Project ideas for 2026

1. Build a GUI Agent with local LLM/VLM and OpenVINO

Short description: You will be required to build an agent application with a graphical user interface as input. It should be able to automatically operate your computer screen or the UI interface of a specific application based on user instructions, and accomplish complex logical goals. In this pipeline, at least one model must be deployed locally using OpenVINO. During this project, you will get free access to AIPC cloud. You can refer to the following projects for ramp up.

• https://github.com/bytedance/UI-TARS-desktop
• https://github.com/X-PLUG/MobileAgent

Expected outcomes: a desktop application that provides a native GUI Agent based on local models

Skills required/preferred: Python, OpenVINO, Prompt engineering , Agentic workflow

Mentors: Ethan Yang, Zhuo Wu

Size of project: 350 hours

Difficulty: Hard

X. Continuous Face-Detection with Automatic Device Switching on AI PCs using OpenVINO AUTO feature

Short description: AI PCs incorporate multiple devices/inference engines for different machine-learning applications. Based on the performance, latency or power consumption requirements, an application may choose to use either NPU, GPU or a CPU for inference tasks. Usually, an application utilizes a single engine/device for the entire lifetime of the process/inference. The machine learning model being used by the application is compiled only for one device. However, it is important for the application to switch between different inference devices during runtime based on user preference, application behavior, and load/stress on the current device in use. Through this project, we want to build a face-detection application that continuously runs on the AI PC while switching between different inference devices during runtime based on user recommendations or evaluating the stress on the current engine. The inference should not end/pause while switching devices and should not lead to BSODs/System Hang/Device Crashes causing other applications to fail.

Expected outcomes:

1. Implement low latency Face-Detection application to run on multiple devices/engines within AI PCs
2. Utilize OpenVINO AUTO feature to demonstrate runtime switching between devices
3. Create a GUI to prompt user to change the device during runtime based on user preference
4. Analyze the device load and recommend user to switch to the most appropriate device to continue inference

Skills required/preferred: Python or C++, Basic ML knowledge

Mentors: Shivam Basia, Aishwarye Omer

Size of project: 175 hours

Difficulty: Easy

X. Implement XLA plugin to run OpenVINO applications on any XLA supported devices (NVIDIA GPUs, FPGA, TPUs)

Short description: Need to implement new OpenVINO plugin for which openvino.compile will convert OpenVINO IR into XLA representation (using HLO or MLIR dialect) and inference request will run the compiled blob on any XLA backend (GPUs, FPGA, TPUs). This feature will allow to infer models on CUDA GPUs, TPUs, FPGA devices using OpenVINO API.

Expected outcomes: It should be new OpenVINO plugin with "XLA" name that is able to infer basic CNN and transformer models on NVidia GPUs, Google TPUs, etc.

Skills required/preferred: well familiar with AI frameworks and tensor operations, understanding and experience with HLO/MLIR dialect, XLA C++ API.

Mentors: Roman Kazantsev, Maxim Vafin, Anastasia Popova, Andrei Kochin

Size of project: 350 hours

Difficulty: Hard

X. Object tracking in MP with OpenVINO inference

Short description: Tracking the objects in a video stream is an important use case. It combines an object detection model with a tracking algorithm that analyzes a whole sequence of images. The current state-of-the-art algorithm is ByteTrack. 

The goal of the project is to implement the ByteTrack algorithm as a MediaPipe graph that could delegate inference execution to the OpenVINO inference calculator. This graph could be deployed in the OpenVINO Model Server and deployed for serving. A sample application adopting KServer API would send the stream of images and would get the information about the tracked objects in the stream.

Expected outcomes: MediaPipe graphs with the calculator implementation for ByteTrack algorithm with yolo models.

Skills required/preferred:  C++ (for writing calculator), Python(for writing client) MediaPipe

Mentors: Dariusz Trawinski, Damian Kalinowski

Size of project: 175 hours

Difficulty: Medium