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My main objective is to remove as much manual work from our day-to-day processes as possible. I know I need reliable task automation, but I have not yet pinned down the exact steps or tools that will achieve the best result. Here is what I can confirm so far: the core goal is pure automation—freeing our team from repetitive actions so we can focus on higher-value work. Beyond that, I’m open to your guidance on where automation will provide the greatest impact, which platforms or languages fit best, and how we can roll everything out smoothly. If you have a solid track record building scripts, bots, or integrated workflows—whether through Python, Zapier, Power Automate, Airtable scripts, or another modern stack—I’d like to hear how you would tackle discovery, implementation, and hand-off. Please outline: • The approach you would take to audit our current processes • How you identify what can be automated first for a quick win • Your preferred tech stack and why it suits this project • Expected deliverables (working automations, clear documentation, and brief team training) I’m looking to get started quickly, so include a realistic timeline for each milestone and any prerequisites you need from me.

I’m rolling out a fully-automated monitoring stack for my ISP/FTTH network and need a specialist who can take it from zero to production on a fresh VPS. The core of the job is LibreNMS—my chosen platform—and I want it configured with: • Device auto-discovery so every Syrotech OLT, switch, and ONU is picked up the moment it appears • Custom alert rules that flag ONU offline events within seconds and drive the rest of the workflow Because SNMP is not yet enabled on the devices, you’ll begin by standardising community strings, security profiles, and MIB placement, then validate visibility from LibreNMS. Once reliable polling is in place, I need the alert pipeline wired directly into Telegram for real-time push notifications. Each critical alert should also trigger an automatic ticket via our help-desk API (Python scripting is fine; a lightweight Flask endpoint already lives on the VPS). Key milestones I expect: 1. VPS hardened and ready for LibreNMS 2. SNMP configured and verified across all Syrotech OLTs/ONUs 3. LibreNMS installed with auto-discovery + custom rules tested 4. Telegram bot and help-desk API integrated, generating tickets from ONU offline alerts 5. Final hand-over: clear runbook, backup schedule, and short video walkthrough If you’ve automated Syrotech or similar GPON gear before, and you’re comfortable mixing SNMP, Python, and webhooks, let’s talk.

I’m looking for a skilled developer to create a Python-based web application that can accept online payments reliably and securely. The core goal is a clean, well-documented codebase that handles the full payment flow—initiation, gateway hand-off, confirmation, and error handling—without exposing sensitive data. Key objectives • Build the web app in Python (Django, Flask, or FastAPI—state your preference and why). • Integrate at least one mainstream gateway for credit/debit cards and leave the code flexible enough to plug in PayPal or other providers later. • Design clear endpoints or views for checkout, payment status callbacks/webhooks, and refunds. • Keep all secrets in environment variables and follow best PCI practices. • Include unit tests for the payment logic and brief developer-level documentation. Nice to have • Pluggable user authentication (email/password and/or social logins) that can be toggled on or off. • Docker or similar container setup for effortless deployment. When you respond, outline the stack you’d use, which payment SDK or API you recommend, and a rough timeline for a working prototype plus polish.

## Job Title Computer Vision Engineer: Custom Multi-Camera Kitchen Automation System (YOLO + Cloud Sync) ## Job Description## Project Overview We operate a commercial kitchen with 8 cooking stations and are looking to build a custom, hands-free quality control system. The goal is to automatically capture a 3-second video clip every time a cook adds a new ingredient into a cooking pot or pan. To make this highly accurate and lightweight, we are standardizing our prep containers. Cooks will transfer ingredients into uniform, highly visible, color-coded prep bowls. The AI needs to track these specific containers, detect when they hover and tilt over a cooking zone, crop a 3-second video clip (1 second before the tilt, 2 seconds after), and upload it to a cloud dashboard. ## System Architecture & Hardware * Inputs: 8 Overhead PoE (Power over Ethernet) Dome Cameras mounted directly above 8 separate cooking stations. * Local Compute: 1 Central Edge AI Server/PC (equipped with a high-end NVIDIA GPU, e.g., RTX 4000 series) processing the 8 live streams simultaneously. * Output: Short 3-second compressed video clips sent directly to a cloud storage bucket. * Frontend: A simple, mobile-friendly web dashboard where management can view clips sorted by Station # and Time/Date. ## Key Responsibilities * Set up the multi-camera RTSP streaming pipeline to the central local processing machine. * Develop and train an object detection/action segmentation model (preferably using YOLOv8/YOLOv10 or similar light models) to identify our specific prep bowls and detect the "tilting/pouring" action. * Implement a rolling video buffer logic that extracts exactly 3 seconds of video surrounding the detected action trigger. * Build the Edge-to-Cloud pipeline to upload compressed video fragments efficiently to a secure cloud platform (AWS S3, Google Cloud, or Azure). * Build a clean, lightweight frontend interface (using tools like Retool, Streamlit, or a basic React app) for cloud video playback. ## Required Skills * Deep expertise in Computer Vision and Deep Learning (Python, OpenCV, PyTorch/TensorFlow). * Extensive experience with real-time object detection models (YOLO workflow is highly preferred). * Proven track record building multi-camera RTSP video pipelines and handling edge processing hardware (NVIDIA Jetson or GPU workstations). * Cloud architecture experience (AWS S3 / Lambda or Google Cloud equivalents). * Experience with video encoding and compression (FFmpeg) to minimize cloud storage costs. ## Project Type & Budget * Project Type: One-time project with potential for ongoing retainer/maintenance contracts. * Budget: Flexible / Competitive based on experience and proposed timeline. * Please include links to any previous computer vision or video-processing projects you have built in your proposal.

Namage ondu basic CSC Tracking System ge bekaada Complete Blueprint illide: ## 1. Website / Software Frameworks (Tech Stack) Nimma developer ge nivu ee kelagina technology upayogisi website ready madalu helabahudu: * Frontend (Software Node): HTML, CSS, JavaScript athava ReactJS (Idu software nodalu tumba clean agi iruttade). * Backend (Logic): NodeJS (JavaScript) athava Python (Django/Flask) - idu nimma 8 jana employees data process madalu help aguttade. * Database (Storage): [MySQL](https://www.mysql.com/) athava PostgreSQL (Grahakara details mattu duddina track safe agi idalu). ## 2. Software Database Structure (Ee Riti Data Save Agabeku) Developer ge ee kelagina Tables create madalu heli: * Employee Table: Employee ID, Name, Username, Password, Role (Admin / Staff). * Customer Table: Token Number, Customer Name, Phone Number, Aadhaar/PAN (if needed). * Services Table: Service ID, Service Name (e.g., Ayushman Bharat, Passport), Government Fees, Center Service Charges. * Transaction Table: Invoice No, Customer ID, Service ID, Employee ID (Yaru kelsa madiddu endu track madalu), Amount Paid, Payment Mode (Cash/UPI), Status (Pending / Completed), Date & Time. ## 3. Website Pages Structure (Niv noduva screen-galu) * Login Page: Employees mattu Admin login madalu secure page. * Admin Dashboard: CSC Owner (Nivu) noduva page. Illi ivattina total collection estu? Yava employee estu kelsa madiddare? Ella report kanuttade. * Employee Dashboard: Staff login adaga kanuva page. Illi avaru hosa customer entry madabahudu mattu status update madabahudu. * Report Page: Month-wise athava Day-wise collections excel sheet tarah download maduva option.

I’m ready to bring my vision for “V Wealth AI Personal” to life and need a full-stack developer (or team) who can own the build end-to-end. The goal is a robust Web application with a companion Android app, both sharing a single codebase where possible for efficiency. Core experience should include React (or similar modern front-end framework), responsive design patterns, Android packaging, and secure back-end work—Node, Python, or comparable stacks are all fine so long as the architecture scales. I plan to keep user sign-in straightforward with email-and-password authentication at launch; social logins and 2FA can remain optional future upgrades. Key capabilities I expect the first production release to cover: • Unified portfolio dashboard pulling live holdings from Zerodha and Dhan for India, and INDmoney for U.S. assets • AI-assisted “Portfolio Doctor,” swing and intraday scanners, plus F&O analytics that surface actionable insights in plain, simple Hindi when requested • Auto-averaging logic, realised / unrealised P&L calculation, dividend tracking, and 52-week high/low visual cues • Candlestick chart rendering with dark-mode support across Web and Android, using a performant charting library of your choice • Clean Excel import for any broker not yet integrated • Mobile-first UX that gracefully expands to desktop screens Deliverables 1. Source code repository with README and environment setup scripts 2. Deployed Web build (AWS, GCP, or similar) and signed Android APK on the Play Console (internal testing track is fine) 3. Post-launch hand-off: brief video walk-through, API keys rotated, and two weeks of bug-fix support Acceptance criteria: • All linked accounts sync correctly and totals reconcile to broker statements • Candlestick charts load in under one second on a standard 4G connection • Responsive layout maintains usability down to a 360 × 640 viewport • Hindi explanation toggle produces context-aware summaries without untranslated jargon If you have shipped fintech dashboards, trading-related scanners, or AI-driven insights before, please share links or screenshots. Let’s discuss a milestone plan that gets an MVP in users’ hands quickly and iterates from there.

I need an end-to-end automation that grabs fresh real-estate market updates from leading UAE news and property portals and publishes them directly to my LinkedIn feed. The workflow should: • Detect and parse new market-update articles on chosen UAE real-estate sites (Realtor.com, Zillow and Trulia are irrelevant here; think Bayut, PropertyFinder, Khaleej Times Property, etc.). • Extract headline, key statistics, and the article link. • Pre-append a short personalized note that I can define in a config file or an Airtable/Google Sheet cell before each post goes live. • Push the final content to my LinkedIn company page through the LinkedIn API, respecting posting limits and avoiding duplication. I’m comfortable running the solution on a small VPS, so Python with BeautifulSoup/Playwright, Zapier, Make.com, or any practical stack is fine—just keep the setup straightforward and well-documented. Deliverables 1. Source code or no-code scenario file. 2. Read-me / screen-share walkthrough showing how to swap in new UAE news sites and edit the personalized message. 3. A quick test proving one live post on my LinkedIn page. Once everything works reliably on a daily schedule, I’ll consider extra milestones for analytics and multilingual support.

I need a Python developer to create an Excel-based trade terminal. Requirements: - Live market data integration - Trade execution - Performance analytics Ideal Skills: - Proficiency in Python - Experience with Excel automation - Knowledge of financial markets and trading systems - Familiarity with data integration APIs Please provide a brief portfolio showcasing relevant work.

I need a straightforward way for my team to send structured information into WhatsApp and have it land neatly in a downloadable CSV. The main data points will be: • Production flow entries • Petty cash vouchers • Maintenance day-to-day work records Here’s the workflow I have in mind. A simple WhatsApp form (or template-based chat flow) captures each submission. I read all messages in Beeper, so the back-end script or integration must pull the content from there—parsing text, dates, numbers, and any attachments—then append everything to a running CSV. If Beeper’s API leaves gaps, I’m open to a comparable approach, but Beeper is my first choice. Key deliverable: • A working integration (bot, webhook, or small app) that automatically converts each WhatsApp message into a structured row in CSV, ready for download or scheduled email. Basic expectations: – No manual copy-paste once it’s live – Clear field mapping for each category above – Timestamps and sender IDs preserved – A short setup guide so I can redeploy or tweak it later If you’ve built something similar with the WhatsApp Business API, Twilio, Python, or Node, you’ll probably move quickly here.

I need a small, reliable bot that crawls the area-guide pages on propertyfinder.ae and bayut every 24 hours, beginning with Dubai Marina and expanding easily to any neighbourhood I add later. The goal is to tell me—at a glance—where propertyfinder’s guides lag behind the competition so my content team knows exactly what to fix next. What the bot must capture • Content structure and layout: pull every section header, keep the heading hierarchy (H1–H6), note order, paragraph length and any calls-to-action above the fold. • Information depth and quality: calculate word counts, Flesch readability, and flag missing or thin sections. • Visual elements: log each image, map, video or infographic, plus the alt text length. • Navigation & overall UX: record presence of sticky menus, breadcrumbs, jump links and internal links that aid exploration. Daily report The script should output a side-by-side comparison (CSV or Google Sheet) plus a short narrative summary emailed or pushed to Slack. I want the report to spotlight section headers and organisation, text formatting and readability, and navigation/user-experience issues for every area guide scanned. Technical expectations • Clean, documented Python (BeautifulSoup + Selenium/Playwright, pandas) or Node.js—your choice—as long as it runs headless, rate-limits requests and respects robots.txt. • Single-command setup on my Mac or a small Ubuntu droplet; include clear instructions to add new areas or tweak selectors when the sites change. • Cron-friendly so it refreshes automatically each day. Acceptance criteria – First run compares the Dubai Marina guides and surfaces at least five structural differences. – Output columns: page URL, crawl date, section title, word count, readability score, visual-element flags, CTA-above-fold (yes/no). – Email/Slack notification lands without manual intervention. If this sounds straightforward to you and you can deliver a maintainable, extensible solution, I’m ready to get started right away.

INTERIORLANE OS — MASTER SRS PROMPT Complete AI-Powered Interior Business Operating System (CRM + ERP + AI + CAD + Production + Franchise) Act as a world-class software architect, AI systems engineer, ERP architect, CRM specialist, CAD engineer, production planner, UI/UX expert, telephony automation architect, and business workflow designer. Build a COMPLETE unified AI-powered cloud operating system for Interiorlane. This is not a simple CRM, website, chatbot, or furniture software. This platform must operate as a complete end-to-end Interior Business Operating System, handling everything from: Lead Generation → Sales → Requirement Collection → Design → Quotation → Closing → Site Visit → Production → Procurement → Installation → Billing → Customer Updates → Warranty → Franchise Operations --- COMPANY DETAILS (FIXED) Interiorlane 34, Vandana Apartment Vishram Nagar, Nagpur – 440026 Phone: 8530634229 Use company branding in: - Website - CRM - ERP - Quote PDFs - Invoice PDFs - BOM - Cutlists - Production sheets - Stickers - Reports - Franchise portals --- CORE OBJECTIVE Build ONE connected ecosystem containing: - AI Website Bot - AI WhatsApp Bot - AI Voice Calling Agents - CRM - Lead Manager - Sales Pipeline - Floor Plan Creator - Floor Plan Analyzer - AI Quote Creator - AI 2D Designer - AI 3D Designer - Invoice & Billing - Taxation - Production ERP - BOM Creator - Cutlist Generator - Cutlist Optimizer - Inventory - Procurement - Vendor Manager - Sourcing Manager - Employee Manager - Salary Manager - Expense Manager - Franchise System - Customer Update Engine - After Sales System Everything must work from one login and one database. No isolated apps. --- TECH STACK Recommended: Frontend: - Next.js - React - TypeScript - Tailwind Backend: - FastAPI / Node - Python AI services Database: - PostgreSQL Storage: - S3 / Cloudinary Realtime: - WebSocket AI: - LLM orchestration - Claude / GPT - Vision models - Voice AI - OCR - RAG CAD / Visualization: - Three.js - Canvas - WebGL PDF: - Server-side PDF rendering Telephony: - Voice API integration WhatsApp: - WhatsApp Business API --- MASTER UI / UX Very important. Primary usage: - Mobile - Tablet - Desktop admin Mobile-first design. Style inspired by: - Livspace - Infurnia - Homelane - HubSpot - Zoho CRM Theme: - White - Grey panels - Red accents --- MASTER DASHBOARD Role-based dashboards. Roles: - Admin - Sales - Designer - Production Manager - Factory Manager - Accounts - Franchise Owner - Installer - Customer Dashboard widgets: - Leads - Conversion - Revenue - Pending Quotes - Production Status - Installations - Expenses - Inventory Alerts - Franchise Metrics --- MODULE 1 — WEBSITE + AI BOT Website sections: - Home - About - Services - Portfolio - Franchise - Contact - Book Consultation AI bot must handle: - Hindi - English - Marathi - Mixed regional language Bot must: - Greet naturally - Understand intent - Ask questions like human sales executive - Qualify lead - Collect requirements - Book consultation --- MODULE 2 — AI WHATSAPP BOT Bot connected to WhatsApp. Must: - Chat like human - Send brochures - Ask requirements - Collect: - Name - Address - Budget - Room sizes - Scope - Timeline - Accept: - Photos - Videos - Floor plans - Voice notes Bot must understand: - Hinglish - Marathi mix - Hindi mix - Typos - Voice converted text --- MODULE 3 — AI VOICE CALLING AGENTS Multiple AI calling agents. Agents: - Lead qualification agent - Follow-up agent - Quote explanation agent - Site visit booking agent - Payment reminder agent - Feedback agent Voice should sound: - Human - Friendly - Natural - Emotional Support: - Hindi - English - Marathi - Regional mix Capabilities: - Ask questions - Handle objections - Negotiate politely - Book meetings - Send WhatsApp follow-up --- MODULE 4 — CRM Complete sales CRM. Lead Sources: - Website - WhatsApp - Calls - Franchise - Referral - Ads - Walk-in Pipeline: New Lead Qualified Requirement Collected Quote Sent Negotiation Site Visit Closed Won Closed Lost Store: - Customer profile - Chats - Calls - Quotes - Notes - Tasks - Follow-ups --- MODULE 5 — FLOOR PLAN CREATOR & ANALYZER Input: - Manual drawing - Uploaded image - PDF floor plan - Auto detection from image AI should detect: - Walls - Doors - Windows - Dimensions Allow manual corrections. Generate: - Room bifurcation - Room labels - Area calculation Rooms: - Kitchen - Bedroom - Living - Office etc. --- MODULE 6 — AI QUOTE CREATOR (DEEP) Collect: Customer: - Name - Address - Number Project: - Budget - Timeline - Scope Scope: - Kitchen - Wardrobe - Bed - Sofa - Curtains - POP - Flooring - Electrical - Plumbing - Civil - Glass - Mattress - Upholstery - Woodwork etc. Room-wise quote. Each room: - Layout - Furniture placement - Door/window direction Use exact item configuration. Show reference images. Generate: - Moodboard - 3D prompts - Quote options Quote levels: - Economy - Premium - Luxury Pricing based on: - Materials - Hardware - Labor - Transport - Installation - Tax - Margin Output: Premium PDF. --- MODULE 7 — AI 2D DESIGNER Create production-grade 2D drawings. Supports: - Kitchen - Wardrobe - Bed - TV Unit - Study - Shoe Rack - Loft - Pooja - Sofa - Curtains Views: - Front - Side - Top - Internal - Section Include: - Dimensions - Hardware positions - Notes Export: - PDF - DXF --- MODULE 8 — AI 3D DESIGNER Generate photorealistic interiors. Styles: - Modern - Luxury - Minimal - Classic Must use exact project specs: - Room size - Window - Door - Furniture - Material - Colors Outputs: - Render - Walkthrough - Design options --- MODULE 9 — INVOICE + BILLING + TAXATION Generate: - Estimate - Quotation - Proforma - Invoice - GST invoice - Payment receipt Tax: - GST - CGST - SGST - TDS - Discounts Track: - Pending payments - Advances - Balance - Due dates --- MODULE 10 — BOM CREATOR Auto generate BOM from design. Include: - Panels - Boards - Acrylic - Glass - Hardware - Fabric - Foam - Wood - Accessories Export: - Excel - PDF --- MODULE 11 — CUTLIST PLANNER Generate cutlist. Include: - Panel dimensions - Thickness - Material - Grain direction - Edge band Support: - Kitchen - Wardrobe - Beds - Sofa - Wood furniture --- MODULE 12 — CUTLIST OPTIMIZER Optimize sheet cutting. Supports: - Ply - MDF - HDHMR - Particle - Acrylic - Laminates Must optimize: - Wastage - Grain direction - Kerf - Gloss direction Generate: - Sheet plan - Utilization % - Scrap --- MODULE 13 — INVENTORY MANAGEMENT Track: - Boards - Hardware - Fabric - Foam - Curtains - Blinds - Lights - Granite - Tiles - Paints Alerts: - Low stock - Reorder --- MODULE 14 — SOURCING / PROCUREMENT Manage: - Vendors - Price lists - Purchase orders - Negotiation - Material comparison PO system: - Create PO - Track delivery - Track payment --- MODULE 15 — ERP / FACTORY MANAGEMENT Track production: Stages: Design Approved BOM Ready Material Ordered Cutting Edge Banding Assembly Polish Packing Dispatch Installation Assign tasks to departments. --- MODULE 16 — SITE UPDATES Field team updates via mobile. Upload: - Photos - Videos - Notes Customer gets updates on: - WhatsApp - App - Email --- MODULE 17 — EMPLOYEE MANAGEMENT Manage: - Employees - Departments - Attendance - Leaves - Payroll Track: - Salary - Incentives - Commission - Expenses --- MODULE 18 — EXPENSE MANAGER Track: - Office rent - Salaries - Fuel - Transport - Marketing - Utilities - Vendor payments Reports: - Daily - Monthly - Annual --- MODULE 19 — FRANCHISE SYSTEM Create franchise model. Manage: - Franchise applications - Franchise leads - Franchise onboarding - Franchise dashboards - Revenue sharing - Territory mapping Franchise CRM: - Leads - Sales - Quotes - Projects --- MODULE 20 — AFTER SALES & WARRANTY Track: - Warranty - Service requests - Repairs - Complaints AI bot can handle support. --- MODULE 21 — ANALYTICS Reports: - Sales conversion - Lead source ROI - Profit margin - Factory efficiency - Inventory turnover - Employee performance - Franchise performance Use charts and dashboards. --- SECURITY Implement: - RBAC - Permissions - Audit logs - Backup - Encryption --- EXPORTS Support: - PDF - Excel - CSV - DXF - PNG All exports must be production-ready. --- FINAL GOAL Build a platform that replaces: - CRM software - ERP software - CAD tools - Quotation tools - Production planners - Inventory software - Telecalling systems - Sales automation tools Interiorlane OS must become the complete operating system for the interior business. It should feel like a combination of: - Livspace backend - Infurnia - Cabinet Vision - Zoho CRM - Odoo ERP - HubSpot AI - AutoCAD workflows But fully customized for Interiorlane. Must provide: AI Sales AI Calling AI Design AI Quote CRM ERP Factory Management Franchise Management Production Automation End-to-End Business Control

Looking for Freelance Generative AI Data Scientist / AI Model Training Expert I am looking for a freelance Data Scientist / AI Model Training Expert who has strong hands-on experience with LoRA training for images and videos. The work is for an AI creative production platform focused on image generation, video generation, character consistency, image-to-video workflows, and animation-style production. What I Need I need someone who can: * Train image LoRAs for characters, styles, products, costumes, props, and environments. * Train or fine-tune video LoRAs / motion LoRAs for movement, camera motion, action, and frame consistency. * Work on image-to-video and video-to-video workflows. * Prepare, clean, caption, tag, and organize image/video datasets. * Improve character consistency across different poses, scenes, outfits, and camera angles. * Reduce issues like identity drift, flickering, bad anatomy, unstable backgrounds, warping, and poor motion. * Test different training settings and improve output quality. * Build a repeatable workflow that can be used for multiple characters and client projects. Required Experience You must have practical experience with: * LoRA training * Image-generation model fine-tuning * Video-generation workflows * Character LoRA / style LoRA / product LoRA / motion LoRA * Dataset preparation for image and video training * Captioning, trigger words, overfitting, underfitting, and model quality testing * Python and machine learning * GPU-based model training Preferred Tools / Tech Knowledge Experience with any of the following is preferred: * ComfyUI * Kohya training workflows * Diffusion-based image/video models * Image-to-video pipelines * Video-to-video pipelines * Pose-guided generation * Face-consistency workflows * Reference-based generation * Python * PyTorch * Docker * Git * Cloud GPU setup Important This is not a prompt-writing job. This is not a traditional dashboard/data analytics job. I need someone who has actually trained models, tested outputs, fixed poor results, and understands how to make AI-generated characters, styles, products, and scenes stay consistent across both images and videos. Please Apply With * Examples of LoRA models you have trained. * Before/after results. * Image or video generation work you improved. * Your training process. * Tools and models you have worked with. * Your hourly rate or project-based pricing. * Your availability.

I have a Data Analytics portfolio with projects in Python, SQL, Power BI, Excel, Machine Learning, Pandas, Scikit-learn, and Predictive Analytics. The projects are complete and functional, but I want my GitHub to look polished, recruiter-friendly, and visually impressive. GitHub: https://github.com/hetachavda -->What I need * Create custom banners for each repository. * Add high-quality dashboard screenshots, charts, icons, and visual assets based on the actual project results. * Rewrite every README into a concise case-study format: * Business Problem * Dataset * Methodology * Key Insights * Business Impact * Technologies Used * Improve repository structure * Add professional badges, consistent branding, and attractive layouts. * Create or improve my GitHub Profile README so all projects have a consistent design. -->Deliverables * Professional GitHub profile * Enhanced READMEs for every project * Custom graphics (not generic templates) * Dashboard screenshots and visualizations * Clean, recruiter-friendly presentation

# AI SEO Content Analysis Agent for a Real Estate Website ## Project Overview We are a leading real estate classifieds website in the UAE (similar to Property Finder and Bayut) and are looking for an experienced AI/LLM engineer to build an intelligent SEO analysis agent. The goal is to improve our rankings for high-value area guide keywords such as: * Dubai Marina * Downtown Dubai * Jumeirah Village Circle (JVC) * Business Bay * Palm Jumeirah * Abu Dhabi communities * Other UAE area guide keywords The agent should automatically compare our area guide pages against competitors, identify content gaps, and generate actionable recommendations to improve our organic search performance. This is **not** a simple web scraping project. We're looking for someone with experience building AI agents using LLMs for content analysis, competitive intelligence, and SEO. ## Scope ### 1. Crawl and Extract Content The agent should: * Scrape our area guide pages * Scrape equivalent competitor pages (starting with Bayut) * Extract: * Main content * Heading structure (H1–H6) * Images and alt text * Internal links * Tables * FAQs * Lists * Structured data (if available) * Metadata (title, meta description) ### 2. AI Content Analysis Using an LLM, compare both pages and identify: * Missing topics and subtopics * Missing sections * Weak or shallow content * Content freshness opportunities * Missing FAQs * Internal linking opportunities * Missing entities and semantic coverage * Readability improvements * EEAT improvement opportunities * Search intent gaps * Competitor strengths * Opportunities to improve topical authority ### 3. SEO Evaluation Assess both pages across factors including: * Heading structure * Keyword coverage * Entity coverage * Topical completeness * Internal linking * Metadata quality * Schema markup * Image optimization * Content depth * Overall competitiveness ### 4. Actionable Recommendations For every page, generate prioritized recommendations such as: * New sections to add * Existing sections to improve or rewrite * Questions to answer * Missing facts or data points * Missing entities, landmarks, schools, transport, etc. * Internal links to add * Suggested content outline * Priority (High / Medium / Low) * Estimated SEO impact ### 5. Reporting Generate a structured report (Markdown or JSON) containing: * Executive summary * Competitor strengths * Our content weaknesses * Prioritized recommendations * Suggested content outline * Overall SEO score for both pages ## Technical Requirements Preferred technologies: * Python * Playwright or Selenium * BeautifulSoup * OpenAI, Claude, or Gemini APIs * LangGraph, LangChain, CrewAI, or similar AI agent frameworks (optional) ## Nice to Have * Strong understanding of SEO and content optimization * Experience building AI agents or automated research tools * Experience with semantic SEO and topical authority * Previous work in real estate or marketplace websites ## When Applying Please include: * Examples of AI agents or LLM applications you've built * GitHub or portfolio * Examples of web scraping or content analysis projects * Any SEO-related experience * Your proposed technical approach * Estimated timeline and budget We are looking for someone who has experience building production-ready AI agents capable of performing high-quality competitive content analysis and generating actionable SEO recommendations, rather than simple scraping scripts.

I have a Data Analytics portfolio with projects in Python, SQL, Power BI, Excel, Machine Learning, Pandas, Scikit-learn, and Predictive Analytics. The projects are complete and functional, but I want my GitHub to look polished, recruiter-friendly, and visually impressive. GitHub: https://github.com/hetachavda -->What I need * Create custom banners for each repository. * Add high-quality dashboard screenshots, charts, icons, and visual assets based on the actual project results. * Rewrite every README into a concise case-study format: * Business Problem * Dataset * Methodology * Key Insights * Business Impact * Technologies Used * Improve repository structure * Add professional badges, consistent branding, and attractive layouts. * Create or improve my GitHub Profile README so all projects have a consistent design. -->Deliverables * Professional GitHub profile * Enhanced READMEs for every project * Custom graphics (not generic templates) * Dashboard screenshots and visualizations * Clean, recruiter-friendly presentation

I need a indivial python developer who can do my project task .

AI Live Commerce Platform – MVP Development Request Introduction Hello, We are building an AI-powered Live Commerce platform that will initially be used for our own TikTok Live sales operations and later offered as a SaaS platform for other businesses. We are looking for a software development partner to build the MVP (Minimum Viable Product). ⸻ Project Goal The objective is to create a system where an AI avatar can host a live shopping session, answer customer questions in real time, and sell products automatically. The system should integrate multiple third-party AI services into one seamless platform. ⸻ Core Components The MVP will integrate the following services: 1. AI Brain * OpenAI API * Generates intelligent responses * Understands customer questions * Uses product knowledge to answer accurately 2. AI Voice * ElevenLabs API * Converts generated text into natural speech * Supports multiple languages 3. AI Avatar * HeyGen Avatar API * Makes the avatar speak using the generated voice * Supports real-time interaction if available 4. Live Streaming * OBS Studio (or another streaming solution) * Streams the avatar to TikTok Live * Displays products, prices, banners, and promotional graphics 5. Live Chat Integration The system should receive TikTok Live chat messages. Example: Customer: “Does this suitcase have a warranty?” ↓ OpenAI generates: “Yes, this suitcase includes a 2-year international warranty.” ↓ ElevenLabs generates speech. ↓ HeyGen avatar speaks the response. ⸻ Product Knowledge Base The platform should include a product database. Each product should contain: * Name * Description * Price * Images * Features * Warranty * Shipping information * Return policy * Frequently Asked Questions The AI should answer only using the provided product information. ⸻ Admin Dashboard We need a simple web dashboard. Features: * Product Management * Avatar Management * Voice Selection * Language Selection * Prompt Management * Live Session Control * Chat Monitoring * Analytics * System Settings ⸻ Multi-language Support Initially: * English * Romanian Future versions: * German * French * Italian * Spanish * Polish * Dutch * Hungarian ⸻ MVP Scope Version 1 should support: * One AI Avatar * One TikTok Live stream * One language * One product catalog * Live chat responses * Admin dashboard ⸻ Future Roadmap The architecture should be scalable to support: * Multiple avatars * Multiple simultaneous live streams * Multiple TikTok accounts * Multiple countries * Multi-language operation * AI customer support * CRM integration * Order management * Warehouse integration * Shopify/WooCommerce integration * Stripe payment integration * API for third-party clients ⸻ Technical Requirements Preferred stack: Backend: * Python (FastAPI) or Node.js (NestJS) Frontend: * React or Next.js Database: * PostgreSQL Cache: * Redis Cloud: * AWS, Google Cloud, or Azure Containerization: * Docker Version Control: * GitHub ⸻ Deliverables Please provide: 1. Technical proposal 2. Estimated development timeline 3. Development cost 4. Team structure 5. API architecture 6. Maintenance and support options 7. Recommendations for improving the architecture This MVP is only the first phase of a much larger AI Commerce platform, so we are looking for a long-term development partner. Thank you.

I need a straightforward Telegram bot that greets users with /start, shows three buttons—Join VIP, Support, and Deposit/Withdrawal—and replies with my predefined messages when any of them is tapped. Every interaction must be logged to a Google Sheet, capturing the user’s Telegram ID, username, chosen button, and timestamp. I’ll supply all message templates, so just wire them in a way that I can swap or extend them easily later. The same flexibility should apply to the button set: I want to be able to add new buttons or remove old ones without touching the core code. Clean, intuitive code is a priority. Please include: • Full source code (Python or another well-supported language that works smoothly with the Telegram Bot API and Google Sheets API). • Step-by-step deployment instructions so I can run the bot on my own server or a simple cloud host. • A brief README explaining where to edit templates and how to adjust the button list. If you’ve built similar Telegram bots or automated Google Sheet loggers before, that experience will be valuable here.

looking for an experienced Python/OpenCV developer to build a desktop application for quantitative analysis of pancreatic ICG fluorescence videos. The software should allow users to import video files, review frames, select regions of interest, and generate objective fluorescence metrics such as intensity over time, peak signal, time to peak, and comparative analysis between pancreatic tissue and surrounding areas. Ideally, the app should have a clean, user-friendly interface, support common video formats, and provide exportable reports and graphs for research use. Experience with image processing, medical video analysis, and desktop application development will be highly valued. Please include your relevant experience, estimated timeline, project cost, and examples of similar work if available.

My current workflow pulls fresh regulatory updates, case-law developments, and legislation changes, then lets an AI model draft a summary that a lawyer refines. I now need this refinement loop to teach the model automatically so each new piece requires less intervention. Your main focus will be automated learning from expert edits: capturing every insertion, deletion, and comment, translating those differences into training signals, and fine-tuning the summarisation model so it steadily improves on content accuracy, relevance, and overall readability. I already handle security and explainability elsewhere in the stack, but the code you deliver must slot into that framework without exposing data outside our private repo. To make this work you will: • Build a diff-tracker that logs lawyer edits against the original AI draft, preserving context and timestamps. • Convert those logs into structured training examples and schedule incremental fine-tuning or reinforcement updates. • Expose an API endpoint that returns the next-best summary plus metadata so the reviewer can interrogate and, if necessary, override any sentence. • Package the training pipeline (likely Python + Hugging Face/LLM-based, but I’m open to suggestions) with clear documentation and unit tests so it runs inside our existing architecture. Acceptance criteria: after integration and one month of live use, the average word-level edit distance between AI draft and final lawyer copy should drop by at least 30% while maintaining legal accuracy (we will measure with our internal checklist). If this sounds within your skill set, outline your proposed approach, the tools you would use, and a rough timeline for delivery.

I need an experienced conjoint-analysis practitioner to uncover what really drives South African consumers when they shop for flooring. The focus is strictly on the flooring category—think hardwood, laminate, vinyl and tiles—so familiarity with product attributes such as finish, durability ratings, installation method and warranty length will be helpful. My objective is to understand customer preferences in depth; price sensitivity is important, but it comes second to mapping the trade-offs buyers make among features. The target respondent group is the everyday homeowner or renter rather than interior-design professionals or retailers, so the design must be clear and engaging for a general-consumer audience. Here’s what I’m looking for you to deliver: • A well-structured choice-based (CBC) or adaptive (ACBC) questionnaire, built in Sawtooth, Qualtrics, or an equivalent platform that can handle advanced conjoint logic. • Guidance on sample size and screening so we reach a representative spread of South African consumers. • Clean, annotated data files plus the utility estimates, importance scores and any segmentation you uncover (R, Python, or Sawtooth output are all fine). • A concise, visually driven report highlighting winning attribute combinations and concrete product or packaging recommendations for our flooring range. I’ll supply existing product specs, reference imagery and any prior research. You’ll handle survey programming, model estimation and insight generation; I can manage panel sourcing if needed. Timelines are flexible within reason, but I’d like an initial survey draft within a week so we can iterate quickly. If you have a proven track record running conjoint studies—especially in consumer durables—let’s talk.

We're building AffirmoBot, a voice-automation product that helps Indian banks confirm suspicious transactions with customers via automated phone calls. The proof-of-concept is built and working (Python/FastAPI backend, tested API, live demo deployment). We need an experienced backend engineer to take it from prototype to production-ready for a bank pilot. WHAT YOU'LL BE DOING 1. Outbound telephony integration Replace our simulated call flow with real outbound calling via Exotel or Tata Tele Smartflo (India-based telephony providers). The bot must dial a customer, run an authentication + confirmation flow, and handle call-status outcomes (answered, busy, switched off, unreachable). 2. Speech recognition integration Integrate Sarvam AI (or equivalent) for real Hindi/Hinglish speech-to-text and text-to-speech, replacing our current rule-based DTMF/keyword matching. 3. Production database Move our in-memory session and audit-log storage to PostgreSQL, with proper schema design for call records, transcripts, and audit trails that must be retained for 5+ years (regulatory requirement). 4. Hardening for a bank security review Review and improve our existing API-key auth, HMAC-signed callbacks, and input validation. We will have a security review before going live with a real bank, so code quality and clear documentation matter. 5. Notifications Wire up our existing SMS (Exotel) and Email (SMTP) notification module into the live call flow for every call outcome. WHAT WE ALREADY HAVE (so you're not starting from zero) • Working FastAPI backend with a deterministic state machine (auth -> capture -> confirm/deny -> done) • A tested, secured API (API-key auth, Pydantic validation, HMAC-signed callbacks) deployed and live • An automated test suite (21 checks) you'll extend as you add features • A browser dashboard for simulating and monitoring calls • Full technical documentation (architecture, API spec, OpenAPI file) • A mock bank API for integration testing WHO WE'RE LOOKING FOR • Strong Python skills, specifically FastAPI or a similar async framework • Prior experience integrating a telephony/VoIP API (Twilio, Exotel, Plivo, Vonage, or similar) — this is the most important specific skill • Comfortable with PostgreSQL schema design • Bonus: experience with speech-to-text APIs, or fintech/banking-adjacent projects • Bonus: based in India or familiar with Indian telecom regulations (DLT, TRAI) • Can work independently and communicate clearly — you'll be the only engineer on this for now, working directly with the founder HOW WE'LL WORK TOGETHER • This is a fixed-scope contract to start (4-6 weeks), with potential for an ongoing arrangement if it goes well • Daily or every-other-day async updates (Slack/WhatsApp), one weekly call • All code reviewed via GitHub pull requests • Payment via milestones (see Milestones section below)

My data-analytics projects already run smoothly; what they lack is a presentation that excites recruiters. The main pain-point is visual polish—banners, eye-catching dashboard screenshots, and charts that instantly show the value of each analysis. Here’s what I need from you: • Rewrite and reorganise every README into a concise case-study format: business question, data sources, methodology, key insights, and final results. • Tidy the folder structure so code, data, notebooks, and assets are separated logically and clone-ready. • Add striking project banners, plus the selected visual elements—dashboard screenshots and charts—drawn from the work I built in Python, SQL, Excel, Power BI, Scikit-learn, and Pandas. • Sprinkle in appropriate badges (build, licence, tools) so each repository looks active and well-maintained. • Create or refine a profile-level README that ties the projects together, keeps tone and styling consistent, and targets roles such as Data Analyst, Business Analyst, and Operations Analyst. Acceptance criteria – Every repo contains a clean, lint-checked README with clear navigation links, installation steps, and usage instructions. – Screenshots are optimised for GitHub’s dark and light themes and load quickly. – Links, badges, and images render correctly when the repo is viewed on desktop or mobile. – No broken notebooks, data paths, or spell-check errors remain. When you reply, please include before-and-after links to GitHub repositories or portfolio work you have already elevated in a similar way. I’m ready to grant access and answer any project-specific questions as soon as we agree on the approach.

I have an existing Python codebase where business logic, routing, and ancillary helpers are all tangled together. I need that clutter untangled so one clearly-defined web service can live in its own repository and be deployed on its own. Here is what I’m after: • A clean separation of concerns inside the current project—controllers, models, utilities, and configuration decoupled so each layer is easy to reason about. • The specific web-facing component extracted into an independent Python package (or micro-service) with its own entry point, virtual-env or Dockerfile, and concise README that explains how to spin it up. • Updated import paths and dependency management so the remaining monolith still runs untouched. • A slim test suite proving both the standalone service and the parent app behave exactly as before. Acceptance criteria 1. Running the extracted service with a single command (e.g., `docker compose up` or `python -m uvicorn ...`) returns the expected health-check endpoint. 2. All existing unit or integration tests in the legacy repo pass. 3. Documentation summarises architecture changes and deployment steps. No third-party APIs are involved, so the work is fully self-contained. If you enjoy thoughtful refactoring, clear module boundaries, and Pythonic best practices, this should be a straightforward yet satisfying project.

Description: We are looking for an experienced full stack developer to help build and improve a web-based application. The ideal candidate should be comfortable working across both frontend and backend development and have practical knowledge of AI integrations. Responsibilities: * Build and maintain responsive frontend interfaces * Develop backend APIs and database logic * Integrate third-party APIs and AI-related features * Work with authentication, user dashboards, and data handling * Debug, optimize, and improve existing code * Communicate clearly and provide regular progress updates Required Skills: * Strong experience with full stack web development * Frontend experience with React, Next.js, Vue, or similar frameworks * Backend experience with Node.js, Python, PHP, Laravel, Django, or similar * Database experience with PostgreSQL, MySQL, MongoDB, or Firebase * Understanding of REST APIs and cloud deployment * Basic to intermediate knowledge of AI tools, AI APIs, prompts, chatbots, or automation workflows * Ability to write clean, maintainable code Nice to Have: * Experience with OpenAI API or other AI model integrations * Experience building SaaS platforms, dashboards, or automation tools * Knowledge of Git, CI/CD, and scalable architecture Please apply with: * Examples of full stack projects you have completed * Details of any AI-related features you have built * Your preferred tech stack * Your availability and communication style We are looking for someone reliable, detail-oriented, and able to turn requirements into a polished working product.

I need a system to automate the receipt of TradingView alerts, store them in an SQLite database, remove duplicates, analyze all tickers at 22:00 GMT using my ChatGPT-based analysis model, rank them by trade probability, and generate a daily report in plain text format. The ranked results should then be emailed to me. The ideal freelancer should have experience with TradingView webhooks, SQLite, Python, and email integration. Familiarity with AI-based analysis and trading strategies is a plus. I have written my ChatGPT analysis model.

Looking for analytics intern who have skill set of python, tableau, sql,api script writing

Successfully developed a Machine Learning model to predict passenger survival outcomes based on historical maritime data from the Titanic dataset. Key Responsibilities & Tech Stack: Data Preprocessing & Cleaning: Handled missing data thoroughly, including advanced imputation for missing ages using passenger titles. Feature Engineering: Extracted meaningful features like passenger 'Titles' and engineered a 'Family_Size' metric to improve model accuracy. Model Training & Evaluation: Implemented and compared classification algorithms (like Random Forest / XGBoost) to optimize performance, achieving a solid accuracy of 76.54%. Tools used: Python, Pandas, NumPy, Scikit-Learn, Matplotlib, Seaborn. This project demonstrates strong capabilities in data analysis, feature engineering, and deploying predictive ML models."

I need a small auttomation tool that logs into my Starmaker account and followws users automatically. The only action required is “Followw,” nothing else. I will supply one or more user sids; the bott should take those sids, locate the corresponding profiles and trigger the followw action for each of them. A clean, head-less solution is ideal—Python with Selenium or a lightweight API approach if you know a reliable endpoint. Whatever method you choose, it must: • accept a single sid or a text file of sids • show basic progress in the console (or a simple log file) so I can confirm who was followed • throttle requests just enough to keep the account safe from blocks Once the run is finished, I expect a brief success report plus any errors captured. Please include the full, editable source code and a quick read-me so I can repeat the process on my own machine.

I need a fully automated signal generator for the Quotex platform that works in both OTC and real-time market hours. The bot must push reliable entry and exit alerts with a target accuracy of at least 90 %. Core logic should combine Moving Average crossovers with Relative Strength Index (RSI) thresholds; you are welcome to layer extra filters if they help sustain the win-rate without adding heavy latency. Key needs • Runs on my desktop or a lightweight web panel and lets me choose asset, timeframe, and whether I’m in the OTC or live session. • Real-time notifications—Telegram, e-mail, or an in-app pop-up—containing direction, strike price, expiry time, and confidence level. • Built-in back-testing or forward demo mode so we can confirm 90 %+ accuracy over at least 100 trades before final hand-off. • Clean, well-commented source code plus a concise setup guide so I can tweak Moving Average lengths and RSI parameters on my own. Acceptance criteria 1. One-week live or demo trial meets or beats the stated accuracy. 2. Signals arrive fast enough for practical execution on Quotex. 3. All documentation and source code are delivered and compile/run without error on a standard Windows machine (Python preferred, but I’m flexible). If you have prior experience building signal engines or trading bots, especially for fixed-time platforms like Quotex, I’d love to see a quick demo or repo link when we start.