Overview We're building an open-source CLI gateway for multi-agent AI orchestration — model-agnostic, MCP-native, and designed to bring any agent framework online with a single command. The repo is active, well-documented, and growing. We need an engineer to accelerate integration coverage and help attract open-source contributors. The Work Build agent templates and runnable examples for LangGraph, CrewAI, and similar frameworks Add LLM provider support (Groq, Mistral, Gemini, etc.) to the Hermes runtime Write clean, contributor-friendly code that models good PR hygiene Submit work via fork → PR → merge workflow on GitHub You Are Strong Python developer with CLI tooling experience Familiar with at least one of: LangGraph, CrewAI, LiteLLM, LangChain Comfortable with open source GitHub workflows (fork, PR, issues, reviews) Self-directed — you read docs, ask good questions, and don't wait to be unblocked Nice to Have Experience with MCP (Model Context Protocol) Familiarity with SSE, OAuth 2.1, or agent credential management Prior open source contributions Engagement Part-time to start, 20 hrs/week Fixed milestones per integration delivered Potential to grow with the project To Apply Share your GitHub profile and one example of open source work or a project that shows your Python and agent framework experience. https://github.com/ax-platform/ax-gateway

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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

**Overview** We are a fast-growing SaaS company with a lean engineering team (~10 devs) utilizing a modern Python (FastAPI/Django) and Node.js backend, React frontend, and PostgreSQL stack. We have already deployed an initial multi-model agent stack—Claude Code, LiteLLM gateway, Git worktrees, and MCP integrations. We need an expert to run an intensive architecture review and optimization session for our current infrastructure. We are not looking for someone to build a full-time, weeks-long project from scratch. Instead, we need a seasoned engineer who has shipped this exact type of infrastructure end-to-end to audit our setup, identify architectural gaps, and guide our team on hardened implementation. This project must move fast. If your timeline is measured in weeks, please do not apply. We want someone who looks at this scope, jumps into a review session, and delivers actionable architectural guidance in days. This starts as a focused, urgent consultation. However, we expect ongoing advisory work—follow-ups, architecture adjustments, and enhancement reviews—as the AI tooling landscape shifts. For the right engineer, this will turn into a recurring relationship. We are completely open to a fixed price per milestone or an hourly structure. **What You Need to Have Actually Shipped and Can Review (Not Just Read About)** * **Full Agentic Coding Harnesses:** The entire loop: orchestrator → subagent → CI gate → merge loops. * **Isolation Layers:** Configured execution layers (such as E2B, Modal, or secure Docker runtimes) as isolated sandboxes for AI-generated code. * **Parallel Claude Code Sessions:** Managed multiple simultaneous subagents on scoped tasks via Git worktrees. * **Self-Hosted LiteLLM Gateways:** Routing to multiple models (Claude, GPT, Gemini, DeepSeek). * **MCP Server Infrastructure:** Connected file system, PostgreSQL, Atlassian, and Slack tool layers for active agents. * **Agent Framework Structures:** Used CLAUDE.md, COMMON\_MISTAKES.md, subagent role definitions, hook scripts, and settings.json. * **Human-in-the-Loop Orchestration:** Built Plan Mode or equivalent approval gates before agent execution. * **Multi-Agent Frameworks:** 7-agent feature factory patterns or frameworks like LangGraph, CrewAI, or Autogen. * **Durable Workflow Engines:** Applied Temporal, n8n, or similar tools for long-running agent workflow execution. * **Mechanical Quality Gates:** Treating CI green as the ultimate gate for agent output quality. \[[1](https://manveerc.substack.com/p/ai-agent-sandboxing-guide)\] **Our Current Stack (What you are reviewing)** * **Backend:** Python (FastAPI / Django) & Node.js (TypeScript) * **Frontend:** React (Next.js) * **Database & ORM:** PostgreSQL / Prisma / SQLAlchemy * **Infrastructure:** Docker Compose, AWS (ECS/EKS) * **CI/CD:** GitHub Actions / GitLab CI * **AI Layer:** Claude Code with shared `.claude/` directory, CLAUDE.md, and LiteLLM gateway in Docker * **MCP:** Atlassian (Jira/Confluence), custom PostgreSQL MCP server, Slack * **Workflow Automation:** Temporal / n8n * **QA Automation:** Playwright / Autonoma **Scope of Work (Review & Advisory Only)** 1. **Comprehensive Audit:** Audit our current agent harness and identify architectural gaps against a production-grade standard. 2. **Sandbox Strategy Consultation:** Review our environment strategy to ensure highly secure, isolated execution runtimes for agent code runs. 3. **Workflow Hardening Review:** Evaluate our parallel agent workflow setup (Git worktrees, subagent role configs, hook scripts, and settings lockdown). 4. **CI Pipeline Integration Strategy:** Advise on wiring our sandbox execution layer into the existing CI pipeline so agent-executed code runs in clean snapshots, not live infra. 5. **Architectural Runbook:** Deliver an optimization report / documented standard that our backend lead can easily own and execute going forward. **How to Apply** Skip the generic pitch. Show us something real to be considered: 1. A GitHub repo, architecture diagram, or Loom walkthrough of an agentic harness you have actually shipped. 2. Specific tools from our stack you have personally configured (E2B, LiteLLM, Claude Code, etc.). 3. One sentence explaining the hardest problem you solved to get full agent loops running reliably. 4. Your availability to conduct this high-impact architectural review session this week.

We are looking for a skilled Full-Stack Developer with experience in AI and software engineering to support client communication and technical requirement gathering. In this role, you will be responsible for engaging directly with stakeholders through calls, understanding their technical and business needs, and accurately translating those requirements into clear, actionable documentation for the development team. The ideal candidate should have a strong technical background in full-stack development, AI systems, and modern software architecture, enabling them to ask the right questions, validate assumptions, and ensure that project requirements are correctly captured without ambiguity. You will act as a bridge between clients and engineering teams, ensuring smooth communication and reducing misunderstandings during project execution. Responsibilities include conducting requirement-gathering calls, clarifying technical specifications, documenting system needs, and collaborating closely with developers and product teams. Strong understanding of AI-driven systems, web technologies, and software development workflows is essential to perform effectively in this role. We are seeking someone who can combine technical expertise with clear communication skills, ensuring that complex requirements are translated into structured, developer-ready specifications that support successful project delivery.

Deliverable Requested from Developer Build a Google Workspace solution that: 1. Monitors incoming emails in slab-sewer@, rough@, and trim@ 2. Uses AI (Gemini, OpenAI, or similar) to classify severity and category 3. Automatically applies Gmail labels 4. Marks RED items as important 5. Runs automatically every few minutes 6. Requires no action from Project Coordinators Success Metric: A Project Coordinator can open their inbox and instantly identify which emails require immediate action versus which can wait until later in the day, without manually reading and sorting every message.

# Full-Stack AI Engineer — Semantic Search + Next.js + Supabase (Long-Term, Contract-to-Hire) ## About We're building an AI-native platform that makes a large archive of recorded talks genuinely discoverable and useful: need-based semantic search over transcribed media, with a subscription product built around it. We have a clear product vision and architecture and are looking for a lead engineer to build the first version and grow with us long-term. Full product details are shared with shortlisted candidates under NDA — this post focuses on the engineering and the skills we need. ## The engineering challenge You'll build a two-part system that shares one database: 1. **A content pipeline (Python):** ingest recorded talks, transcribe them, chunk and enrich the transcripts with metadata using an LLM API, generate embeddings, and store everything in Postgres. 2. **A web app (Next.js):** fast, crawler-friendly, SEO-strong content pages with structured data; retrieval-based search that returns relevant source material with links/citations; user accounts; and Stripe-gated paid content. We care a lot about retrieval *quality* and clean, maintainable architecture — this is a real product, not a prototype. ## Required tech stack - **App:** Next.js (App Router), TypeScript, Vercel. Strong SSR/SSG, SEO, and JSON-LD structured-data experience. - **AI/backend:** Python; production RAG (embeddings, chunking, retrieval quality); LLM API integration. - **Data:** Postgres + **pgvector** (via Supabase); embeddings via a hosted model (Voyage/OpenAI). - **Auth & gating:** Supabase Auth with row-level security. - **Payments:** Stripe (subscriptions + one-time). ## Required skills - Shipped production Next.js (App Router) + TypeScript apps with strong SSR/SEO. - Built a real RAG / vector-search system in production — not a tutorial clone. - Comfortable in Python for data pipelines. - Postgres + pgvector and Supabase in production. - Stripe integration. - Plans before building; communicates clearly in writing. ## Nice to have - Audio/video transcription experience (Whisper / faster-whisper / Deepgram / AssemblyAI). - Agentic coding workflows (e.g., Claude Code). - Content-heavy SEO products or media libraries. ## Engagement - Hourly, contract-to-hire. ~20–40 hrs/week to start; long-term for the right person. - We start finalists on a **small paid test project** (a single self-contained slice of the pipeline) before the full engagement — that's how we evaluate fit. ## Confidentiality This is a proprietary product. Shortlisted candidates sign a mutual NDA before we share full scope and context. Please don't expect complete product details in the first exchange — strong technical applicants will have everything they need to be evaluated, and the rest follows the NDA. ## How to apply Applications that skip these are ignored: 1. **Start your proposal with the word `pgvector`** so we know you read this. 2. Link **two** projects: one live Next.js/SSR app, and one RAG/embeddings or LLM-integration project. Tell us what *you* personally built. 3. Answer briefly: *An offline embedding pipeline and a live search query must use the same embedding model — why does that matter, and how would you guarantee it?* 4. One line on your approach to chunking long-form audio/video transcripts for good retrieval.

I am looking for a developer or agency to build a web platform called First Responder Academy. The goal is to create the most comprehensive AI-powered training platform for firefighters, EMTs, and paramedics. The platform will initially focus on: 1. Firefighter Oral Board Preparation 2. EMT NREMT Preparation 3. Paramedic NREMT Preparation 4. Firefighter I Certification Preparation 5. Firefighter II Certification Preparation Core Features Required: * User accounts and login system * Membership and subscription management * AI-powered training simulator * Progress tracking * User dashboard * Mobile-friendly design * Modern, professional interface * Ability to scale over time AI Training Features: The AI should be able to: * Conduct firefighter oral board interviews * Score responses * Provide detailed feedback * Generate EMT and paramedic scenarios * Generate Firefighter I and II questions * Create multiple-choice exams * Provide remediation and study recommendations * Increase or decrease difficulty based on performance Knowledge Base Features: The system should allow: * Uploading NREMT skill sheets * Uploading EMS protocols * Uploading Firefighter I and II standards * Uploading department-specific hiring packets * Uploading study guides and training documents The AI should reference uploaded materials when generating questions and grading answers. EMS Features: * EMT training mode * Paramedic training mode * NREMT-style testing * Practical scenario evaluations * Protocol-based learning * Trauma and medical assessment training * Airway management training * Cardiology training Cardiology Module: A major future feature will be ECG interpretation. Requirements: * ECG image library * Random ECG presentation * Student interpretation * AI grading and feedback * Rhythm recognition training * Treatment decision evaluation Firefighter Features: * Firefighter Oral Board Simulator * Firefighter I Exam Preparation * Firefighter II Exam Preparation * NFPA / IFSAC-based content * Scenario-based learning * Multiple-choice testing * Leadership and decision-making exercises Department-Specific Training: The platform should eventually allow users to: * Upload local EMS protocols * Upload department hiring packets * Upload mission statements and values * Receive customized training based on those documents Progress Tracking: The platform should track: * User scores * Weak areas * Practice history * Completion rates * Recommended study topics * Improvement over time Admin Features: * Upload and manage training content * Upload and manage protocol documents * Upload and manage ECG images * Create and edit question banks * View user analytics * Manage subscriptions Technology Preferences: * Modern scalable architecture * AI integration using OpenAI API * Secure user authentication * Cloud-hosted * Responsive design * Easy content management Design Style: * Professional * Modern * Clean * Fire and EMS themed * Dark mode preferred * Red, black, gray, and white color palette Important: I am looking to build an MVP first, not every feature immediately. Phase 1 priorities: 1. User accounts 2. AI training simulator 3. Oral board preparation 4. EMT preparation 5. Paramedic preparation 6. Firefighter I and II preparation 7. Progress tracking 8. Membership system Future phases can include ECG image testing, advanced protocol integration, department-specific customization, and additional first responder training programs. Please provide: * Estimated timeline * Estimated cost * Recommended technology stack * Examples of similar projects * Suggestions for MVP development

We are building a next-generation workflow automation platform that combines deterministic business rules, artificial intelligence, document intelligence, and human review workflows into a single operating system. This is not a traditional CRM project. Our vision is to develop a doctrine-driven platform where business rules serve as the system authority, AI serves as an analytical and drafting layer, and human reviewers serve as the final compliance checkpoint. We are seeking an experienced engineer or engineering partner who can help architect and build the platform from the ground up. Project Objectives The platform will: • Ingest and analyze large volumes of structured and unstructured documents • Extract data from reports, PDFs, and supporting documentation • Apply rule-based workflow logic • Generate AI-assisted recommendations and draft outputs • Maintain complete audit trails and workflow transparency • Route work through human review checkpoints • Support future deployment of local AI infrastructure for privacy and performance Core Architecture The system will be built around four primary layers: 1. Rules Engine * Deterministic business logic * Workflow orchestration * State management * Trigger and escalation logic * Audit tracking 2. AI Layer * Document analysis * Classification * Pattern detection * Summarization * Draft generation * Structured outputs 3. Local Processing Layer * OCR * Document parsing * Data extraction * Vector search * Local inference capabilities * Privacy-first processing 4. Human Review Layer * Quality assurance * Workflow approvals * Compliance review * Exception handling Initial Development Priorities Phase 1 • User authentication • Client record management • Document upload system • OCR and document extraction • Workflow engine • Rule-based status management • Review dashboard Phase 2 • AI-powered document analysis • Automated classification • Recommendation engine • Draft generation workflows • Response parsing Phase 3 • Local AI infrastructure • Vector database integration • Knowledge retrieval system • Multi-agent workflow orchestration • Advanced automation Desired Technical Experience Required • React / Next.js • Node.js, Python, or similar backend framework • PostgreSQL or equivalent relational database • REST APIs • Cloud infrastructure (AWS, Azure, or GCP) • Workflow automation systems • Document processing pipelines Preferred • OpenAI APIs • Anthropic APIs • Retrieval-Augmented Generation (RAG) • LangGraph, LangChain, or similar frameworks • Vector databases • OCR technologies • AI agent architectures • NVIDIA AI ecosystem • Local model deployment What We Are Looking For We are not looking for someone who simply builds forms and dashboards. We are looking for a builder who understands how to combine: • Rules engines • Artificial intelligence • Workflow automation • Human review systems • Scalable software architecture The ideal candidate enjoys solving complex business process problems and translating expert decision-making into software systems. Engagement Structure Open to: • Fractional CTO • Lead Architect • Senior Full-Stack Engineer • AI Systems Engineer • Development Agency • Long-term strategic technology partner To Apply Please provide: • Relevant project examples • Experience building workflow automation platforms • Experience with AI-powered applications • Technology stack recommendations • Estimated availability • Preferred engagement structure

I need a developer to build an AI visibility audit tool for destination marketing. The core logic is already defined and I have a full spec. I need someone who can build it clean and ship it. What the tool does: it queries ChatGPT, Gemini, Claude and Perplexity with a fixed set of real traveler questions, captures whether a destination shows up and where its competitors land, scores the result, and drafts a short report. Roughly 15+ questions, each run a few times per platform, with web search enabled. What I need built: The query engine across all three platforms, running on my own API keys Integration with my existing scorecard backend A gated flow: a personal emailed link that runs once per user, results delivered by email A saved-run database I can log into and review, so every run is stored from day one Built to be re-run on a schedule later (this becomes an ongoing monitoring product) Two non-negotiables: It runs entirely on my API accounts and keys. Billing and ownership sit with me. I own all code and IP outright. This is a defined, finish-and-ship project, not open-ended. I'll share the full spec with candidates who look like a fit. US-based candidates only. Skills LLM / OpenAI API, Gemini API, Perplexity API, API integration, Python (or your stack — tell me), backend development, database design, prompt engineering If interested, please respond with the following answers to be taken seriously: Describe a tool you've built that calls LLM APIs in production. What did it do and what was your specific role? How would you handle the fact that AI answers vary run to run? How do you make a score that holds up to scrutiny? What's your approach to keeping per-query API costs controlled at volume? Rough estimate on timeline and cost for a project scoped like this.