FrantzMaster AI is an all-in-one intelligent personal, business, transportation, finance, savings, reminder, and daily-life assistant designed to help people save money, make better decisions, stay organized, and manage everything in one place. The main purpose of FrantzMaster AI is to become a user’s everyday AI assistant for finding the best opportunities, comparing prices, tracking important expenses, managing bills, discovering affordable transportation and fuel options, monitoring business activities, and reminding users about important tasks before they forget them. FrantzMaster AI should be simple enough for anyone to use while being powerful enough to help individuals, families, drivers, truck drivers, small-business owners, entrepreneurs, and companies. The application should use artificial intelligence, automation, location-based services, personalized recommendations, price comparisons, calculators, alerts, reminders, dashboards, and intelligent tracking to help users save time and money. 1. SMART AI PERSONAL ASSISTANT FrantzMaster AI should have a central AI assistant that users can communicate with naturally. Users should be able to type or speak requests such as: * “Find me the cheapest gas near me.” * “Find the best load for my truck.” * “Find me a cheap ride.” * “Remind me to pay my insurance tomorrow.” * “How much money did I spend this month?” * “How much will this trip cost me?” * “Find the best loan for me.” * “Compare these insurance prices.” * “Remind me about my car payment.” * “What bills do I have coming up?” * “How much can I save this month?” * “Find the cheapest route.” * “Calculate my profit.” * “Help me manage my business.” * “What should I pay first?” * “Show me everything I need to do today.” The AI should understand the user’s request and automatically direct them to the correct feature. ⸻ 2. BEST LOAD FINDER FrantzMaster AI should include a powerful Load Finder designed especially for truck drivers, box-truck drivers, owner-operators, carriers, and transportation businesses. The system should help users find available loads and compare them. Users should be able to enter: * Truck type * Truck size * Maximum weight * Current location * Destination * Available dates * Preferred distance * Fuel economy * Minimum desired payout * Return-trip preferences * Empty-mile preferences The AI should help compare loads based on: * Total payout * Miles * Estimated fuel cost * Tolls * Estimated driving time * Deadhead miles * Estimated profit * Profit per mile * Profit per hour * Pickup location * Delivery location * Broker information * Load requirements The AI should help users understand which loads may provide better potential profit. The app should never guarantee that a load is profitable. Instead, it should provide calculations and estimates so the user can make an informed decision. ⸻ 3. CHEAPEST GAS FINDER FrantzMaster AI should include a Best Gas Finder. The user should be able to see nearby gas stations and compare fuel prices. The application should allow users to search for: * Cheapest regular gas * Cheapest mid-grade * Cheapest premium * Cheapest diesel * Gas stations near the user * Gas stations along a route * Gas stations near a destination The AI should consider: * Distance * Fuel price * Estimated amount of fuel needed * Vehicle fuel economy * Potential savings * Route convenience The system should help users avoid driving far out of their way just to save a few cents per gallon. ⸻ 4. LOAN COMPARISON FrantzMaster AI should include a Loan Finder and Loan Comparison Tool. Users should be able to enter: * Loan amount * Credit score range * Desired loan term * Monthly income * Monthly expenses * Down payment * Interest rate * Existing debt The AI should help users compare loan offers based on: * APR * Interest rate * Monthly payment * Total interest * Total repayment * Loan term * Fees The AI should clearly explain that loan offers, approval decisions, interest rates, and eligibility depend on the lender and the user’s financial information. The app should help users understand loans rather than promise approval. ⸻ 5. INSURANCE COMPARISON FrantzMaster AI should help users organize and compare insurance information. The app can support categories such as: * Car insurance * Truck insurance * Commercial insurance * Home insurance * Renters insurance * Business insurance * Life insurance * Other insurance Users should be able to save: * Insurance company * Policy number * Monthly payment * Due date * Renewal date * Coverage information * Agent information * Customer service information The AI should remind users before payments and renewals. ⸻ 6. CAR AND TRUCK MANAGEMENT FrantzMaster AI should have a Vehicle Manager. Users should be able to add: * Car * SUV * Van * Pickup truck * Box truck * Commercial truck * Trailer * Motorcycle For every vehicle, users should be able to track: * VIN * License plate * Mileage * Registration expiration * Insurance expiration * Inspection * Oil changes * Tire rotations * Brake service * Maintenance * Repairs * Fuel expenses * Loan payments * Vehicle value * Service history The app should automatically remind users when important vehicle tasks are approaching. ⸻ 7. BILL MANAGEMENT FrantzMaster AI should include a powerful Bill Manager. Users should be able to add every recurring bill they have.

We are seeking a highly experienced Advanced Artificial Intelligence instructor and curriculum consultant to design, customize, and deliver a private, instructor-led AI training program for a group of six participants. The program is expected to begin in October 2026 and may be delivered onsite in San Francisco, San Diego, or Los Angeles, California. The final location and schedule will be determined with the selected instructor and client. Program Overview This engagement is intended for experienced AI practitioners who can connect advanced technical concepts with practical enterprise implementation. The selected instructor will work with the client and NobleProg to determine the appropriate program duration, sequence the curriculum, customize the content, prepare hands-on labs, and deliver the training. The program should provide participants with practical knowledge of modern AI engineering, enterprise AI architecture, AI governance, large language models, production AI systems, and the infrastructure required to deploy and manage advanced AI solutions. Requested Curriculum Areas The proposed program should address the following subjects: AI governance, Responsible AI, and AI risk management Large language models and foundation models LLM training, fine-tuning, and distributed training LLMOps and production AI MLOps and AI platform engineering On-premises AI, private AI, and data sovereignty Agentic AI and multi-agent systems Retrieval-Augmented Generation Knowledge graphs and enterprise knowledge integration AI infrastructure and GPU clusters Distributed computing for AI workloads High-performance model inference AI security and model security Regulatory compliance and enterprise AI controls Enterprise AI architecture and deployment strategy The final curriculum may be adjusted based on participant backgrounds, available infrastructure, program duration, and the client’s technical priorities. Training Requirements The training should preferably be delivered as a private, instructor-led program and include: Practical exercises and demonstrations Instructor-guided hands-on labs Realistic enterprise AI use cases Participant training materials Lab instructions and supporting resources Recommendations for continued learning Certificates of completion Opportunities for participant questions and technical discussion Instructor Responsibilities The selected instructor will be responsible for: Participating in a client alignment and discovery meeting Assessing the participants’ technical backgrounds and learning objectives Recommending an appropriate course duration and training schedule Developing a proposed course outline Customizing the curriculum for the client Identifying participant prerequisites Defining all hardware, software, cloud, and GPU requirements Preparing practical exercises and hands-on labs Delivering the training virtually or onsite Providing training materials and supporting resources Recommending certificate or continuing education options Coordinating with NobleProg throughout the engagement Program Details Anticipated start: October 2026 Number of participants: Six Potential onsite locations: San Francisco, California San Diego, California Los Angeles, California Delivery options: Private onsite instructor-led training Private virtual instructor-led training A blended or hybrid schedule, when appropriate Applicants should indicate which locations and delivery formats they can support. Ideal Instructor Qualifications The ideal instructor will have substantial professional experience in several of the following areas: Enterprise artificial intelligence Machine learning engineering Large language model development and deployment Foundation model training or fine-tuning Distributed model training LLMOps and MLOps AI platform engineering Agentic AI and multi-agent architecture Retrieval-Augmented Generation Knowledge graphs GPU infrastructure and AI clusters High-performance model serving and inference Private or on-premises AI deployments Data sovereignty and regulated data environments AI governance and Responsible AI AI security, compliance, and risk management Enterprise AI architecture Previous experience delivering advanced instructor-led training to corporate, government, engineering, or technical audiences is strongly preferred. Relevant certifications, publications, conference presentations, open-source contributions, enterprise projects, or academic credentials should be included in the application. Proposal Requirements Please provide the following information with your application: A summary of your relevant AI experience Your instructor-led training experience Your experience with enterprise or government clients Your recommended course duration Your proposed training schedule A preliminary course outline Recommended technical prerequisites for participants Required participant software and development environments Required cloud platforms, GPU resources, or local infrastructure Your ability to support virtual delivery The onsite locations you can support Your availability during October 2026 Relevant certifications and professional credentials Examples of similar training programs you have delivered Certificate or continuing education options you can support Any applicable corporate, government, or group-training considerations Confirmation that you accept the stated daily rate Please clearly identify which curriculum topics you can teach directly and whether any portions of the program would require an additional specialist. Compensation Training rate: $1,200 per day of training delivery The final project compensation will be determined by the approved number of training days. The agreed compensation is expected to cover: One client alignment and discovery meeting Curriculum planning Training material preparation Reasonable curriculum customization Hands-on lab preparation Instructor-led training delivery Reasonable local travel expenses, including gas, Uber, and parking, will be reimbursed when necessary and approved in advance. Any airfare, lodging, extended-distance transportation, specialized cloud environments, GPU resources, software licenses, or other significant expenses must be disclosed and approved before the engagement begins. Payment Terms Payment milestones and invoicing terms will be finalized before the engagement begins. Applicants should disclose any required deposits, cancellation terms, or other payment conditions in their proposals. Important Links Additional program and curriculum details: https://docs.google.com/document/d/1EZ4KRYCaZZYtg3JX4eSu82H3pmjCRxEguHCbbTD7yJ0/edit?usp=sharing NobleProg instructor process and SOP: https://share.synthesia.io/a0788c6e-56d5-4da8-92c6-0d5c03ad6d52 Please review both links before submitting your application. How to Apply Begin your application with the phrase “Advanced Enterprise AI Instructor” to confirm that you have reviewed the complete posting. Please submit a focused proposal that explains how you would structure this program for six participants, including the number of days you recommend, the balance between lectures and labs, and the infrastructure required to complete the hands-on exercises successfully.

Need a basic ai bot built on high level. I will work with you one on one so it’s perfect. - need it to quote. - if someone signs up for service, push the contact info into a slack channel. Need someone who is experienced with high level ai bots

I’m seeking an expert for a project-based engagement to design and build an agentic AI-powered investor relations (IR) capability that automates key aspects of the IR function while maintaining appropriate regulatory and disclosure compliance. The objective is to create a system that can develop and consistently reinforce a strategic corporate narrative, identify relevant industry themes and opportunities, and generate high-quality investor-facing content that positions the client company as a recognized subject-matter expert and thought leader in embodied intelligence. The ideal candidate will have experience with agentic AI systems, workflow automation, LLM-powered content generation, financial communications, and/or investor relations, with a strong understanding of the compliance considerations associated with communications by public companies.

I am looking for an experienced Python developer or algorithm specialist to rigorously test and validate a few scripts that I have. The script locates stocks that have been trending for 2-3 days on the MACD zero line PRE BREAKOUT . Your primary goal will be to stress test the script, identify edge cases, very output accuracy, and ensure robust performance under various conditions. Code review: Evaluate the existing code base for efficiency, security, and best practices. Functional Testing: run the script against sample data sets to verify output, accuracy Edge case testing: intentionally push the algorithm to its limits to find potential bogs, bottlenecks or failure points. Documentation: provide the detailed report of your findings, including reproduction steps for any bogs and recommendations for optimization. Requirements: proven experience in algorithm, testing the bargaining and performance optimization. Strong proficiency in python and relevant testing frameworks. Familiarity with API’s or database Excellent and analytical skills and attention to detail. To apply: please submit a brief proposal, including: 1. Examples of past projects where you tested de BAIRD or optimize an algorithm. 2. Your preferred mythology for testing this type of script.. 3. Your estimated turnaround time for this project..

We are looking for an experienced AI Architect / Senior LLM Engineer to design and build an enterprise-grade AI platform for the healthcare industry. You will lead the architecture and implementation of intelligent AI solutions that improve clinical operations, automate administrative workflows, and enable healthcare professionals to access trusted medical knowledge through advanced AI technologies. The ideal candidate has hands-on experience building production-ready Agentic AI systems, Multi-Agent architectures, RAG pipelines, and LLMOps using modern AI frameworks and cloud platforms. Responsibilities Design and develop scalable Agentic AI solutions for healthcare applications. Build Multi-Agent Systems using LangGraph, CrewAI, or AutoGen. Develop enterprise Retrieval-Augmented Generation (RAG) pipelines for medical knowledge retrieval. Create AI agents for clinical knowledge assistance, document intelligence, workflow automation, and care coordination. Build and integrate MCP servers and custom AI tools with internal healthcare systems. Optimize prompt engineering, retrieval strategies, and response quality for high accuracy. Implement AI guardrails, evaluation pipelines, monitoring, and observability for production deployments. Deploy secure, scalable AI infrastructure on AWS using Infrastructure as Code and CI/CD best practices. Collaborate with engineering, product, and healthcare stakeholders to deliver reliable AI solutions. Required Skills 5+ years of experience in AI/ML or Generative AI development. Strong expertise in Python and backend API development. Experience with LangGraph, CrewAI, AutoGen, or similar multi-agent frameworks. Hands-on experience with AWS Bedrock, Azure OpenAI, or Vertex AI. Strong understanding of RAG architectures, vector databases, embeddings, and semantic search. Experience with Pinecone, Weaviate, pgvector, or similar vector databases. Knowledge of LLMOps, evaluation frameworks, prompt engineering, and AI observability tools. Experience with Docker, Terraform, CI/CD, and cloud-native deployments. Familiarity with healthcare compliance, security, and responsible AI practices is highly preferred. Preferred Technologies LangGraph CrewAI AutoGen AWS Bedrock Claude GPT-4o Gemini Pinecone pgvector LangSmith Arize Phoenix FastAPI Docker Terraform GitHub Actions MLflow Nice to Have Experience developing AI-powered healthcare platforms. Knowledge of healthcare workflows, clinical documentation, or medical knowledge systems. Experience integrating AI solutions with enterprise applications through APIs and MCP. Familiarity with AI governance, model evaluation, and production monitoring. If you are passionate about building enterprise-scale AI solutions that transform healthcare through Agentic AI and Generative AI, we'd love to hear from you.

Hello! I’m looking for a thoughtful and reliable developer to create a simple Document AI application for a personal collection of articles, academic papers, and book chapters. The app should accept images containing text and use both **OCR and an LLM** to: 1. Extract the text from each image while preserving headings, paragraphs, and reading order as accurately as possible. 2. Organize the extracted text into logical sections. 3. Generate a concise summary for each section. 4. Generate an overall summary for the full article, paper, or chapter. 5. Save the original extracted text and summaries in an easy-to-use format, such as Markdown, TXT, or DOCX. The extracted text may later be used with text-to-speech software, but text-to-speech is **not part of this project**. The images may vary in quality and layout, so I would appreciate someone who can recommend an appropriate OCR approach and thoughtfully handle issues such as page order, columns, headings, footnotes, and repeated headers or page numbers. ## Preferred Qualifications * Experience with Python and OCR tools or services * Experience integrating LLM APIs * Familiarity with document structure, reading order, and long-document summarization * Ability to create a simple, approachable interface * Clear and patient communication * Respect for the privacy of uploaded documents ## Initial Deliverables * A working application that can process one document containing multiple images * Extracted and organized text * Section-level summaries * One complete document or chapter summary * Exportable output files * Basic setup and usage instructions * Source code I would be happy to provide several sample images so we can first confirm the OCR quality and discuss the best approach before building the full application. When applying, please share: * A brief explanation of how you would approach the project * Examples of similar OCR, document-processing, or LLM applications you have created * Which OCR and LLM technologies you would recommend * Whether you would suggest a desktop, local web, or cloud-based application * Any questions you have about the documents or desired output Thank you very much for taking the time to read this. I’m hoping to find someone who is careful, kind, and genuinely interested in making the application accurate and pleasant to use.

About us: Luxe Intelligence is a Baltimore based AI consulting firm. We design and deliver custom AI agent systems for business clients, including regulated industries, with a growing security and government-adjacent practice. We design the system and own the client relationship. You build to spec. The kind of work: Real examples of project types on our roadmap: - Data matching and compliance checking agents that cross-reference large lists (10,000+ rows) with no shared ID, using fuzzy name matching, confidence scoring, and human review flags - Research agents that pull from defined sources and produce structured memos with citations, and say "unverified" instead of guessing - Workflow automations across webhooks, spreadsheets, CRMs, Slack, and email - Read and write-back integrations with systems of record like Salesforce - Deployments inside client cloud environments with audit logging and security review support Must haves: - Strong Python, including pandas and API work - Hands-on experience with LLM APIs (Anthropic, OpenAI): prompt design, structured outputs, cost control - Fuzzy matching or entity resolution experience on real data - Cloud deployment on AWS, Azure, or GCP - Security-minded engineering as a habit, not an afterthought: secrets management, least-privilege access, encryption in transit and at rest, audit trails, human-in-the-loop review steps - Clear written English and documented handoffs Nice to have: - A real cybersecurity background: security engineering, compliance frameworks (SOC 2, NIST, FedRAMP awareness), or secure deployment in regulated environments - US citizenship with eligibility for a government security clearance, or an active clearance, is a plus and worth mentioning - Make.com or similar automation platforms - Salesforce API - Experience answering client security questionnaires How we work: Fixed-price milestones scoped from agreed hour estimates, paid on delivery and approval. NDA signed before any project details are shared. No client contact; all communication runs through Luxe. Some overlap with US Eastern hours. Every engagement starts with one small paid test milestone. Strong performance can grow into a larger ongoing role. To apply, answer these four things, and start your reply with the word CHARCOAL so we know you read this far: 1. Describe a fuzzy matching or entity resolution project you built. How big was the data, and how did you score confidence? 2. Describe an LLM-powered system you deployed into someone else's environment. What broke, and how did you fix it? 3. Estimate this: two lists, about 10,000 rows and 2,000 rows, no shared ID. Need matches, confidence scores, and a monthly flagged-items report. Roughly how many hours, broken down however makes sense to you? 4. Your hourly rate, your weekly available hours, and any security or clearance background.

I need a freelancer to build an AI agent for one simple task. The task involves scraping data from a webpage on a daily basis and then posting that data to an excel document on a daily basis. The task would then run automatically and keep copies of the screen shots so that the data can be verified.

Senior Computer Vision / AI Engineer — Proof-of-Concept Development AiChairWatch™ is seeking an expert-level Computer Vision / AI Engineer to develop a focused proof of concept for a patent-pending recreational seating-management technology. This is a paid, project-based independent contractor engagement. The initial objective is to establish technical feasibility and build a controlled POC—not a full commercial SaaS platform. Project Scope The POC will use computer vision and video analytics to detect people and recreational seating, establish and maintain temporary person-to-seat associations, track movement within defined spatial areas, generate state-based events, and support a lightweight operational dashboard. The engineer will be expected to help determine the appropriate technical architecture rather than simply implement a predetermined technology stack. Initial work will include: Review confidential technical requirements under NDA Evaluate technical feasibility and recommend the POC architecture Process live and/or recorded camera video Detect people and recreational seating Establish temporary person-to-seat associations Maintain associations as people move through the monitored environment Handle temporary occlusion and tracking loss Determine movement relative to configurable spatial zones Generate defined system events and state transitions Implement basic event/timing logic Create a lightweight staff-facing POC dashboard Log system events and test results Measure and document performance, limitations, and failure conditions Provide recommendations for subsequent pilot development Required Experience We are specifically looking for someone with substantial hands-on experience in real-world computer vision/video analytics. Strong experience should include several of the following: Python OpenCV YOLO or comparable object-detection frameworks Multi-object tracking Person tracking and/or re-identification Occlusion handling Spatial reasoning / regions of interest / geofencing Video-stream processing RTSP/IP camera integration Event-driven application development REST APIs/backend development Git-based source control Experience with technologies such as ByteTrack, BoT-SORT, DeepSORT, NVIDIA DeepStream, Jetson, ONNX, TensorRT, or comparable CV/edge-AI technologies is desirable but not mandatory. What We Are NOT Looking For This is not primarily a generative-AI, ChatGPT, LLM, chatbot, or prompt-engineering project. The primary technical challenge involves computer vision, persistent object/person tracking, spatial reasoning, and reliable state determination from video. Initial Development Approach The engagement is expected to begin with a paid technical feasibility and architecture milestone. The selected engineer will review the detailed requirements, evaluate representative video/test conditions, identify technical risks, recommend the detection/tracking architecture, and define measurable POC acceptance criteria. Upon successful completion of that milestone, the engagement may proceed into POC development. The initial POC will intentionally use a small controlled environment rather than attempt to build the complete commercial platform. Intellectual Property & Confidentiality AiChairWatch™ involves patent-pending technology. Detailed technical specifications and proprietary operating logic will be provided only to selected candidates after execution of a confidentiality agreement. Development work will be governed by written provisions addressing confidentiality, source code, work product, intellectual property, third-party/open-source components, and ownership/assignment of applicable development results. When Applying Please answer the following: Describe the most relevant computer-vision system you have personally designed or implemented. What experience do you have with multi-object/person tracking? How have you handled temporary occlusion, lost tracks, and re-identification? Have you processed live IP-camera/RTSP streams in a production or prototype environment? Please describe. Consider this simplified scenario: A camera observes several recreational chairs. A person occupies one chair, gets up, walks through the monitored area, is temporarily obscured by other people, and later leaves the area. At a high level, how would you maintain the person's association with the original chair and determine when that person has actually left the monitored area? What would you want to evaluate before selecting the computer-vision architecture for this POC? Please provide links to relevant GitHub repositories, demonstrations, publications, portfolio examples, or other technical work where available. Clearly identify which portions of the examples you personally designed or implemented. What is your availability during the next 60 days? Please provide your hourly rate and/or preferred structure for an initial paid technical-feasibility milestone. U.S.-based candidates preferred. AiChairWatch™ AI-Powered Recreational Seating Management Patent Pending