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.

We're looking for a contract engineer to lead that transition. The core integrations and workflow already exist and pass their tests. The work now is making them run reliably against live customer systems, hardening the platform for day-to-day operation, and setting up onboarding so we can bring on additional customers without rebuilding. You'll work closely with our CTO, who owns the architecture and data model. Your focus is integration, implementation, and reliability.

Frontend: React Native (Expo SDK 54), TypeScript Backend: Firebase — Firestore, Cloud Functions (Node, v1), Firebase Auth, Firebase Storage Architecture: Multi-tenant data model (multiple client organizations sharing one Firestore database, isolated by security rules) Auth: Email/password via Firebase Auth, role-based access (general contractor / subcontractor roles) Firebase / React Native SaaS (Contract, with potential for ongoing work)

We need a programmer to help with the WEBYep login and convert pages from .php to .html. The work includes improving the login functionality and updating the page structure for better performance and compatibility. This is a small, part-time project for someone who can work efficiently and deliver clean, reliable code.

About Us We are a forward-thinking AI enterprise software company building governance solutions. Our systems combine Python engineering, Natural Language Processing, and Machine Learning to deliver secure governance solutions. We’re seeking a Back-End Python Engineer with expertise in AWS deployed applications, GITHUB CI/CD pipelines, DJANGO, ML Pipelines, Endpoint Integration, Sagemaker, containerization, Use of AI to design front end applications and debug code. Key Responsibilities Design, develop, and maintain back-end services in Python to support software application Debug Application for Quality and Assurance Build Data Connectors for Application Integration Implement new features with front end design as needed Containerize and deploy services across AWS infrastructure. Build and scale RESTful APIs and microservices (Django + DRF) that integrate into automated pipelines. Tune system performance (network, I/O, memory, GPU utilization) for optimization. Architect and maintain databases (SQL & NoSQL), ensuring query optimization, high availability, and caching (Redis). Integrate background processing (Celery) and real-time communication (WebSockets) into containerized environments. Collaborate with DevOps, front-end, and AI/ML teams to deliver end-to-end automated workflows. Apply best practices in system design (SOLID, DRY, KISS), Python standards (PEP8), and secure infrastructure deployment. Qualifications Core Skills Proficiency in Python (OOP, async, functional programming, data structures). Expert-level knowledge of AWS Infrastructure (deployment, operators, CI/CD, scaling). Strong background in containerization (Docker, Podman) and Kubernetes-native orchestration patterns. Experience supporting AI Dev automation workflows and integrating back-end services with automated pipelines. Deep knowledge of Django & DRF: ORM, serializers, view sets, permissions, HTTP methods. Advanced database design & optimization for high-throughput applications. Familiarity with Redis caching, Celery task queues, and uWSGI/ASGI communication layers. Solid testing skills (pytest/unittest) and CI/CD pipelines with Git. Preferred Expertise Hands-on experience with GPU-enabled workloads and hardware acceleration in containerized environments. Familiarity with infrastructure automation tools (Ansible, Terraform, or similar). Agile/Scrum team experience and use of task tracking (Jira, Trello). What We’re Looking For We want an engineer who: PRIORITIZES SECURITY OF SYSTEMS AND INFRASTRUCTURE ACROSS SECURITY FRAMEWORKS Builds automation-first systems that support AI Dev workflows from code to deployment. Thinks about performance and scalability at the infrastructure + software level. Collaborates across teams (DevOps, AI/ML, product) to deliver fully integrated, automated platforms.

We are looking for a U.S.-based senior React/Firebase technical lead or software architect to help review and guide an existing SaaS product to pilot launch. The product is already built and deployed in staging. This is not a greenfield build. We need an experienced technical partner who can inspect the current architecture, review contractor work, identify launch risks, and help sequence the remaining work before pilot. Current stack: * React / TypeScript * Firebase Auth * Firestore * Firebase Storage * Firebase Cloud Functions * Firebase Hosting / GCP * GitHub The app is a multi-tenant SaaS platform with organizations, locations, employees, role-based permissions, training modules, SOPs, communications, request-off workflows, handbook acknowledgements, and admin consoles. We need help with: * Reviewing the existing React/Firebase architecture * Reviewing Firestore rules, Storage rules, Cloud Functions, auth/custom claims, and tenant isolation * Reviewing PRs and contractor work * Advising on remaining pilot-readiness work * Helping sequence frontend cleanup, UX/UI polish, admin console structure, and final security review * Identifying risks before real pilot customers are onboarded Important experience: * React + TypeScript SaaS applications * Firebase Auth / Firestore / Cloud Functions * Multi-tenant architecture * Role-based access control * Security-minded code review * GitHub branch / PR workflow * Ability to explain technical tradeoffs clearly We would like to start with a small paid technical review first. If it is a good fit, this may turn into an ongoing technical lead/advisor role. We are not looking for someone to blindly rebuild the app. We need senior judgment, architecture review, launch-risk identification, and practical guidance.

I have built out an MVP using lovable for the wedding content creator community. It is a gallery platform where they upload their iphone videos and it displays in a gallery (Like pic-time, pixieset, etc). Our users primarily record 4k short form iphone videos and the upload pipeline is the most important piece. I dont have background in any of this and have only build with lovable, but the business is growing quickly and I would love some help to fix bugs and harden the pipelines/overall architecture. We use Cloudflare, Backblaze, Mux, Fly, and Lovable. We haave also built out an app that is on Testflight (Primarily what our users are using to upload).

Note: We are a well-funded startup with a very high engineering bar, working alongside senior engineers with experience from leading AI labs. This is a smaller initial paid task, but we pay well for excellent work and there is potential for a much larger collaboration if the fit is strong. Your PRs will be reviewed by strong engineers, so we are looking for someone who takes ownership, thinks clearly, and cares about shipping clean, production-ready code. Please only apply if you can hold yourself to that standard. We are not looking for generic AI-generated output or low-effort execution. # Implement Probabilistic Attribution Between Marketing Website and Electron Desktop App We have a marketing website where users can click to download our Mac desktop app. The app is distributed as a standard Mac DMG and built with Electron. We use PostHog for product analytics, and we also run Google Ads. Users may eventually sign in inside the desktop app through ChatGPT/auth, but many users will first be anonymous. We want to implement a simple first version of attribution that helps us understand which website visitors / ad campaigns / download clicks later become desktop app users. ## Goal Build a lightweight probabilistic matching system that connects: 1. A user visiting the marketing website 2. The same user clicking “Download” for the Mac DMG 3. The desktop app being opened for the first time 4. The user later signing in, when applicable The goal is not perfect identity matching. The goal is good-enough attribution for our current low-volume flow, roughly around 100 download clicks per week. ## What needs to be figured out The developer should determine the best simple implementation for: - Capturing enough information on the marketing website when someone clicks the Mac download button - Capturing enough information from the Electron app on first open - Matching those two events probabilistically on the backend - Passing useful attribution information into PostHog events - Associating the attribution with the authenticated user once the user signs in - Testing that the full flow works end-to-end The likely matching signals are things like timestamp proximity, hashed IP, platform, timezone, language/locale, and other non-invasive browser/app context. The implementation should avoid overcomplicated or privacy-invasive fingerprinting. ## What we should do Implement a simple backend-backed attribution flow: - When someone clicks “Download for Mac” on the website, create a download-attribution record. - Capture campaign data such as UTMs, Google Ads click ID if present, landing page, referrer, and PostHog anonymous/browser ID where available. - When the Electron app first opens, create or retrieve a persistent app install ID. - Send a first-open event from the app to the backend. - Backend attempts to match that first app open to a recent download click. - Store the match with a confidence level such as high/medium/low/unmatched. - Send attribution metadata as properties on relevant PostHog events. - Once the user signs in, connect the app install and attribution record to the authenticated user ID. ## What we should not do in this version We do not want to overbuild this. Do not: - Generate a unique DMG per user - Modify the signed Mac app bundle - Inject tokens into the installer - Implement custom deep links yet - Build a full deterministic attribution system - Use probabilistic matching to permanently merge PostHog user identities - Send raw IP addresses to PostHog - Add invasive browser fingerprinting Probabilistic attribution should be treated as estimated attribution, not as guaranteed user identity. ## Expected deliverables The task is complete when: - The website download flow records download intent and campaign metadata. - The Electron app records first-open/install metadata. - The backend can probabilistically match app first opens to recent website download clicks. - PostHog receives app events with attribution properties when a match exists. - The system links the app install to the authenticated user after sign-in. - There is a way to inspect/debug attribution matches. - The implementation is tested locally or in staging with realistic flows: - normal download → immediate app open - delayed app open - no matching download - multiple download clicks from the same network - user signs in after opening the app ## Important constraint This is a first version. We prefer a simple and maintainable solution that gives us useful attribution data over a complex solution that tries to be perfectly accurate.

one qudio talk a live audio talk application where talk volume is set based upon content of conversation, thus if your talking about cats other users talking about cats will be heard loudest where everyone is on same audio channel this seperates content into what is most audible by result users per their content can talk clearly to others based upon their own content all other talk is at a lower volume where can be accessed based upon you varying your conversation to that topic thus the volume is then then higher on that content with your previous content on a medium volume users can easily have multiple conversations and enjoy talking to many people i would also like to have a reactive to audio motion graphic for the website that is the total extent of the UI similar to this shown where each user has a unique channel in the circle

I need help with finding and installing a plugin that will work with my current setup to change the price of our rentals. The task involves troubleshooting and ensuring compatibility with the current setup. The ideal candidate should have experience in WordPress and plugin installation.