Authority Hacker AI Accelerator / Claude Code Consultant Needed for Financial Services Lead Generation & Automation Overview I am looking for an experienced consultant who is familiar with the Authority Hacker AI Accelerator ecosystem, Claude Code, AI agents, automation workflows, and modern lead-generation systems. This is not a traditional SEO project. My goal is to build practical AI-powered systems that help generate qualified leads, automate repetitive tasks, improve prospect outreach, and allow me to spend more time meeting with clients. Ideal Candidate You have hands-on experience with: • Authority Hacker AI Accelerator • Claude Code • AI Agents • Anthropic Claude • OpenAI / ChatGPT • n8n • Make.com • GoHighLevel • LinkedIn Sales Navigator • CRM Automation • Lead Enrichment • Workflow Design • API Integrations • Prompt Engineering • SOP Creation Bonus Experience Experience working with: • Financial Advisors • Insurance Agents • Medicare Agents • Wealth Management Firms • Compliance-Sensitive Industries Initial Objectives I want help building and implementing: Phase 1: AI Prospect Research System Build a workflow that: • Identifies ideal prospects • Researches prospects automatically • Summarizes relevant information • Generates personalized outreach suggestions • Creates prospect profiles Phase 2: LinkedIn Lead Generation System Build a workflow that: • Supports LinkedIn prospecting • Generates personalized first-touch messages • Generates follow-up messages • Helps maintain ongoing conversations • Creates content ideas relevant to target audiences Phase 3: CRM & Follow-Up Automation Connect with: • GoHighLevel • Redtail CRM • Calendly or appointment scheduler • Email systems Objectives: • Automate follow-up • Automate reminders • Improve lead tracking • Reduce manual work Phase 4: Content & Marketing Automation Create systems that help generate: • LinkedIn posts • Educational content • Seminar marketing materials • Email campaigns • Client nurturing content Deliverables I am looking for someone who can: • Recommend the best architecture • Build workflows • Document workflows • Train me to use them • Create simple SOPs • Record Loom videos explaining the setup Important Please only apply if you have actual experience with: • Authority Hacker AI Accelerator • Claude Code • AI Agent workflows In your proposal, please answer: 1. Have you completed or participated in Authority Hacker AI Accelerator? 2. What Claude Code projects have you built? 3. What AI agent systems have you implemented? 4. Which automation platforms do you prefer and why? 5. Share examples of AI workflows that generated measurable business results. 6. How would you approach this project for a financial advisor focused on retirement income and Medicare planning? Engagement • Initial paid consultation • Followed by project implementation • Potential ongoing monthly advisory relationship

## Project at a Glance - Project stage: Partially built; bootstrapped project where efficiency, speed, cost, and lean execution matter. - Expected project duration: Approximately 1 to 2 months. - Engagement type: Independent contractor / hourly engagement only. - Scale target: The app should be architected with future growth in mind, including a target capacity of 80,000+ monthly active users with reasonable headroom to scale without avoidable rework, security degradation, or performance issues. - Security posture: SOC 2 readiness should be considered from day one; certification may occur later. - Meetings: One weekly video sync of up to 60 minutes may be scheduled by mutual agreement and should be accounted for in the contractor’s proposed budget. ## Overview We are building a production-grade, multi-tenant SaaS web application designed for enterprise-level scale, security, and reliability. The application may handle employee data and is being built with SOC 2 readiness in mind from day one. This is a bootstrapped project. Speed and lean execution are critical, and every decision should balance quality with pragmatism. The product is being designed to last, scale, and withstand technical, security, and operational scrutiny. We are seeking a U.S.-based individual senior full-stack engineer who can personally lead the web app development scope through production-ready implementation, end to end. No outsourcing outside the U.S. No agencies. ## Technology Stack - Front-end: Next.js App Router, TypeScript, Tailwind CSS - Backend / API: Next.js API Routes, Supabase Edge Functions, FastAPI / Python - Database: Supabase PostgreSQL - Authentication: Supabase Auth, OAuth, SSO, MFA - Authorization: RLS / Row-Level Security, RBAC - Realtime: Supabase Realtime - LLM / AI: OpenAI / Anthropic / Gemini-compatible LLMs - Billing: Polar.sh - HRIS Integration: Unified third-party connector - Email Delivery: Resend - Analytics: PostHog - Error Monitoring: Sentry - Infrastructure: Vercel + Supabase - CI/CD: GitHub Actions - Testing: Vitest / Jest, Playwright - AI Agents: Agentic workflows, tool use, and related architecture - MCP Integrations: MCPs for ChatGPT, Claude, Gemini, or similar environments - Additional technologies may be used. ## What We Are Looking For - Experienced full-stack SaaS product engineer with a strong, verifiable portfolio - Deep expertise in Next.js and TypeScript - Production-grade Supabase experience, including RLS, Realtime, and Edge Functions - Python back-end development experience with LLM integration, including RAG pipelines, memory, or fine-tuning workflows - Experience implementing subscription lifecycle flows and seat-based access control end to end - A genuine standard for clean, well-organized, maintainable code - Demonstrated ability to design systems that can scale horizontally without structural rework - Demonstrated ability to work efficiently in a lean, bootstrapped environment - Security-first development practices, especially when handling sensitive or regulated data - Clear, prompt professional communication - Experience building AI agents or agentic workflows - Experience building MCP integrations for ChatGPT, Claude, Gemini, or similar platforms - Experience designing hierarchical multi-tenant account structures with seat-based access control ## Strongly Preferred - Hands-on experience building RAG pipelines and LLM fine-tuning workflows in production - Experience handling employee or HR data, including PII access controls, audit trails, and data residency considerations - Experience building toward SOC 2 readiness in a prior engagement - HRIS or enterprise HR system integration experience - Familiarity with OWASP, NIST CSF, or CIS Controls ## This application is being built to last and may handle sensitive employee data. We are looking for an engineer who takes code quality, data responsibility, lean execution, and long-term system health seriously. ## Please answer all 5 screening questions in your response. ## Communication - Contractor should identify normal availability windows and provide timely responses, generally within one business day during those agreed availability windows. - Day-to-day communication will generally be async through the applicable platform or contract workroom. - One weekly video sync of up to 60 minutes may be scheduled by mutual agreement for status updates, completed-work summaries, blockers, and upcoming priorities. Contractor should account for this meeting time in the proposed hourly rate or budget. - Pre-engagement sales, proposal, or introductory scoping discussions are not billable. ## Confidentiality and IP Project details are shared only after the client’s NDA is executed. If both parties decide to move forward, a separate IP Assignment Agreement is required before any offer is accepted or substantive work begins. This is an independent contractor / hourly engagement only and does not include full-time employment, salary, benefits, equity, revenue share, product ownership, or any ongoing engagement beyond the agreed scope.

DESCRIPTION; I'm building a data infrastructure product for ontology-driven AI context: object types, properties, and relationships materialized ahead of query time, so AI systems retrieve connected context fast instead of rebuilding it from raw sources on every request. I need experienced eyes on the ingestion foundation before anything gets built on top of it. The deliverables are fixed (below); hours are flexible — propose what you think the work honestly takes. Rate: my budget is $50–75/hr. That's a hard ceiling — proposals above that range can't be afforded and won't be considered, regardless of quality __________________________________________________________________________ WHO SHOULD APPLY A data engineer / data infrastructure engineer who understands what an ontology and a knowledge graph are and why they matter for AI systems — connected entities and relationships as first-class context, not just tables. You don't need graph database experience; you need to get why pre-materialized, relationship-aware data beats rebuilding context from raw sources on every query. If that framing clicks for you, you're the right kind of applicant. __________________________________________________________________________ THE PRODUCT, HIGH LEVEL: The platform deploys on a client's own infrastructure — we never see their data. Clients connect their data sources, define an ontology (object types, properties, relationships), and the platform materializes it across tiered storage. Later phases add a binary serve layer, SSD/RAM caching, and GPU-parallel query execution so AI systems and data applications retrieve connected context at very low latency. Target customers: companies running AI on complex connected data (security operations, healthcare, financial services) where privacy demands private deployment and speed matters. Storage note: the current prototype uses Iceberg on GCS for development convenience, but the architecture is intentionally built for any S3-compatible storage (on-prem S3, private cloud VPC, MinIO, etc.). Portability is a design requirement, not an afterthought — the platform must never be tied to a single cloud provider. __________________________________________________________________________ WHAT EXISTS TODAY: A working Python prototype: FastAPI, PyIceberg, PyArrow, Postgres, Supabase (metadata + sync ledger), GCS as the Iceberg warehouse. Architecture and design docs are provided for orientation. The cold path is functional and tested: a 31-test production suite ran against live infrastructure at 1M–5M row scale — core correctness, concurrency, failure injection (kill mid-sync, storage outages, lease expiry), idempotency/replay, rollback, a 50-sync soak, and audit checks. All passing, with a written sign-off document you'll receive. That's exactly why I'm hiring you: tests confirm behavior I anticipated. You're here for what I didn't anticipate — structural weaknesses, hidden risks, and edge cases that a test suite written by the same mind that wrote the pipeline can't catch. I'm strong on product and systems design, not low-level data engineering. The codebase is AI-assisted, and I want a professional to find what that typically accumulates. This is a prototype built from the ground up — no live client today. The goal: ensure the ingestion foundation is genuinely solid (data coming in from source correctly, at scale, repeatedly) so a scoped MVP pilot and beta release won't break under real usage. You are validating the foundation before anything gets built on top. __________________________________________________________________________ YOUR SCOPE — THE COLD PATH, END TO END Data source → validation → identity merge → materialized ontology in Iceberg on S3-compatible storage. The data connectors are in scope — they ARE Milestone 1. The platform supports exactly three ways data comes in, and your job includes confirming each one is genuinely production-grade, not just demo-grade: Postgres — full refresh and incremental watermark sync S3-compatible object storage (CSV) — currently GCS via S3 interop, but must work against any S3-compatible store (on-prem, MinIO, private VPC) Manual CSV upload — primarily for testing/onboarding For each connector, production-grade means: real error handling (bad credentials, unreachable source, permission failures, malformed/garbage data, schema drift), clear failure messages that tell a user what broke, no silent partial ingests, and sane retry/recovery behavior. If a connector swallows errors, loses rows quietly, or fails confusingly — that's exactly the finding I'm paying for. No other connectors are planned for this milestone. Three connectors that work correctly under stress beats ten that mostly work. Focus areas across the pipeline: Connectors — production-readiness and error handling as described above Identity & matching — entities staying consistent across syncs (PK merge, fingerprint mode, composite keys) Sync semantics — full refresh vs incremental watermark sync, replay idempotency, delete behavior Relationships — FK→PK edge materialization, rebuild triggers, orphan handling, stable node identity Versioning & audit — Iceberg snapshots, rollback, schema change lineage, sync ledger completeness Reliability — failure modes, partial writes, lock/lease behavior, silent wrong-data risks Code structure — dead code, duplication, coupling, fragility; source-specific logic must stay contained in each connector and never leak into the shared pipeline Explicitly out of scope: GPU execution, query kernels, binary serve formats, caching layers, query-time serving, and any new connector types — all future phases. Your scope ends at correct, versioned, audited data in Iceberg. __________________________________________________________________________ DELIVERABLES (in priority order) Prioritized written assessment — what's pilot-ready as-is, what must be fixed before a real pilot customer (with specific recommendations), and what the existing test suite missed (edge cases, risks, gaps). Active code changes — implement fixes for the highest-priority issues you find, directly in the repo. You'll have full repo access. I'm open to architecture changes and refinements as long as they're clearly explained with reasoning. A change log that teaches — for every change: what you changed, why it mattered, what it fixes or prevents, and what to watch for going forward. This isn't paperwork — I'm making a local engineering hire for the next milestone, and your write-ups become the onboarding record. Everyone who touches this codebase after you should learn from what you found. Fixes go deepest-risk-first. What you get from me: repo access, architecture/design docs, the test suite + sign-off report, and async availability for questions. __________________________________________________________________________ ***REQUIRED EXPERIENCE: 1)Production Python data pipelines 2)Apache Iceberg, Delta Lake, or Hudi (or strong Parquet/data-lake work) 3)Postgres 4)Merge/upsert, idempotency, watermark/CDC patterns Building or hardening data connectors that real users depend on************* __________________________________________________________________________ WHERE THIS CAN GO: This starts as a fixed-scope review. Separately, I plan to make my first part-time/full-time engineering hire locally (Dallas) to build Milestone 2 and beyond — SSD caching, serve layers, containerization, and microservices as the platform scales. For the right freelancer, there's opportunity to stay engaged on recurring scoped work — reviewing the foundation as it evolves and working in conjunction with that future hire. Not required, not promised — but the door is open if the work is strong. __________________________________________________________________________ *********HOW TO APPLY — READ CAREFULLY***** Answer this one question in your proposal, briefly and in your own words: "You're building a pipeline that ingests from Postgres and S3-compatible storage and materializes a connected ontology (entities + relationships) into Iceberg. How do you design the sync process to be reliable and idempotent — especially around watermarking, commits, and failure handling between steps?" Include your proposed hour estimate for the deliverables above. Get creative — attachments and notes welcome. Note on AI-generated proposals: I use AI heavily myself — but if your proposal or screening answer is clearly AI-generated boilerplate, you will be automatically rejected without consideration. I'm hiring your judgment and experience, not your ability to paste a prompt. Short, direct, human answers. __________________________________________________________________________ A NOTE ON TECHNOLOGY BOUNDARIES: ***QUICK EXAMPLE*** FastAPI and Iceberg are what the platform uses today, not permanent decisions. As the product scales, we may want to run FastAPI alongside a second framework, replace it entirely, or eventually move away from Iceberg toward a custom storage format optimized for the GPU serve layer. Those should be engineering decisions made on merit, not decisions we're forced into because the current code makes swapping painful. What I need confirmed: is the codebase modular enough that a change like that stays contained? Core business logic (validate, merge, materialize, version) should never be tangled directly with infrastructure. API routes should be thin entry points that hand off to service logic, not where business logic lives. Iceberg writes should be isolated behind a single abstraction. If those boundaries are clean, replacing or extending a technology layer is a focused engineering effort. If they're not, it touches everything and becomes a mess under deadline pressure with a full team. Flag anywhere that boundary is broken. That's a priority finding. __________________________________________________________________________ FINAL REMARKS: NDA & IP protections This engagement requires signing an NDA and IP assignments agreement before work begins; standard protections given you'll have full repo access to a pre-launch product. Documents are provided on day one; nothing unusual in them. If that's a dealbreaker, please don't apply.

We are Bowlful a new fresh bowl concept coming to the SODO district of Orlando. We we are aiming to open our doors in October/November 2026. Our BlazeAI account is already set up, connected to all our social channels, and has our branding loaded. Now, we need an expert to activate it. The Goal: We need to start a weekly "Coming Soon" campaign (1 post per landing page week) to tease our arrival and build anticipation in the local market. We want to keep our exact address a mystery until our sign is installed in approximately 60 days. Your Mission: You will be the pilot of our BlazeAI platform. Your primary task is to set up, manage, and fine-tune a 5-month campaign that generates a weekly post. You will use the AI to create content that: Teases our arrival in the SODO area. Highlights our "fresh" concept. Drives our audience to our website for a "sneak peek." Directs the AI to adapt the message as we progress (e.g., sharing pictures of construction, the sign installation, and other milestones). We want to start with one post per week, but as we get closer to opening, we plan to increase the frequency. What We're Looking For: We need a BlazeAI power user. Not just someone who can post, but someone who understands how to train the AI to produce exactly the right tone and content strategy. Key Responsibilities: Campaign Setup: Configure the initial "Coming Soon" campaign within BlazeAI, setting the right goals, topics, and output formats for our brand. Content Direction: Provide ongoing, creative direction to the AI so each weekly post feels fresh, mysterious, and engaging. Quality Control: Review and approve all content generated by the AI before it goes live. Weekly Review: Hop on a 30-minute Sunday call with me to review the upcoming week's posts and discuss any necessary adjustments to the campaign strategy. Performance Analysis: Monitor basic analytics to see what's working and adjust the AI's direction accordingly. Expert-level proficiency with BlazeAI. Strong understanding of AI content generation and prompt engineering within the Blaze ecosystem. Experience managing social media campaigns, especially for local businesses or restaurants. Excellent communication skills for our weekly strategy calls. This is 1-2 hours total a week as the content will generate based on your input and depending how well you lead the AI it will become more and more efficient and require less tweaks. I will review the content on Saturday send over anything I see that I would like adjusted etc. Then our call on Sunday should be very short just to ensure we are on the same page. Long term could be a long term weekly campaign management and grow with our hot start up. As we get closer to the opening we will have many other strategies to employ and the role will expand for the right person as well as the compensation.

**SEO & AEO Strategist** **The role** As Lead SEO & AEO Strategist, you bring a strong SEO background with a deep understanding of AEO and you're the person who raises the analytical bar for the whole team. You are the analytical expert of the team and the voice everyone lean on to make sense of how they show up in AI prompts. You deeply understand our data, reading what's moving and why, and create an actionable plan of recommendations we can act on. You also set the standard for how the rest of the team reads the data and advises clients: mentoring analysts, building the playbooks others run on, and turning your judgment into something we can scale. This is a hands-on, data-first, client-facing role with a clear enablement mandate: you supply the analytical judgment, you deliver it as strategy, and you teach others to do the same. What you'll do Get our brand and clients up and running. Onboarding and get us and clients to value fast, so their accounts are working hard for them from week one. Read the data, find the signal. Analyze how client brands surface across AI answer engines. Identify citation gaps, ranking shifts, and the content patterns that move Share-of-Prompt and our other visibility metrics. Lead working sessions where you take a client's growth and brand goals and map them to a concrete AEO plan, then push on the parts that will move the needle most. Deliver insights and recommendations. Produce the briefs, performance reviews, and optimization plans clients rely on. Translate complex visibility data into clear narratives for both technical and executive audiences. Set the standard and level up the team. Mentor and train others on how to read the data, run a client session, and turn a messy dataset into a recommendation that lands. Define the analytical methods and quality bar others work to, and bring newer team members up to speed on AEO fast. Build the playbooks. Turn your judgment into reusable assets, including analytical frameworks, content playbooks, session templates, and ways of measuring what's working, so the pod's best thinking scales beyond any one person. Support Fix Pack work. Help diagnose what needs fixing and validate that deployed Fix Packs are moving the metrics they should. Flag the exceptions and opportunities that need the team's attention. Work shoulder to shoulder with the pod. Give the Partners and the wider pod the analytical inputs they need: the right numbers, the right context, the right recommended next move. Track the category. Stay current on AEO / SEO developments and how AI answer engines are evolving, so our recommendations stay ahead of a landscape that is constantly shifting. What you need to know (SEO / AEO) This is the foundation of the role. You should already think in terms of how brands earn visibility in AI answers, not just traditional search rankings, and be able to explain that thinking to others. SEO foundations. Solid, hands-on fundamentals: keyword and content strategy, structured data, site architecture, and organic performance analysis, with growing expertise in AI search. Metrics literacy. Comfort reasoning about visibility and performance metrics: reading a shift and understanding what it implies. Category awareness. Familiarity with the AI-visibility tooling landscape, or genuine eagerness to get up to speed fast. What you bring Advisory instinct. You're at ease guiding clients, walking them through a recommendation, and making a technical idea land for a marketing or brand team in plain language. A teaching mindset. You like making other people better at their craft. You can break down how you reached a conclusion, give useful feedback, and turn your own instincts into repeatable methods. Data fluency. Comfortable working with data to find signals. You don't just report numbers, you interpret them. Prompt literacy. Familiarity with prompt engineering and LLM behavior, enough to work effectively inside agent workflows and reason about their outputs. Storytelling. Strong written and verbal communication. You can turn a messy dataset into a clear recommendation for a stakeholder who has thirty seconds. Organized under load. Proven ability to juggle multiple accounts and deadlines without dropping quality. Detail obsessed. Every lift, every point of impact piques your curiosity. Resourceful and proactive. A self-starter who finds creative solutions, adapts quickly, and is comfortable navigating ambiguity and learning new tools. Who you likely are * 4+ years of hands-on SEO or organic-growth work behind you. Agency pedigree is a plus or a comparable data-heavy, client-facing role at a SaaS or AI company. * Someone with an SEO or digital-marketing background who is leaning hard into AI search, or a sharp analyst who's genuinely excited about the AEO category. * Confident on a client call, in a working session, or in a written recommendation, and used to high-touch accounts. * Someone others naturally come to for answers. You've informally or formally mentored, trained, or set the bar for teammates before. * A craftsperson who takes pride in the details while keeping the big picture in mind. * Looking for a role with a clear growth path toward deeper technical and team leadership. 
 Bonus points * You've built reusable things for clients or teams before: playbooks, content frameworks, onboarding material, or ways to measure what's working. * You've worked with enterprise or other high-touch accounts. * You've operated inside a fast-moving, startup.

EroFlow Intelligence is an enterprise-grade, autonomous AI orchestration pipeline designed to mitigate global supply chain disruptions for aerospace manufacturing. Built using a multi-agent framework, the system automates the entire lifecycle of risk detection, impact analysis, and procurement mitigation without requiring human intervention for standard operational anomalies. The architecture coordinates three specialized, asynchronous AI agents that communicate via a centralized event bus to solve complex logistical bottlenecks in real-time. Core Agent Architecture & Workflow 1. The Sentinel Agent (Data Ingestion & Extraction) Role: Continuous Global Monitoring. Function: Utilizes advanced LLM-driven web scraping and unstructured data extraction to monitor global news feeds, geopolitical shifts, weather anomalies, and shipping port telemetry. Trigger: If it detects a disruption (e.g., a port strike or critical mineral shortage), it extracts key entities (materials affected, estimated delay times) and passes a structured JSON payload to the orchestration layer. 2. The Impact Assessment Agent (Predictive Modeling) Role: Deep Cross-Referencing & Analytics. Function: Upon receiving a trigger, this agent cross-references the disrupted material with the company’s internal ERP (Enterprise Resource Planning) database and current inventory levels. Output: It runs a predictive analysis to determine exactly which production lines will stall and calculates the financial risk, assigning a high/medium/low priority score to the event. 3. The Mitigation & Logistics Agent (Autonomous Execution) Role: Operational Resolution. Function: If the risk score exceeds a specific threshold, this agent is authorized to take action. It autonomously queries pre-vetted alternative suppliers via APIs, negotiates standard volume pricing based on historical contract data, drafts a comprehensive procurement proposal, and queues the purchase order for final human sign-off (or executes it automatically if under a certain dollar cap). Technical Stack (The Blueprint) Frameworks: LangGraph / CrewAI (for multi-agent state management and deterministic routing). Core Language: Python 3.11+ Data Layer: PostgreSQL (for ERP syncing) & Pinecone / Qdrant (Vector database for storing and querying supplier contract PDFs and historical compliance documentation). LLM Orchestration: OpenAI GPT-4o / Anthropic Claude 3.5 Sonnet utilized via structured outputs (Pydantic parsing) to ensure strict API data integrity. Hosting & DevOps: Containerized via Docker, orchestrated via Kubernetes, and deployed on AWS with asynchronous task queues managed by Celery and Redis. Quantifiable Business Results (The Hook) 92% Reduction in supply chain anomaly response time (from 48 hours down to 14 minutes). Automated Recovery: Successfully mitigated over 140 potential production line stalls autonomously in simulated stress tests. Cost Efficiency: Saved an estimated $1.2M in expedited shipping fees by predicting bottlenecks 10 days before they impacted manufacturing floors.

Colony Mobility LLC is a Florida-based technology company preparing a Phase I Small Business Innovation Research (SBIR) proposal for the U.S. Department of Transportation under Topic 26-FT1: Person-Centered, Carefree, Complete Trip Planning — Powered by AI. We are seeking a Senior AI Systems and Algorithm Researcher to serve as a named research subcontractor on this federal proposal. This is a research design and documentation role — no production software build is required. What you will research and document: Rider preference engine algorithm — design a machine learning system that learns individual traveler needs over time, including stated preferences, observed behavioral patterns, and inferred preferences for new users Success probability mathematical model — design and write a proof of correctness for a weighted scoring algorithm that calculates the probability a specific rider will successfully complete a specific trip given real-time conditions AI orchestration architecture — document the multi-agent coordination system that assembles, monitors, and replans multimodal trips in real time Outcome learning algorithm — design the reinforcement learning loop that improves system recommendations based on real trip outcomes Trip assembly algorithm pseudocode — document the step-by-step logic for building complete door-to-door journeys from multiple transportation sources LLM integration architecture — document how large language models are used within the system for normalization, preference reasoning, and conversational interfaces What the federal report specifically requires from this role: Algorithm pseudocode for all AI components Mathematical notation and proof of correctness for the success probability model Summary of how ML methods have been used to solve trip-planning problems similar to this solicitation — literature review contribution Justification of how prior research is extended and improved by this system What we need from you before July 3, 2026: A short professional bio (3–5 sentences) describing your relevant background A brief letter of commitment confirming your availability and intent to perform the described work if the contract is awarded Required qualifications: Graduate degree (Master's or PhD) in Computer Science, Applied Mathematics, Data Science, or Artificial Intelligence — or equivalent research experience Demonstrated experience designing machine learning algorithms — preference learning, recommendation systems, optimization, or routing Ability to write mathematical notation fluently — probability models, weighted scoring functions, proofs of correctness Experience writing technical research documentation — academic papers, federal research reports, or technical deliverables for a non-technical audience Familiarity with large language models and their practical limitations in production research contexts Strong plus (not required): Background in multimodal routing algorithms, operations research, or transportation optimization Experience with reinforcement learning or multi-agent systems Prior SBIR, federal research, or government contract experience Published research on routing algorithms, preference learning, or mobility AI Contract details: Hours: 180 hours over 6 months Rate: $120/hour Location: fully remote Start date: September 2026, upon DOT SBIR Phase I award notification Total contract value: $21,600 plus 20% overhead = $25,920

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.

We are looking for a highly efficient AI-First Content Writer to join our editorial team. We run a network of product review sites (The Digest Network) that help consumers make buying decisions through "Winner vs. Loser" comparison articles. This is not a traditional copywriting role. We do not want you to spend hours writing from scratch. We want you to use AI (Claude, ChatGPT, etc.) to generate high-quality content based on our proven winning templates, and then act as the editor/fact-checker to polish the final output. Crucially, you must also be able to generate realistic AI imagery of the products reviewed. Lastly, you will upload the articles to Webflow CMS as a final step; instructions will be provided. Responsibilities: AI-Assisted Writing: Generate ~1,000-word articles using our specific outlines and detailed creative briefs. Articles will be a mix of research paper summaries and product comparison reviews. You will use AI to do 90% of the writing, then manually refine the hook, tone, and formatting. Follow the "Winning Style": We have a specific article structure that converts. You must be able to prompt the AI to follow this structure rigidly (e.g., specific headings, "Winner" callouts, bolding key benefits). AI Image Generation: For each product comparison article, you must generate 3 high-quality AI images featuring the products. You must be skilled in prompting (Midjourney, Ideogram, or similar) to create realistic-looking product packaging that matches the brand vibes of the companies we review. Fact-Checking: AI hallucinates. You are responsible for ensuring pricing, ingredients, and product claims are 100% accurate before submission. Requirements: Advanced AI Proficiency: You are comfortable using Claude 3.5 Sonnet, ChatGPT-4o, or similar to generate long-form content that doesn't sound robotic. AI Art Skills: You know how to prompt for specific product photography styles (e.g., "studio lighting," "clean background," "bottles on a counter"). Attention to Detail: You can spot when an AI drifts off-template and correct it immediately. Speed & Consistency: We value high output. This role is perfect for someone who has mastered their AI workflow. Payment: We will pay on a per-article basis. Budget is for ~30 articles.

We’re looking for experienced AI professionals to provide short, original quotes, practical insights, and light content feedback for our educational articles and guides. Your real-world perspective will help make the content more accurate, useful, and trustworthy for readers. The initial project involves reviewing and contributing to one guide, with the possibility of ongoing work. Example guide: onlinemastersdegrees.org/best-programs/information-systems/ **What You’ll Do:** * Review AI education content for accuracy and clarity * Leave light feedback through Google Docs comments * Provide brief expert quotes, usually 2–5 sentences each * Offer practical insights based on real-world AI, machine learning, or data science experience * Help add context around AI careers, degree programs, certifications, skills, tools, and industry expectations **For the Initial Project:** We’re looking to add approximately 3–4 short expert quotes to one AI guide. Quotes should be original, practical, and based on your professional experience. **Details:** * $30 per page * Pages typically take 20–30 minutes * Clear guidelines and examples provided * Contract, flexible, and ongoing work **Relevant Experience May Include:** * Artificial intelligence * Machine learning * Data science * Generative AI * Natural language processing * Computer vision * AI product development * MLOps * AI governance, risk, or compliance * Responsible AI * AI education or workforce development **In your submission, please include:** 1. A few sentences about your AI background, professional experience, and areas of expertise 2. Any relevant degrees, certifications, credentials, or notable AI projects 3. Link to your LinkedIn profile To help us sort through automated submissions, please put the name of Shopify’s CEO at the top of your submission.