Ollama jobs
...850 000 000 tokens every day while experimenting with Vibe coding projects that rely on Claude Code + Skills, and the bill is piling up fast. Most of that waste appears when the code calls Claude and, to a lesser extent, Ollama; Qwen 3.8 is in the mix too, but its footprint is smaller. Locally the workflow runs, but it still eats tokens, and on the H200 server I spun up on Vast the same stack becomes painfully slow as well as expensive. Here’s what I need: • A careful audit of the current prompt chains and function calls to pinpoint exactly where Claude Code and Ollama are over-allocating tokens. • Concrete code-level optimisation techniques—prompt refactoring, context window trimming, token-length guards, caching strategies or any other proven ...
...Docker Stack Create a ready-to-run Docker Compose deployment of an Agentic AI Stack with Open WebUI, Ollama, NanoClaw. It will be used in a training with approximately 10 participants. The deployment should require only .env configuration and a docker compose file, so that all I have to do is put in the configurations and run docker compose. It is important for me that the freelancer takes care all the necessary testing and delivers a working solution. My Server: 32 GB RAM, CPU-only, configurable with 1–16 CPUs, approximately 10 users. I will do the deployment on the server, your task only is to provide the project folder with .env file and docker-compose.yml. Components: Open WebUI Ollama + Qwen3 14B, model configurable via .env, automatic initial download, p...
...Tech Stack: Node.js (v20+ / ES Modules), Plain Node HTTP / REST APIs, PowerShell scripts, React 19 / Vite / Express / tRPC / Drizzle ORM ( reference prototype), and local LLM integrations (Ollama). Immediate Next Tasks (Phase 1): Primary Machine Validation: Validate and test our Node.js runtime (v0.1.3) on the primary Windows computer (verifying persistent signed sessions, local task creation, and approval holding queues). First Worker Deployment: Deploy and validate the portable worker runtime () on a secondary machine, configuring capability profiles, claim/lease handling, and local Ollama integrations. Future Roadmap: Configure secure private remote access via Tailscale Serve (HTTPS) and expand multi-worker scalability. Required Skills & Experience Core Backend...
...AI models (+ a web GUI for submitting requests via chat). Nothing else. It's a stand-alone VM. The final VM will have the right models to allow me to use models offline + how to update it. For my IT tasks, i'm using Claude Code (excellent but it has strong limits), OpenCode (but it's a command line tool), Gemini (but is not stronger specialized on coding). I read that is possible to integrate Ollama and Claude Code but I not understand IF Claude Code available () is last one or old. I don't know if exists a Deepseek version specialized in coding (I read that DeepSeek V4 Pro is not a good choice). It seems that Claude Sonnet 4.5 is one of right model. Please pay attention on environment performances (probably we could use cloud models). HW available:
AI Model Integration – Local and Cloud Support The system should support both cloud-based AI models and locally hosted models. The goal is to give us the flexibility to use a powerful cloud model when needed or run the entire system privately on a local machine or server. Local and Self-Hosted Models The system should support local AI model platforms such as: Ollama LM Studio vLLM Hugging Face models Other local LLM servers or OpenAI-compatible APIs The developer should ensure compatibility with popular open-source models, including: Llama Mistral Qwen Gemma DeepSeek Other suitable open-source models Cloud AI Models The system should also support cloud-based AI providers, including: OpenAI Anthropic Claude Google Gemini The AI architecture should be flexible enough to...
I have developed a high-...live in the frontend panels. Biomedical Reflection: Refine the "Reflection Agent" (currently using a fine-tuned biomedical Llama model) to perform a secondary quality check on the literature review output. Technical Stack: Backend: Deno v2 (TypeScript), WebSockets. Frontend: React, Vite, Tailwind CSS, Framer Motion. AI Logic: LangGraph, LangChain. Models: Groq (Llama 3.3 70B Orchestrator) & Local Ollama (Biomedical fine-tuned SLM). What’s Already Done: ✅ Stable Multi-agent state graph and orchestration logic. ✅ Professional, responsive dashboard with glassmorphism effects. ✅ Backend/Frontend communication via WebSockets. I am looking for a developer who understands medical data sensitivity and has deep experience in Agentic RAG and Acad...
...upon successful delivery and alignment on future expansion. Key Responsibilities & Core Scope: 1. Data Ingestion & Local RAG Architecture: - Build a pipeline to ingest, parse, and index corporate documents (PDFs, Excels, Docs) using an Open-Source Vector DB (e.g., Qdrant, Milvus, or Chroma). 2. Offline AI Core Deployment (Air-Gapped Containerization): - Package and deploy Local LLMs (via Ollama or vLLM) in a zero-dependency, containerized environment using Docker & Docker Compose. - Ensure 100% functionality without external internet access. 3. Executive Front-End Dashboard: - Develop a sleek, clean, and responsive user interface (React or ) displaying project status metrics, data insights, and an interactive local AI assistant. Requirements & Qual...
...activities whose triggers fire. The engine is domain agnostic, so additional workflows are data rather than code. Work routes to queues with load balanced distribution across approximately 20+ users. The system handles deadline ticklers, confirmable dates, and hold and close behavior. AI ARCHITECTURE A two-tier design. You must be comfortable with both: - Local tier: a dedicated Mac server running Ollama, handling high volume, routine, and privacy sensitive work including document assembly from templates, field extraction, classification, and summarization. Zero marginal cost, and data does not leave our infrastructure. - Cloud tier: the Anthropic Claude API for work requiring higher level reasoning. You will build a routing and abstraction layer that selects the tier based ...
...response parsing and prompt handling, improving validation and exception handling, and ensuring API credentials/configuration are managed securely through environment variables. The existing CRM frontend, database structure, customer records, and normal CRM functionality should remain unchanged. The implementation should also keep the AI provider layer reasonably modular so Anthropic or a local Ollama model could be added later without redesigning the CRM. Expected deliverables: Working CRM AI summary feature Working AI suggested-reply feature Correct OpenAI API integration Improved prompt and response handling Timeout and API error handling Environment-based API configuration Basic testing of existing CRM workflows Changes pushed to our GitHub repository Short setup/configur...
I need an end-to-end Retrieval-Augmented Generation platform, powered by Ollama-hosted language models, that lets a user drop in a document—TXT, PDF, or DOCX—then automatically searches my custom knowledge bases for exact matches and contextually related material, finally returning a concise, well-structured summary. Scope • Data ingestion: raw folders of text must be cleaned, chunked, embedded and stored in a vector store you recommend (Faiss, Milvus, or similar). • User workflow: drag-and-drop upload, progress feedback, and a chat-style window that displays the sourced passages beside the generated synopsis. • Retrieval logic: combine exact-match look-ups with semantic search so the AI can cite both literal hits and topic-level associations. &bu...
Antigravity is already installed and my Ollama daemon is serving four-five local LLMs flawlessly. Antigravity’s agents talk to those models entirely offline. We will hop on AnyDesk or Zoom—my preferred interface for this job—and you’ll: • Point Antigravity to the localhost endpoints exposed by Ollama • Verify bidirectional communication with at least one sample prompt/response per model • Leave me with the edited config files or environment variables and a quick note of the steps taken No other integrations are required—just Antigravity ↔ Ollama. Once we can pull responses from the local models without touching the cloud, the task is complete.
Fixed budget 10usd and 2 hours complete setup I need a small, fully-offline proof-of-concept that runs on my own PC. It must link a WhatsApp number through the regular “Link device” method (i.e., the same flow you see in WhatsApp Web) and start a local Ollama agent that speaks only in Bangla. The Ollama prompt has to be editable from an admin panel. Inside that same panel I also want to maintain: • product list • price of each item • delivery charge • estimated delivery time On WhatsApp the bot should do just six things, in Bangla: 1. tell the product name 2. state the fixed price and refuse any attempt to bargain 3. share a sample design image when asked 4. quote the delivery charge 5. insist on ৫০% advance for any order ...
Crear Agente de IA corriendo Local Expert in AI Agents (Ollama, OpenClaw, n8n) needed to build Master Agent Orchestrator & Local Infrastructure Optimization Busco un ingeniero de software o experto en DevOps/AI con experiencia comprobable en arquitecturas de agentes locales para estructurar, optimizar y conectar mi entorno actual en Linux (Pop!_OS). Ya tengo varias herramientas instaladas (**Ollama con Llama 3, OpenClaw, n8n**), pero necesito un profesional que limpie la configuración actual, resuelva conflictos de recursos y cree una arquitectura de orquestación sólida. #### **Objetivos y Entregables del Proyecto:** 1. **Orquestación de Agentes Maestros:** * Desarrollar un **Agente Maestro "CLAW"** (utilizando el framework OpenCla...
I am looking for an experienced Python, local AI, macOS, and browser-automation developer to build one integrated private AI and knowledge-archive system for my MacBook Air M1 with 16 GB unified memory. The project has two connected parts: 1. **AI-Native MacBook Setup** Build a lightweight, practical local AI environment optimized for Apple Silicon. The setup should include: - Ollama or another suitable local AI runtime - A user-friendly interface such as AnythingLLM, Open WebUI, or a better alternative - Efficient local language and embedding models suitable for an M1 MacBook with 16 GB memory (e.g., Gemini, Chat GPT, Claude) - Private local document search and question answering - Separate knowledge workspaces for: - ShivHeals - Webgroww Digital - Personal...
I will pay only 5 usd and Complete it in 2 hours I need a production-ready WhatsApp chatbot running on my own VPS (Ubuntu, root access available). The agent must rely on Ollama for its natural-language understanding so replies feel fully conversational rather than keyword-driven. Core functions • Customer support – the bot should recognise free-form questions and return helpful answers drawn from a knowledge base file or URL I will supply. • Order processing – customers must be able to check an order status, place a new order, or update an existing one. A simple REST hook to my back-office API is sufficient; I can extend it later. • Image generator – when a user requests an illustration (“/image a red sports car”), the bot should c...
...delivery tracking, AI assistance, telemedicine, analytics, fraud detection. Must be production-ready for national rollout with low-bandwidth, offline support, scalability, security & compliance. Core Architecture Frontend: Flutter (3 apps), (portals) Backend: NestJS microservices + Kong gateway + Keycloak auth ERP: ERPNext DB: PostgreSQL, Redis, OpenSearch, Qdrant, MinIO AI: LangChain + Ollama + Tesseract Streaming: Kafka Infra: Docker + Kubernetes, GitLab CI/CD, Prometheus/Grafana, Cloudflare Key Microservices: User, Pharmacy, Inventory, Search, Order, Payment, Delivery, Notification, AI. Agency Portfolio Requirements Must demonstrate 2+ similar production healthtech/pharmacy/marketplace platforms successfully launched at national scale in Africa or emerging markets. Pro...
I am completely new to both Ollama and Claude, so I need a patient instructor who can walk me through the very first steps—from installing the software to getting that first “Hello World” response. My priority right now is mastering the basic setup and installation process; we can save coding tricks and advanced features for later sessions. I learn best when I can watch the workflow in real time and ask questions as we go, so a step-by-step live demonstration is essential. I’d like to meet over Google Meet, share screens, and replicate everything on my own machine while you guide me. If you have a recommended checklist of prerequisites (OS requirements, dependencies, command-line tools, API keys, etc.), please let me know before our call so I can have everyt...
...choice. 2. Build a data pipeline: we will provide raw monitor images; help us set up an auto-labeling step (using an external vision API to generate first-pass labels) plus a human-verification workflow. 3. Run the fine-tune using LoRA/QLoRA (free GPU environments like Kaggle/Colab are acceptable — keep training cost near zero). 4. Export the final model to GGUF format so it can run locally via Ollama. 5. Deliver a deployment guide so the model runs offline on BOTH macOS (Apple Silicon) AND Windows machines — no internet and no cloud cost. 6. Provide an accuracy report on a held-out test set. CONSTRAINTS (IMPORTANT) - Final model must run OFFLINE and CROSS-PLATFORM — it must work on both macOS (Apple Silicon) and Windows PCs (with or without an NVIDIA GPU). Pat...
AI Mental Health Chatbot Website (Python + Flask + Ollama) Project Description I am looking for an experienced Python/AI developer to help me build the MVP (first version) of an AI-powered mental health chatbot. The chatbot should provide supportive and empathetic conversations in both Arabic and English. It is designed to help users who need emotional support, while clearly stating that it is not a replacement for a licensed mental health professional. Required Features Python (Flask) HTML, CSS, JavaScript Ollama integration (Llama 3, no OpenAI API) Smart conversational AI (not keyword-based) Arabic & English support User registration and login Save chat history for each user SQLite database Responsive and modern UI Future Features (Not required in this phase) Voice conv...
...pdfplumber), and Ollama running a local model (such as Qwen2.5-7B or Llama-3.1-8B). Variable Layouts: The documents have similar text data but the visual layouts, formatting, and specific wording change significantly from file to file. Traditional coordinate-based PDF parsing will not work. Structured Output: The local LLM must be configured using Pydantic or Ollama’s structured JSON output feature to guarantee a strict schema response. Validation Layer: A Python post-processing layer needs to run a fast local fuzzy-matching script (RapidFuzz or TheFuzz) to map extracted text entities back to a Master database reference table. Scope of Work: Text Extraction Script: Read digitally selectable PDFs efficiently. Local LLM Integration: Prompt engineering and schema enforcem...
...Requirements The platform should intelligently analyze user preferences (budget, location, amenities, lifestyle, etc.) and rank the most suitable properties using a weighted matching algorithm. An AI assistant should also answer rental questions, compare properties, and provide personalized recommendations. Preferred Skills • Flutter • Python / FastAPI • Firebase • AI / Machine Learning • LLMs (Ollama/Open-source models) • Recommendation Systems • REST APIs Deliverables • Complete source code • Production-ready Flutter application • FastAPI backend • AI recommendation engine • LLM chatbot integration • API documentation • Deployment guide Ideal Candidate We're looking for someone with experience building A...
...You're comfortable moving between Discord management, Reddit, and X, and you don't need hand-holding to figure out a new platform. Real fluency with technical / AI / open-source communities. You don't need to code, but you need to be at home around local AI, GPUs, and a knowledgeable crowd. A generic community manager won't land here. Ideally, you already use Dream Server (or tools like it) — Ollama, LM Studio, , vLLM, etc. The best person for this is probably already running local AI. Strong written English and a natural, non-corporate voice Proven experience growing at least one online community or audience Self-directed and good at reading a room Nice to have You're already a Dream Server user or active in the local AI community Exper...
I’m moving our AI inference off OpenAI and onto a Tesla P100 16 GB box that already runs qwen2.5 7B/14B on Ollama. The backend is wired to switch between local and remote per-prompt, so the infrastructure work is minimal; the real task is model selection, tuning and validation. Cost reduction is the driving motive, but I will only flip a prompt when the dashboards show zero drop in the accuracy of CV match scores (real interview rate is the secondary check). We have four production prompts; each will run in shadow mode until its metrics are indistinguishable from the current OpenAI baseline. What I need from you • Pick or fine-tune the best qwen2.5 variant—or another model that will fit and perform on a single P100—then set quantisation, context window a...
...my site and other approved sources • hold live discussions with the user • provide thoughtful, Biblically grounded insights when the conversation turns to Christian topics • scale gracefully so I can add additional skills later BACKEND CONSTRAINTS My priority is to keep monthly spend close to zero. I am therefore leaning toward open-source or very low-cost models (e.g. Llama 3, Mistral, Ollama, LocalAI) running on my own VPS or a lightweight managed service. If you can demonstrate that a small-tier commercial plan (Gemini Pro Lite, OpenAI 3.5 free quota, etc.) fits the cost target, I am open to it, but the default should be free/open. TECH STACK FACTS • Current agent: Python command line • Repo includes basic vector-store retrieval but no pro...
...MobileNetV2 (TFLite INT8) for crop disease detection, FAO-56 Penman-Monteith + Random Forest for irrigation planning, Gradient Boosting for yield forecasting, Random Forest classifier for crop recommendation, Isolation Forest for sensor anomaly detection, stage-aware NPK fertilizer scheduling, and a composite Farm-Plan ensemble A three-tier LLM cascade (Anthropic Claude Haiku → Claude Code CLI → Ollama Qwen2.5:0.5B) with silent rule-based fallback for offline resilience A trilingual voice assistant (English / Hindi / Telugu) with offline STT and online TTS Custom PCB design (v1→v2) with solar MPPT (LT3652), power management, and full sensor suite Validated results including sensor latency, LLM tier hit-rate, CPU/RAM profiling, and multi-hop mesh performance The c...
...solutions to build a scalable intelligent ecosystem. The ideal candidate should have strong Python development skills along with hands-on experience in API integrations, automation workflows, and AI-based applications. ________________________________________ Key Responsibilities • Develop and maintain backend services using Python • Integrate AI tools and open-source platforms such as ERPNext, n8n, Ollama, Dify, and Chatwoot • Build APIs and automation workflows for lead generation and sales processes • Work on AI model integrations and automation pipelines • Develop integrations for email campaigns, WhatsApp communication, scheduling systems, and CRM platforms • Collaborate with internal teams for feature implementation and deployment • Optimi...
I am currently building an advanced AI assistant platform called OpenClue / JARVIS AI, designed as a premium AI operating system with automation, voice interaction, intelligent agents, and enterprise AI capabilities. The system is being developed using: • React + TailwindCSS frontend • FastAPI/Node.js backend • Ollama local LLM integration • AI agent workflows • Voice assistant automation • Real-time WebSocket communication • Modern cinematic UI/UX inspired by Iron Man HUD and Apple Vision Pro Key capabilities include: * AI chat assistant * Voice command execution * Multi-agent AI workflows * Document and OCR processing * AI automation pipelines * Resume/CV ATS optimization tools * Enterprise AI assistant concepts * Local/private AI deployment *...
...YouTube/video links * Other text-based files * Automatic indexing of uploaded content * Re-indexing functionality * User management AI / RAG FUNCTIONALITY: * AI must answer ONLY using uploaded content * No internet/web search * Preferably based on open-source models * Minimal or no recurring monthly costs * Fully deployable on AWS PREFERRED TECHNOLOGIES: * Python * LangChain or LlamaIndex * Ollama / vLLM / open-source LLMs * ChromaDB, Qdrant, or FAISS * FastAPI or similar * Simple frontend using React/Vue or similar INFRASTRUCTURE: * Development will be done directly on my AWS instance * SSH access via .pem key will be provided * Developer must work directly on that server IMPORTANT: I have a limited budget. I am looking for a simple, functional, scalable, and low-mainte...
...ChromaDB, or LlamaIndex). Create a workflow to ingest/sync folders of business data (Product Specs, OSHA manuals, Pricing, Order History). Ensure the agent can "retrieve" relevant facts from these files before answering. Agent Framework (Claw / OpenClaw) Install and configure the agentic framework on my machine. Connect it to the Knowledge Base as a "tool." Connect to an LLM provider (API or local Ollama). Complex Custom Workflows (Skills) Lead Research: Agent searches my local catalog to suggest products for a specific customer request. Drafting: Agent uses my company templates and past quotes to draft new proposals. Compliance Bot: Agent cross-references current product designs against local OSHA/ANSI PDFs. Safety & Private Handoff Air-Gapped Privacy: Da...
I need an AI agent that runs entirely on my VPS, loads through Ollama, and uses the Qwen‑3 14B or 32B model. There must be zero external API calls for inference everything stays local. The agent must listen to my Office 365 mailbox via a Microsoft Graph Webhook and process every new message within a maximum delay of five minutes. Workflow requirements When an email arrives, the agent should: • parse intent, entities, urgency, required actions, sentiment, and deadlines • decide whether to: (a) create a follow‑up task in Corteza CRM (b) schedule a meeting (c) draft a context‑aware reply • push the chosen action back to the relevant system through its API (Corteza or Microsoft Graph) The agent must be idempotent and avoid double‑processing emails (s...
...filtering by department, year, document type, etc. Conversation History & Memory. High Security: All data stays on local server/machine. Preferred Technology Stack: LLM: Ollama (Qwen3, Llama 3.1/3.3, or any strong Vietnamese-supported models). Framework: LangChain or LlamaIndex. Vector Database: ChromaDB, FAISS, or LanceDB. Embedding Model: nomic-embed-text or equivalent. Frontend: Streamlit, Gradio, Open WebUI, or AnythingLLM. Report Export: python-docx, WeasyPrint, ReportLab, Jinja2. Requirements for Freelancer: Proven experience building Local LLM + RAG systems that run fully offline. Strong expertise with Ollama + LangChain/LlamaIndex. Experience in model optimization (quantization, long context, performance tuning). Good prompt engineering skills, especially f...
I need OpenClaw + Nerve UI deployed with multiagent AI Models & Skills configured on an Ubuntu server so it can drive a series of robotic-automation jobs via API integration Tasks requirement 1. Backup current server working files 2. Install and configure the latest stable OpenClaw with required config (all API Keys will be provided) i. AI Models API - will be provided ii. Skills - Telegram, Web Search, Browser Support, Google Workspace & 3rd party API iii. Multi Agent workflow & live interaction in group chat 3. Setup schedule tasks to AI functionality to work with 3rd party API. Acceptance criteria 1. `systemctl status openclaw` shows active on boot. 2. Messaging via Telegram & Email could be responded by AI Models 3. A sample API call returns a stubbed inferenc...
I need OpenClaw (ClawX) deployed with multiagent AI Models & Skills configured on an Ubuntu server so it can drive a series of robotic-automation jobs via API integration Tasks requirement 1. Backup current server working files 2. Install and configure the latest stable OpenClaw with required config (all API Keys will be provided) i. AI Models API - will be provided ii. Skills - Telegram, Web Search, Browser Support, Google Workspace & 3rd party API iii. Multi Agent workflow & live interaction in group chat 3. Setup schedule tasks to AI functionality to work with 3rd party API. Acceptance criteria 1. `systemctl status openclaw` shows active on boot. 2. Messaging via Telegram & Email could be responded by AI Models 3. A sample API call returns a stubbed inference r...
Ollama & comfyUI possibly already installed but receiving error message. May need re-install. Synology NAS, macbook Have some container issue. The goal for this session is simple: • Ollama and ComfyUI installed on the NAS, running reliably • Both services reachable from my MacBook via LAN for quick model tests
We are looking for an experienced AI develo...language) - curriculum-bound responses * Build APIs and backend for integration with classroom system * Develop synchronization system (offline → central server) * Provide documentation and training Technical Requirements * Experience with LLMs (LLaMA, Mistral, etc.) * Experience with RAG frameworks (LangChain / LlamaIndex) * Vector databases (FAISS / Qdrant / Chroma) * Local AI deployment (Ollama / ) * Python backend (FastAPI preferred) Deliverables * Fully functional AI engine (offline capable) * Documentation * Deployment support * Training for internal team Preferred Experience * EdTech AI systems * Multilingual AI systems * Offline AI deployments Engagement Type * Fixed cost / milestone-based * Long-term engagement poss...
...served through Ollama (Qwen2.5, Llama3.2, Phi3 for the first iteration). The interface must surface at least two key numbers for each model on every query—its latency and the text response itself—so I can judge speed against output quality at a glance. No cloud calls, no telemetry: everything runs offline on the host machine for maximum privacy. Deliverables • Clean, well-commented Python codebase (Streamlit UI, LangChain pipelines, FAISS setup, Ollama integration) • Instructions to add or swap local models with minimal edits • A sample dataset and walkthrough that prove PDFs, CSVs, and DOCXs index and query correctly • Read-me covering environment setup, hardware requirements, and how latency is captured/reported If you have prior...
...served through Ollama (Qwen2.5, Llama3.2, Phi3 for the first iteration). The interface must surface at least two key numbers for each model on every query—its latency and the text response itself—so I can judge speed against output quality at a glance. No cloud calls, no telemetry: everything runs offline on the host machine for maximum privacy. Deliverables • Clean, well-commented Python codebase (Streamlit UI, LangChain pipelines, FAISS setup, Ollama integration) • Instructions to add or swap local models with minimal edits • A sample dataset and walkthrough that prove PDFs, CSVs, and DOCXs index and query correctly • Read-me covering environment setup, hardware requirements, and how latency is captured/reported If you have prior...
...served through Ollama (Qwen2.5, Llama3.2, Phi3 for the first iteration). The interface must surface at least two key numbers for each model on every query—its latency and the text response itself—so I can judge speed against output quality at a glance. No cloud calls, no telemetry: everything runs offline on the host machine for maximum privacy. Deliverables • Clean, well-commented Python codebase (Streamlit UI, LangChain pipelines, FAISS setup, Ollama integration) • Instructions to add or swap local models with minimal edits • A sample dataset and walkthrough that prove PDFs, CSVs, and DOCXs index and query correctly • Read-me covering environment setup, hardware requirements, and how latency is captured/reported If you have prior...
Title: Set up offline AI research and drafting tool on macOS (Ollama + Kotaemon + local RAG) Project — "Legal X" I am building a fully offline, zero-API-cost AI research and drafting assistant for legal work, called Legal X. It will run entirely on my MacBook Air M-series (16 GB unified RAM, 10-core GPU, 512 GB), with no cloud dependency and no ongoing subscription cost. The system needs to do three things over a private corpus of approximately 40,000 readable PDF and HTML files (~5 GB): 1. Answer research queries with numbered footnotes, citing the exact source file and highlighting the passage in the original document for verification. 2. Produce drafts grounded in retrieved source material, in a consistent style. 3. Support long-form writing (articles, book c...
...installation and configuration of OpenClaw on this Windows machine. • Hook it cleanly into Ollama so I can call the models from OpenClaw without manual file shuffling or extra scripting. I am comfortable following clear, step-by-step instructions—screen-share or written guide is fine—so long as we end with a fully working setup. Success for me means: 1. OpenClaw launches from the command line with no missing-dependency errors. 2. Ollama models are discoverable and can be invoked directly through OpenClaw. 3. A quick sanity test (e.g., running a sample clawfile against an Ollama model) completes without issues. If you know your way around Windows terminals, environment variables, and the OpenClaw-Ollama workflow, this should be a conc...
...(Governance, Risk & Compliance) platform for small enterprises. The system automates regulation discovery, document analysis (with translation), offline mobile evidence capture, continuous control monitoring, and proactive risk remediation using agentic AI. Tech stack (all open‑source): - Backend: FastAPI (Python), PostgreSQL 16 + pgvector, Redis, Keycloak (OIDC/MFA), n8n workflows - AI & ML: Ollama (llama3, nomic-embed-text, phi3) + Opus‑MT (local NMT) - Frontend (web): 14 (React, TypeScript, TailwindCSS) - Mobile: SwiftUI (iOS), Kotlin/Jetpack Compose (Android), SQLite for offline sync - Infrastructure: OCI Always Free (Ampere A1, 4 OCPUs, 24 GB RAM) in Johannesburg region - Monitoring: Prometheus, Grafana, OpenTelemetry - Deployment: Docker Compose (dev) ...
...Intel Core Ultra 5, RTX 5070 Ti 16GB, 64GB DDR5. Ubuntu Server 24.04 LTS and NVIDIA drivers already installed. Server setup (your main job): You will use a provided AI-assisted installation script (Claude Code) to deploy the following via Docker — the agent does most of the typing, you supervise and handle anything it cannot do automatically: • RAID 1 on two 4TB NAS drives • Tailscale, Portainer, Ollama (4 AI models), Open WebUI, Nextcloud, Twenty CRM, n8n Device setup (3 devices after server is done): • Microsoft Surface Pro 8 Windows 11 — Tailscale, Claude Code, Nextcloud, browser bookmarks • iPhone 17 Plus and iPhone 16 — Tailscale, Nextcloud, Claude app, home screen shortcuts What I provide: Full installer brief, automated setup scrip...
Looking to setup locally on windows (intel Core i7 10th generation) any LLM that can give good quality responses as ChatGPT without internet connection. Preferred is Ollama however it is very slow for llama 3 70B. NOT sure if it can be done via quantize versions of LLMs.
...upgrade hardware - if I connect a new tool/domain/workflow, it can expand into that area through controlled prompt-driven upgrades - it behaves like a real digital AI assistant platform, not a one-time script bundle CURRENT STACK: - Mac mini M4, 16 GB RAM - Ollama running locally - local models already available - Claude Code installed - OpenCode installed - OpenClaw available - n8n available - custom Jarvis v1 controller already created - local dashboard already created - Telegram bot already connected PARTLY WORKING: - local Ollama inference works - Telegram /health works - Jarvis controller runs - dashboard runs - launchd services are running - safety blocking exists for dangerous commands - task execution works - registry/workflow creation partially works IMPORTANT ...
...(Claude API), Local (Ollama/Mistral on client infra), or Hybrid (operational docs via cloud, sensitive docs flagged by category to local). Mode switch takes effect immediately, no redeploy. - Connectors: toggle OneDrive, Gmail, Outlook, Slack, WhatsApp per tenant. Each stores its own OAuth credentials encrypted. - Branding: logo initials, accent colour, company name, subdomain. - User roles per tenant: admin, analyst, viewer. - Pipeline confidence threshold per tenant (default 0.80). Six-phase pipeline: ingest, acquire (OCR/Whisper/pdf-parse), classify, extract entities, summarise, embed (pgvector). Routing logic: if mode=hybrid, classification phase checks document category against tenant's sensitive-category list and routes accordingly to Claude API or local Ollama...
...something my local LLM (running through Ollama) can understand and answer questions on with reliable accuracy. The goal is a hands-off pipeline: I drop a fresh PDF into a folder, run a command, and then query the model for any figure—whether it sits in the balance sheet, income statement, or cash-flow section—and get a clean, correct response every time. What I need built • A script (Python preferred) that parses the PDF, captures every table and key figure, and outputs a structured data store (CSV, JSON, or SQLite—whatever best supports downstream use). • Validation logic that cross-checks totals so obvious extraction errors are caught automatically. • An indexing or embedding step that wires the cleaned numbers and text into my on-prem ...
I want to turn my workstation into a self-hosted “AI coworker” that can take over repetitive development chores for me. Its core mission is task automation, specifically around code generation, bug detection, and test automation for building mobile apps in React Native. End output is an apk. Here is what I need from you: • Ollama is already installed locally with a UI to accept prompts. Version Queen 14b. Ram 32 GB Flowise in setup with docker. VS code, python is installed. • Wire the model into an agent loop that accepts natural-language requests, produces clean code, automatically runs unit tests, and flags or fixes defects it spots in existing repositories. • Expose the agent through a lightweight interface—CLI is fine, simple web UI is even ...
Phase 2 of the OMA Platform has been completed and delivered on sche...Platform has been completed and delivered on schedule. This phase transformed the visual prototype from Phase 1 into a fully operational AI-powered website evaluation engine, implementing the complete Digital Content Efficiency Index (DCEI) methodology across 83 measurement standards, 11 axes, and 2 perspectives. The platform integrates Google PageSpeed, DataForSEO, GTmetrix, and an on-premise Ollama AI model to evaluate websites automatically. All results are displayed on a live bilingual (Arabic/English) dashboard with full RTL support. The scoring engine implements the exact weights and axis structure confirmed by the client, with Content Quality weighted at 35% and Technical Competence at 65% of the overall D...
I need a freelancer to install and run this project on my personal desktop: Scope: - Review the GitHub repo - Install and configure the app locally - Fix any setup issues - Confirm it runs correctly Access: - Work will be done via Chrome Remote Desktop Notes: - Minimal communication — rely on the repo instructions - Must be able to work independently Deliverable: - Fully working installation on my machine Timeline: - 1–2 days To apply: - Confirm you’ve reviewed the repo - Share your price and relevant experience
