and beyond. We are looking for someone who can progressively teach him how to use AI to create, build, and solve problems. Topics may include: • Learning how to communicate effectively with AI and using AI to support academic success • Critical thinking and verifying AI responses • Research and creative writing • Brainstorming and problem solving • Website design and development with AI • Creating simple games with AI • Building apps and digital tools as his skills grow • Learning basic programming concepts using AI as a coach • Entrepreneurship and business ideas • Using AI to help businesses become more efficient • Marketing and content creation • Responsible and ethical use of AI • Developing confidence as a creator—not just a consumer—of AI technology The ideal tutor enjoys helping young people build real-world skills and can gradually increase the difficulty as Jordan grows. WHAT WE ARE LOOKING FOR IN YOU • Demonstrated experience teaching AI—not simply using it • Strong prompt engineering knowledge • Comfortable teaching both an adult professional and a motivated 12-year-old • Patient, engaging, and adaptable • Able to build a long-term curriculum instead of isolated lessons • Reliable, organized, and an excellent communicator Bonus experience: • Programming or software development • Website development • AI-assisted coding • Game development • Digital marketing • Entrepreneurship • Small business consulting LOGISTICS • Two weekly sessions (one for Jordan and one for me--45–60 minutes each) • Zoom • Weekly to start • Start date: ASAP • Budget: Please include your hourly rate. TO APPLY Please include: Your hourly rate. Your experience teaching AI and prompt engineering. An example of how you would structure Jordan's first month of lessons. An example of how you would structure my first month of lessons. What you think will be the most valuable AI skills for a motivated 12-year-old to develop over the next five years. Applications that do not answer these questions will not be considered. We are looking for someone who enjoys teaching, stays current with AI, and is excited about helping both a business owner and a young learner become confident, capable AI users and creators.

Seeking an experienced AI Solutions Architect to provide consultation and design an enterprise-grade Agentic AI platform capable of automating business workflows, retrieving knowledge from enterprise data sources, and integrating with existing business systems. This engagement focuses on solution architecture, technical design, technology selection, implementation planning, and best practices rather than full-scale application development. Scope of Consultation The consultant will: Assess current business processes and identify high-value AI automation opportunities. Design an enterprise Agentic AI architecture aligned with business and technical requirements. Define multi-agent workflows, agent responsibilities, and orchestration strategies. Design a scalable Retrieval-Augmented Generation (RAG) architecture for enterprise knowledge retrieval. Recommend the appropriate Large Language Models (OpenAI, Claude, Gemini, AWS Bedrock, Azure OpenAI, etc.) based on cost, performance, and use cases. Recommend vector database technologies and semantic search architecture. Design secure integrations with enterprise applications, APIs, and internal knowledge repositories. Define prompt engineering strategies, AI guardrails, evaluation methodology, and governance practices. Recommend cloud architecture and deployment strategies for AWS, Azure, or Google Cloud Platform. Provide guidance on LLMOps, monitoring, observability, security, model lifecycle management, and scalability. Develop an implementation roadmap, including phases, estimated effort, risks, and technical recommendations. Required Expertise Enterprise AI Solution Architecture Agentic AI Multi-Agent Systems Retrieval-Augmented Generation (RAG) Large Language Models (LLMs) LangChain LangGraph Prompt Engineering OpenAI API Anthropic Claude Google Gemini AWS Bedrock Python Vector Databases Enterprise System Integration AWS Microsoft Azure Google Cloud Platform AI Governance LLMOps MLOps Workflow Automation Deliverables Enterprise Agentic AI solution architecture document Multi-agent workflow design and orchestration diagrams RAG architecture and knowledge management design Vector database recommendation and data flow architecture Cloud deployment architecture Integration strategy for enterprise systems AI governance, security, and LLMOps recommendations Implementation roadmap with milestones and estimated effort Architecture review presentation and knowledge transfer session Project Outcome Delivered a comprehensive enterprise AI architecture and implementation strategy that provides a scalable foundation for deploying Agentic AI solutions. The consultation enabled stakeholders to make informed technology decisions, reduce implementation risks, accelerate development, and establish best practices for governance, security, and long-term operational success.

Looking for someone to help me learn AI for my construction company. We want to streamline workflows and relieve back office duties.

As Exponentials is the first startup in the world to solve the AI backlash, we feel that it make sense and is realistic for us to choose our own investors rather than them choosing us. And to select those investors who are most aligned with our scope of activities, strategies and values long term, or at least will support them at a high level of capability, even if much of the AI industry currently remains in AI extraction as opposed to AI collaboration between humans and AI co-evolution in the service of human needs mode. Exponentials is seeking a senior freelance strategist and operator to coordinate the inner networks of Dario Amodei, Sam Altman and Elon Musk, with the specific aim of positioning Exponentials as a natural “home investment” for all three empires. Exponentials investment thesis: 1. Exponentials is solving the AI backlash via the co-evolution of AI and humans in the service of human needs, and thus moving from the current extraction model of AI to a collaborative model. For Exponentials, this is moving past discovery silos to create unified discovery across (initially, $25 trillion TAM) Ecommerce, healthcare, education and media. This is accomplished through the combination of Search, LLM's and World models 2 AI can't be (optimally) successful if too many of its (potential) customers are fearful of or dislike AI 3 AI is feared and disliked (in addition to loved), as customers are smart enough to realize that AI is employing an extraction model on humans rather than a collaborative model with humans in the service of human needs 4 Major tech CEO's telling the public that they are wrong to have negative views about AI is insulting one's customer 5 If the AI industry wants to get into a war with the public it will be a stalemate at best. AI has enough perceived benefits already and the AI companies are powerful enough that they can impose their will on the public to a certain degree, but 6 It is inevitable that the AI companies who actually give the customers what they want and what truly benefits them, by flipping from the push to the pull model, will have a sustainable competitive advantage, with both inevitability and defensibility. 7 Famously, the future is already here. It is just not evenly distributed. And famously, there is nothing more powerful than an idea whose time has come. 8 We are not selling technology. We are not selling a model of AI. We are selling an empowered path for humanity that is inevitable and defensible because the AI backlash is real and not sustainable long term. What the research says about Exponentials Book Excerpt How to Avoid the AI Backlash Leading to the AI Bubble Hartfield, Solis & Elridge In 2026, artificial intelligence stands at a strange crossroads. On paper, it is the most powerful general-purpose technology humanity has ever built. In practice, a growing share of the public now experiences AI as something done to them rather than for them — and that emotional turn is reshaping the technology's future as surely as any breakthrough in model architecture. When AI is built on top of architectures that treat humans as targets for extraction — of attention, data, or money — it inevitably triggers backlash, and that backlash in turn increases the odds of a violent AI bubble. When AI is built as infrastructure for discovery that co-evolves with humans in the service of our actual needs, we get a different trajectory: compounding value, durable trust, and a stable foundation for long-term innovation. Exponentials, for example, is building a different kind of infrastructure: a universal personal discovery engine that integrates large language models, adaptive search, and world models to help people navigate complex needs across commerce, health, education, and media. Instead of pushing content to you based on what is most profitable to show, such a system starts from your articulated intent, your evolving context, and your long-term goals. Pull discovery uses the same core components that power today's AI wave — but arranges them differently. It treats you as an integrated being whose health, work, learning, and media consumption are interconnected, rather than as a series of separate "users" in separate verticals to be monetized independently. An AI-driven discovery engine built on pull, not push, gives us a way to answer, credibly, the question that will increasingly be asked by citizens, workers, and regulators: "Who is this for?" If we build the missing layer of AI for human needs — a discovery infrastructure that co-evolves with us, across the whole pyramid of human need — AI becomes a tool that communities defend rather than attack, that regulators shape rather than suppress, that individuals choose rather than endure. Book Excerpt The Co-Evolution of Artificial Intelligence and Humans Kai Mercer & Lena Voss We are not passengers in the age of AI. We are in the earliest stages of a co-evolutionary relationship — one in which humans and artificial intelligence are changing each other in continuous feedback loops. The question is not whether this co-evolution will happen. It is already happening. The question is what kind of co-evolution we will choose. For two decades, the dominant model has been "push discovery" — systems that decide what you should see based on what maximizes platform profit or engagement, not what serves your needs. These systems are powered by sophisticated AI. But they are not designed to serve human flourishing. The AI gets smarter at manipulation. Humans adapt by becoming more skeptical, more weary, more prone to decision fatigue. This is co-evolution in the wrong direction. Exponentials demonstrates a fundamentally different architecture — one built around understanding and serving human intent. The platform uses large language models to understand queries in context (not just keywords, but intent, constraints, and values), world models that maintain continuously updated representations of options, and hybrid search across structured and unstructured data. Critically, the person evolves too: getting better at articulating needs, learning to trust well-matched recommendations, navigating abundance efficiently rather than being overwhelmed by it. Critically, Exponentials proves this model is economically viable. Operating on subscriptions rather than advertising means the platform succeeds when you find what you need efficiently — not when you're kept scrolling indefinitely. When economic incentives align with human wellbeing rather than against it, co-evolution can move in a fundamentally different direction. The co-evolutionary trajectory is not determined by technological capability. It is determined by design philosophy, business model, and the values embedded in the system's optimization function. Exponentials proves the second path is viable — and that aligned AI, genuinely designed to serve human flourishing, can work at scale.

Project Capital Advisors (PCA) is seeking an experienced AI Solutions Architect to support a government modernization consulting engagement. The initial project involves helping a county government modernize its citizen telephone operations using Voice AI, workflow automation, and enterprise system integrations. The selected consultant will work directly with executive leadership and public sector stakeholders to assess existing operations, develop technical recommendations, and help define an implementation roadmap. Responsibilities include: * Leading technical discovery sessions with client stakeholders * Evaluating existing phone systems and call workflows * Designing Voice AI and Contact Center AI solution architectures * Recommending enterprise platforms and integration strategies * Creating technical documentation and architecture diagrams * Supporting project scoping, budgeting, and executive presentations Preferred experience: * Enterprise Voice AI or Contact Center AI * Twilio, Amazon Connect, Genesys Cloud, Five9, Retell AI, ElevenLabs, Azure AI, or OpenAI APIs * API integrations and workflow automation * Experience with government or other regulated industries is preferred Engagement Details: * Contract/Fractional * Approximately 10–20 hours per week initially * Remote with occasional travel * Opportunity for additional engagements as PCA expands its Government AI consulting practice Please review the attached position description for complete responsibilities, qualifications, and application requirements.

We're looking for an AI & Data Analysis expert to lead the integration of intelligent tools within the business platform. You'll connect Google Ads, marketing data files, and operational data sources to build AI agents via Claude that support business decision-making across our pet retail operations. Key Responsibilities Design and configure Claude-powered agents using tool use, structured prompts, and automated workflows for data analysis Integrate the Google Ads API to extract campaign metrics and feed decision-making dashboards Ingest, clean, and structure CSV, Excel, and other marketing data formats for agent processing Generate automated narrative reports and actionable visualizations for the executive and marketing teams Maintain and iterate on data pipelines connecting advertising, sales, and inventory data Required Technical Skills Claude API / Anthropic MCP (Model Context Protocol) Prompt engineering and LLM tool use / function calling Google Ads API Python or JavaScript (for pipelines and integrations) SQL / PostgreSQL / Supabase Pandas / NumPy or equivalent data libraries REST API consumption and integration Advanced Excel / Google Sheets Nice to have: Google Analytics, BigQuery, Looker, Power BI Ideal Profile Proven experience building data pipelines or LLM-powered tools in a production environment Hands-on familiarity with the Anthropic API and agent/tool-use patterns Ability to translate raw data into clear, actionable business recommendations Self-directed — can propose and build solutions without exhaustive specs Initial Projects Campaign ROI Agent — connects Google Ads + business sales data to generate automatic performance alerts and recommendations Marketing File Pipeline — ingests CSV/XLSX marketing files and produces AI-generated summaries and insights Executive Dashboard — decision-support interface with Claude-generated action recommendations based on live data

The Role: As a Software Engineer on our AI Infrastructure team, you will help design the core systems that power Prism AI’s generative AI platform. You will help build infrastructure and tools that ensure the reliability, performance, quality, and availability of our AI system. Our mission is to make Prism AI the most reliable and user friendly generative AI platform in the world. You will partner closely with our cloud infrastructure team, product team, and performance team to deliver infrastructure that bridges the gap between our customer and the ultra-performant proprietary Prism inference engine. Key Responsibilities: Contribute to the design and development of scalable backend infrastructure that supports distributed training, inference, and data pipelines Build and maintain core backend services such as LLM CI/CD pipeline, control plane, and model serving systems Support performance optimization, cost efficiency, and reliability improvements across compute, storage, and networking layers Building frameworks and safeguards to ensure Prism AI has the best model quality in the industry Collaborate with performance, training, and product teams to translate research and product needs into infrastructure solutions Participate in code reviews, technical discussions, and continuous integration and deployment processes Minimum Qualifications: Bachelor’s degree in Computer Science, Engineering, or a related technical field (or equivalent practical experience) 3 years of experience in software engineering, with a focus on infrastructure or machine learning systems Strong programming skills in Python, Go, or a similar language Proven experience in ML infrastructure and tooling (e.g., PyTorch, MLflow, Vertex AI, SageMaker, Kubernetes, etc.). Basic understanding of LLM knowledge (e.g., context length, disaggregated prefill, KV cache memory estimation, etc) Preferred Qualifications: 5+ years of experience in software engineering, with a focus on infrastructure or machine learning systems Experience with open source inference engine like vLLM, Sglang, or TRT-LLM Contributions to open-source infrastructure or ML projects Experience in building large scale ML/MLOps infrastructure

We are seeking a Claude expert to develop and enhance our AI tool. to integrate all of my marketing using constant contact email and texting. also wanting to integrate all work so I don't have to deal with the mundane. The ideal candidate will have extensive experience with Claude and be able to integrate it into our existing systems. Responsibilities include developing new features, optimizing performance, and ensuring seamless integration with our current infrastructure. The role requires a strong understanding of AI technologies and the ability to work independently.

26ers is building Human + AI operating systems that help organizations improve decision quality, execution speed, and organizational leverage. We are seeking a customer-facing AI Architect who can work directly with executives, operational leaders, and technical teams to design practical AI solutions that solve real business problems. This role helps organizations identify high-value AI opportunities, redesign workflows, modernize operations, and implement Human + AI operating systems that improve execution, decision-making, and organizational effectiveness. The ideal candidate can move fluidly between customer conversations, workflow discovery, solution design, governance considerations, and implementation planning. Responsibilities • Participate in customer discovery and solution design conversations • Analyze current-state workflows and identify AI transformation opportunities • Design Human + AI operating models, agentic workflows, and operational systems that improve execution and decision-making • Create solution blueprints, implementation plans, and statements of work • Collaborate with implementation developers and technical delivery teams • Consider data governance, security, compliance, and operational requirements throughout solution design • Contribute to the development of reusable 26ers methodologies, frameworks, and institutional knowledge • Design systems that capture, structure, and operationalize organizational knowledge and institutional learning Ideal Experience • Experience designing AI-powered business workflows and operational systems • Strong understanding of OpenAI, Claude, and modern LLM-based solution design • Experience with workflow orchestration platforms, AI agents, automation systems, and API-based architectures • Strong understanding of data governance, information security, and enterprise AI deployment considerations • Experience translating business requirements into solution architectures, implementation plans, and statements of work • Customer-facing experience in consulting, solution engineering, professional services, digital transformation, or technical advisory roles • Experience conducting discovery workshops, workflow assessments, and current-state/future-state design exercises • Understanding of operating model design, workflow modernization, and organizational transformation • Strong written and verbal communication skills with executive stakeholders • Ability to leverage AI tools to rapidly produce architecture drafts, blueprints, requirements documents, implementation plans, training materials, and customer deliverables Nice to Have • Experience with Gemini, MCP, LangGraph, CrewAI, AutoGen, or similar orchestration frameworks • Experience with n8n, Make, Zapier, or workflow automation platforms • Experience with vector databases, RAG architectures, and organizational knowledge systems • Experience building or deploying multi-agent systems • Government, healthcare, financial services, or other regulated industry experience • Startup, founder, or early-stage company experience • Experience designing systems that capture institutional knowledge, operational learning, or organizational intelligence • Military, consulting, enterprise software, or transformation leadership experience Success in this role • Quickly understand a client's operating environment, workflows, and business objectives • Identify high-value opportunities for AI-enabled transformation and operational leverage • Translate customer goals into practical solution designs, implementation plans, and delivery roadmaps • Balance innovation, governance, security, and operational realities • Help organizations move from AI experimentation to operational execution This role may begin on a contract basis and expand into a longer-term strategic partnership as 26ers grows.

We are seeking a skilled freelancer to develop a Tavus AI agent capable of interviewing people and recording conversations directly to cloud storage. The ideal candidate will have experience in AI development and cloud integration, ensuring seamless and secure data storage.