Forum Intelligence: Project Brief & Initial Rollout 1. Executive Summary & Objective Forum Intelligence is a beginning as a localized data retrieval, processing, and archiving system designed to scrape public municipal records and state legislative data for public oversight. The immediate objective is to build a functional, highly resilient prototype focused on the Tri-Cities region (Burbank, Glendale, and Pasadena, California). The system will autonomously ingest messy, unstructured municipal data (City Council meeting minutes, agendas, public notices, and legislative PDF text, recorded mp4), clean it, and make it fully searchable and queryable via a localized AI agentic framework. 2. Phase 1 Scope: The Tri-Cities Rollout Th engineer will be responsible for building two primary pillars: A. Resilient Scraper Bots • Target Ingestion: Monitor and pull data from Burbank, Glendale, and Pasadena municipal portals and California legislative feeds. • Data Types: Brittle HTML sites, heavily nested tables, public notices, legislative drafts, and massive unstructured PDF archives. • Requirements: The scraping architecture must be exceptionally robust, utilizing intelligent error handling, retry semantics, and pagination tracking to handle frequent municipal website layout changes without breaking the pipeline. B. Ingestion & Vector Pipeline • Parsing: Extracting clean text from poorly formatted documents and scanned PDFs. • Local RAG (Retrieval-Augmented Generation): Chunking and embedding the data locally into a vector database (e.g., pgvector, Chroma, or Milvus) to enable semantically accurate entity linking and contextual search. 3. Targeted Hardware Stack To ensure maximum data security, strict public oversight integrity, and predictable operational costs, Forum Intelligence is skipping commercial cloud APIs in favor of an on-premise, localized NVIDIA enterprise deployment. The production roadmap aligns precisely with the new computing patterns detailed in NVIDIA’s latest hardware roadmap: • Inference & Token Generation: Running local open-weight frontier models (e.g., Neotron 3 Ultra or Claude/Llama equivalents) optimized for reasoning and long-context tool use. • Compute & Orchestration: The backend infrastructure is architected around NVIDIA’s dedicated agentic architecture, utilizing high-instructions-per-clock (IPC) Vera CPUs paired with Vera Rubin GPUs. • Memory & Storage Processing: Utilizing NVIDIA’s unified memory fabric and data processing units (DPUs) for ultra-low latency context management, KV caching, and fast vector database retrieval. 4. Immediate Milestones for the Engineer 1. Architecture Design: Map out the database schema and local inference ingestion loop. 2. Tri-Cities Scraper Deployment: Write and deploy the initial automated bots for Burbank, Glendale, and Pasadena. 3. Local MVP Pipeline: Demonstrate a local RAG pipeline where a user can query the Tri-Cities scraped records and receive grounded answers with exact source attributions. The above was AI generated from months long conversations with Gemini. The goal is to prove the concept then roll out to LA County, state of CA, and then the country.

Looking for an elite problem solver, either an ex-MBB consultant with deep technical fluency or a seasoned AI Product Manager, to act as the crucial bridge between business operations and technical AI implementation. You will be responsible for dissecting client operations, prototyping the AI logic, writing flawless engineering briefs, and driving the development team to the finish line. What You Will Do: Workflow Audits & Discovery: Lead deep-dive discovery sessions. Map out existing operational workflows, identify bottlenecks, and pinpoint high-ROI opportunities for AI automation. Prototyping & Technical Translation: Because you are an AI power user, you will prototype the initial AI prompts and logic. You will then translate your operational findings into crystal-clear engineering briefs (or PRDs) so developers know exactly what to build. Development Management: Act as the project lead during execution. Manage the engineering workstream, clear roadblocks, and ensure the final solution is delivered on time, within budget, and achieves the strategic goal. Stakeholder Alignment: Act as the primary liaison between non-technical business leaders and the technical development team. Requirements & Qualifications: Top-Tier Pedigree: 1+ years of experience at a top management consulting firm (MBB, Big 4, Tier 2) OR proven experience as a Technical/AI Product Manager. Advanced AI Fluency: You are an AI power user. You possess advanced prompt engineering skills (e.g., chain-of-thought, few-shot) and know how to force LLMs to output reliable, structured data. Elite Structured Thinking: You excel at turning highly ambiguous, messy business processes into clean, logical frameworks (using Lucidchart, Miro, etc.) and comprehensive technical requirements. Project Leadership: Proven track record of managing technical resources, tracking deliverables, managing budgets, and driving teams to a deadline.

We're building an internal operations platform to automate utility account management for a large real estate portfolio. Today, much of this work is manual. Information about utility accounts exists across multiple systems, and employees spend significant time identifying missing bills, reconciling account data, researching exceptions, and coordinating follow-up work. We're building a system that automates these processes by synchronizing data between our operational system and accounting system, applying business rules to identify exceptions, and presenting actionable work queues and dashboards for our operations team. Examples include: Utility accounts that exist in one system but not another Missing or delayed utility bills Accounts requiring setup or closure based on occupancy changes Autopay and e-bill tracking Operational exceptions that require human review Dashboards, work queues, assignments, notes, and status tracking Our internal product manager owns the business requirements and workflows. Your role is to work closely with them to design and implement the technical solution, not to perform business process discovery. What You'll Do Design and use AI to build the application's backend and frontend. Design a clean, maintainable application architecture. Use AI to build dashboards and workflows that allow operations teams to efficiently manage exceptions. Translate product requirements into production-ready software. Leverage AI development tools (Codex, Claude Code, Cursor, or similar) as a core part of your workflow to accelerate development. Review, validate, and refine AI-generated code to ensure quality and maintainability. What We're Looking For We're looking for an experienced software engineer with strong software engineering fundamentals who embraces AI-assisted development. You should understand how modern software applications are architected, designed, built, and deployed, and be comfortable making sound technical decisions while moving quickly. Experience in many of the following areas is preferred: Full-stack application development Application architecture and system design APIs and system integrations SQL databases and data modeling Authentication and security Cloud-hosted applications Testing and debugging Source control and collaborative development We care much more about engineering judgment, speed of execution, and the ability to effectively leverage AI than expertise in any particular language or framework. Nice to Have Experience building internal business applications or operations platforms Experience working with accounting, ERP, or workflow systems Experience building dashboards and operational tooling To Apply Please include: A brief summary of your experience building business applications. The AI development tools you use regularly (Codex, Claude Code, Cursor, Windsurf, etc.) and how they fit into your workflow. Examples of projects where AI significantly accelerated your development process. Your availability over the next 2–3 months and your expected hourly rate.

Seeking an experienced freelancer to scope an AI project focused on generating building floor plans. The ideal candidate will have a strong background in architectural design and AI technologies, with the ability to assess project requirements and provide detailed proposals.

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

Join our team as a senior AI Architect working closely with our product and engineer teams to design practical AI capabilities within our SaaS platform. This is a hands-on role focused on building reliable, production-grade conversational and AI-assisted features — not experimental research projects. You will work closely with product and engineering teams to design scalable AI patterns, integrate modern LLM technologies, and help shape how AI capabilities are embedded into real operational workflows. You will focus deeply on architecture, implementation quality, reliability, usability, scalability, observability, and operational robustness. This role is ideal for someone who understands both modern AI tooling and the realities of shipping enterprise SaaS software in production environments. We value people who can think critically about architecture, tradeoffs, operational realities, and long-term maintainability — not just prototype AI demos.

We are looking for a senior, enterprise-grade Software Engineer to build a high-performance Proof of Concept (POC) for an Autonomous B2B Insurance Compliance & Asset Monitoring Agent. The system will bridge the gap between complex commercial insurance policy guidelines (unstructured data via RAG) and real-time operational telemetry (structured IoT log streams in Snowflake). The goal is an intelligent agent that dynamically analyzes asset anomalies against policy definitions to flag compliance breaches instantly. This is a highly intensive 1-week, 50-hour sprint for an engineer who understands complex data schemas, time-series IoT data gravity, and deterministic agent routing. Core Architecture Requirements (What You Will Build) The Multi-Agent Orchestration Layer: Build an autonomous agent framework (using LangGraph, AutoGen, or native Python execution loops) capable of multi-step reasoning, tool-calling, and error self-correction. Snowflake & IoT Data Lake Integration: Grant the agent secure access to a Snowflake environment containing structured IoT asset monitoring data (e.g., machine telematics, temperature logs, usage hours, maintenance schedules). The agent must dynamically discover schemas and write optimized SQL queries to aggregate this data. Insurance Compliance Hybrid RAG Pipeline: Implement a semantic search architecture to parse and embed dense, unstructured insurance underwriting guidelines, warranty contracts, and B2B compliance policies into a vector store. Deterministic Tool Use & Evaluation: The agent must safely look up a specific policy compliance rule (via RAG) and then automatically generate and execute a targeted query against the Snowflake IoT data lake to verify if real-world asset telemetry violates that specific rule. Required Technical Stack & Expertise Languages & Core Frameworks: Python (FastAPI / Typer), LangChain / LangGraph, LlamaIndex. AI & Embeddings: OpenAI GPT-4o / Anthropic Claude 3.5 Sonnet APIs, vector databases (Pinecone, pgvector, or Snowflake Cortex search). Data Architecture: Deep production experience with Snowflake, cloud data lakes, time-series IoT data manipulation, and advanced text-to-SQL generation patterns. Observability: Implementation of tracing tools like LangSmith, Phoenix, or Arize to monitor prompt tokens, routing paths, and costs. Project Timeline & Deliverables (50 Hours) Phase 1 (Hours 1–15): Environment setup, ingestion of sample insurance policy PDFs into the vector store, and secure connection string setup to a Snowflake mock IoT dataset. Phase 2 (Hours 16–35): Core development of the multi-agent execution loop, prompt tuning for text-to-SQL generation against time-series data, and policy-to-telemetry mapping logic. Phase 3 (Hours 36–50): Observability integration, edge-case testing for hallucination mitigation (ensuring false compliance flags are eliminated), clean code delivery, and a walkthrough video demonstration. If this sprint goes smoothly, there is an immediate opportunity to extend this contract into long-term architecture maintenance, production scaling, and integration into our core platform. Please include real examples of multi-agent, IoT, or insurtech data pipelines you have personally built in your application.

We are not looking for someone to create a single avatar. We are looking for a long-term creative partner to help build one of the world's most recognizable AI media brands. If you're excited about combining AI, storytelling, and positive global impact, we'd love to hear from you. Bonus experience: News media Animation Digital influencers Entertainment branding Intellectual property development To Apply Please include: Your portfolio of AI characters. Examples of consistent AI characters you've developed. The AI tools you use most often. Your process for maintaining character consistency across thousands of images and videos. Your favorite AI character you've created and why. A brief description of how you would design a globally trusted AI news host. Finalists will complete a paid concept assignment. Design one original AI news host for Global Positive News that includes: One hero portrait Three alternate expressions A short biography A sample script introducing the character Three example prompts showing how the character can be recreated consistently

Need an experienced developer to integrate an AI-powered feature into an existing application. Small scope, fast turnaround

I need a advanced agentic system built with persistent memory and up to 6 agents that work together. I am building a franchised coffee shop business. there is so much data that can be pulled together and harvested from customer spending habits and also what is the highest grossing items that sell , vs the most profitable hours of the day. All that data needs to be meshed with the actual Quickbooks data and financials. All that then needs to be balanced with real world site selection for new coffee shop locations. Here is what I need: Agent 1. Pulls information Directly from clover POS automatically. Agent 2. takes Agent's 1 information and cross references with Margin Data from Quickbooks. Recommends New drinks that are both on trend AND High Margin. Agent 3 is the financial Agent. It works directly with Quickbooks. It monitors cash flow and alerts when labor percentage exceeds parameters. It also stress tests expansion and " what if" scenarios. Agent 4. the site selection agent. agent 4 monitors LoopNet, Costar, and parcel data for commercial land available. It cross references traffic count and demographics, it checks competitor coffee presence etc. Agent 5 is the capital strategist. when agent 4 finds a location, it consults with agent 3 which is connected to Quickbooks , it models out loan scenarios, cash flow impact. and helps run " what if " scenarios that it gets asked. Agent 6 is the main Orchestrator that runs everything that I would communicate soley with through Whatsapp. It connects all the agents and pulls data collectively and makes them all work together and stress tests ideas that one agent might find.