We're looking for professionals who use documents, sheets, and presentations (such as Microsoft 365 or Google Workspace) to give us feedback on River AI (rivereditor.com) - a new AI workspace for iterating on business documents faster. River allows you to upload your files, and then make precise edits to your documents (with the full context of the project you're working on). We're looking for professionals across the board - from legal writers to marketing bloggers.

Hello, I'm looking for an experienced automation engineer or systems integrator to build a cloud-based automated messaging system for our restaurant operations. We operate 10 restaurant locations with approximately 40 Motorola TLK 25 radios using WAVE PTX. The goal is to use Radio.co to schedule already created operational voice mp3 reminders (opening procedures, food safety checks, labor reviews, closing tasks, etc.) and automatically broadcast those messages to designated WAVE PTX talk groups. The proposed solution would utilize: * Radio.co for scheduling and audio content * Azure Windows VM as a dedicated cloud-hosted dispatch workstation * WAVE PTX Dispatch for radio communications * Virtual audio routing and PTT automation to transmit scheduled messages to Motorola TLK 25 devices We're looking for someone who can design, deploy, test, and document the complete solution, including Azure configuration, audio routing, automation, monitoring, and scalability for future growth. If you have experience with cloud infrastructure, automation, streaming audio, dispatch systems, or Motorola WAVE PTX, please let me know how you would approach this project. We have a clear vision and proposed architecture for this project and are looking for a skilled developer to help execute, validate, and deploy it. The objective is to create a cloud-based automated messaging system that uses Radio.co for scheduled voice reminders and broadcasts those messages to Motorola TLK 25 radios through WAVE PTX Dispatch. We anticipate utilizing an Azure Windows VM, virtual audio routing, and automated Push-to-Talk activation to create a reliable, scalable solution for restaurant operations. At this stage, we need a technical expert who can review our proposed workflow, identify any improvements or challenges, and then build, test, document, and deploy the solution. The ideal candidate has experience with cloud infrastructure, automation, audio streaming, browser automation, dispatch systems, or Motorola WAVE PTX. We value practical execution and problem-solving and are looking for someone who can take an existing concept and turn it into a production-ready system.

Summary 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. Here is the Exponentials investor demo (in progress) https://pull-discovery-core.base44.app/ Exponentials is seeking a senior virtual assistant or project manager. We find that our best hires have a natural culture fit. Are you an individual who sees solutions over problems? If so, how does that build a foundation for your work? How proactive are you, and what does that mean? Do you believe that world-class teams win in business and in life? What does it mean to be a part of and help lead a world-class team? Whare are your personal qualities and beliefs that contribute to this? Are you a fit to Exponentials as detailed below. What do you understand about Exponentials from the below that others miss? What is Exponentials trying to do, and why are you clearly the best candidate to do it? 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.

DESCRIPTION We're a small applied AI lab running a live, production-track AI product for an institutional financial services client. The work is technical, fast-moving, and high-stakes. We need to fill a critical infrastructure role with someone senior, collaborative, and genuinely excited about building in the current AI tooling ecosystem. THE ROLE You'll own the data infrastructure layer for an AI-powered intelligence platform built on the Microsoft Azure ecosystem. This is a hands-on engineering position — you're responsible for designing, building, and maintaining the pipelines that feed a live AI scoring engine. The environment is agentic. Data moves from 15+ heterogeneous external sources (APIs, PDFs, regulatory filings, web) through Bronze, Silver, and Gold layers into a scoring and inference system. The hard problems are extraction quality, schema normalization, pipeline reliability, and getting the right data to the scoring engine in the right shape. You'll work directly with the technical lead and engagement lead. No layers. Fast decisions. WHAT YOU'LL OWN + Data pipeline architecture and delivery across Bronze (raw ingestion), Silver (normalization, NLP extraction, entity resolution), and Gold (unified output, scoring-ready) layers + Microsoft Fabric lakehouse implementation — OneLake, Data Pipelines, Dataflows Gen2, Warehouse, and downstream system integration + Microsoft Foundry (formerly Azure AI Studio) — agent orchestration, prompt pipelines, and AI model integration within a secure Azure tenancy + Azure Data Factory orchestration for structured source ingestion +Salesforce integration via Snowflake native connector — field mapping, custom object schemas, sync reliability Extraction pipelines for unstructured sources (PDFs, regulatory filings, web content), coordinating with Azure OpenAI-based extraction agents +Data governance and security posture — all data stays within the client's Azure tenancy; data residency is non-negotiable REQUIRED: Technical Skills + Microsoft Fabric — production experience, not sandbox. You should be able to speak to Lakehouse vs. Warehouse tradeoffs, OneLake architecture, and real pipeline implementation. Microsoft Foundry / Azure AI Studio — hands-on with agent deployments, prompt flow, model endpoints, and Azure OpenAI integration within an enterprise Azure tenancy + Azure Data Factory — pipeline authoring, trigger management, connector configuration, monitoring +Snowflake — Gold layer data warehousing, schema design, query optimization, native connector usage (specifically Salesforce) + Python — data engineering contexts: pandas, PySpark, API clients, extraction scripts + SQL — complex joins, window functions, schema design; SQL Server preferred + Azure Blob Storage / ADLS Gen2 — Parquet/Delta format, access control, lifecycle management REQUIRED: AI-Augmented Development This is a hard requirement. You should be actively using AI coding tools to multiply your output — fluency with Claude Code, Cursor, and OpenAI Codex as part of your daily development workflow. If these aren't already in your stack, this isn't the right fit. We hire for multiplied output, not raw hours. REQUIRED: Demonstrable Work We don't evaluate resumes alone. Bring something — a GitHub repo, a deployed pipeline, an architecture document you authored, a case study with real numbers. We should be able to look at your work and understand what you built, what decisions you made, and why. Work under NDA is fine if you can describe it in enough detail to convey complexity and ownership. ATTITUDE & WORK STYLE Comfortable with Agile Scrum and its accompanying ceremonies. You raise issues early and help solve them. You communicate tradeoffs clearly without over-explaining. You're comfortable with evolving specs and don't need to win the architecture argument — just build the right thing within the approved stack. We're a small, senior team with low friction and direct communication. That's the environment; it works if you work with it. THE STACK The client environment has specific technology approvals. Production work runs on Azure OpenAI (client-hosted), Microsoft Fabric, Microsoft Foundry, Snowflake, Azure Data Factory, ADLS Gen2, Salesforce via Snowflake native connector, and SQL Server. LangChain, DeepSeek, and the external Claude API are not approved for this environment. NICE TO HAVES Experience with financial services or institutional investment data (SEC EDGAR, public pension filings, regulatory documents), familiarity with InvestorFlow or Salesforce Financial Services Cloud, unstructured document extraction at scale, or Azure Purview.

I need an experienced AI video producer to recreate an approximately three-minute patient-education video for parents of pediatric dental patients using a realistic digital twin that reflects my current appearance and a high-quality clone of my voice. The project will be generated from an updated script rather than by editing the old footage frame-by-frame. The final video should look natural, professional, and reassuring—not like a generic AI avatar or overly polished promotional video. The scope should include: - Creating or guiding creation of a realistic custom digital twin - Creating or integrating a high-quality voice clone - Generating the video from a written script - Correcting pacing, pauses, lip synchronization, and pronunciation - Accurately handling medical terminology - Adding simple professional branding, captions, and lower-third identification - Delivering a final 16:9, 1080p MP4 suitable for patient parent education I would like to begin with a paid 15–20 second proof of concept before proceeding with the full three-minute video. Important: My face, voice, photographs, recordings, script, avatar, and voice model may not be reused, published, displayed in a portfolio, or retained for other projects without my written permission. After completion, any copies stored outside my controlled account should be deleted unless I specifically authorize otherwise. I prefer that the digital twin and voice model be created in an account that I control. I want the ability to use the completed digital twin and voice model for future patient-education projects. No patient-identifying information will be included, and the final video must remain appropriate for a healthcare patient-education setting.

I am looking for an experienced and honest mentor who can teach me how to create sustainable income online using AI, digital products, Amazon, and other legitimate online opportunities. My main goal is to build financial freedom through products and systems that can be created once and sold repeatedly. I understand that no income is completely passive and that marketing, updates, and customer service may still be required. However, I want to focus on scalable opportunities instead of work that only pays me for each hour I spend. I am especially interested in: Using AI to create useful digital products E-books, workbooks, planners, guides, templates, and printables Amazon KDP and other Amazon income opportunities Selling digital products through Etsy, Shopify, or other platforms Canva templates and downloadable resources AI-assisted online or freelance jobs Affiliate marketing and content-based income Other realistic online income streams that can grow over time I already have experience in accounting, bookkeeping, business operations, Shopify, print-on-demand, social media, Etsy, Amazon Seller Central, Canva, and building my clothing brand, SPARGI. I have many ideas and I am willing to do the work, but I need someone to help me determine which opportunities are realistic, profitable, and best suited to my skills. I do not need a generic list of ideas. I need personalized, step-by-step guidance. The person I hire should help me: Review my experience, skills, current businesses, budget, and available time Identify the best one or two online income streams for me to focus on first Explain the advantages, risks, costs, and realistic income potential of each option Create a clear 30-day and 90-day action plan Help me select and validate profitable digital-product ideas Teach me how to create, package, price, list, and market the products Explain how AI can help with research, writing, design, automation, and productivity Guide me through Amazon KDP or other suitable Amazon opportunities Show me legitimate AI-assisted online jobs or freelance services I could offer Review my work and provide feedback as I complete each step Help me avoid scams, oversaturated ideas, and unnecessary expenses My first goal is to choose a clear direction and successfully create and launch one income-producing digital asset. I would then like to build additional income streams using a repeatable process. I am not looking for a get-rich-quick program, cryptocurrency opportunity, expensive course, dropshipping package, or someone whose main goal is to sell me additional services. I want practical, ethical, realistic guidance from someone who has personally created online income or helped others do it. Please include the following in your proposal: Which online income streams have you personally created or managed? What experience do you have with AI, digital products, Amazon KDP, Etsy, Shopify, or online freelancing? Based on my description, which two opportunities would you explore first? What would we accomplish during the first three to five hours? Can you provide examples of products, businesses, or clients you have helped? What ongoing work is normally required after a product is launched? I would like to begin with a small paid trial. The first deliverable should include a personalized assessment, recommendations for the best opportunities, and a clear 30-day action plan.

I need a full-stack developer to build a simple AI-powered chat assistant. The app should allow users to type a question and receive a response from the ChatGPT API. The UI should be clean, responsive, and built with React and TypeScript. Main Requirements: - Build a simple React + TypeScript frontend - Add a chat input and response area - Connect the app to the ChatGPT/OpenAI API - Show loading state while the AI is responding - Handle basic API errors - Keep the code clean and easy to understand - Provide short setup instructions in a README file

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.

I am a fashion business owner/designer looking for an experienced AI creative professional to help me integrate AI into my fashion design and product development workflow. I am specifically looking for someone who understands both: * fashion/apparel design * AI tools such as ChatGPT, Midjourney, and related visual AI platforms I want hands-on guidance and training using my actual collections and products.

We're expanding our team to better support our clients as AI-powered search continues to evolve. Some of our customers are looking to improve their visibility in platforms like ChatGPT, Google AI Overviews, Perplexity, and other answer engines. We're looking for someone with a strong background in GEO, AEO, or AI search optimization to join our team. Responsibilities: -Develop AI search optimization strategies -Improve visibility across answer engines and LLMs -Optimize content for AI-generated search results -Stay current on emerging GEO/AEO best practices Requirements: -Experience with GEO, AEO, or AI search optimization -Strong SEO and content optimization background -Excellent research and analytical skills -US-based preferred Note: This position is open to US-based freelancers only. Applications from freelancers located outside the United States will not be considered.