I need someone to build reporting in GA4 that will identify and remove bots to see a clear picture of actual bounce rate. The task involves creating a report to filter out bot traffic and provide insights on bounce rates. The ideal candidate should have experience with Google Analytics 4 and data analysis.

We are looking for an experienced Lead Crypto/AI Engineer who can also effectively manage team operations and client accounts. Your responsibilities will include: Leading the development and optimization of blockchain solutions and AI systems. Overseeing technical project delivery, ensuring alignment with client expectations. Managing client account processes, including setup, communications, and deliverables. Coordinating and supervising workflows among global team members. Requirements: Proven experience in blockchain technology, cryptocurrency projects, and AI development. Strong leadership, project management, and account management skills. Proficiency in programming languages such as Python, Solidity, Solana, JavaScript, or equivalent. Excellent communication skills and a track record of effectively managing client relationships.

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.

We are looking to do a new build of our patch bay label designer tool. Customers are currently able to pick their model of patch bay, add text labels, colorize, and group. They can then checkout and it creates print ready artwork for our team to produce. The UI is a bit clunky and there are some features we want to add such as an admin tool to add and edit labels. You can see the current tool here: https://create.traceaudio.com/

We need a developer to build a simple AI chatbot MVP using Next.js and the OpenAI API. The chatbot should allow a business owner to enter FAQ or support content, then let users ask questions through a chat interface. The AI should answer based only on the provided content.

I’m looking for a senior AI app developer who can help me build an AI-powered MVP while also guiding me through the technical decisions. This is not just a coding task. I want someone who can think through the product, recommend the right architecture, explain tradeoffs, and build the first working version. The ideal person should be comfortable with OpenAI/LLM integrations, full-stack development, database design, authentication, deployment, and startup-style MVP execution. I’d like to work with someone who can act almost like a technical partner: build the product, teach me what is being done, and help me understand how to maintain or scale it later.

I'm an AI automation expert with a growing roster of clients, and I'm bringing on a skilled freelancer to help handle the smaller projects so I can keep up with demand. This isn't a new or one-off operation. I work with many clients already, and bring on new ones every week. I'm looking for someone reliable I can hand work to consistently, not just for a single project. You should be comfortable building AI automations independently and delivering clean, working solutions for client-facing work. To apply, please: - Send a short Loom introducing yourself - Share examples of your previous automation work I review every application personally, so a quick, genuine intro goes a long way. If we're a good fit, there's steady, ongoing work here.

I am looking for a freelancer to help me set up a personal AI-powered workstation and workflow system. I work in banking, credit underwriting, trade finance, and real estate. I use a Windows/PC environment and need a practical setup that helps me organize documents, prompts, templates, workflows, and repeatable tasks using ChatGPT, Claude, Microsoft Office, OneDrive/SharePoint, and Windows tools. The goal is not theory. I need a working system that helps me draft emails, credit memos, financial analysis prompts, presentation prompts, document review workflows, and repeatable instructions. Deliverables: 1. Recommended workstation/software setup 2. Folder and file organization structure 3. Prompt library by workflow category 4. Repeatable SOPs for common tasks 5. Basic training on how to use the system This should be fixed-price and milestone-based. Please include examples of similar workflow systems you have built.

We are looking for an expert backend developer and automation engineer to extend an existing, production-grade Model Context Protocol (MCP) server and overhaul its orchestration layer. The headline correction for this project: the existing Lawfather MCP is to be retained and extended, not rebuilt. It already exposes deterministic, parameterized Playwright tools for every required county portal (District Clerk, HCSO, HCDAO) and a client database. Those backend tools are the reliable layer and are not the source of the instability this project exists to fix. The instability lives entirely in the orchestration layer — the model-driven layer that decides when and how to call the tools. The fix is to move deterministic control out of model-followed prose and into code, and to host the agent on an always-on machine with persistent memory. Core Project Principles • Extend, Don't Rebuild: Retain and extend the existing MCP; do not re-implement portal scrapers from scratch. • Code Over Prompts: Deterministic logic lives strictly in tool code, never in instructions the model must remember each session. • No Caller Loops: Batch operations must run to completion server-side. No operation may require the caller (model) to loop. • Agnostic Architecture: The system must remain model-agnostic and host-agnostic. No single provider — Anthropic, OpenAI, Z.ai/GLM, or Nous — may be a hard dependency. • Privilege First: Client data stays on owned hardware; the model is never the gatekeeper of which case a file belongs to. Existing Tool Inventory (To Be Inherited As-Is) The following tools already exist on the production MCP (containerized on a local Synology NAS) and are in daily use. Re-deriving their behavior is completely out of scope: • hcdc_get_docket: Court settings by date range + bar number (District Clerk). • hcdc_check_filings: Per case: standard defense filings present vs. missing. • hcdc_download_filings: Images-tab documents: bulk OR selective by filters; dest_subfolder; dry_run. Note: The parameterized download tools already cover most retrieval requests. "All filings," "this filing," "all subpoenas," "all resets," and "everything filed that day" are argument combinations on this tool, not separate features. • hcso_locate: Defendant custody location (facility / floor / pod) by SPN. • hcdao_grab_file: Download a single named file from the DA portal Files tab. • hcdao_download_discovery: Batch / delta discovery download from the DA portal. • hcdao_download_media_alert: Batch-download files listed in a 'New Media Available' portal email. • hcdao_case_summary: Scrape the Case Jacket quick summary / DAO narrative. • hcdao_plea_offer: Scrape current plea offer + full offer history. • hcdao_assigned_ada: Assigned ADA name / email / phone on a case. • lookup_client / list_clients: Resolve / list clients from the shared client database. Scoped Work (Paid Deliverables) 1. County Case Resolver (New Tool): Find a case from partial identifiers — any subset of (name, SPN, DOB, court, cause). Searches county systems (not just the local client DB). MUST return a ranked candidate list for the user to choose from; MUST NEVER auto-select. Wrong-defendant selection is a privilege failure, not a cosmetic bug. 2. Latest-Version Retrieval: Add scope=latest to hcdao_grab_file so 'most recent' selects the newest among supplements instead of the first match. 3. Async Transcribe Tool (Skill to Tool Promotion): Build a deterministic MCP tool using Gemini 3.1 Pro Preview for transcription, followed by a second pass that sends the transcript back with case context for cleanup (speaker mapping, defense-moment preamble). Long-running: implement as an async job (submit to job id to poll to fetch), NOT a synchronous call. 4. OCR Tool (Skill to Tool Promotion): Implement a readability check on ingest. If a document is not cleanly readable, FLAG it and ASK before sending to Gemini 3.1 Pro Preview for OCR. OCR must be gated and confirmed, never automatic. 5. Server-Side Batch Jobs: Move all chunk, loop, delta, and throttle logic OFF the caller and INTO the tool code. One call runs the batch to completion. 6. Queued HCDAO Fixes: For hcdao_download_discovery, add a portal_ids filter for targeted single-file pulls and a custom output-path / Drive-folder destination feature. Known Portal Quirks to Handle from Day One • hcdc_get_docket returns a broader date range than requested; results must be filtered to the requested window. • hcdao_download_discovery delta detection is blind to files organized into dated subfolders and must be explicitly handled. • Court DG7 does not surface through standard bar-number docket lookup and requires separate handling. • The Playwright Node.js driver subprocess can die silently while database tools respond; you must health-check the driver proactively. Orchestration, Host Layer, & Deployment Topology • Target Host: Hermes Agent (Nous Research) running as the persistent shell, providing persistent memory, the scheduler, and messaging surfaces. The MCP server will plug directly into it. • Agnostic LLM Routing: Default the agent/dispatch role to the most reliable tool-calling model (currently Claude Opus). Route bulk, non-critical generations (draft summaries, transcript cleanup) to a cheaper model (e.g., GLM-5.2). No provider may be hard-wired. Per-tool pins are allowed strictly for transcription/OCR tasks (pinned to Gemini 3.1 Pro Preview). • Memory Fencing: Hermes's persistent memory and learning loops must remain enabled to accumulate facts and user preferences. However, the agent must be strictly fenced from self-editing or rewriting its own mechanical execution paths (portals, downloads, filings), which must remain frozen in MCP tool code. • Hardware Deployment Infrastructure: • Always-on Brain: M1 Pro MacBook Pro (16 GB, mains-powered, lid open) running the Hermes gateway, Messages.app, and a BlueBubbles iMessage bridge. Must be fully automated via launchd services to handle headless crash recovery, auto-login, and sleep prevention (pmset autorestart / caffeinate). • Tools and Storage: Synology NAS (10.0.0.149) hosting the Lawfather MCP container, local client folders, and Drive sync. • Private Network: Tailscale mesh across all devices for secure remote access without open inbound ports. Acceptance Criteria for Sign-Off • No batch operation requires the caller to iterate. • The case resolver returns ranked candidates and never auto-selects. • Transcription runs seamlessly as an async two-stage job surviving multi-hour files without timing out. • OCR never fires automatically on low-readability files without gated confirmation. • Zero regressions on the existing MCP tool inventory. • The Resiliency Test: The full stack successfully restarts completely unattended after a host reboot or simulated power loss, and is reachable via iMessage/SMS immediately after. • Self-editing is fenced on mechanical download/filing paths. Hard Guardrails • Privilege: Downloads route strictly to the correct client folder; a wrong-case match is treated as a severe defect, not a warning. Privileged audio/discovery data stays on owned hardware where the chosen model allows. • Determinism: Repeatable steps live entirely in tool code, never in prompts. • Agnosticism: Model and host layers must remain fully swappable without modifying the core MCP tools. Before quoting "done," you will be expected to confirm live portal behaviors regarding District Clerk document labels, DA portal stable identifiers, and county search surfaces. How to Apply Please submit a proposal detailing your specific experience with MCP architectures, Playwright browser automation, and macOS/Docker DevOps automation. Anti-Bot Filtering: To prove you read this entire scope, please start your application with the phrase "PROTECT THE LAW" in all caps. Automated or generic copy-paste applications will be instantly rejected.

Project Overview We are an energy distribution and marketing company running Microsoft Dynamics 365 Business Central. We are evaluating whether we can build an internal cash application automation workflow instead of purchasing a third-party SaaS solution. The objective is to automate the matching of incoming customer payments to open invoices using: Bank transaction feeds (BAI2 files from Frost Bank) Remittance emails from Outlook Open AR invoices in Business Central AI-assisted matching where appropriate This is not a chatbot project. This is an accounting workflow automation project. Current Process Treasury downloads bank activity. Treasury reviews remittance emails. Accounting manually identifies customer payments. Accounting matches payments to open invoices. Accounting creates and posts entries in Business Central. We would like to reduce manual effort while maintaining accounting review and approval controls. Desired Future State Data Sources Frost Bank BAI2 files Outlook shared mailbox containing remittance emails Business Central: Open AR invoices Customer master Vendor master Payment journals Workflow Read incoming remittance emails. Extract: Customer name Payment amount Invoice numbers Payment references Read bank transactions. Match payments to customers and invoices. Generate confidence scores. Present suggested matches for review. Push approved entries into Business Central Payment Reconciliation Journal or Cash Receipts Journal. Maintain audit trail of AI recommendations and user approvals. Deliverables Requested Phase 1 – Discovery Provide: Recommended architecture Estimated implementation effort Recommended technology stack Estimate of expected match rates Build vs buy recommendation Phase 2 – Proof of Concept Build a prototype that: Reads remittance emails Parses BAI2 transactions Pulls open invoices from BC Suggests invoice matches Provides confidence scoring Phase 3 – Production Build Optional based on successful POC. Preferred Technology Strong preference for: Microsoft Business Central Power Automate Azure OpenAI Azure Functions BC APIs SQL Server/Azure SQL Open to alternatives if justified. Required Experience Please provide examples of: Microsoft Business Central projects Payment reconciliation projects Cash application automation Bank integration projects AI-assisted document processing Accounting or ERP workflow automation Screening Questions How many Business Central implementations have you personally worked on? Have you integrated BAI2 bank files before? Have you built payment reconciliation workflows? What match rates would you expect before and after AI assistance? Would you use Power Automate, Azure Functions, or another architecture? What are the biggest risks in this project?