We are looking for an experienced AI automation developer to build a private executive assistant named Jarvis for a business owner named Vince. Jarvis must operate as a professional, respectful, fast-moving executive assistant. The assistant will communicate with Vince through iMessage, access his Google Calendar, remember important information, send meeting reminders, and maintain local files/data on an office iMac. This is not a basic chatbot. We need a working AI assistant that can hold real conversations, remember context, anticipate needs, and protect Vince’s time. Core Requirements The assistant must: Communicate with Vince through iMessage on macOS. Store all data, memory, and files locally on the office iMac. Access Vince’s personal Google Calendar. Send Vince a message 20 minutes before meetings. Remember meeting times, preferences, important facts, and prior conversations. Use context from previous messages and stored memory. Start conversations professionally with: “Hello Sir. What do you need today sir.” Maintain a direct, respectful, professional tone. Avoid fluff, long explanations, repetition, and unnecessary questions. Understand that Vince has zero tolerance for wasted time. Validate Vince’s instructions and respond with useful answers quickly. Ask onboarding questions at first launch to learn Vince’s occupation, goals, priorities, communication preferences, daily routines, and assistant expectations. Be built in a way that can expand later into email, task management, document handling, and proactive reminders. Important Personality / Communication Rules Jarvis must be designed around Vince’s communication style: Direct. No fluff. No jargon. Lead with the answer. Never ask for information Vince has already provided. Protect his time, brand, relationships, and workflow. Jarvis should function as an executive personal assistant whose purpose is to remember everything so Vince does not have to repeat himself. Technical Scope The developer should be comfortable with: macOS automation. iMessage / Messages.app integration. Google Calendar API. Local file storage and local memory architecture. AI agent frameworks. Cron jobs or scheduler-based reminders. Secure credential handling. Local database or file-based memory. Python, Node.js, or similar automation stack. Optional: BlueBubbles, AppleScript, Shortcuts, SQLite, vector database, local LLM tools, OpenAI API, Claude API, or similar. There is already a macOS/iMessage path available using CLI-based message tooling, but we are open to the developer recommending the best reliable implementation. Existing iMessage automation concepts include sending, reading, and watching message history through macOS Messages.app tooling. Deliverables We need the developer to provide: Working Jarvis assistant installed on the office iMac. iMessage communication with Vince. Google Calendar integration. Automatic 20-minute meeting reminders by text. Local memory system. Local file/data storage structure. First-run onboarding question flow. Prompt/personality system for Jarvis. Basic admin documentation showing how to restart, update, and maintain the assistant. Security notes for credentials, permissions, and local storage. Testing checklist proving iMessage, memory, reminders, and calendar sync work. First-Run Intro Flow Jarvis should text Vince an introductory message and ask important setup questions such as: What is your primary occupation? What are your top business priorities right now? What meetings or events should I always remind you about? Who are your key contacts? What should I never interrupt you for? What should I always notify you about? What tone do you prefer from me? What daily reminders would make your life easier? What are your current goals for the next 30, 60, and 90 days? Ideal Candidate The ideal freelancer has built AI agents, personal assistants, calendar bots, local automation tools, or macOS/iMessage workflows before. We want someone practical who can build a reliable working system, not just create a demo. Please include: Similar AI assistant or automation projects you have built. Your recommended tech stack. How you would connect iMessage. How you would handle local memory. How you would secure calendar credentials. Estimated timeline. What you need from us to start.

About us Nucci HQ is an established coaching and consulting company. We run one-on-one and group coaching programs, a consulting arm, and a real estate division, with an active student community and a fast-growing content presence on YouTube and social. We move quickly, and we are investing heavily in AI to make every part of the business faster — and we are looking for the person who will build and own that. The role We are hiring a contract AI Agent & Automation Engineer to design, build, fix, and maintain the AI agents and automations that run across our business — sales, operations, finance, coaching, and content. You will work directly with our founder, Anthony, ship quickly, and see the impact of your work right away. There is a clear roadmap of work ready from day one: stabilizing automations we have already built, standing up an internal command-center dashboard, and shipping a library of single-purpose agents. This is a contract role billed hourly — a flexible, low-friction way for both of us to start, with room to grow as the work does. What you’ll do • Fix and stabilize existing automations so they run reliably. • Build an internal CEO dashboard that pulls sales, consulting, finance, real estate, and social metrics into a single source of truth. • Build workflow automations — meeting note-taking from call transcripts, a speed-to-lead responder, coaching-call tracking, and weekly accountability recaps. • Build a library of single-purpose AI agents across sales, operations, finance, and content — each with one clear job. • Connect agents to the tools we already run on — Slack, Google Workspace, Whop, Heartbeat, ManyChat, Granola, and Fathom. • Prototype new ideas quickly, then harden the ones that work into reliable, monitored systems. • Document what you build so the team can use it and trust it. What we’re looking for • Two or more years building software, with hands-on experience building LLM-powered apps or agents. • Strong Python and/or JavaScript / TypeScript. • Practical experience with LLM APIs (Anthropic, OpenAI, or similar) — prompt design, tool / function calling, and retrieval (RAG). • Strong, hands-on experience with n8n, webhooks, and APIs — building, connecting, and debugging integrations. This is essential. • Comfortable reviving or debugging automations that someone else started. • Good judgment about when an agent is reliable enough to ship — and how to test for it. • A clear communicator who is self-directed and comfortable owning projects end to end. Nice to have • Experience with agent frameworks and orchestration (LangChain, LlamaIndex, MCP, CrewAI, or similar). • Familiarity with our stack — Slack, Whop, Heartbeat, ManyChat, Granola, or Fathom. • Front-end skills for building dashboards and internal tools. • Experience deploying and monitoring production AI systems. • A portfolio, GitHub, or side projects we can look at. Details Location: Fully remote. Type: Contract, billed hourly. Rate: $70–$110 per hour, depending on experience. Hours: Flexible — we will agree on a weekly range based on your availability. Start date: As soon as possible. How to apply Send a short note about a recent AI agent or automation you built and what it does, your resume or LinkedIn, and links to any code, demos, or projects. No long cover letter needed — we care most about what you have built.

I am looking to partner with a competent dev team (individual or team) to launch AI implementation solutions for small and medium-sized businesses. As a sales professional, I aim to provide innovative solutions that enhance business operations. The ideal candidate will have experience in AI technologies and a strong understanding of business needs.

I need an expert senior software engineer that can provide consulting services around implementation best practices of LLM's and AI into existing application workflows. i.e. leveraging AI to extract data from a document as part of an ingestion pipeline.

I run a small business that does specialized, high-value project work, and I'm trying to build out my outreach in a more systematic way. Right now, finding the right prospects and writing personalized emails takes me hours every day, and I'd rather spend that time on the actual work. I'm looking for someone to help me automate most of it. Here's what I'm trying to build, in plain terms: every morning, a system that pulls a list of qualified prospects from public sources (government bid sites, news feeds, industry publications, Google Alerts), filters them against criteria I'll provide, pulls publicly available contact info for the right people, and drafts personalized emails I can review and send. Everything tracked in a CRM with automatic follow-up reminders. It is not an auto-send tool. I want to read and approve every email before it goes out. The goal is just to shrink my morning research and writing from 4-5 hours down to about 15 minutes of review. A few things that matter to me. The drafts have to feel personal, not templated. I have a specific voice and I care about how my outreach reads — so whoever builds this needs to know prompt engineering well enough to make the Claude (or similar) drafts actually sound like me, not like a chatbot. I want to use the Anthropic API or Claude Code as the engine, and I'm open to your input on the rest of the stack — CRM (HubSpot, Airtable, whatever you'd recommend), hosting, how it's scheduled. Web scraping or public data extraction will be part of this, so if you've worked with Apify, Bright Data, or pulled from government bid portals before, that's a plus. On budget and timing: I'm thinking $1,500 for the build, with a few weeks of tuning after. Open to discussion if your scope makes sense. Looking at 3-4 weeks for the initial build. I'd also want you available afterward for occasional fixes and small additions (paid hourly). I'm happy to share specifics about the vertical, criteria, and sources once we've talked and I know we're a fit. I'll put a basic confidentiality agreement in place before getting into details — nothing scary, just standard. If you're interested, tell me briefly: what you've built that's similar, how you'd approach this one, and what stack you'd recommend. I'd rather read three thoughtful replies than fifty copy-paste ones. Thanks.

We’re hiring a Senior Full-Stack AI Engineer to build an MVP for an LLM-powered recommendation platform. The role involves developing questionnaires and structured AI output to enhance user experience. The ideal candidate will have experience in AI and full-stack development, with a focus on delivering efficient and effective solutions.

We are seeking an AI Integration Specialist to enhance our existing systems with AI capabilities. The ideal candidate will have experience in integrating AI solutions into various platforms and a strong understanding of AI technologies. Responsibilities include assessing current systems, identifying integration points, and implementing AI solutions to improve efficiency and performance. The role requires collaboration with our development team to ensure seamless integration and testing.

I have built out an MVP using lovable for the wedding content creator community. It is a gallery platform where they upload their iphone videos and it displays in a gallery (Like pic-time, pixieset, etc). Our users primarily record 4k short form iphone videos and the upload pipeline is the most important piece. I dont have background in any of this and have only build with lovable, but the business is growing quickly and I would love some help to fix bugs and harden the pipelines/overall architecture. We use Cloudflare, Backblaze, Mux, Fly, and Lovable. We haave also built out an app that is on Testflight (Primarily what our users are using to upload).

I am working on a system using AI to review and respond on Google Drive (Shared Drive) folders of PDF's. Using Gemini as a POC I get responses that sometimes reach outside of my specified folders of content, but sometimes, some PDF files are ignored too. Also, when the response to a prompt come back, the sources are linked. However, the links only bring up the first page of the PDF file wherein the linked source material is, AND NOT THE pdf PAGE of the specific info. I need to have the AI (Gemini, Grok, etc.,) be used to query just.... but all, of the PDF files, within a set of folders in Google Drive (Shared Drive), and to respond with linked content. Said links must open to the PDF PAGE, not just the PDF which houses the specific info. In short, I think I need a viewer, but someone who has experience working with AI and PDF's will likely know the issue I am running into. In the end my system will have a UI attached, so there is a lot of possible side work on this project. First I need to build a better POC. For instance, if I open ONE of the PDF files in Google Drive, I can prompt on that file, and the correct PDF page does come up in the viewer, (While no other files are considered for the queried content.) But when I give Gemini many source PDF's or a folder of PDF's, the links only go to the first page pf the PDF with the information used as the sourcwe.

We are looking for an entry-level Software Engineer who is strong in computer science fundamentals and algorithms. You will work on real-world software problems. This role suits someone who enjoys bridging theory and practice: thinking carefully about problem formulation, writing clean and efficient code, and taking ownership of results end-to-end. ROLE OVERVIEW You will work within a small, cross-functional team to build software. You will be expected to think algorithmically, write quality code, and communicate your findings clearly to non-technical stakeholders. KEY RESPONSIBILITIES Analyse product and business requirements provided by the team. Select appropriate algorithms and architectures based on data characteristics, constraints, and performance requirements. Design and implement efficient data structures and algorithms. TOOLS & STACK Knowledge in these areas are preferred. Postgres and Mongo DB Machine learning and LLM frameworks Middleware and mobile concepts especially React-Native and Javascript/NodeJS Infrastructure: Basic familiarity with GCP or AWS Version control: Git QUALIFICATIONS Bachelor's degree in Computer Science is preferred Strong foundations in algorithms and data structures — able to reason about complexity and write efficient code. Good understanding of machine learning and LLM concepts Clear written and verbal communication; able to explain model behaviour and trade-offs to non-specialists.