**Title:** Build Automated Weekly Business Listing Scraper + Email Digest **Budget:** $100–$200 (fixed price) **Overview:** I'm an active buyer looking to acquire a small business in the $800K–$3M range. I need a simple automated system that scrapes business-for-sale listings from multiple websites weekly, filters them by my criteria, and emails me a clean digest every Monday morning. **Sources to scrape (at minimum):** - BizBuySell.com (Wisconsin + Minnesota + broader Midwest filter) - Sunbelt Business Advisors (sunbeltnetwork.com) - Lake Country Advisors - BusinessBroker.net (Wisconsin/Midwest) - BizQuest.com (Wisconsin/Midwest) **Filter criteria:** - Location: Milwaukee area first, Wisconsin/Minnesota second, Midwest broadly - Industries: Manufacturing, Landscaping, Trades (HVAC, plumbing, electrical, similar) - Seller's Discretionary Earnings (SDE): $200K–$800K - Asking price: $800K–$3M - Exclude: Restaurants, bars, franchises, retail - Established businesses only (no startups) **Email digest format (every Monday ~8am CT):** - Business name/title - Asking price - SDE or cash flow (if listed) - Location - Brief description (1-2 sentences) - Direct link to listing - Source site - Date first seen (flag NEW listings vs. ones already sent) **Technical requirements:** - Deployed on a cloud server (e.g. DigitalOcean, AWS, Heroku) — I should NOT need to run anything on my own computer - Scheduled to run automatically every week with no intervention from me - Email sent to my Gmail address - Duplicate suppression — don't re-send listings I've already received - Basic error handling so it doesn't silently fail - Simple documentation so another developer could maintain it if needed **Deliverables:** 1. Working scraper deployed and running on cloud infrastructure 2. First test email sent to confirm it works 3. Brief setup doc explaining what was built and how to update criteria in the future **Nice to have (bonus):** - A simple way for me to update search criteria without needing a developer (e.g. a config file or Google Sheet I can edit) - Alerts if a listing matches ALL criteria especially well (e.g. SDE over $500K) **About me:** I'm a serious, qualified buyer — not a casual browser. I've been evaluating acquisitions for over a year. I want this tool to surface deals efficiently so I can focus my time on outreach and diligence. Please include in your proposal: - Which sites you've successfully scraped before - Your preferred tech stack for this project - Estimated timeline - How you'll handle sites that block scrapers

Title: Backend Developer — AI Data Pipeline, Vector DB & Real-Time Push API Post: We are building an automated backend system that continuously crawls public web sources, processes and indexes content using AI, and delivers updates via webhooks. Looking for someone who has built this type of system before and can move fast. NDA required before project details are shared. What you’ll build: • Web crawler network —. • AI processing pipeline — cleans, deduplicates, chunks, and embeds ingested content into a vector database using an LLM embedding model. Quality scoring and incremental updates required. • Push API — monitors for significant content changes and delivers updates via webhook endpoints automatically. Includes configurable push schedules per subscriber, REST query endpoint, API key authentication, and token usage tracking per key. Tech stack (flexible — use what you know best): • Python (FastAPI) or Node.js • Any vector DB — Pinecone, ChromaDB, Supabase • Any LLM API — Anthropic or OpenAI • Any scheduler — n8n, APScheduler, cron • Redis for queue management • Railway, Render, or AWS for deployment Requirements: • NDA signed before kickoff — non-negotiable • Must have built RAG pipelines or vector DB systems in production — not tutorials • Must have experience with web crawlers and scheduled job pipelines • Must have experience with webhook delivery systems • GitHub or portfolio showing relevant deployed work required • 95%+ Job Success Score preferred • Individual contractors only — no agencies To apply include: • Example of a similar system you’ve built — web crawler, RAG pipeline, or push notification API • Your preferred stack for this type of build • Brief technical approach in 3–5 sentences • Hourly rate and availability to start Budget: $50–$80/hr Timeline: 3 weeks — focused sprint with daily check-ins

Python backend developer wanted to join our team. You will help build backend systems, APIs, databases, and data workflows. Experience with Python, PostgreSQL, FastAPI/Django, and data processing is preferred. We need someone who can work independently and communicate clearly. Long-term remote opportunity for the right developer.

We need a senior Python developer to build a custom internal research tool for our healthcare AI consultancy. The tool runs 75 standardized queries across six AI platforms in parallel, captures structured responses and citations, and writes results to an Excel template for analysis. What you're building A Python application with three components: Six API adapters: OpenAI (ChatGPT), Anthropic (Claude), Perplexity, Google (Gemini), xAI (Grok), and SerpAPI (for Google AI Overviews). All hit the official APIs and return a normalized response object. Async orchestrator that fires all 75 queries in parallel across the six platforms, handles retries and rate limits, tags client domain and competitor mentions in real time, and writes results to a provided Excel template. Streamlit UI for click-to-run operation, with engagement metadata input, live progress monitoring, results preview, plus Google Drive upload and Slack notification integrations on completion. Deliverables Six platform adapter modules with normalized response interface Async orchestrator with error handling and retry logic Excel writer (we provide the template) Streamlit dashboard Google Drive and Slack integrations README, setup guide, and operator documentation Test suite covering 70%+ of code paths All code in a GitHub private repo owned by us What we provide at kickoff Detailed architecture document and UI specification AHS Excel scan template, production-ready Full 75-query taxonomy All six API keys, provisioned and shared via 1Password Sample completed scans for reference Direct Slack access to the project lead Timeline and process 10 business days from kickoff to delivery Fixed price, paid in three milestones (30% / 40% / 30%) $500 bonus if delivered on or before business day 8 Daily commits to the repo required Weekly Friday sync at 10am ET Mutual NDA signed before access to query taxonomy Required skills 5+ years Python with strong asyncio experience Hands-on experience with at least 3 of: OpenAI API, Anthropic API, Perplexity Sonar API, Google Gemini API, xAI Grok API, SerpAPI openpyxl for Excel manipulation Streamlit for UI Google Cloud OAuth for Drive integration Slack API for notifications Strong Git/GitHub workflow Out of scope Query design, AI scoring synthesis, deck generation, mobile, and multi-user authentication. This is one focused tool with a clear endpoint. Please answer in your proposal Walk us through a recent Python project that integrated 3+ third-party APIs. What broke and how did you fix it? Which of the six APIs in scope have you used in production? What's your approach to rate limits and transient failures across async API calls? What scope questions do you have before bidding? Not a fit if You've never integrated more than two LLM APIs, you're uncomfortable with fixed-price contracts, or you can't commit to daily commits on a 10-business-day timeline.

Overview We are looking for an experienced Python developer with strong technical expertise and exceptional communication skills. This role is ideal for someone who is comfortable discussing technical concepts with clients, participating in interviews, and collaborating closely with stakeholders. We value developers who can not only write clean, scalable code but also clearly explain their thought process, ask the right questions, and represent our team professionally during client meetings and technical interviews. Responsibilities - Design, develop, and maintain Python applications and backend services. - Build and integrate APIs, databases, and third-party services. - Participate in technical discussions with clients and internal teams. - Attend interviews with excellent verbal communication. - Write clean, maintainable, and well-tested code. - Troubleshoot and optimize existing systems. Required Skills - 5+ years of professional Python development experience. Strong knowledge of: Python, FastAPI, Django, or Flask, REST APIs and microservices, PostgreSQL, MySQL, or MongoDB, AWS, Docker, and CI/CD practices - Experience with system design and scalable architectures. - Excellent English communication skills (written and spoken). - Comfortable participating in technical interviews and client-facing discussions. - Ability to explain technical concepts clearly to both technical and non-technical stakeholders. Nice to Have - Experience with cloud infrastructure and DevOps practices. - Experience with AI/ML integrations or data pipelines. - Previous consulting or agency experience. - Experience working with distributed remote teams.

Project Brief: Horse Racing Handicapping Automation – MVP Project Title: Build an MVP Python script to automate horse racing handicapping using Thoro-Graph, Brisnet, and EquinEdge data. Overview / Goal I have developed a working handicapping model in Python (handicap_race_v39) that ranks horses for a race using normalized metrics from EquinEdge (Win%, GSR), Beyer figures, projected Thoro-Graph figures, and bonus signals (Thoro-Graph patterns, trip quality, hidden/sneaky good performances). The goal of this project is to create a Minimum Viable Product (MVP) that automates the end-to-end process: Input: A full race card’s documents (Thoro-Graph PDF + Brisnet PDF + EquinEdge screenshots) Output: Ranked horse selections per race with transparent bonus explanations. This is the cheapest realistic path — I want a functional working script first, not a polished GUI or production-grade system. Current State (What Already Exists) • A verified core handicapping function (handicap_race_v39) that normalizes inputs and produces ranked selections with estimated win probabilities. • Proximity-based bonus logic that assigns Thoro-Graph patterns and trip comments to the correct horse. • Bonus configuration values (e.g., Top-Pair-Top = +18, troubled but strong trip = +6 to +10, hidden trip = +12, X/bounce = -8). • Human-readable bonus summary logic. I will provide all existing code to the developer. MVP Scope (Cheapest Realistic Path) Build a single, reliable Python script that can: 1. Accept a race card’s documents (one Thoro-Graph multi-page PDF, one Brisnet PDF, and multiple EquinEdge screenshot images). 2. Extract key structured data: • Horse names • Today’s projected Thoro-Graph figure (or relevant pattern) • Beyer figures • EquinEdge Win% and GSR • Thoro-Graph patterns (Top-Pair-Top, Pair-Pair-Pair, X, bounce, etc.) • Trip quality / hidden trip signals 3. Run the existing handicap_race_v39 model with proximity-based bonuses. 4. Output clean ranked selections per race, including: • Rank • Horse name • Composite score • Estimated win probability • Key bonus explanations (why the horse received positive or negative bonuses) Key Deliverables • One working Python script (or small set of scripts) that processes a full race card. • Clear output in CSV and/or readable text format. • Basic documentation on how to run the script. • The script should handle the most common cases cleanly (even if it needs occasional manual help on very difficult pages). Technical Preferences • Python 3 (pandas, numpy, etc.) • Use of AWS Textract is acceptable for PDF parsing (I can provide AWS access or the developer can suggest alternatives). • The existing code I provide should be used as the foundation for the scoring engine. • Keep it simple and maintainable — this is an MVP. Out of Scope (to keep cost down) • Web interface or GUI • Fully automated daily processing / scheduling • Perfect accuracy on every single page (some manual review or overrides are acceptable in MVP) • Back-testing framework • Advanced machine learning models Success Criteria • The script can process a complete race card (Thoro-Graph + Brisnet + EquinEdge) and produce ranked selections for all races. • Bonus logic is applied at the horse level (not race level). • Output is clear enough that I can understand why each horse received its ranking and bonuses. • The script runs reliably on new race cards with reasonable accuracy. Timeline & Budget Guidance (Cheapest Realistic Path) • I am looking for the most cost-effective realistic solution, not the most polished version. • Realistic budget range for this MVP: $3,500 (depending on experience and location). • Timeline: 3–5 weeks is acceptable. How to Apply Please include the following in your proposal: 1. Your relevant experience with PDF parsing, OCR, or document automation (especially dense tables or racing/sports data). 2. A short description of how you would approach the Thoro-Graph PDF parsing challenge. 3. Your proposed timeline and total cost for the MVP as described. 4. Any questions you have about the existing code or scope.

I need a Python developer to configure and deploy an existing open-source Google App Engine app to my Google Cloud account. The app fetches products from the Lightspeed eCom API and generates a Google Merchant Center feed. Estimated 1-2 hours of work. There is a Github repo of the app here: https://github.com/guilhermechapiewski/lightspeed-custom-google-feed.

**Project Overview:** I am looking for an expert developer to build a lightweight desktop stock ticker application (Windows/macOS preferred) where the MAIN focus is high-utility, fully customizable AUDIBLE alerts. I want to monitor the markets by ear without constantly staring at my screen. The app will feature a customizable "Quote Builder" layout running on fast user-defined refresh loops, but the sound engine is the absolute priority of this project. **Core Audio Requirements (The Main Point):** * Event-Driven Sound Profiles: I need to assign distinct, custom text-to-speech (TTS) speeds, pitches, or triggers based on user-defined price movements. * Directional Audio Logic: Distinctly different tone pitches or speech profiles for "Up" ticks versus "Down" ticks so I can instantly hear market direction. * Speech Profiles: A drop-down menu to toggle between "Standard Mode" (reads full labels: "Tesla 100, up 2, bid 99...") and "Pro Mode" (strips all labels for high-speed tracking: "TSLA, 100, up 2, 99..."). * Global Panic Mute: Hitting the Spacebar or a dedicated hotkey must instantly mute/unmute all active audio feedback immediately. * API Key Settings & Data Feeds: The app must use a "Bring Your Own Data Feed" architecture. It must feature a configuration settings screen where users input their personal, API credentials (keys and tokens) to feed data into the ticker. * Brokerage Dropdown Selector: The UI must include a simple drop-down menu allowing users to choose which data provider or brokerage connection to activate (e.g., [Dropdown: Alpaca Markets, Polygon.io, Interactive Brokers, Yahoo Finance]). The developer must build modular data adapters for these connections. **Data & Interface Requirements:** * Custom Quote Builder: Ability to save layout templates choosing from fields like Symbol, Last Price, Up/Down, Bid/Ask Size, Day High/Low, Open, and Close. * Fast Polling Loops: Drop-down selector for data intervals per ticker: 1 second, 5 seconds, 30 seconds, 1 minute, or 5 minutes. * Multi-Monitor Support: Global hotkeys to switch saved templates instantly without needing the app window to be in active focus. * Ticker Looping: Supports inputting a single ticker or a comma-separated list to cycle through multiple stocks on the interval loop. **Budget & Contract Setup:** * Contract Type: Fixed-Price * Total Project Budget: $3,000 (To be broken into milestones upon signing an NDA) ⚠️ CRITICAL: You must start the very first word of your cover letter/proposal with the word "TICKER" to prove you are a human and not an automated bot. If your proposal does not start with the word "TICKER", it will be instantly declined without review. Please reply by explaining your experience with asynchronous programming, audio-based desktop systems, or handling high-speed financial APIs (like Alpaca or Polygon.io). Selected candidates will be asked to sign an NDA before receiving the full requirements document.

Looking for a Python developer with sports analytics experience to build a college football grading engine. The full specification includes 34 custom metrics, requiring a deep understanding of football analytics and data modeling. The project involves creating a grading engine that can process large datasets efficiently and accurately. Ideal candidates will have experience with data visualization and machine learning.

Project Overview: I am building Reseller Bro, a mobile utility application designed for on-the-go users to analyze product images and instantly retrieve live, accurate marketplace valuation metrics. The front-end user interface and layout are largely complete. The application uses an AI workflow for visual recognition and keyword generation, but the backend data retrieval and sync pipeline needs optimization for speed and absolute accuracy. I am looking for a highly skilled backend developer to conduct a code audit, repair a broken data aggregation pipeline, and optimize our database caching architecture. The Core Tasks: Database Cache Failure: The app currently fails to properly save or recall data on repeat queries. We need a high-speed caching layer so that if an item has already been searched once, the second lookup pulls from the internal database instantly (under 1 second). Data Sync & Engine Accuracy: The live data gathering process is currently unstable and occasionally returns empty results even for common consumer items with deep market history. Once the AI identifies the item keywords, the data pipeline must reliably sync and aggregate live pricing data from major public e-commerce and secondary marketplaces. Data Array / Tier Mapping Mix-up: The app currently handles item condition mapping poorly, sometimes displaying irrational pricing tiers (e.g., valuing low-tier condition higher than top-tier condition). We need a clean pre-fetch matrix that normalizes marketplace conditions into three clean categories on the first search and automatically filters out pricing anomalies or extreme statistical outliers. Required Technical Skills: Strong backend development experience using Node.js or Python. Advanced expertise in data extraction, handling complex HTML/JSON payloads, managing dynamic connection protocols, and optimizing marketplace data synchronization. Deep experience with database optimization and high-speed caching layers (such as PostgreSQL, Redis, or similar). Ability to step into an existing codebase, audit backend logic, and refactor code without disrupting the current frontend UI layout. 💰 Budget & Performance-Based Milestones ($700 Total) This contract is fixed-price and strictly milestone-driven based on functional performance metrics. No milestones will be released without successful live-testing verification. Milestone 1 ($150) - Code Review & Cache Repair: Audit the inherited repository. Fix the database caching system so that previously queried items bypass the live search entirely and load data instantly (under 1 second). Milestone 2 ($250) - Data Pipeline Optimization & Matrix Mapping: Fix the live data aggregation process to consistently pull reliable pricing from target market channels using the AI-generated keywords. Implement data-cleaning and normalization rules to ensure price tiers always reflect a logical tier structure while removing extreme outliers. Milestone 3 ($300) - Final Integration & Testing: Fully connect the optimized database cache, clean data sorting logic, and application backend into a finalized, stable build ready for deployment testing. To Apply: Please respond with your relevant experience in optimizing high-speed database caches and managing high-volume marketplace data aggregation. Please briefly explain your typical approach to ensuring stable, reliable data connections with public e-commerce platforms. Full project source files and existing code repositories will be shared with top candidates during the interview phase.