I'm an independent inventor (Massachusetts LLC, patent-pending) developing a portable sports-training device that uses a projected laser line and a global-shutter mono camera to measure the angle of a small metal striking surface at the moment of impact. Target accuracy is ±0.5° on the angular measurement, with measurement latency under 2 seconds, and direct-sunlight robustness as a key engineering risk. This is an end-to-end Phase-0 feasibility engagement. The working assumption is laser-line + global-shutter mono camera with bandpass filtering, but I want your read on whether that's the right approach for this accuracy and these conditions. I have a strong lean, not a closed decision, and I'd rather you push back early than build something the wrong way. Once we align on the approach, you'll spec the bench rig (camera model, laser modules, filters, optics, baseline geometry, target mounting); I'll source the parts from your BOM and either ship the components for you to assemble or assemble and ship a built rig, your preference, whichever fits your workflow best. From there you capture data under controlled and outdoor conditions, develop the detection and calibration pipeline, and deliver a working codebase plus a written accuracy/robustness report. Hardware is returned to me on completion (or retained for a follow-on engagement if we both want to continue). What you'll deliver: 0. Approach review + rig spec. A short written deliverable (2–4 pages) covering: (a) your read on the proposed sensing approach, affirm + refine, or argue for an alternative with reasoning and a specific recommendation; (b) a bench-rig BOM with specific parts (camera model, laser modules, bandpass filters, optics, mounting, target plate) sized for the working distance and accuracy spec; (c) laser-to-camera baseline geometry with your reasoning, and recommended calibration targets. I'll source the parts from your BOM. We'll decide together whether I ship components for you to assemble or assemble and ship a built rig, whichever you'd rather. 1. Rig assembly or acceptance + baseline capture. Receive shipped parts (or built rig), assemble or validate alignment as appropriate, confirm basic optical performance against the M0 spec, then capture a baseline dataset (~200 frames per configuration) under controlled indoor lighting. Photos of the as-built rig and a setup diagram included. 2. Detection pipeline. A Python/OpenCV module that extracts the projected laser line with sub-pixel accuracy from frames at 60–100 fps. Sub-pixel line fit (Steger, Gaussian, parabolic) or weighted centroid, your choice with a short justification. 3. Calibration framework. Documented procedure and accompanying script for mapping pixel displacement to angular displacement of the target plate, accounting for camera intrinsics, lens distortion, and laser-to-camera baseline geometry. Validation against ground-truth rig angles. 4. Robustness data capture + analysis. Re-capture under (a) bright indoor with mixed daylight and (b) direct outdoor sunlight, for both laser variants with and without matched bandpass filters. Quantified accuracy + jitter per condition. 4–8 page PDF report comparing visible-red + bandpass vs. near-IR + matched bandpass. 5. Stretch (optional milestone): First cut at deriving angle-at-impact from a short pre/post-impact image sequence, pseudocode or working prototype, whichever fits the time budget. Deliverable format: Well-commented Python module(s) in a Git repo I'll provide, a README that walks a junior engineer through running the pipeline end-to-end, the captured datasets (raw frames + ground-truth angles), and a PDF report. What I'm looking for: - Comfort giving an unambiguous engineering recommendation: "use this approach with these parts" or "don't and here's why, and here's what to do instead." Phase 0 succeeds or fails based on the judgment in Milestone 0 as much as the algorithm in later milestones. - 5+ years of practical computer vision work, with shipped projects involving line/edge detection, sub-pixel feature localization, or structured-light triangulation. - Comfort doing your own benchtop work; mounting, alignment, basic optics handling. - Strong Python + OpenCV; comfort with NumPy/SciPy for the line-fit and calibration math. - Camera calibration experience (OpenCV calibrateCamera, distortion coefficients, projective geometry). - A workspace where you can run an outdoor sunlight test safely and legally with a Class-2 visible-red laser and a Class-1 IR laser module. - Bonus: prior work with laser triangulation, structured-light scanning, or sports/motion-tracking applications. - Bonus: experience deploying CV pipelines to Raspberry Pi or ESP32-S3-class hardware (potential follow-on scope). Engagement: - Fixed-price (preferred): $5,000–$7,500 total, paid across 5 milestones (approach review + rig spec → baseline capture → detection pipeline → calibration → robustness report). - Hourly alternative: $70–$140/hr with a 75-hour cap, then re-scope. - Duration: 5–7 calendar weeks (approach-review phase happens up front; ~1 week round-trip shipping after rig build). - Weekly 30-min check-ins (US Eastern preferred; flexible). - Hardware: shipped to you fully insured at my cost. Returned (insured, my prepaid label) on completion, or retained for follow-on engagement. - Possible follow-on: porting the pipeline to Raspberry Pi / ESP32-S3, IR laser variant tuning, integration support for the next prototype phase. Before we start: Short NDA + IP assignment signed before I ship the kit, share the technical design doc, or grant repo access. Upwork's standard terms transfer IP on payment, but I want a standalone signed PIIA on file as well, routine, less than 1 hour of your time. To apply, please include: 1. 1–2 examples of prior CV work involving sub-pixel localization, line fitting, or laser/structured-light triangulation. Paragraph + GitHub or paper link. 2. Three or four sentences on your approach to extracting a sub-pixel laser line centroid from a single frame. 3. Confirm you have a workspace where you can run both indoor and outdoor (direct-sunlight) image captures with a small bench rig, and that you're comfortable assembling the shipped kit. 4. Whether you prefer fixed-price or hourly, and your proposed milestone breakdown. 5. Without committing to a final answer until you've seen the full spec, a quick take: do you think projected laser line + global-shutter mono camera is the right sensing approach for ±0.5° angular accuracy at 60–100 fps under direct sunlight, or would you steer me toward a different approach? Two or three sentences. Looking forward to talking with strong candidates. Jason

We are looking for an experienced API Integration Engineer to help finalize and optimize integrations for our security platform. The ideal candidate will have strong experience working with third-party APIs, authentication mechanisms, cloud-based AI services, and troubleshooting production integrations. Your primary responsibility will be to validate and configure API credentials for URL classification and IP reputation services, identify and integrate the correct Large Language Model (LLM) endpoint (OpenAI, Claude, Azure OpenAI, or custom/internal models), and ensure the overall system is secure, reliable, and high performing. This is a short-term contract with the potential for ongoing work if the engagement is successful. Responsibilities 1. Verify and configure API credentials for: - URL Classification services - IP Reputation services - Threat Intelligence APIs 2. Validate authentication methods including: - API Keys - OAuth 2.0 - Bearer Tokens - JWT 3. Identify the correct LLM provider and endpoint, including: - OpenAI - Claude (Anthropic) - Azure OpenAI - Google Gemini - Internal/custom LLM deployments 4. Confirm that all required API keys, secrets, and access tokens are correctly configured. 5. Test API connectivity and verify successful authentication. 6. Troubleshoot integration issues across development and production environments. 7. Optimize API performance, latency, retry mechanisms, and error handling. 8. Collaborate closely with our development team to resolve integration challenges. 9. Document the configuration process and provide recommendations for future maintenance. 10. Ensure best practices for credential management and secure secret storage. Required Skills 1. Strong experience integrating REST APIs 2. Experience with authentication protocols: - API Keys - OAuth2 - JWT - Bearer Tokens 3. Experience working with AI APIs including one or more of: - OpenAI - Anthropic Claude - Azure OpenAI - Google Gemini 4. Familiarity with URL reputation and threat intelligence services 5. Experience integrating IP reputation APIs 6. Strong debugging and troubleshooting skills 7. Knowledge of HTTP/HTTPS, JSON, webhooks, and API testing tools (Postman, Insomnia, etc.) 8. Experience with Python, Node.js, or similar backend technologies 9. Familiarity with cloud environments (AWS, Azure, or GCP) To Apply Please include the following in your proposal: - Brief overview of your experience with API integrations. - Examples of projects involving OpenAI, Claude, Azure OpenAI, or other LLM integrations. - Experience integrating URL classification, IP reputation, or cybersecurity APIs. - Your preferred development stack. We are looking for a highly skilled engineer who can quickly identify integration issues, ensure secure API connectivity, and help us deliver a robust, production-ready solution. If you have strong experience with API authentication, AI integrations, and troubleshooting complex systems, we'd love to hear from you.

Colony Mobility LLC is a Florida-based technology company preparing a Phase I Small Business Innovation Research (SBIR) proposal for the U.S. Department of Transportation under Topic 26-FT1: Person-Centered, Carefree, Complete Trip Planning — Powered by AI. We are seeking a Senior AI Systems and Algorithm Researcher to serve as a named research subcontractor on this federal proposal. This is a research design and documentation role — no production software build is required. What you will research and document: Rider preference engine algorithm — design a machine learning system that learns individual traveler needs over time, including stated preferences, observed behavioral patterns, and inferred preferences for new users Success probability mathematical model — design and write a proof of correctness for a weighted scoring algorithm that calculates the probability a specific rider will successfully complete a specific trip given real-time conditions AI orchestration architecture — document the multi-agent coordination system that assembles, monitors, and replans multimodal trips in real time Outcome learning algorithm — design the reinforcement learning loop that improves system recommendations based on real trip outcomes Trip assembly algorithm pseudocode — document the step-by-step logic for building complete door-to-door journeys from multiple transportation sources LLM integration architecture — document how large language models are used within the system for normalization, preference reasoning, and conversational interfaces What the federal report specifically requires from this role: Algorithm pseudocode for all AI components Mathematical notation and proof of correctness for the success probability model Summary of how ML methods have been used to solve trip-planning problems similar to this solicitation — literature review contribution Justification of how prior research is extended and improved by this system What we need from you before July 3, 2026: A short professional bio (3–5 sentences) describing your relevant background A brief letter of commitment confirming your availability and intent to perform the described work if the contract is awarded Required qualifications: Graduate degree (Master's or PhD) in Computer Science, Applied Mathematics, Data Science, or Artificial Intelligence — or equivalent research experience Demonstrated experience designing machine learning algorithms — preference learning, recommendation systems, optimization, or routing Ability to write mathematical notation fluently — probability models, weighted scoring functions, proofs of correctness Experience writing technical research documentation — academic papers, federal research reports, or technical deliverables for a non-technical audience Familiarity with large language models and their practical limitations in production research contexts Strong plus (not required): Background in multimodal routing algorithms, operations research, or transportation optimization Experience with reinforcement learning or multi-agent systems Prior SBIR, federal research, or government contract experience Published research on routing algorithms, preference learning, or mobility AI Contract details: Hours: 180 hours over 6 months Rate: $120/hour Location: fully remote Start date: September 2026, upon DOT SBIR Phase I award notification Total contract value: $21,600 plus 20% overhead = $25,920

Project Overview: I am seeking an expert Python developer to build a lightweight, local automation script that bridges custom TradingView indicator alerts to Interactive Brokers (IBKR) for automated options execution. Core Deliverables: Webhook Listener: A local Python script that securely listens for JSON payloads sent via TradingView webhooks. Dynamic Option Chain Scanner: Upon receiving a payload containing specific parameters (Ticker, Target Delta, DTE), the script must query the live IBKR option chain (using ib_insync or ib_async), find the exact or closest available strikes matching that Delta, and calculate secondary strikes based on distance to the current spot price. Complex Multi-Leg Order Routing: The script must dynamically assemble these strikes into a specific 5-leg complex combo order (combining a credit vertical spread, two debit vertical spreads, and a naked wing) and route it to the market as a single transaction using mid-price algorithmic fills. Rigorous Error Handling & Logging: The code must include strict safeguards (rate limits, bid/ask spread checks, and fallback logic if an exact strike or delta is unavailable) to ensure no rogue orders are fired. Developer Requirements: Demonstrated expertise with the Interactive Brokers API (ib_insync framework heavily preferred) and TWS/IB Gateway. Deep understanding of options mechanics, including how to handle complex multi-leg combinations and delta sourcing. Excellent, proactive communication. Weekly availability during standard New York market hours (EST) for live testing. Must be located in the United States (required for IP protection and NDA enforcement). Workflow & Milestones: Initial development and testing will be done exclusively using an IBKR Paper Trading account. The project will be managed via two strict milestones: 1) Full execution validation in a paper environment, and 2) Live market deployment verification using a single test contract. If you have read this entire post and have experience with IBKR options routing, please start your proposal with the word "DELTA" and briefly describe a past algorithmic trading project you have built.

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

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

I’m working on an early-stage startup concept in the counter-drone / critical infrastructure security space. The product is not drone jamming, takedown, spoofing, or mitigation. The concept is a civilian-compliant drone incident management platform that helps data centers, utilities, ports, stadiums, prisons, refineries, airports, and industrial campuses detect, document, and respond to unauthorized drone activity. I’m looking for a technical advisor with experience in one or more of the following areas: Counter-UAS / drone detection RF sensing or passive RF detection Radar systems Sensor fusion Computer vision / object detection Security camera systems Physical security technology Aerospace / unmanned systems Remote ID / drone telemetry Critical infrastructure security systems The goal is not to have you build the full product right now. I’m looking for a technical expert who can review the concept, help shape the MVP, identify technical risks, and advise on what would make the product credible to customers, investors, and pilot partners. The product vision is a software/workflow layer that connects to existing cameras, sensors, employee reports, Remote ID feeds where available, and security operations tools. The platform would help security teams determine whether a drone incident is real, assess risk, preserve evidence, create an incident timeline, and coordinate response. I’d like help answering questions such as: What sensor inputs should the MVP support first? What technical claims should we avoid making early? What is feasible for a software-first prototype? How should we think about false positives: birds, aircraft, weather, reflections, etc.? What would make a pilot technically credible? What metrics should we measure during field testing? What existing hardware or sensor vendors could we integrate with instead of building hardware ourselves? What architecture would make sense for an edge gateway + cloud dashboard model? What technical risks would investors or customers challenge us on? What technical talent would we eventually need to hire? Deliverables I’m looking for: 1–2 advisory calls Technical feasibility review MVP recommendations Sensor/integration recommendations List of technical risks and assumptions Suggested pilot metrics Recommended technical hiring profile Optional written summary after the call Please let me know: Your relevant background in drones, C-UAS, RF, radar, computer vision, sensor fusion, or security systems Whether you have experience with commercial, defense, or critical infrastructure customers Your availability for an initial advisory call Your hourly rate or fixed price for a concept review What information you would need from me before the call I’m looking for someone who can be direct, practical, and honest. I do not need hype. I need someone who can tell me what is technically realistic, what is risky, and what the first credible version of this product should look like.

**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.

I’m building Elevyn, a platform designed to help people better understand themselves through connected data, intentional reflection, and AI-generated insights. This is not a social media platform. This is not a productivity app. This is not just another trading journal. Elevyn is an operating system for self-mastery, with trading serving as the first environment where personal growth becomes measurable. Most apps collect data. Elevyn is designed to observe. The goal is to connect information across different areas of a person’s life and uncover meaningful patterns that help them improve their decision-making, habits, discipline, and overall performance. The Vision Users will log information such as: • Sleep • Mood • Daily reflections • Workouts • Morning routines • Trading activity • Goals • Habits • Personal notes Instead of simply displaying this data, Elevyn will analyze relationships between it and surface meaningful insights. Examples include: • “You slept less than 5 hours before 7 of your last 10 losing trades.” • “Your win rate increases after completing your morning routine.” • “You tend to become more impulsive after multiple winning days.” • “Your consistency improves when workouts and journaling happen on the same day.” The long-term vision is an intelligent system that continuously helps users become more self-aware. Current Stage I already have: • Brand identity • Vision and philosophy • User flows • Interactive prototype • Core feature planning • Long-term roadmap I’m now looking for a developer or small team (2-3 people) to build the first production version. What I’m Looking For I’m looking for someone who can think beyond simply building screens. I want someone who can: • Build scalable architecture • Recommend the best technologies • Think through user experience • Ask thoughtful questions • Help solve technical challenges • Build for future AI integrations • Create clean, maintainable code Experience with AI, analytics, mobile applications, and scalable backend systems is a significant advantage. Technology (Open to Recommendations) I’m open to your recommendations, but I’m considering: • Flutter or React Native • Node.js or Python backend • PostgreSQL • Firebase/Supabase • AWS • AI integrations (OpenAI or similar) • Secure authentication and cloud infrastructure Long-Term Vision Trading is only the beginning. The underlying engine should eventually support entrepreneurs, athletes, creators, students, and anyone pursuing mastery in a specific area of life. The core idea remains the same: Help people understand themselves by connecting patterns across their behaviors, decisions, and outcomes. Who I’m Looking For I’m looking for someone who believes in building meaningful products—not just completing tasks. If you’re excited about creating a platform that combines psychology, data, AI, and personal development into something people genuinely use to improve their lives, I’d love to hear from you. Please include examples of similar work, your recommended tech stack, and how you would approach building a scalable MVP that can grow over time.

We’re looking for a developer to add 2 small features to an existing application built with Python, FastAPI, React, and AWS. This is a short task and should take about 3–4 hours for someone familiar with the stack. The work will involve understanding the current codebase, implementing the requested updates, and making sure everything works properly after the changes. Experience with full-stack development and working inside existing applications is important. Please include a brief summary of your relevant experience and your availability to start soon.