I am working on a system using AI to review and respond on Google Drive (Shared Drive) folders of PDF's. Using Gemini as a POC I get responses that sometimes reach outside of my specified folders of content, but sometimes, some PDF files are ignored too. Also, when the response to a prompt come back, the sources are linked. However, the links only bring up the first page of the PDF file wherein the linked source material is, AND NOT THE pdf PAGE of the specific info. I need to have the AI (Gemini, Grok, etc.,) be used to query just.... but all, of the PDF files, within a set of folders in Google Drive (Shared Drive), and to respond with linked content. Said links must open to the PDF PAGE, not just the PDF which houses the specific info. In short, I think I need a viewer, but someone who has experience working with AI and PDF's will likely know the issue I am running into. In the end my system will have a UI attached, so there is a lot of possible side work on this project. First I need to build a better POC. For instance, if I open ONE of the PDF files in Google Drive, I can prompt on that file, and the correct PDF page does come up in the viewer, (While no other files are considered for the queried content.) But when I give Gemini many source PDF's or a folder of PDF's, the links only go to the first page pf the PDF with the information used as the sourcwe.

Looking for general help building out a platform for an AI saas.

I am looking to partner with a competent dev team (individual or team) to launch AI implementation solutions for small and medium-sized businesses. As a sales professional, I aim to provide innovative solutions that enhance business operations. The ideal candidate will have experience in AI technologies and a strong understanding of business needs.

Looking for an expert developer to build a private, highly secure creative and financial workspace using the Google AI Studio / Gemini 1.5 Pro API. The absolute priority of this project is a 100% complete data migration. You will take a 6-week raw chat log containing precise financial checking/savings ledgers, business blueprints, and long-form biographical story notes, and ingest it flawlessly into a permanent vector database. You will connect this repository to the Gemini API so the system maintains permanent context retention across both a laptop and an Android phone without losing a single digit or syllable. Must deliver a clean, private, password-protected web interface that allows for seamless text dialogue, image uploads (for inventory tracking), and audio processing. Security, privacy, and flawless data retention are non-negotiable.

We need to have a AI developer who is readily available on slack to fix any errors that may arise from our app and to do monthly check and updates. we want this to be long term and to be a partnership. Occasionally we may have a new project to work on as well.

We are looking for 2-3 AI engineers/developers to help us build/complete an AI go-to-market (GTM) tool that looks at both structured and unstructured data, runs it all through our AI engine, and provides insights and recommendations straight to sales people. The tool is able to handle large volumes of data and provide actionable insights for business decision-making. Our engine consists of a prioritization algorithm, pattern matching and sentiment analysis. We use our recommendation engine to deliver the output (insights) straight to our tool which is "Apple-simple", intuitive and gamified. No more sales time wasted on looking at dashboards and trying to agree on which insights are important and which should be acted on. What you'll do Own features end-to-end across theFastAPI backend (IEngine) and Next.js 15 / React 19 frontend— from Claude prompt design to UI polish. Extend the intelligence pipeline: meeting ingestion (Google Drive + Deepgram realtime), Claude-driven action card generation, Neo4j relationship graph, and Supabase-backed state. Build customer-facing dashboard surfaces — deals, gamification, coaching, trust graph — with TypeScript, Tailwind, shadcn/ui, and D3. Operate WebSocket transcription sessions and async job pipelines reliably under real meeting load. Instrument with Sentry, harden auth (NextAuth :left_right_arrow: JWT :left_right_arrow: FastAPI service-to-service), and keep deploy pipelines green. Build with simulators: when you can't test against live meetings, generate realistic synthetic transcripts through our simulator service. Stack you'll work in Backend: Python 3.11, FastAPI, Uvicorn, PyJWT, Anthropic SDK, Deepgram, Neo4j, Supabase (Postgres + realtime), WebSockets Frontend: TypeScript, Next.js 15 (App Router), React 19, NextAuth 5, Tailwind, shadcn/ui, D3, Stripe Infra: Fly.io, GitHub Actions, Sentry, Supabase AI: Claude (Opus/Sonnet) for transcript analysis, action card generation, and agentic dev workflows What we're looking for 4+ years building production web applications, ideally across Python and TypeScript. Comfort designing and shipping features against an LLM API — prompt iteration, structured outputs, evals, cost/latency tradeoffs. Real Claude or OpenAI production experience required. Experience with multi-tenant SaaS patterns, JWT auth, and one or more of: graph databases, realtime systems, audio/transcription pipelines. Comfort with AI-assisted development workflows (Claude Code, Cursor, etc.) — not just as a code-completion tool, but as a way to plan and ship features. Bias toward shipping. Small surface area, high ownership, no committee.

We’re looking for an experienced engineer or trainer to help deliver hands-on AI coding training for engineering teams. The focus is on moving engineers beyond basic Copilot/autocomplete usage into agentic workflows with tools like Claude Code, Codex, GitHub Copilot, and related tooling. Scope: Run practical workshops for engineers Teach Claude Code, Codex, and Copilot workflows Cover task scoping, prompting, reviewing diffs, and code validation Help create assets like CLAUDE.md, agents.md, slash commands, or workflow templates Advise on best practices, cost awareness, security, and team adoption Ideal candidate: Strong software engineering background Hands-on experience with Claude Code, Codex, or similar tools Experience training engineering teams Clear communicator with practical examples Please apply with relevant examples of AI coding workflows, trainings, or engineering teams you’ve supported.

Overview We run an AI voice assistant for self-storage operators. We have an internal, AI-assisted workflow for triaging call feedback — investigating what happened on a call, diagnosing the root cause in our codebase, and drafting fixes. We’re looking for someone technical to run that AI-assisted workflow day to day and help us make it better. You’ll be driving AI coding agents, reading real code to understand behavior, and improving the process and tooling itself. What you’ll do Use our AI agent tooling to work through a queue of customer feedback on AI voice calls. Read our TypeScript/Node codebase (voice-agent prompt assembly, workflow/“SOP” engine, tool implementations) to diagnose why the agent behaved a certain way — not just guess. Draft fixes: workflow-instruction edits, knowledge-base entries, or code changes via pull request with a clear verification plan. Improve the triage process itself — refine the AI agent prompts/skills, conventions, and the internal MCP tooling that powers it. Write clear, customer-facing summaries of what changed for our team to review and approve. You’re a great fit if you Read and reason about code confidently — ideally TypeScript/Node; React a plus. Have hands-on experience driving AI coding agents (Claude Code, Cursor, or similar) and understand how LLM prompts/tools/agents fit together. Think in cause-and-effect: “the agent did X because line Y / instruction Z.” Write precisely and concisely for both technical and non-technical audiences. Are process-minded — you spot the repetitive thing and turn it into a better workflow. Bonus: prompt engineering, LLM tool/agent development, or voice/conversational AI experience. How we work We’ll start with a paid trial on a small batch, then scale steady ongoing volume. To apply: Tell us about a time you used an AI coding agent to diagnose or fix something non-trivial in a codebase you didn’t write — what you did, and how you verified it worked. A link to relevant work is a plus.

We are building a semiconductor manufacturing intelligence platform designed to help engineers rapidly identify yield excursions, investigate root causes, and capture institutional process knowledge. A working foundation already exists, including yield dashboards, lot tracking, process-route visualization, maintenance-event correlation, and investigation timelines. We are now looking for a highly capable developer to extend and refine the system into a production-grade engineering decision-support tool. This is not a basic dashboard project. The goal is to enhance an existing platform into a system that connects manufacturing data, equipment history, and engineering knowledge with lightweight AI-assisted analysis. Key Objectives Help engineers answer questions such as: * Why did yield drop? * What changed before the excursion started? * Which tools or chambers are most likely responsible? * Have we seen a similar issue before? * What corrective actions worked previously? Scope of Work Investigation Workspace * Improve investigation timelines * Correlate process events, SPC/FDC signals, maintenance activity, and yield changes * Enhance interactive debugging workflow Historical Excursion Search * Simple similarity matching using rules or embeddings/API-based methods * Retrieve past investigations and outcomes Engineering Knowledge Layer * Searchable notes, documents, and reports * Store corrective actions and process changes AI-Assisted Summaries (lightweight) * Generate investigation summaries using an LLM API * Suggest possible contributing factors based on available data Ideal Candidate * Strong full-stack or data engineering experience * Comfortable working with existing codebases * Experience with analytics dashboards or industrial systems * Familiarity with APIs, databases, and data modeling * Bonus: exposure to manufacturing or semiconductor data Notes * This is an extension of an existing platform, not a rebuild * Focus is on practical implementation rather than complex architecture * Speed and execution matter more than theoretical design * Potential for ongoing work if collaboration goes well

I’m running a real estate investment platform called ToInvested.com. The project is about 90% finished, and most of the code was built with Claude together with another engineer. Now I need a senior engineer to step in, review the full product carefully, test every major workflow, and help verify that everything is working correctly before it goes live. This is not just a “write more code” role. I need someone who can look at the platform like a real product, find hidden bugs, catch weak logic, test edge cases, review the AI-generated code, and tell me honestly what is ready and what still needs fixing. Because this is a real estate investment platform, accuracy and trust matter a lot. Users may rely on property data, investment logic, calculations, and AI-driven insights, so even small issues can create a serious problem later. The ideal person has strong full-stack experience, understands AI-assisted development, and has a good testing mindset. Real estate tech experience would be a big plus, especially with property platforms, investment tools, marketplaces, mortgage systems, or financial workflows. My main goal is simple: I want someone to break the project before real users do. If you’re the kind of engineer who can take a nearly finished product, test it deeply, clean up weak areas, and help make it production-ready, I’d be happy to talk.