I need a full-stack developer to build a simple AI-powered chat assistant. The app should allow users to type a question and receive a response from the ChatGPT API. The UI should be clean, responsive, and built with React and TypeScript. Main Requirements: - Build a simple React + TypeScript frontend - Add a chat input and response area - Connect the app to the ChatGPT/OpenAI API - Show loading state while the AI is responding - Handle basic API errors - Keep the code clean and easy to understand - Provide short setup instructions in a README file

About ZeremAI ZeremAI is a premier AI and Automation implementation consultancy that future-proofs businesses. We specialize in cutting-edge AI agents, intelligent systems automation, and real-time AI-assisted KPI dashboards across all business departments. Our "Align–Automate–Achieve" methodology bridges the gap between technology and measurable impact, helping teams reclaim up to five days of productivity weekly per employee, allowing organizations to focus on their core mission. Position Overview: We are looking for an AI Technical Account Manager who thrives at the intersection of systems architecture and client partnership. This role is for a high-presence communicator who takes pride and care about Customer Success and can translate complex AI and automation workflows into clear, actionable business wins and communicate them with the AI Development team. You are an AI Technical Account Manager at heart—someone who possesses the technical depth to design sophisticated systems but whose real superpower is leading the client relationship. You’ll be the face of our delivery team, ensuring our clients don't just get a tool, but a transformed business process they actually enjoy using. Key Performance Indicators (Success Metrics): -100% successful deployment rate of AI and automation solutions. -Ensure systems adoption success scores reach 95%+ -Increase customer productivity by 20% to 100%+ through AI automation. -Increase customer KPI achievement rate by 2x through intelligent dashboards. -Maintain 97% or higher customer satisfaction rating for solution delivery. Core Client Responsibilities: Strategic Client Leadership: - Relationship Management: Act as the primary technical point of contact, managing expectations and leading clients through the "Align–Automate–Achieve" journey with confidence. - High-Impact Communication: Lead discovery calls and project updates with clarity and brevity. You are the concise expert—getting to the point quickly and making complex concepts clear and simple to non-technical customers. - Client Success Orientation: Prioritize the "human" side of the implementation, ensuring the solution solves the client’s actual pain points rather than just being technically correct. Solution Architecture & Discovery: - Workflow Mapping: Conduct process audits to identify gaps and translate messy business problems into clean system requirements. - High-Level Design: Architect automation ecosystems using Make, Zapier, Airtable, HubSpot, and other systems, ensuring all pieces of the tech stack talk to each other seamlessly. - AI Integration: Leverage LLMs (OpenAI, Claude, etc.) to enhance productivity and build smarter business logic for clients. Delivery & Adoption: - Implementation Oversight: Work with the internal dev team to ensure the blueprints you designed are built to spec and delivered on time. - The Final 10%: Lead the hands-on training and documentation phase, ensuring the client’s team feels empowered and capable, not overwhelmed. - Iterative Optimization: Monitor performance post-launch and proactively suggest enhancements to deepen the client relationship. Key ZeremAI Operations Responsibilities: - Collaborate with the Chief AI Officer and Strategy Officer to evolve ZeremAI’s systems playbook. - Lead the evaluation and adoption of new AI technologies and platforms. - Ensure solution designs align with the Align–Automate–Achieve methodology. - Create internal system frameworks, templates, and automation best practices. - Mentor and collaborate with AI Systems Developers to maintain high-quality standards. - Analyze reporting on architecture performance, ROI metrics, and adoption rates. The Ideal Profile: Technical Fluency (The "How It Works" Knowledge): - Systems Thinker: You understand the Big Picture of how APIs, CRMs, and databases interact. You don’t need to be the world’s fastest coder, but you must be a world-class architect. - Automation Stack: Deep familiarity with CRM, Project Management, Integration, and AI tools. - AI Expert: A solid grasp of how to practically apply AI/LLMs to business workflows. The Superpowers (Client & Communication): - Professional Presence: You carry yourself with a demeanor that builds immediate trust. You are concise, articulate, and mindful of the client’s time. - Concise Communicator: You avoid the Solution Architect Trap of over-explaining. You know how to give a "Yes/No" or a "Bottom Line Up Front" (BLUF). - Leadership: You can guide, educate, and—when necessary—firmly pivot a client toward a better strategic path. Qualifications: Required Skills & Experience - 5+ years of experience in systems consulting, operations, business automation, or a similar role. - 2+ years of AI expertise in a business context - Strong hands-on experience with automation tools (JSON, Zapier, Make, Airtable, etc.) and system integrations. - Working knowledge of project management and CRM platforms (e.g., Monday.com, HubSpot, Salesforce). - Familiarity with API usage, data flows, and basic scripting or logic-based automation. - Excellent problem-solving and process-mapping skills. - Clear written and verbal communication, especially with non-technical stakeholders. - Strong client-facing presence with the ability to guide, educate, and influence clients. Preferred Qualifications: - Experience in a fast-paced startup or agency environment. - Exposure to AI tools and understanding of their application in business settings. - Technical certifications in automation platforms (Zapier Expert, Make Partner, etc.). - Understanding of business functions like marketing, sales, operations, and finance. Soft Skills: - Demonstrate creativity in problem-solving by identifying innovative solutions to client challenges and operational obstacles. - Systems thinker with a knack for breaking down complex problems. - Highly organized, self-motivated, and able to manage multiple client projects simultaneously. - Collaborative team player who thrives in a remote-first work environment. - Passionate about technology, continuous improvement, and client success.

We are looking for an experienced AI trainer / speaker to deliver a 2–3 hour live, remote Introduction to AI training session for a B2B field sales team The audience will be group of sales professionals. The client is in the protective packaging and packaging automation industry. Their sales team works with customers on packaging materials, packaging processes, damage reduction, labor efficiency, sustainability, throughput, and automation-related opportunities. The goal of the session is to provide a practical and engaging introduction to AI usage in sales workflows. This should not be a highly technical AI course. The focus should be on helping sales professionals understand how AI can support their daily work and improve sales productivity. Desired session focus: Practical introduction to AI and generative AI for non-technical sales users How field sales teams can use AI safely and effectively AI for account research and customer meeting preparation AI for improving discovery questions and understanding customer pain points AI for writing better follow-up emails, summaries, and sales messaging AI for preparing customer-specific value propositions AI use cases relevant to B2B consultative sales Responsible AI use, including confidentiality, accuracy, and human review Live examples and practical demonstrations The ideal trainer should be able to make the session engaging, practical, and relevant to a sales audience. Experience training sales teams, B2B commercial teams, or business users on AI adoption is strongly preferred. Experience in manufacturing, packaging, industrial sales, logistics, automation, supply chain, or similar B2B industries would be a strong plus, but is not mandatory if the trainer can tailor examples appropriately. Trainer responsibilities: Prepare and deliver a 2 hour session Tailor examples to a B2B field sales audience Include practical AI demonstrations that sales professionals can relate to Explain AI concepts in simple business language Provide guidance on safe and responsible use of AI tools Keep the session interactive and engaging for the group Coordinate with us in advance to align the session with client goals Ideal candidate qualifications: Strong experience delivering AI, generative AI, or digital productivity training Comfortable presenting to business and sales audiences Ability to explain AI concepts without unnecessary technical complexity Strong communication and facilitation skills Experience with tools such as ChatGPT, Microsoft Copilot, Claude, Gemini, or similar AI platforms Ability to tailor training examples to client-specific business scenarios Prior experience with sales enablement, B2B sales workflows, or customer-facing teams is preferred Please include the following in your response: Brief summary of your AI training experience Examples of similar business or sales-focused AI sessions you have delivered Your approach for making a 2–3 hour AI session practical and engaging Any relevant industry experience with B2B sales, manufacturing, packaging, logistics, supply chain, or automation Your availability in August for this training session

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.

With all the advancements in ai, I dont need a coder but someone who knows how to run multiple Ai platforms so as to execute business plans for new ventures

Overview I have a Next.js website with a newsletter signup form that currently submits directly from the browser to HubSpot's Forms v3 endpoint. I want to add a lightweight LLM-based spam filter that inspects each submission *before* it reaches HubSpot, and silently rejects (or flags) anything that looks like spam/bot/junk input. Current setup - Framework: Next.js (App Router, TypeScript, React client component) - The form component (`NewsletterForm.tsx`) POSTs directly to `https://api.hsforms.com/submissions/v3/integration/submit/[portalId]/[formGuid]` - Fields collected: `firstname`, `lastname` (optional), `jobtitle`, `email` - Portal ID and Form GUID are public form identifiers (no secrets today) What I want you to build 1. Create a server-side API route in the Next.js app (e.g. `app/api/subscribe/route.ts`) that: - Receives the form fields from the client - Runs an LLM spam/quality check (e.g. OpenAI or similar) to classify the submission as legit vs. spam — checking for gibberish names, fake/disposable emails, nonsense job titles, injection attempts, etc. - If legit → forwards the submission to HubSpot (server-side) - If spam → rejects gracefully with a generic message (no HubSpot write) 2. Update the existing `NewsletterForm.tsx` to POST to the new internal API route instead of calling HubSpot directly. 3. Keep the LLM API key server-side only (use an environment variable — never expose it to the client). 4. Preserve the existing UX: loading / success / error states should still work. Deliverables - Working API route with the LLM spam check + HubSpot forwarding - Updated form component - Brief note on which env vars to set (`OPENAI_API_KEY`, etc.) and how to configure them - Clean, typed TypeScript that matches the existing code style Nice to have (optional) - Basic rate limiting / honeypot field as a cheap first line of defense before the LLM call - Configurable spam threshold or a logged "reason" when something is rejected Requirements to apply - Strong Next.js App Router + TypeScript experience - Experience calling an LLM API (OpenAI or equivalent) from a server route - Familiarity with HubSpot Forms API is a plus To apply, please briefly answer: 1. Which LLM/provider would you use and roughly what would it cost per submission? 2. How would you handle the case where the LLM API is slow or down — do you fail open (let it through) or fail closed (block it)? 3. Have you integrated with HubSpot Forms before? (yes/no is fine)

About the Role Assembly Software is a B2B SaaS company serving law firm customers and is actively expanding its internal AI capabilities. We are seeking a highly skilled AI contractor to serve as our embedded AI program lead — someone who can own and advance the design, implementation, and governance of AI tooling across the entire organization. This is a hands-on, strategic role. You will work directly with IT leadership and cross-functional teams to assess our current AI landscape, close gaps, and build a mature, secure, and operationally excellent AI program. We are a heavy Anthropic/Claude shop. Strong familiarity with Claude, the Anthropic API, and the Model Context Protocol (MCP) ecosystem is a significant advantage for this role. Core Responsibilities • Audit existing AI tool usage and identify overlaps, gaps, and shadow IT • Design and implement a company-wide AI governance framework • Lead MCP server setup, integration, and lifecycle management • Configure and manage Claude Teams/Enterprise deployments • Build and maintain an internal AI Skill Library for staff use • Define AI security policies and data access controls • Evaluate and recommend new AI tools and vendors • Establish prompt engineering standards and best practices • Connect AI tooling to internal business systems (Salesforce, M365, Asana, and others) • Support AI integrations with sensitive data sources including our data warehouse and CRM • Produce documentation, SOPs, and executive-ready reporting • Train internal staff and stakeholders on AI capabilities and safe usage Required Qualifications • Hands-on AI implementation experience in enterprise environments • Deep familiarity with large language model platforms, particularly Anthropic Claude and OpenAI • Proven experience building and managing MCP (Model Context Protocol) servers and integrations • Strong understanding of AI security — data exposure risks, access scoping, governance controls, and audit logging • Experience integrating AI tooling with business systems such as Salesforce, Microsoft 365, or similar platforms • Ability to author clear governance documentation, security policies, and executive-facing deliverables • Comfortable operating independently with minimal oversight while maintaining strong stakeholder communication Preferred Qualifications • Hands-on experience with the Anthropic Claude API, including system prompt design, tool use, and agentic workflows • Background in B2B SaaS, legal technology, or other regulated industries • Familiarity with SOC 2 compliance requirements as they relate to AI tooling and data access • Prior experience standing up internal AI assistants or Copilot-style tooling connected to live business data • Knowledge of data warehousing and secure query patterns for LLM-to-database integrations • Familiarity with CI/CD workflows and lightweight DevOps for deploying AI services

We are seeking an experienced Workflow Automation Specialist to design and implement a scalable email automation system that streamlines the processing of inbound requests and outbound communications for Data Subject Rights Requests. Our team currently handles a moderate-to-high volume of incoming emails from multiple sources. Much of the workflow involves repetitive administrative tasks, including reviewing emails, extracting key information, manually logging data, and sending standardized responses. We are looking for a consultant/developer who can evaluate our existing process (combination of GMail & Monday.com), recommend the best tech to use, and build a solution that significantly reduces manual effort while maintaining accuracy, visibility, and auditability (this is key!). Full job & project details in the attached PDF

**** Agencies are welcome to apply, but put your GenAI solutions architect on the very first interview call. Not a salesperson who then hands off **** We are an AWS partner company focussed solely on Data & AI implementation work. We are specifically looking for a GenAI Solutions Architect to lead pre-sales engagements with customers. Typical duties include: 1/ Executing pre-sales discovery calls with customers 2/ Demoing our proprietary production GenAI demo environments and mapping customer use-cases 3/ Performing deep GenAI assessments and helping provide roadmap for implementation and planning 4/ Compile Statement of Work for customer projects We have a number of tools that will assist in this process to ensure compliance with our patterns and standards. The ideal candidate should have at least 3 yrs of hands on GenAI implementations, understanding of RAG, MCP, bedrock, agentcore experience, strands/langchain experience, and the ability to clearly understand business needs and articulate/map out to technical implementations. Our interview process is unique since this is a LONG running engagement: Step 1: Interview with a technical executive Step 2: Interview with a Sr. technical engineer Step 3: After NDA is signed, train on one of our IP environment (5 hr commitment from your side, but likely faster if you are already familiar with GenAI & AWS) Step 4: Present to a panel on the concept. This is where we evaluate your ability to present what you have learned and earn trust, similar to how you would be doing customer facing.

We're looking for a developer to build a lean, working Proof-of-Concept of an automated pipeline that ingests podcast episode audio, generates a clean transcript with speaker diarization and timestamps, and uses an open-source NotebookLM alternative (Notex or Open Notebook) to automatically produce a suite of repurposed content assets — show notes, episode summaries, social media posts, blog drafts, and pull quotes. The goal is to validate the end-to-end workflow on 2–3 sample episodes, not to build a full production platform yet. We want to see the plumbing work cleanly before investing in scale. Envisioned stack: n8n for orchestration, a speech-to-text API (Deepgram, AssemblyAI, or Whisper), a lightweight DB (Supabase or PostgreSQL), and an open-source NotebookLM alternative as the content generation engine. The whole system should be self-hostable via Docker. We're open to the developer's recommendations on the best tools and tradeoffs. Deliverables include a working n8n workflow, Docker-compose setup, a short README, demonstration on 2–3 sample episodes we provide, and a brief written recommendation on Notex vs. Open Notebook for scaling this pipeline to ~500 episodes/year. Required skills: n8n (or similar orchestration), speech-to-text APIs, Docker / self-hosted deployments, hands-on experience with NotebookLM alternatives or RAG-based content engines, LLM prompt engineering for structured output, and PostgreSQL / Supabase basics. Nice to have: Prior podcast or media-tech automation work, pgvector / RAG experience, structured output via JSON schema or function calling, and experience scaling automation pipelines. To apply, please include: a short overview of your automation / AI pipeline background, specific experience with n8n + STT APIs + open-source NotebookLM alternatives, links to GitHub or prior workflows, a 2–3 sentence note on whether you'd recommend Notex or Open Notebook for this use case and why, and your estimated turnaround time. This is a fixed-budget POC (~$100). If the workflow is clean, reliable, and well-documented, we plan to expand it into a full production build (client portal, human-in-the-loop editor, admin dashboard, scaling to 500+ episodes/year) with a significantly larger budget.