C++ Projects on Upwork: Find Your Next Job

I’m looking for help turning an ESP32 into a BACnet-enabled sensor node dedicated to data acquisition and monitoring. The board must join an existing BACnet network and expose a full set of HVAC parameters so our BMS can poll them in real time.

Scope of work
• Port or configure a reliable BACnet stack on the ESP32 (C/C++ or ESP-IDF preferred).
• Create BACnet objects that report temperature, humidity, airflow, CO₂ levels, and any other standard unit parameters typically required for a complete HVAC profile.
• Ensure each object is properly readable over BACnet/IP and supports basic COV subscription.
• Provide a sample MQTT or serial debug output so I can cross-check readings during bench testing.
• Supply clear build instructions and commented source code that compiles in PlatformIO or ESP-IDF.

Acceptance criteria
1. ESP32 announces itself on the network and passes a BACnet Who-Is/I-Am test.
2. All defined objects return live sensor data in the correct engineering units.
3. COV or periodic polling returns values without timeouts for at least 24 h.
4. Documentation covers pinout, dependencies, and steps to flash future firmware updates.

If you’ve previously integrated BACnet on microcontrollers or worked with HVAC field devices, this should be straightforward. Let me know your approach and a realistic timeline, and we can get started right away.

Skills: C Programming, Electronics, Microcontroller, C++ Programming, Debugging, MQTT, Embedded Systems, HVAC

Builds the hardware POCs and physical-world integrations + connected products, sensors, proximity flows.

What you’ll do
- Ship POC hardware projects to Early Stage connected products
- Integrate BLE (mesh, beacon, GATT), NFC (HCE, peer-to-peer, tag programming), and LoRaWAN (TTN, ChirpStack) with cloud and client systems.
- Full-stack: firmware on the device, edge compute, backend APIs, lightweight web/app dashboards so a non-technical client can see the POC live.
- Bridge physical hardware to AI agents — sensor data in, agent decisions out.
- Partner with AI Developers to translate physical-world events into the same agent infrastructure they already run.
- Ship demo-ready POCs the can put on a desk, in a store, or on a trade-show floor in 15-30 days.

What you bring
- Claude Code ninja. You drive AI-pair coding fluently — full-stack features shipped in days, not weeks. Non-negotiable.
- Production embedded experience (Embedded C/C++, Rust, MicroPython) on ESP32, Nordic nRF, STM32, or Raspberry Pi.
- Strong BLE protocol fluency — mesh, beacon, advertising, GATT services, pairing.
- NFC working knowledge: HCE on Android, peer-to-peer, tag write/read, NDEF.
- LoRaWAN deployment experience: device classes, gateways, network servers (TTN, ChirpStack, Helium).
- Full-stack web/app: TypeScript, Node, Next.js, React Native. Can ship the dashboard the client sees, not just the firmware.
- Calm with prototype-grade hardware — soldering, breadboarding, basic oscilloscope and logic-analyzer work.

Nice to have
- PCB design (KiCad, Altium) — even at the schematic-and-handoff level.
- Industrial design / 3D modeling (Fusion 360, OnShape) for enclosures.
- AWS IoT Core or Azure IoT Hub for production-grade fleets.
Edge ML / computer vision (TensorFlow Lite, ONNX Runtime, edge inference).
- Past work that went from POC to manufacturing (DFM mindset).
- Bluetooth Auracast, UWB, Matter / Thread — emerging protocols.

BUILD A PORTABLE AI VISION & DESK ASSISTANT
Raspberry Pi 5 | Computer Vision | OpenAI | OCR | Python

Project Type: Fixed Price with Milestone Payments

Budget: $1500 - $2,000 FIRM — All hardware shipped to you. If any additional hardware items are needed, I will make sure you get those right away.

Timeline: 6 weeks required.

OVERVIEW

I need an experienced developer to build a standalone AI-powered vision and problem solving desk assistant on Raspberry Pi 5 hardware. This is an accessibility and productivity tool for my personal professional use — not a hobbyist project.

The device must operate completely independently with its own screen, camera, and AI pipeline. No host computer. I am not allowed do any software installation on client machines... so this needs to be stand alone just for me.

I want to auto focus camera (which I will place 5 to 10 feet behind me to focus on my computer screen, take a snap shot of my screen at the push of a button, and once it runs what I see through AI...I need it to give the answers on my 2.5 inch display screen siting on my desk.

*This device will only be used ethically and not in any academic setting. I am dyslexic and work in fast-paced environments where I need to read documents, analyze visuals, solve math problems (like liquid does conversation and physical length, hight, and width Conversions when dealing with American and European clients. When I'm work work video calls with 25 impatient people... I need to get AI-powered answers on demand — quickly and reliably.


WHAT IT NEEDS TO DO

- Capture images of documents, screens, diagrams, and drawings, math problems
- Extract and read text using OCR
- Analyze images and answer questions using OpenAI
- Display responses clearly on a built-in touchscreen
- Start fast and run reliably in real work environments


HARDWARE — I SHIP IT TO YOU

You will receive:
Raspberry Pi 5 8GB
Raspberry Pi 5 Power Supply-
Cooler
Arducam 64MP Autofocus Camera-
Small HDMI Display
Breadboard + Jumper Wire Kit-
Push Button Kit
DJI Mic Mini

If you need additional components (camera module, enclosure, etc.), include those costs in your proposal. Ideally within the $1500 - $2,000 budget.


SOFTWARE STACK

- Python (primary language)
- OpenCV (image processing)
- OCR engine — Tesseract or equivalent
- OpenAI API (primary AI engine — open to recommendations)
- Simple touchscreen UI with large, readable buttons


MILESTONE PAYMENT STRUCTURE

Milestone 1 — $300
Architecture plan and written technical spec. Approved before any build begins.

Milestone 2 — $400
Hardware assembled, camera functional, touchscreen UI framework running.

Milestone 3 — $500
OCR working, OpenAI integrated, full image-to-answer pipeline functional.

Milestone 4 — $800
Final testing, polish, documentation, device shipped back to me.

Total: $2,000. Payment released only when each milestone is demonstrably complete.

WHO I AM LOOKING FOR

You should have real, demonstrable experience with:
- Raspberry Pi hardware builds
- OpenAI API integration
- Computer vision with OpenCV
- OCR systems
- Python for embedded or AI projects

Please do not apply if you are learning on the job. I need someone who has built something like this before and can prove it.

HOW TO APPLY

Please answer all of the following — applications without answers will not be considered:

1. Describe a similar project you have personally built.
2. Share GitHub links, photos, or videos of relevant prior work.
3. What is your location and time zone?
4. How long will this take from hardware receipt to delivery?
5. Do you accept the $2,000 firm budget?
6. Any recommendations to improve the design? I'm all ears!

Thank you for your interest.

Adam Corn
Monterey, California

I have a Windows executable that was created with Nuitka from a Python project. My goal is to retrieve the original source code, focusing only on the main executable module rather than the entire code-base.

You will need to reverse the packing that Nuitka performs, extract the embedded byte-code, and convert it back into clean, human-readable .py files. I am interested specifically in the main executable file; configuration or resource files are not required.

Please work with whatever toolchain you prefer—unpacking scripts, custom loaders, ida-python, or a dedicated Nuitka extractor—as long as the end result meets the following acceptance criteria:

• A fully readable .py file (or set of files if the main module was split) that recreates the logic of the original main executable.
• Code must run under a standard Python interpreter (3.x) without relying on the compiled EXE or Nuitka stubs.
• A brief note (markdown or text) outlining the steps you took so I can reproduce the process if needed.

Let me know your estimated turnaround time and any questions you have about the executable itself.

Skills: C Programming, Python, Software Architecture, Software Testing, C++ Programming, Software Development, Scripting, Reverse Engineering

I have an open-source baseline in mind (the Ipernov face-swap repository) and need a truly real-time desktop application built from that starting point. The core of the job is to fork the repo, strip out any unnecessary overhead, and rebuild the inference pipeline so that live-streaming and video-call scenarios run smoothly with minimal latency and zero dropped frames.

What matters most
• Desktop focus – the executable must run locally on a typical gaming-class PC without cloud round-trips.
• Target Hardware Baseline – The application must be optimized to achieve these metrics natively on an RTX 4060 Laptop GPU (8GB VRAM) and an i7-14650HX CPU with 16GB RAM.
• Full face swap is the single non-negotiable feature; partial swaps or expression transfer are optional nice-to-haves if they do not compromise performance.
• Real-time speed – think webcam input at 720p/1080p and a sustained 30 FPS or better. This will involve CUDA, TensorRT/ONNX optimisations, lightweight encoder/decoder tweaks, and clever GPU memory management to ensure stable, long-term live streaming within a strict 8GB VRAM budget without memory leaks.
• Seamless output – the program should expose a virtual webcam so that OBS, Zoom, or Discord can pick it up instantly.

Deliverables
1. Clean forked codebase with all performance improvements clearly documented.
2. One-click desktop build (installer or packaged binaries).
3. Benchmark report showing FPS, latency, and system specs used.
4. Quick-start README plus comments explaining every critical optimisation step.

Testing & Sign-off
I will test the build on my own machine and sign off once the metrics match the benchmark you provide. If you enjoy squeezing out every last millisecond of inference time, this will be a fun project.

To Apply:
Please start your proposal with the words "Low Latency Achieved" so I know you have read this entire specification. In your proposal, briefly highlight your specific experience optimizing ONNX Runtime, TensorRT, or custom C++/Python pipelines for real-time video processing.

Skills: Python, CUDA, Machine Learning (ML), Computer Vision, Deep Learning, AI Rendering, AI Text-to-video, AI Chatbot Development, AI Model Development, AI Development

I'm looking for a Quotex bot that specializes in signal generation. The primary task of this bot will be to generate trading signals that are highly accurate.

Ideal Skills and Experience:
- Proficiency in trading bot development, particularly for Quotex.
- Strong background in algorithm design for signal generation.
- Knowledge of trading strategies, even if not specifically requested, can be a plus.
- Experience in ensuring high accuracy in signal predictions.

Please note, while the bot will be focused on signal generation, understanding of market dynamics and ability to incorporate different trading strategies can enhance the bot's performance.

Skills: C Programming, Matlab and Mathematica, Algorithm, C# Programming, C++ Programming

I'm building Kypros — a professional-grade Arduino-compatible development board based on the NXP MIMXRT1176DVMAB (dual-core Cortex-M7 + M4, 1GHz). This is a custom PCB The goal is to make Kypros as easy to program as a Raspberry Pi Pico — plug in a USB-C cable, drag and drop a .uf2 file, or programme directly via arduino ide or mcuxpresso. done.
I need an experienced embedded software developer to build the software stack from scratch alongside the hardware.
---
What I need built
Milestone 1 — UF2 Bootloader (EVKB bring-up)
Port tinyuf2 (or equivalent) to the MIMXRT1176DVMAB
Target: NXP MIMXRT1170-EVKB first, then Kypros PCB
USB OTG Serial Downloader → drag-drop .uf2 from any OS (Windows/Mac/Linux) — no drivers, no software, no button presses
Flash layout: first 256KB protected bootloader, remaining ~15.7MB user firmware
Deliverable: working .uf2 bootloader on EVKB + documentation
Milestone 2 — Arduino Core (ArduinoCore-kypros)
Build on ArduinoCore-API + NXP MCUXpresso SDK
Reference: arduino-pico (RP2040 core) for structure/approach
Must support: GPIO, PWM, UART, SPI, I2C, analogRead/Write, millis/micros, interrupts
Board variant file for Kypros pinout
Board Manager JSON (`package_aventen_index.json`) — installable via Arduino IDE URL
Deliverable: Arduino IDE installs Kypros board, blink.ino compiles and runs
Milestone 3 — Basic peripheral support
USB Serial (CDC) — Serial.print() works over USB-C
SD card (USDHC2 — Micro SD)
Ethernet (RTL8211E PHY — RMII)
WiFi/BT (Murata 2EL module — SDIO + UART)
Deliverable: working examples for each peripheral
---
The board — key specs
MCU: MIMXRT1176DVMAB — Cortex-M7 @ 1GHz + Cortex-M4 @ 400MHz
Flash: W25Q128JVPIQ 16MB (QSPI)
PSRAM: APS6404L 8MB
Wireless: Murata Type 2EL (WiFi 5 + BT 5.0, soldered on, SDIO)
Ethernet: RTL8211E 1G PHY
HDMI: ADV7535 DSI-to-HDMI bridge
USB: USB-C (OTG1 — programming + power)
SDK: NXP MCUXpresso SDK 2.x
Important: This is the RT1176, not the RT1062 or RT1064. Teensy 4.x experience is useful context but this is a different, newer chip. Do not apply if you have only worked with RT106x.
---
What I'm NOT looking for
CircuitPython / MicroPython support (not on roadmap)
Porting Teensyduino
Someone who will disappear after Milestone 1
---
Ideal candidate
Proven experience porting Arduino Cores to custom NXP i.MX RT boards
Familiar with tinyuf2 or similar UF2 bootloader implementations
Comfortable with MCUXpresso SDK, CMSIS, and bare-metal C/C++
Has done USB HID/CDC firmware before
Can write clean, well-documented code
Bonus: experience with FreeRTOS on Cortex-M7
---
Working style
NDA required before any files are shared (same as our partner engineer)
Milestones paid on delivery via Upwork
Clear written communication — I review everything before it goes into the repo
GitHub (private repo) for all deliverables
You'll work alongside us as the board approaches prototype and for testing on our prototype as well as the EVK b
---
Budget
Hourly: open to discussion — looking for the right person, not the cheapest
Milestone 1 estimated: 15–25 hours
Long-term engagement preferred — Kypros V2 and SDK work will follow
---
To apply, please answer:
Have you ported an Arduino Core or tinyuf2 bootloader to an NXP i.MX RT chip before? Which one?
What is your approach to USB OTG bring-up on a bare custom board?
Link to any relevant GitHub repos or open source work.
Applications without answers to these three questions will not be considered.

I’m building a small desktop tool for Windows that lets users drop in any portrait and instantly receive a perfectly aligned passport-size JPEG. The key is accuracy and speed, so I need robust AI face detection that automatically centres the head, levels the eyes, and corrects shoulder tilt without manual tweaking. Once the crop and alignment are finished, the file should be saved straight to JPEG at the standard passport dimensions I’ll supply.

While the core focus is the one-click crop, I’d like the codebase structured so secondary edits—brightness or contrast tweaks, red-eye fixes, even full background removal—could be added later without rewriting the detection workflow. If you already have modular models or libraries that handle those, let me know; otherwise, neat hooks or an extension interface will be enough.

Deliverables
• Windows installer (or portable EXE) ready to run on current versions of Windows
• Source code with clear comments and a short README that explains model training or external AI services used
• A quick user guide illustrating the drag-and-drop flow, the AI alignment in action, and the final JPEG export path

I’ll test by running a batch of sample portraits with varying orientations; every output must respect the official passport frame and appear level to the naked eye. Let me know which frameworks or AI libraries you plan to leverage, and your timeline to a working prototype.

Skills: Graphic Design, Photoshop, Photo Editing, C++ Programming, Image Processing, Desktop Application, AI Image-to-text, AI Image Editing, AI Model Development, AI Development

Experienced Unreal Engine / Unity Developer Needed for Multiplayer FPS Shooter Project

Project Overview

I am looking for an experienced game developer to help revive and further develop a multiplayer FPS shooter project that was started some time ago but never fully completed.

The game already has an existing foundation, and my goal is to continue development, improve overall quality, and expand the game’s features. I am seeking a reliable developer who can work with me to bring the project to a polished and modern state.

Scope of Work

Key areas of focus include:

* Improving existing gameplay systems and mechanics
* Enhancing multiplayer functionality and networking performance
* Designing and implementing a modern, polished user interface (UI/UX)
* Adding new weapons and weapon systems
* Introducing additional gameplay features and content
* Optimizing performance and stability
* Fixing bugs and improving overall player experience
* Assisting with future scalability and feature planning

Ideal Candidate

The ideal developer should have:

* Strong experience in multiplayer FPS game development
* Experience with Unreal Engine or Unity (please specify)
* Knowledge of networking, replication, and multiplayer architecture
* UI/UX implementation experience
* Experience creating weapon systems and gameplay mechanics
* Strong problem-solving skills and attention to detail
* Good communication and the ability to collaborate long-term

Project Goal

My goal is to transform this unfinished project into a high-quality multiplayer FPS experience with improved gameplay, new content, and a modern presentation. I am interested in building a long-term working relationship with the right developer if the collaboration is successful.

When applying, please include:

* Relevant FPS or multiplayer projects you have worked on
* Your preferred game engine
* Examples of UI or gameplay systems you have developed
* Your availability and hourly/project rate

I look forward to hearing from talented developers who are passionate about FPS games and interested in helping bring this project to life.

• Describe your recent experience with similar projects
• Include a link to your GitHub profile and/or website
• What frameworks have you worked with?
• Describe your approach to testing and improving QA

I have a Pine Script indicator running on TradingView that performs real-time data analysis based on a custom pattern-recognition algorithm. I now need it rewritten as a C++ custom study for Sierra Chart so I can keep the exact same logic and speed up execution on my desktop.

You will receive the original Pine source plus notes that explain each detection rule and how the pattern-matching logic drives the on-chart signals. Your job is to translate that logic line-for-line into the Sierra Chart C++ API (sc.dll), respecting its event-driven structure and efficient memory handling. All calculations must update tick-by-tick without repainting or lag.

Deliverables
• Complete, well-commented C++ source code (.cpp / .h) that compiles with the current Sierra Chart Advanced Custom Study SDK
• Compiled DLL ready to drop into the /Data folder
• A brief setup guide showing how to add the study, set any inputs, and verify that results match the original Pine plot in real time

Acceptance criteria
• Visual output on Sierra Chart mirrors the Pine Script on identical symbol, timeframe, and data feed
• No runtime errors or excess CPU load while updating tick data
• Code passes Sierra Chart’s Sanity Check and loads without security warnings

I am available to answer questions about the pattern rules and to run side-by-side tests during development.

Skills: C Programming, Algorithm, C# Programming, C++ Programming, Software Development, Documentation, Pine Script, API Integration

I need an experienced robotics software engineer or integration consultant to support an early-stage robotics project.
The work is part-time and can be remote. The initial requirement is to review robot SDK/API documentation, advise on the correct technical setup, and help design safe, repeatable workflows using existing robot hardware.
This is not a project to build a robot from scratch. I need someone who can work with existing robotics platforms, SDKs and APIs, then help develop practical control, monitoring and demonstration workflows.
Initial work may include:

* Reviewing robot SDK/API documentation
* Advising on the correct development environment
* Recommending hardware/software setup
* Creating simple control or monitoring tools
* Supporting camera/sensor capture workflows
* Advising on safe scripted demo routines
* Supporting remote testing and debugging
* Estimating development time for a first proof-of-concept
Required skills:
* ROS or ROS 2
* Python
* C++
* Linux / Ubuntu
* Robotics SDK/API integration
* Git / GitHub
* SSH and remote debugging
* Experience working with real robot hardware
Strongly preferred:
* Humanoid or quadruped robot experience
* Unitree SDK experience
* Mobile robotics experience
* Computer vision
* Depth cameras, LiDAR or sensor integration
* Motion planning / navigation
* SLAM / mapping
* Teleoperation
* Docker
* NVIDIA Jetson or edge AI devices
* Gazebo, Isaac Sim or similar simulation tools
This role can be mainly remote. A local operator will handle the physical robot during testing where required. You should be comfortable supporting remotely via video, logs, SSH and development tools.
I would like to start with a small paid discovery phase of 5–10 hours to review the technical requirements, advise on SDK/API setup, recommend practical demo workflows, identify risks or limitations, and estimate the work needed for a first proof-of-concept.
Please include:

1. Your robotics experience
2. Your ROS / ROS 2 experience
3. Your Python and C++ experience
4. Any humanoid, quadruped or Unitree experience
5. Examples of real robot hardware you have worked with
6. Whether you can support remotely through video, logs and SSH
7. Your hourly or day rate
8. Your availability over the next 4–6 weeks
Please start your reply with “Robot SDK” so I know you have read the brief.

We are seeking a skilled freelancer to develop a sorting program utilizing Facial Recognition and computer vision. The ideal candidate will have experience in C# and Windows App Development, with a strong understanding of OpenCV. The project requires an intermediate level of proficiency and involves creating a program that can efficiently sort images based on facial features and read name tags on people. The program should be user-friendly and capable of handling large datasets.

supertrader is an AI-powered trading journal used by thousands of traders to automatically track performance, identify mistakes, and improve decision-making.

We've already built the post-trade analytics layer.

Now we're building something bigger:
An AI-powered browser extension that helps traders before mistakes happen.

Our vision is to create a real-time trading copilot that understands trader behavior across platforms like TradingView and broker websites, detects patterns, and delivers personalized coaching directly inside the trading workflow.

The Project
We're looking for a senior engineer to lead the development of a browser extension that captures trading-related activity and sends structured behavioral events to our existing platform.

The extension will serve as the foundation for real-time coaching, intelligent pattern recognition, and future AI interventions.

Existing Stack

Backend:
- Python
- Flask
- PostgreSQL
- Gemini AI

Frontend:
- React

Extension (proposed):
- WXT
- TypeScript
- React
- Chrome Extension (Manifest V3)

We're open to alternative recommendations if you have strong reasoning.

Phase 1 Deliverables
Build a production-ready browser extension that:
- TradingView Integration
- Capture behavioral and contextual events such as:
Symbol viewed
Timeframe changes
Watchlist interactions
Session duration
Chart interactions
- Other relevant trading signals
- User Authentication
- Login using existing SuperTrader accounts
- Secure token handling
- Account linking
- Event Streaming
- Send structured events to our backend in real time.

Build a lightweight overlay that can display:
- Coaching messages
- Risk alerts
- Trading insights
- Future AI recommendations

Phase 2 (Future Scope)
The successful candidate may continue with:
- Platform Expansion to 50+ brokers
- Power systems that detect:

Requirements

Must Have:
- WXT
- Strong TypeScript experience
- React
- Browser extension development
- Chrome Extension Manifest V3
- Content scripts
- DOM instrumentation
- API integrations
- Git/GitHub workflow

Nice to Have:
TradingView familiarity
Trading or fintech experience

Please include:
- Pricing for phase 1 & 2 and timelines
- Examples of browser extensions you've built.
- Your preferred extension stack and why.
- Experience with TradingView, broker platforms, fintech, or data-heavy applications.

I want to create an MT4 code on XAUUSD for broker compatible for 2 decimal symbols. For trading , we should follwing trend filter as supertrend 10 3. For buy signal , it will take buy entry for 150 pips target. if price falls below then it should average the entry with below combinmation. Entry lot- 0.02. Next averaging lots 0.04, 0.08, 0.03, 0.06, 0.12,0.04,0.08,0.16,0.05,0.10,0.20a nd so on. ALso, keep averaging TP 150 points above avg value. Keep on averaging loosing position till the trend is on the same side. if the trend flips and there are open position, open equal amount of hedge position. again target of 150 points and averaging at 500 points. Keep two seperate counter for running profit and hedging profit. Obce the postion enters in hedging mode, calculate hedging profit. If hedging profit 1.02 X floating loss, then exit all open position. and start new trades as per the trend again.

I'm seeking a developer to create Windows-compatible software for personalizing dual interface EMV cards. The software should handle:

- Data Encoding
- Key Management
- Application Loading

Ideal Skills & Experience:

- Experience with EMV technology and card personalization
- Proficiency in Windows software development
- Knowledge of cryptographic key management and data encoding techniques
- Familiarity with application loading processes for EMV cards

Please provide examples of similar work done and ensure you have a strong background in security protocols.

Skills: C Programming, C# Programming, Software Architecture, Cryptography, C++ Programming, Software Development, Software Engineering, Data Protection

Summary

We currently have a complete multi-layer trading architecture primarily based on NEAT and designed for crypto, forex, and equity markets.

The architecture is already implemented and functional. This engagement is not focused on building a new trading system from scratch. The objective is to optimize, improve, and validate the existing architecture while maintaining strict out-of-sample robustness requirements.

Architecture Overview

1. LSTM Forecasting Layer

This layer consists of LSTM-based forecasting models whose purpose is feature enrichment rather than direct trade generation. The models produce structured future-looking estimates, directional probabilities, expected movement metrics, and uncertainty-related outputs.

2. NEAT Decision Layer

The core decision-making layer is based on NEAT. Forecasting outputs are not treated as trading signals. Instead, evolutionary agents learn how to utilize forecasting information together with market-derived features in order to generate trading behavior. The forecasting and decision processes are intentionally separated to reduce overfitting and future-data leakage.

3. Multi-Model Aggregation Layer

Multiple independently evolved models are combined through an aggregation framework. Rather than relying on a single agent, the system combines models trained under different conditions, datasets, and market environments.

4. Context / Regime Layer

A higher-level contextual layer evaluates market conditions, volatility environments, uncertainty levels, and broader regime information. This layer acts as a validation mechanism for lower-level decisions.

5. Execution and Risk Layer

The final layer combines upstream outputs and determines trade execution, risk exposure, position management, and abstain/no-trade behavior.

This architecture already exists. The purpose of this project is to improve performance, robustness, generalization quality, and out-of-sample behavior.

Optimization Scope

We are looking for someone with strong experience in quantitative trading systems, evolutionary algorithms, NEAT, machine learning for financial markets, and robust validation methodologies.

Potential optimization areas include, but are not limited to:

* NEAT evolution logic
* Fitness design
* Multi-model aggregation
* Feature selection and feature engineering
* Regime handling
* Trade filtering
* Risk management behavior
* Generalization improvements
* Overfitting reduction
* Validation methodology improvements
* Architecture-level optimization where appropriate

The goal is not to redesign the system from scratch, but to improve the existing architecture while preserving its overall structure and design philosophy.

Validation Methodology

All optimization work must be evaluated using a strict walk-forward process.

Historical data must be separated into train, test, validation, and holdout datasets.

In addition, market regimes must be treated independently. Regime intervals are considered separate datasets and must not be merged simply because they belong to the same regime category.

For example, if a market contains multiple bull-market periods occurring in different years, each bull-market interval must be treated as its own dataset rather than combined into a single larger bull-market dataset.

Models should be trained using regime-specific datasets and evaluated only on appropriate unseen periods.

The same overall methodology must also be applied to the aggregation layer. Regime-specific models and the higher-level aggregation logic should both be evaluated under the same train/test/validation/holdout framework.

Validation periods may be used for experimentation, refinement, and optimization. However, final performance evaluation must be based exclusively on holdout data.

If significant architectural or behavioral modifications are introduced during validation, additional unseen periods should be reserved for final confirmation testing in order to maintain a realistic separation between optimization and final evaluation.

Holdout Evaluation

Backtesting must be performed using non-cumulative position sizing.

Performance evaluation should not depend on compounding effects, account growth, or equity curve amplification. The objective is to evaluate the quality of the decision process itself rather than the effects of compounding.

The architecture must demonstrate robustness across multiple market environments.

Final evaluation will be performed on crypto, forex, and equity markets.

For each market category, multiple assets will be selected for final evaluation. Asset selection will occur after optimization work is completed and before final holdout testing begins.

All final condition checks must be performed exclusively on holdout data.

Performance Targets

Performance will be evaluated using rolling 3-month holdout windows.

For each 3-month holdout window:

* If benchmark return is positive, system return must exceed benchmark return by at least +8 percentage points.

* If benchmark return is negative, system maximum drawdown must not exceed 50% of the benchmark drawdown during that same period.

These requirements apply to the holdout evaluation process and are the primary acceptance criteria for the optimization effort.

The objective is not to maximize return at any cost, but to consistently demonstrate superior risk-adjusted behavior relative to the benchmark across different market conditions.

Application Requirements

When applying, please include:

1. Directly relevant experience with algorithmic trading systems.
2. Experience with NEAT, neuroevolution, genetic algorithms, or similar evolutionary methods.
3. Similar optimization, quantitative research, or trading-system projects.
4. What parts of the architecture you would investigate first.
5. Your approach to improving out-of-sample robustness and reducing overfitting.
6. Any experience with walk-forward validation, regime-based modeling, or portfolio-level evaluation.

This project is intended for candidates who are comfortable working on an existing quantitative trading architecture and improving its performance under strict holdout-based evaluation criteria

1️⃣ Current structure:
(100 concurrent sockets // no phone numbers & telephony redundancy)

Currently we have an agent with a 100 call concurrency limit using these tools:

TTS - Eleven labs
STT - Soniox
LLM - Grok 4.0
Database - MongoDB
Concurrency - Railway
Telephony - Telnyx
Coder - Antigravity

2️⃣ Your PROPOSED structure:
(For 8000+ concurrent sockets AND PROVEN phone numbers & telephony redundancy) //

ANSWER EACH line below as ROOTED in YOUR real experience as possible:

1️⃣ TTS - Which one based on your extensive experience?

What is the fallback TTS in case this one fails? Have you tested Gradium before?

We want to use voices that have NO cost here which. Is totally possible while still sounding equal or better to Eleven labs

Answer based on real experience not theory please

We’re extremely advanced (not new at this)

We will know if this a non researched answer

2️⃣ STT - Which one based on your extensive experience and why?

Also what is the fallback STT for lowest latency while still being accurate in case this one fails?

3️⃣ LLM - Which one based on your extensive experience and WHY?

Also what is the fallback LLm in case this one fails?

4️⃣ Database - Which one based on your extensive experience?

What is your proposed fallback DB case this one fails at this scale?

5️⃣ Concurrency - Which one based on your extensive experience?

Do NOT suggest AWS here // we want the lowest cost of maintenance possible while still being relaiable and AWS isn’t either!!!

We want max Lowest possible cost in this and still being reliable (8000 concurrent sockets isn’t that crazy of a scale)

What is your 2Nd alternative to this in case it fails?

6️⃣ Telephony - Which one based on your extensive experience?

Do NOT answer twillio // lowest cost plus higher deliverability here is the concern

Answer with your #1 preferred provider
Then #2 and #3 and why please (be impressive)

7️⃣ Coder - Which one based on your current workflow? Answer with your current PARALLEL development workflow - what are you using to manage 8+ instances at a time while you code and which coder and model?

Proposed NEW structure & tools:
(tell us here in your answer 👆👆)

REMEMBER we’re NOT new at this so carefully choose your answers and tell us WHY you’re choosing each piece

Lastly do let me know if this is too challenging to answer from your experience!!!

If you don’t have extensive experience with some of these questions please be honest and tell me please

Answer each question in here as extensively and as tactically as possible (impress us)

Hi,

Before I place an order, can I check whether this is something you can build?

I have an Excel spreadsheet containing around 130 UK property companies.

I would like a Python workflow that:

* Reads the spreadsheet
* Uses the Companies House API to pull director, PSC, filing history and charge information
* Adds the results to the spreadsheet
* Creates a scoring system based on criteria I provide
* Outputs an updated Excel workbook

Have you worked with the Companies House API before?

If not, are you comfortable building this using a documented public API?

Could you briefly explain how you would approach the project?

Many thanks,
Rita

I'm seeking an experienced Roblox scripter to develop character abilities for a game.
The game is inspired based of Infectious smiles on roblox
Im looking for someone to make the exact same game mechanics eg the infection system, the team system and guis
Requirements:
- Script character abilities like special moves, skills, etc.
- Ensure smooth integration with existing game mechanics.
- Optimize scripts for performance and minimal bugs.

Ideal Skills:
- Proficiency in Lua scripting for Roblox.
- Experience with character abilities development.
- Strong problem-solving skills and attention to detail.

Please include relevant experience in your application.

Skills: C Programming, Game Design, C++ Programming, Debugging, Game Development, Lua, Scripting, Roblox, Performance Tuning, Character Design

# ESP32 NFC Audio Player Firmware Development (Children's Audio Box)

## Project Overview

We are developing BAYANI, a screen-free educational audio box for children inspired by products such as Toniebox.

The hardware platform already exists and includes:

* ESP32
* NFC reader (PN532 or compatible)
* MicroSD card
* Audio DAC (I2S)
* Audio amplifier
* 3 control buttons
* LED indicators
* Li-Ion battery
* USB-C connection

At this stage, we are looking for an experienced embedded firmware developer to create the V1 firmware.

---

## Firmware Features Required

### NFC Figurine Detection

* Detect NFC figurine placement
* Read NFC UID
* Associate UID with audio content
* Start playback automatically when a figurine is placed

### Playback Control

* MP3 playback from microSD card
* Play / Pause
* Previous / Next track
* Volume control
* Smooth audio behavior

### Figurine Removal

* Immediate pause when figurine is removed
* Save playback position
* Resume from last position when figurine is placed again

### Button Management

* Play / Pause button
* Volume Up
* Volume Down
* Long press functions if needed

### LED Management

* Playback status
* Charging status
* Battery indication
* Error indication

### Battery Management

* Battery level monitoring
* Low battery warning
* Charging detection

### USB Mass Storage Mode

* Device appears as USB storage when connected to a computer
* Easy audio content management
* No dedicated application required

---

## Deliverables

* Fully documented firmware source code
* Build instructions
* Firmware architecture explanation
* Demonstration video
* Tested firmware running on hardware (or equivalent validated setup)

---

## Required Skills

* ESP32
* Embedded C / C++
* NFC (PN532 preferred)
* I2S Audio
* MicroSD file systems
* USB Mass Storage
* Battery-powered devices
* Consumer electronics products

---

## Important

This is not an academic exercise.

The goal is to create a reliable firmware foundation for a children's commercial product.

When applying, please provide:

1. Examples of ESP32 projects you personally developed.
2. Examples involving NFC, audio playback, or consumer devices.
3. Your proposed timeline.
4. Your proposed budget.
5. Whether you can test on real hardware.

Important

The budget for this firmware V1 project is fixed at €300.

Please do not apply if your minimum budget exceeds this amount.

This project is intended as a first functional firmware version for a prototype and future product development. Additional work may be available later depending on results and collaboration quality.

When applying, please provide:

Examples of ESP32 projects you personally developed.
Examples involving NFC, audio playback, or consumer devices.
Your proposed timeline.
Confirmation that you can work within the €300 budget.
Whether you can test on real hardware.

Please begin your proposal with the word BAYANI so that I know you have read the entire project description.

Skills: C Programming, Electronics, Technical Writing, Microcontroller, Arduino, Software Development, Documentation, Embedded Systems, Signal Processing, Prototyping

Quiero llevar mi nevera de maduración al siguiente nivel con un controlador Arduino que mida y gestione tres variables críticas: temperatura, humedad y concentración de gases. Los valores deberán mostrarse en una pantalla LCD y, si alguno sale de los rangos que definiré, se activará una alarma sonora y encenderá-apagar un sistema de ventilación para restablecer el ambiente ideal.

El ajuste de los parámetros será totalmente manual mediante botones físicos en el frontal; nada de apps ni control remoto. El objetivo es mantener el sistema sencillo y robusto.

Qué necesito de ti
• Código Arduino bien comentado (C/C++ en Arduino IDE) que lea sensor de temperatura, sensor de humedad y sensor de gas, muestre los datos en la LCD, gestione los relés del ventilador y dispare la alarma.
• Rutinas de calibración simples accesibles mediante los mismos botones.
• Diagrama de conexiones claro con referencias a pines, relés, resistencias, fuente de alimentación, etc.
• Breve guía de prueba para que pueda verificar funcionamiento antes de instalarlo definitivamente.

Aceptación
El proyecto se considerará completo cuando cargue el sketch, vea las lecturas estables en la LCD, pueda navegar por los menús con los botones, y el ventilador y la alarma respondan al sobrepasar los umbrales fijados.

Si tienes experiencia previa con sensores de temperatura (por ejemplo DS18B20), humedad (DHT22 o similar) y sensores de gas (MQ series) y te manejas con pantallas LCD 16x2 o I2C, dímelo: agilizará la selección.

Skills: C Programming, Electronics, Microcontroller, C++ Programming, Circuit Design, Embedded Systems, Automation

We're looking for a versatile Mid-Level Full Stack Engineer to join our growing team. You'll work across the entire stack — building responsive, performant frontend interfaces with React and TypeScript, developing robust backend services in Python/Django and Node.js, and deploying scalable infrastructure on the cloud. Experience with Java or Kotlin, mobile development (iOS/Android), and a curiosity for AI/ML integrations are a strong plus.

We are seeking an experienced AI Engineer to develop a conversational assistant for our retail business. The ideal candidate will have expertise in n8n, Odoo, LLMs, and AI agents, with a proven track record of building production-ready AI solutions. You will be responsible for integrating AI capabilities with our existing systems, enabling natural customer interactions, automating workflows, and improving operational efficiency. Strong experience with API integrations, conversational AI, and scalable automation is highly preferred.

We are seeking an expert to help resolve a '0 view issue' on our TikTok account. The ideal candidate will have experience in social media marketing and TikTok-specific strategies to improve content visibility. Responsibilities include analyzing current content performance, identifying potential issues, and implementing effective solutions to increase engagement and views.

# Optimization of Stepper Motor Control for a Low-Power Thermostatic Radiator Valve

## Project Context

We have developed a working electronic product that uses a stepper motor to actuate a thermostatic radiator valve. The product is battery-powered and uses an STMicroelectronics STSPIN motor driver.

The product is already functional, but we would like to improve its energy efficiency and its behavior when the batteries are weak. Benchmarking against similar products on the market shows that our current solution could be optimized, especially regarding the trade-off between power consumption, available torque/thrust, and movement reliability.

We are looking for an experienced freelance engineer in embedded electronics, stepper motor control, and low-power optimization to support us on a short and focused mission.

## Project Objectives

The main objective is to optimize the stepper motor control strategy in order to:

1. Reduce power consumption during motor movement.
2. Limit current peaks to improve operation with weak batteries.
3. Adjust the motor torque/thrust to the actual functional requirement, avoiding unnecessary excessive force.
4. Maintain movement reliability, including under unfavorable conditions such as weak batteries, mechanical friction, load variation, cold starts, etc.
5. Provide driver settings and/or firmware strategies that can be integrated into the existing product.

## Technical Scope

The product uses a stepper motor controlled by an STMicroelectronics STSPIN driver. The mission will mainly focus on:

* Analyzing the current motor control strategy.
* Identifying the parameters that influence consumption, current peaks, torque, and thrust.
* Optimizing the motor driver settings.
* Evaluating strategies such as:

* reducing motor current,
* dynamically adapting current depending on the movement phase,
* acceleration/deceleration ramps,
* microstepping or step mode adjustment,
* limiting inrush current at startup,
* holding-current reduction or motor current shutdown after movement,
* indirect detection of missed steps or overload, if applicable.
* Providing concrete firmware recommendations and, if possible, modifying the existing code or supplying example configuration code.

## Material and Resources Provided

We can provide:

* A fully functional product sample.
* The necessary tools to program or modify the firmware.
* Relevant information about the STSPIN driver used.
* The means to reproduce motor movements.
* The expected functional constraints of the product.
* Internal benchmark measurements after signature of a confidentiality agreement.

Detailed schematics, full source code, and internal performance data will only be shared with the selected freelancer under a confidentiality agreement.

## Expected Deliverables

At the end of the mission, we expect:

1. A clear diagnosis of the current motor control strategy.
2. A justified optimization proposal.
3. Recommended configuration parameters for the STSPIN driver.
4. Concrete firmware recommendations.
5. If possible, a patch, pseudo-code, or example code that can be integrated into the existing firmware.
6. A test protocol to validate the improvements.
7. A concise report including:

* measured or estimated gains,
* potential trade-offs,
* limitations of the optimization,
* recommendations for industrialization.

## Success Criteria

The mission will be considered successful if the freelancer can demonstrate a significant improvement in one or more of the following areas:

* Reduced power consumption during motor actuation.
* Reduced current peaks.
* Improved operation with weak batteries.
* Reduced excessive thrust without loss of functionality.
* Maintained movement reliability.
* Recommendations that can be clearly integrated into the existing product.

Precise quantified targets will be shared after a confidential technical discussion.

## Desired Profile

We are looking for a freelancer with proven experience in several of the following areas:

* Low-power stepper motor control.
* STMicroelectronics STSPIN drivers or equivalent motor drivers.
* Battery-powered products.
* Embedded firmware in C/C++.
* Power consumption optimization.
* Current measurement on embedded systems.
* Compact mechatronic products.
* Industrial constraints and market-ready products.

Experience with motorized valves, thermostatic radiator valves, linear actuators, motorized locks, low-power IoT products, or similar devices would be a strong advantage.

## Duration and Availability

We are looking for a fast resolution.

Target duration: maximum 2 weeks.

The freelancer must be available quickly and able to work efficiently on an existing product, with short and structured technical exchanges.

## Confidentiality

This project concerns a commercial product. We want to avoid sharing information that could be used by competitors.

The selected freelancer will be required to sign a confidentiality agreement before accessing sensitive elements such as firmware, detailed schematics, internal measurements, full product architecture, and benchmark data.

At the first stage, only the minimum information required to assess the mission will be shared.

## Questions to Answer in Your Proposal

Please include the following information in your proposal:

1. Your experience with low-power stepper motor control.
2. Your experience with STSPIN drivers or equivalent motor drivers.
3. The methods you would use to reduce current peaks.
4. The methods you would use to adjust motor torque or thrust.
5. The measurement equipment and methods you would recommend.
6. Your availability to complete the mission within 2 weeks.
7. Examples of similar projects you have completed, if possible.
8. Your approach to working with confidential information.
9. What you would need in order to start quickly.

## Budget

Budget to be discussed depending on experience, profile, and ability to start quickly.

We will prioritize an experienced freelancer who can deliver concrete results quickly rather than a long exploratory assignment.

Skills: Electronics, Microcontroller, Embedded Software, Mechatronics, Electronic Design, Embedded Systems

I need an audio expert to extract whispering from approximately 6 hours of audio. The goal is to focus on low-volume sections and remove interference.

Requirements:
- Extract whispering with medium quality
- Clean only whispering parts, not entire file
- Remove background noise, echoes, and volume spikes

Ideal Skills:
- Audio editing experience
- Proficient in noise reduction techniques
- Familiarity with editing software (e.g., Audacity, Adobe Audition)

Skills: Audio Services, C++ Programming, Sound Design, Audio Production, Audio Processing, Audio Editing, Adobe Audition, Audio Engineering

I'm looking for an experienced Roblox developer to create a resource collection game focused on manual mining of minerals and ores.

Key Features:
- Players mine minerals and ores using tools
- Engaging resource collection mechanics
- Customizable mining tools

Ideal Skills:
- Proficiency in Roblox Studio
- Experience with Lua scripting
- Game design experience, particularly in resource collection mechanics
- Strong understanding of player engagement and retention strategies

Please include portfolios showcasing similar projects.

Skills: Java, Python, Game Design, C++ Programming, Game Development, Lua, Scripting, Roblox

PROPOSED TITLE
USB-C Diving Camera EVT Testbed - Embedded Firmware + Electronics Engineer (milestone-based, visual proof required)

PROJECT TYPE: Hourly or Fixed-price (milestone-based)
CATEGORY: Electronics / Embedded Firmware / PCB Design
EXPERIENCE LEVEL: Expert

SUMMARY
Build a bench-level EVT electronics testbed (not a production or waterproof device) for a USB-C diving-camera accessory, and validate every high-risk subsystem before any custom PCB is committed.

1) PROJECT OVERVIEW
We are building Universal Pro Max, a smartphone-based diving camera accessory. Before committing to a custom PCB, we need a bench-level EVT (Engineering Validation Test) testbed assembled from dev boards and off-the-shelf modules to prove that the riskiest subsystems actually work on real phones.
This contract is NOT a production PCB, NOT a diver-use device, NOT a waterproof or certified product, and NOT a high-pressure system. It is a controlled lab testbed used to retire technical risk.
You will be paid and evaluated per milestone. Every milestone has a concrete, eye-verifiable deliverable (a video, photo, oscilloscope or logic-analyzer capture, log file, or screenshot) plus written pass/fail evidence. We do not accept "it works" without visual proof.

2) BACKGROUND AND HARD CONSTRAINTS (non-negotiable)
- Communication is USB-C only. No BLE, no Wi-Fi, no wireless fallback.
- Smartphone app data AND firmware update must run through the internal smartphone USB-C pigtail.
- The same USB-C connection must validate app data PLUS 5 V smartphone charging from the testbed power system.
- Primary power source is a commercial protected, replaceable 21700 Li-ion cell (nominal 3.6/3.7 V, typically 4,000 to 5,000 mAh). The cell must NOT be soldered or glued.
- Power rails must be separated (system 3V3, backup 3V3, 5 V phone boost, auxiliary power for pump/valve/haptic). No single shared 5 V rail.
- Depth sensing uses the MS5849-30BA pressure/temperature sensor.
- Half-shutter uses Hall sensors; the rotary dial uses magnetic-field angle sensing (optical and mechanical encoders are not primary methods).
- Haptic validation is mandatory (LRA plus driver preferred; ERM comparison optional).
- Firmware update over USB-C must be fail-safe, resumable, chunk-verified, and rollback-capable with A/B slots.
- Pump is testbed-only (low pressure), with hardware-level emergency stop that cuts pump power even if firmware is frozen.
- Android is the first validation platform. iPhone 15+ is a separate feasibility gate and must not be assumed to work.

3) PROBLEM STATEMENT
The team cannot safely commit to a custom PCB because the highest-risk behaviors - USB-C charge+data role on real phones, protected 21700 behavior under peak load, USB-C firmware update reliability, haptic interference with magnetic sensors, and pump safety - have not been proven on hardware. Committing a custom PCB before this validation risks an expensive, un-fixable board respin and schedule loss. We need a freelancer to build the testbed and produce visual, repeatable evidence that each subsystem works (or document exactly why a strategy fails) so we can make a go/no-go decision.

4) GOALS AND SUCCESS METRICS
- Prove USB-C charge+data on real phones: Android phone charges at 5 W while the app streams telemetry for 30 min with zero USB disconnects.
- Prove protected 21700 power path: no MCU reset, no USB disconnect, no nuisance cell trip under all defined load cases.
- Prove USB-C firmware update: update survives disconnect at 10%, 50%, 99%; previous firmware stays bootable; resume and rollback verified.
- Prove sensor and input subsystems: MS5849, Hall shutter, magnetic dial, and haptic all stream correct values over USB-C with documented tolerance.
- Prove pump safety: overpressure, sensor fault, watchdog reset, and emergency stop all force pump off and vent open.
- Enable go/no-go on custom PCB: all 5 validation gates pass with archived video/log evidence.

5) SCOPE OF WORK
In scope:
- Component selection and BOM for an off-the-shelf modular testbed (Version A).
- Wiring/harness assembly, breakout integration, and bring-up.
- Firmware for MCU (sensors, inputs, haptic, pump control, USB-C telemetry/commands, A/B firmware update).
- Android-first USB-C app-side test harness sufficient to demonstrate charge+data, telemetry, command, and firmware update (a simple test/utility app or script is acceptable; full production app is out of scope).
- iPhone 15+ feasibility assessment (documented findings plus any proof-of-concept).
- Structured validation runs across all 5 gates with recorded evidence.
- Documentation: schematics/wiring tables, test procedures, results, and a custom-PCB recommendation memo.
Out of scope:
- Production/custom PCB layout and manufacturing (a later, separate engagement; Version B).
- Waterproof or pressure-certified enclosure or any diver-use safety certification.
- High-pressure (60 m internal compensation) systems or any pressure above the defined testbed limits.
- Full consumer app store release of the smartphone app.
- Industrial design / cosmetic housing.

6) REQUIRED SKILLS AND EXPERIENCE
- Embedded firmware in C/C++ on a native-USB MCU (e.g. STM32, RP2040, or ESP32-S3 with wireless disabled).
- USB-C / USB 2.0 and Type-C CC/PD fundamentals (power role, data role, DRP/DRD, PD negotiation).
- Bootloader / A/B firmware update / DFU design with external SPI/QSPI NOR flash.
- Sensor integration over I2C/SPI/ADC (pressure, Hall, magnetic angle, humidity, temperature).
- Motor/pump driver and valve control with current sensing and flyback protection.
- Haptic driver experience (LRA/ERM, DRV2605L-class).
- Lab instrumentation: oscilloscope, logic analyzer, multimeter, current measurement.
- Clear English documentation and willingness to record video/screen-capture evidence.
- Nice to have: Android USB host/CDC/accessory development; iOS ExternalAccessory/MFi knowledge.

7) WHAT WE PROVIDE
- This brief and the full internal V1.2 EVT architecture document (telemetry schema, wiring table, BOM candidates, state machine, validation gates).
- Target sensor part (MS5849-30BA) and required sensing approach.
- Test phones (Android first; iPhone 15+ for the feasibility gate) or a budget to acquire them.
- Component/module budget (reimbursed against the approved BOM).
- Timely review and milestone sign-off.

8) MILESTONES AND DELIVERABLES
Acceptance rule for every milestone: the deliverable is accepted only when (1) the visual proof is provided (video / photo / scope or logic-analyzer capture / log file / screenshot) AND (2) the acceptance criteria are met with written pass/fail notes. Each milestone is small enough to verify in one review session.

M0 - Kickoff, Plan and BOM
- Tasks: review architecture, confirm MCU and module choices, produce the testbed BOM and a block/wiring plan.
- Visual deliverable: a shared BOM sheet plus a one-page block diagram image, and a short screen-recorded walkthrough of the plan.
- Acceptance: BOM lists every module with part number, qty, and purpose; block diagram matches the required separated-rail architecture; reviewer approves before purchasing.

M1 - Power Subsystem: Protected 21700 plus Separated Rails
- Tasks: assemble protected 21700 holder, fuse/polyfuse, reverse protection, 3V3 system rail, 5 V boost, and rail separation.
- Visual deliverable: photo of the wired power bench plus multimeter/scope captures of each rail at idle and under a test load.
- Acceptance: all rails within spec; reverse-polarity protection demonstrated; battery telemetry (voltage/current/SOC) readable; no rail collapse under a defined step load.

M2 - MCU Bring-up and Service Interface
- Tasks: flash MCU, bring up debug (SWD/UART), confirm external SPI/QSPI flash.
- Visual deliverable: screen recording of the MCU booting, printing version/heartbeat, and reading/writing external flash.
- Acceptance: stable boot, heartbeat at 1 Hz, external flash read/write verified.

M3 - USB-C Charge-Only (Android)
- Tasks: implement Type-C source/CC logic plus VBUS load switch/eFuse; validate 5 W / 7.5 W / 10 W charge modes.
- Visual deliverable: video of an Android phone charging from the testbed plus current/VBUS scope or meter captures at each mode.
- Acceptance: correct current limiting, stable VBUS, no reverse current, acceptable thermal rise; charge state logged.

M4 - USB-C Data-Only Telemetry (Android)
- Tasks: implement USB-C telemetry link (CDC JSON Lines preferred; HID/vendor fallback).
- Visual deliverable: screen recording of the Android test app/script receiving live telemetry for 30 min, including an unplug/replug recovery.
- Acceptance: 30-min stable stream; at least 10 unplug/replug recoveries; no MCU reset.

M5 - USB-C Charge plus Data Simultaneously (Strategy A)
- Tasks: validate the target role (testbed = source plus USB device; phone = sink plus host) with charge and data together.
- Visual deliverable: video showing the phone charging while the app streams telemetry, with on-screen current and telemetry, for 30 min.
- Acceptance: no data drops, correct phone current/charge telemetry, zero USB disconnects.

M6 - USB-C Role Feasibility Matrix plus iPhone 15+ Gate
- Tasks: document Strategy A/B/C outcomes; test power/data role swap, DRP/DRD/PD needs, unplug/replug; run the iPhone 15+ feasibility assessment (accessory enumeration, app-level USB data, coexistence with charging, MFi/ExternalAccessory needs).
- Visual deliverable: a results table plus supporting clips/captures, and a written iPhone feasibility memo with evidence.
- Acceptance: each strategy marked pass/fail with proof; clear recommendation on whether Strategy A holds on target phones and what iOS requires.

M7 - MS5849-30BA Depth Sensor Bring-up
- Tasks: PROM/CRC read, compensated pressure/temp, surface baseline, depth-from-delta; distinguish sensor temp vs validated water temp.
- Visual deliverable: screen recording of live pressure/temp/depth telemetry at 1 to 2 Hz plus a chart of a controlled pressure change.
- Acceptance: PROM CRC valid; values plausible and stable; fault flags (e.g. port blockage, stuck value) trigger correctly in a forced-fault demo.

M8 - Hall Shutter (Half/Full)
- Tasks: implement released/half/full detection with thresholds, hysteresis, debounce; expose raw values and thresholds over USB-C.
- Visual deliverable: video of 100 press cycles with on-screen event log plus a raw-value plot across magnet distance/offset.
- Acceptance: no missed or chattering events over 100 cycles; raw values and thresholds logged; threshold tolerance map produced.

M9 - Magnetic Rotary Dial
- Tasks: magnetic angle sensor bring-up; map angle to 24 detents per revolution; CW/CCW, step count, velocity, wraparound, missed/extra-step handling.
- Visual deliverable: video of 100 rotations with on-screen direction/step/velocity log plus raw-angle plot.
- Acceptance: correct direction and step count over 100 rotations; no missed/extra steps beyond tolerance; raw angle telemetry available.

M10 - Haptic Subsystem plus Interference Test
- Tasks: LRA driver plus pattern library; measure interference with Hall/dial/MS5849/USB/charging using an accelerometer or vibration sensor.
- Visual deliverable: video of each haptic pattern plus an interference test matrix with measured data (latency, current, recognition).
- Acceptance: input-to-haptic latency under 50 ms; no false shutter/dial events; no USB disconnect or MCU reset during haptic; recognition targets documented.

M11 - Pump Control plus Hardware Safety
- Tasks: pump driver, vent valve, current sensing, flyback; pressure targets +5/+10/+20/+30 kPa; software limit +35 kPa; hardware emergency stop.
- Visual deliverable: video of pressurize/hold/vent cycles with live pressure telemetry plus a recorded safety demo (overpressure, sensor disconnect, watchdog reset, e-stop).
- Acceptance: targets reached with controlled overshoot; every fault condition forces pump off and vent open; e-stop cuts pump power in hardware even with firmware frozen.

M12 - A/B Firmware Update Over USB-C
- Tasks: bootloader with A/B slots, chunk CRC, resumable progress, full-image verification, pending-boot confirm, rollback, USB recovery mode, redundant metadata.
- Visual deliverable: screen recording of a full update plus forced disconnects at 10%, 50%, 99% showing resume, plus a rollback and a recovery-mode demo.
- Acceptance: previous firmware always bootable; resume from last verified chunk; corrupt chunk/image rejected; failed boot rolls back; both-invalid-slots enters USB recovery.

M13 - Full Validation Gates plus Final Report
- Tasks: run Gates 1 to 5 end-to-end; compile evidence; write the custom-PCB go/no-go recommendation.
- Visual deliverable: a consolidated evidence pack (videos/logs/captures indexed by gate) plus final report and updated schematics/wiring tables.
- Acceptance: all 5 gates pass with archived proof; report clearly states what is proven, what failed, and the recommendation for Version B (custom PCB).

9) VALIDATION GATE REFERENCE
- Gate 1 - Minimum power and USB validation: M1 to M5.
- Gate 2 - High load and transient validation: M5, M10, M11.
- Gate 3 - Firmware update validation: M12.
- Gate 4 - Pressure and pump safety validation: M11.
- Gate 5 - Sensor and input validation: M7 to M10.

10) NON-FUNCTIONAL REQUIREMENTS
- Reliability: no unexpected USB disconnects or MCU resets during any defined test run.
- Safety: pump defaults to off on reset, fault, update mode, backup mode, and USB disconnect; vent fails open where hardware allows.
- Traceability: every test produces a timestamped log and an indexed evidence file.
- Maintainability: firmware is modular and documented; build/flash instructions provided.
- Portability: telemetry/command protocol documented so the production app team can reuse it.
- Reproducibility: each test includes a written procedure so we can re-run it independently.

11) RISKS AND MITIGATIONS
- USB-C charge+data role fails on target phones (top risk): test Strategy A/B/C with jumpers/DNP/eval modules before any PCB commitment.
- iPhone 15+ blocks app-level USB data: separate feasibility gate; document MFi/ExternalAccessory requirement early.
- Protected 21700 nuisance trips or brownout under peak: measure peak current per load case; derate charging before unsafe conditions.
- Haptic corrupts magnetic sensing or USB: quantitative interference matrix with accelerometer before sign-off.
- Firmware update bricks device: A/B slots, chunk CRC, rollback, and independent USB recovery mode.

12) ENGAGEMENT, IP, AND HOW TO APPLY
Terms:
- Engagement: milestone-based fixed-price or hourly with milestone gates (your proposal).
- Budget / rate: please propose per-milestone pricing and total.
- Timeline: please propose duration per milestone group.
- IP and confidentiality: all deliverables, firmware, schematics, and findings are work-for-hire and our exclusive property. NDA required before sharing the internal architecture document.
Screening questions (please answer in your proposal):
1. Describe a USB-C charge+data (or USB host/device) project you delivered on real phones. What broke and how did you fix it?
2. Which MCU would you choose for this testbed and why (native USB, A/B update, external flash)?
3. How would you prove a firmware update is rollback-safe after a mid-transfer disconnect?
4. Have you done iOS ExternalAccessory/MFi USB work? Summarize the constraints.
5. Share links/photos/video of a comparable hardware bring-up you can show.

We are looking for unreal engine developer that can help enhance our game.

I need a complete system that can turn data written in the ancient Brahmi and Sharda scripts into a scannable, QR-style matrix and then read it back reliably. The work splits into two tightly linked parts:

1) Pattern engine
Build an algorithm that takes Brahmi or Sharda text (Unicode strings supplied by me) and outputs a square, high-contrast image resembling a QR code. Include error-correction logic so the pattern survives moderate camera tilt, blur, and 20 % visual noise. A lightweight CLI or library in Python, Java, or C++ is fine as long as I can call it from other projects later.

2) Dual-platform scanner
Create an app for both iOS and Android whose main task is to scan and decode these patterns. After scanning, the screen should show an informative read-out: the detected script, original text, romanised form, and any embedded metadata. Users won’t create codes inside the app for now—scanning and decoding is the priority.

Key capabilities
• Fast camera preview with live detection
• Automatic perspective correction, de-skew, and brightness adjustment (OpenCV, ML Kit, or similar)
• On-device decoding in under 500 ms for a 300×300 px pattern
• Clear feedback when a scan fails and suggestions to improve focus or lighting

Deliverables
• Source code for the generator module and mobile apps
• Build/run instructions plus a short video demo
• Sample patterns and a test report showing ≥95 % decode accuracy across 100 varied photos

If you are fluent in image processing, computer vision, and cross-platform mobile frameworks such as Flutter, React Native, or native Swift/Kotlin—and you’re excited by the idea of reviving ancient scripts in modern tech—I’d love to work with you.

Skills: Python, Mobile App Development, Android, C++ Programming, Image Processing, OpenCV, iOS Development, Computer Vision

HELP WANTED: Electronics/Automation Student for Paid Workbench Project
Location: Bradenton, FL (Zip 34203) MUST LIVE NEAR BRADENTON FL. 34203

Job Description:
Local manufacturing workshop is looking for a tech-savvy student, hobbyist, or maker to assist with the final low-voltage assembly and firmware flashing of a custom 5-channel automated machinery console. All structural frames, heavy metal box enclosures, linear guide slides, and power supplies are already mounted to a heavy steel backbone plate.

Tasks Include:
- Wiring low-voltage logic lines between an Arduino Uno R3, Uno rev 4 with wifi and industrial TB6600/DM860T stepper motor drivers.
- Connecting SPI data lines to onboard digital potentiometer chips (MCP41010).
- Installing Arduino IDE on a workshop laptop and uploading pre-written Python/C++ scripts to microcontrollers.
- Conducting safe workbench testing to verify stepper motor step counts and calibration limits.

Requirements:
- Basic experience with Arduino/microcontroller programming and the Arduino IDE environment.
- Ability to read straightforward point-to-point electrical schematic wiring maps.
- Comfortable working alongside the shop owner and an off-site senior engineering team via text/chat interface.

This is a fun, clean, highly educational hands-on project that can be completed