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This program pairs one or more mentees with an experienced mentor to deliver an AOSP 12 emulator for 64-bit RISC-V. RISC-V International has setup Android SIG and released RISC-V Android github repo as https://github.com/riscv-android-src. Current RVI Android codebase is based on AOSP 10, while the latest AOSP is 12 now. We hope to catch up with latest AOSP 12 codebase, and as part of our plan, an emulator is required first as test and run environment. Deliverables (bullet list of components and the changes expected): - Add RISC-V target in the emulator. - Porting Ranchu virtual board to support RISC-V machine. - Corresponding unit tests implementation - Regression tests pass Note: upon work should be based on AOSP 12. 3. acceptance criteria Acceptance criteria (bullet list with measurable results defined): - Phase I: Virtual peripherals of emulator can work with RISC-V 64bit cores and pass unit tests. - Phase II: Emulator can boot-up with Linux plus basic rootfs w/o GUI for AOSP 12. - Phase III: Emulator can boot-up with Linux plus rootfs w/ GUI for AOSP 12. Note: due to AOSP 12 rootfs is still in developing, a AOSP 10 rootfs from Android SIG repo can be used instead to help verification, but the kernel version should be matched with AOSP 12.

Enhance z/vm Prometheus exporter, as https://github.com/zvmexporter/zvm_exporter is pretty old and some stuffs like xcat is not maintained anymore, we propose to use Feilong as base to enhance Prometheus exporter and to provide more metrics to help ecosystem build up

The llama nexus project is an API proxy to provide OpenAI-compatible and unified API endpoints for multiple downstream API servers, including LLamaEdge API servers running open-source LLMs. https://github.com/LlamaEdge/llama-nexus Currently, the Llama Nexus supports the stateless /chat/completions API endpoint for LLMs. We would like to expand this to support the /responses stateful API from OpenAI as well. https://platform.openai.com/docs/api-reference/responses https://platform.openai.com/docs/guides/responses-vs-chat-completions In particular, we aim to implement support for MCP Code interpreter Web search File search Browser use (optional) Expected Outcome: 1. New features for the Llama Nexus proxy server.

Description: Connectors are tremors interface to the outside world, they allow us to integrate with third-party systems. Currently, tremor only has a limited set of connectors for databases, we support s3 and google cloud storage for object stores, and have a k/v connector that offers a simple integrated key-value store. While this is a good starting point interfacing with more databases will make tremor easier to use for our end users. The primary target will be integrating with Yandex Clickhouse.

[CRI stats KEP](https://github.com/kubernetes/enhancements/issues/2371) is an effort to take the container stats and metrics collection from cAdvisor and move it to the CRI implementations. CRI-O will soon have support for stats and metrics collected through CRI, but work needs to be done to verify and validate these fields, and make sure their collection is performant as possible. - Expected outcome: A test suite verifying the correctness of CRI-O's stats and metrics collection, as well as data verifying performance regressions are minimal at worst.

This internship is about O-RAN-SC code quality improvement and clean-up. The work involves: Tagging consistency - work with LF-RelEng and PTLs Documentation (release notes, broken-links, etc.) Fix Stale dockerfiles - containers to be rebuilt. Artifacts publishing (proper links to containers for every release) User-guide evaluation - ensure the steps are still valid on Kubernetes Cluster. Appropriate (PTL/Project defined) actions around CVEs Evaluate and analyze reports Document the policy. Possibly fix the ‘critical’ ones - based on inputs from PTLs. Document best-practices, new-ptl onboarding, etc.

To improve the user experience of the KubeEdge official website, this project will focus on homepage design enhancements, the addition of new pages, and improvements to community resources. The goal of this project is to enhance the website's usability, increase user engagement, and attract more users to KubeEdge by enhancing training content and hardware compatibility support. Expected Outcome: 1. Design and optimization of the homepage, including design and code updates. 2. New page: Showcase for KubeEdge course videos, including design and code updates. 3. New page: "Hardware Compatibility" page, including design and code updates. 4. Design and optimization of the partner page, including design and code updates. 5. Optimization of community resources, improving documentation and onboarding experience to ensure users can easily get started and effectively use KubeEdge.

This project aims to design and build a suite of automated scale tests for kgateway that would run as part of our builds and releases process. These tests will help us understand how kgateway behaves under a large number of Kubernetes gateway resources and kgateway extensions, and load tests to ensure it performs reliably as usage scales. You'll gain hands-on experience with performance testing, infrastructure automation, and Kubernetes-based systems. Expected Outcome: 1. Design and a scale testing suite 2. Build a scale test suite for kgateway 3. Analyze test results to identify bottlenecks or failure points 4. Write developer-facing documentation 5. Explore Oracle Developer cloud and determine if it is suitable for kgateway’s scale tests.

The zlib is required for compiling and running many existing C / C++ / Rust apps in Wasm. Most noticeably, it is [needed in the Python port to Wasm](https://github.com/python/cpython/issues/93819). The VMWare Wasm Labs team is using a zlib port from [Singlestore](https://github.com/singlestore-labs/python-wasi) in [their Python Wasm runtime](https://wasmlabs.dev/articles/python-wasm32-wasi/). In WasmEdge, we could support the zlib host functions through our [plugin system](https://wasmedge.org/book/en/plugin.html). This way, any existing zlib apps can be compiled to Wasm and runs inside WasmEdge. - Expected outcome: Create a new [WasmEdge plugin](https://wasmedge.org/book/en/plugin.html) that exports all public functions in `zlib`. Implement SDK (in C/Rust) that uses the C ABI to generate corresponding headers for the above plugin. Generate the unit tests and pass the unit tests. >80% of code coverage for verification.

Today, most users adopt Blockchain in order to collaborate with each other, however, it is difficult to manage a large blockchain network because of multiple organizations and nodes. Hyperledger Cello is aiming to resolve the following challenges, 1) Facilitate creation of blockchain network, can help user without blockchain background to setup their network nodes quickly. 2) Cross organization communication, can connect blockchain networks among multiple organizations. This project targets to design and implement a practical operational system equipping with decentralized functionalities to solve above challenges, based on Hyperledger Cello code base. Learning Objectives 1) Work closely with community experts and developers to learn the open-source culture and skills; 2) Learn the advanced knowledge inside the blockchain and distributed ledgers; 3) Practice hand-on experience with web application design and implement. Expected Outcome Help design and implement the blockchain operation dashboard and api server.

kpm is a package management tool for KCL. kpm currently lacks checksum verification for dependencies, so this part needs to be completed to support package integrity verification and package source verification Expected Outcome: Complete the workflow of adding checksum through `kpm` when uploading package and verifying checksum through `kpm` when downloading package

With WASI-NN plugins, WasmEdge is well-suited for running AI applications. However, AI applications are more than just the model. The application must pre-process data (such as images, audio and video) into TFLite / PyTorch formats, and convert the inference results back into application data in post-processing. Here are some examples: * The [mediapipe-rs](https://github.com/WasmEdge/mediapipe-rs) project provides a Rust SDK to build applications for the mediapipe AI models. * The [llama2.c](https://github.com/karpathy/llama2.c) application is [compiled to Wasm and runs in WasmEdge](https://medium.com/@michaelyuan_88928/running-llama2-c-in-wasmedge-15291795c470) to generate text using the [llama2](https://ai.meta.com/llama/) models. In this project, we would like to build a Rust SDK to support applications on the [YOLO models](https://pjreddie.com/darknet/yolo/). - Expected Outcome: - A Rust SDK that implements the pre-processing and post-processing functions required for the YOLO models. Those functions are implemented in OpenCV and Python in the official YOLO release. - Both image and video inputs should be supported. - Examples and documentation should be provided. - Recommended Skills: - OpenCV - Rust - Tensorflow / Pytorch - WebAssembly

This project focuses on enhancing the KubeStellar UI with WDS-related functionalities, allowing users to manage and deploy workloads effectively. Objectives: - Implement UI components for managing workloads in WDS. - Integrate real-time workload deployment tracking. - Enhance UI design for better user experience and usability. - Display visual indicators for workload placement across multiple clusters. Expected Outcomes: - A user-friendly UI for managing WDS workloads. - Real-time feedback on deployment status. - Improved visualization of workload distribution.

Move2Kube is a command-line tool for automating creation of Infrastructure as code (IaC) artifacts. It has inbuilt support for creating IaC artifacts for replatforming to Kubernetes/OpenShift. Move2Kube has a very plugin friendly architecture, users can write custom logic in the form of "Transformers" that Move2Kube can integrate seamlessly into its transformation pipeline. So far we have support for both Starlark and container image based transformers. We would like to support writing transformers as WASM modules that Move2Kube can run. WASM provides extensive sandboxing for security, it allows writing transformers in different language stacks like Rust, C/C++, etc. other than Golang, and WASM is just as lightweight and fast as Starlark. - Expected Outcome: - Implement a feature in Move2Kube CLI to allow running WASM modules as custom transformers.

OPNFV VSPERF already includes comprehensive Monitoring solution. However, it lacks the Log-Analysis and Alert-Management solution. This project aims to include log and alert management solutions based on opensources - ELK Stack. This will enhance the ability of VSPERF to: 1. Perform post-test correlation/causation analysis. 2. Adapt the Test-Traffic patterns based on the Alerts. The first aim is to create custom kibana dashboard analyzing and synthesizing VSPERF logs/events. For every test-run, VSPERF generates following logs: 1. vsperf-overall 2. vswitchd 3. trafficgen 4. host-cmds 5. guest-cmds These logs have useful information that can help user to understand the test behavior, and analyze the performance results. VSPERF will have Alerting from the Monitoring component, and lacks alerting from the logs. As the next step, this project aims to use the same information - The logs - and create alerts than will be notified to VSPERF.

<p>Currently, urunc launches sandbox monitors, such as Firecracker and QEMU</p><p>through command-line invocations. This approach offers limited control over the</p><p>sandbox lifecycle once the process is started. On the other hand, most</p><p>monitors expose remote management interfaces, typically through a socket-based</p><p>API.</p><p><br></p><p>These interfaces provide access to the same operations currently performed via</p><p>CLI, but also enable further control over the sandbox lifecycle. In particular,</p><p>they allow more fine-grained lifecycle management of the sandbox, including</p><p>querying and monitoring its state, performing device hotplug and unplug</p><p>operations and interacting with the guest.</p><p><br></p><p>This project aims to extend urunc's sandbox integration layer to support remote</p><p>management interfaces and to explore each monitor's capabilities in order to</p><p>extend the functionalities of urunc sandboxed containers.</p><p><br></p><p>Expected Outcome:</p><p>&nbsp;- A design document describing the updated architecture and workflow for</p><p>&nbsp;&nbsp;spawning and managing sandbox monitors in urunc.</p><p>&nbsp;- Implementation of the necessary changes in urunc to manage sandbox monitors</p><p>&nbsp;&nbsp;through their respective APIs.</p>

The component-model proposal merges and supersedes the Module Linking and Interface Types proposals. With this feature, WasmEdge can execute multiple modules wasm with Module Linking and and more flexible types with Interface Type.

<p>Lima launches Linux virtual machines with automatic file sharing and port forwarding (similar to WSL2). Currently, Lima supports Linux, macOS, and FreeBSD as guest operating systems. The primary goal of this project is to expand this capability by adding support for Windows guests. Furthermore, the project aims to improve the stability and user experience of running Lima on Windows hosts. This will be achieved by removing dependencies like `cygpath.exe`, researching and developing a native Hyper-V(or a [HCS](<a href="https://learn.microsoft.com/en-us/virtualization/api/hcs/overview" rel="noopener noreferrer" target="_blank">https://learn.microsoft.com/en-us/virtualization/api/hcs/overview</a>) driver to provide optimized, native virtualization on Windows hosts.</p><p><br></p><p>Expected Outcome:</p><p><br></p><ul><li>Primary:</li><li class="ql-indent-1">The ability to successfully launch and run Windows guest virtual machines using `limactl start template:windows` using the QEMU driver on any host.</li><li class="ql-indent-1">Deliver a seamless installation experience on Windows hosts by automating configurations and eliminating the need for [manual setup notes](<a href="https://github.com/microsoft/winget-pkgs/pull/356038/changes#diff-9c16ba1b4e8cfc88d634b6cc436e0041d2201ac93e1d4fcd78fca09e0667ca3aR29" rel="noopener noreferrer" target="_blank">https://github.com/microsoft/winget-pkgs/pull/356038/changes#diff-9c16ba1b4e8cfc88d634b6cc436e0041d2201ac93e1d4fcd78fca09e0667ca3aR29</a>)</li><li class="ql-indent-1">Complete removal of the `cygpath.exe` dependency.</li><li>&nbsp;Secondary (if time permits):</li><li class="ql-indent-1">Investigate and decide between Hyper-V and HCS for the native driver: HCS is the basis of WSL2 and may be available in Windows 11 Home Edition, while Hyper-V is only available in Pro/Enterprise editions. Research availability and integration feasibility.</li><li class="ql-indent-1">Potentially integrate the chosen driver (Hyper-V or HCS) as an [external VM driver](<a href="https://lima-vm.io/docs/dev/drivers/" rel="noopener noreferrer" target="_blank">https://lima-vm.io/docs/dev/drivers/</a>).</li><li>Tertiary Goals (if time permits): Upgrade the existing WSL2 driver to drop image restrictions and allow users to run multiple instances, as well as exploring a simple graphical interface (`limagui.exe`) to launch virtual machines.</li></ul><p><br></p>

All In is a community dedicated to advancing diversity, equity, and inclusion within open source. All In for Students is a paid, 12-month program for select college students from underrepresented backgrounds. During the program, students will receive professional development training, open source education, a structured 12-week open source project, and ultimately have the opportunity to join one of our corporate partners for a summer internship experience.

Envoy is an open source edge and service proxy, designed for cloud-native applications.

FINOS (FinTech Open Source Foundation - part of the Linux Foundation) is looking for a summer marketing intern to assist with: - Marketing research (members, projects, BD) - Social media (research, operations) - Content for website (research, operations) - Podcast production (research, operations)

The RISC-V Mentorship Program enables one or more 12-week internship-style projects per session, funded by RISC-V, to match mentors/project leaders together with mentees/interns . Mentees are guided through a series of milestones by one or more project mentors, with whom the mentees meet on a weekly basis. Convolution is the core operation of deep learning models and computer vision applications. MLIR supports various convolution operations. Our project is to vectorize them for the RISC-V backend. There are several methods to implement convolution vectorization, such as optimizing nested loops, implementing vectorization algorithm, converting to GEMM, etc. This project needs to choose a vectorization method and implement a conversion pass for the convolution operations. As for the vector semantic support, MLIR has the “Vector” dialect for the general vector abstraction, and it also allows the backend-specific vector dialect, such as the “x86vector” dialect, “arm_neon” dialect, and “arm_neon” dialect. Like these dialects, the project also needs to propose an “RVV” dialect and work with existing dialects and tools. Deliverables: - An MLIR “RVV” Dialect. (Operations in the dialect can support the convolution vectorization) - A conversion pass to vectorize convolution operations in “Linalg” dialect with “RVV” dialect enabled. - A conversion pass to lower the operations in “RVV” dialect to “LLVM IR” dialect. - Unit tests for “RVV” dialect and conversion passes.

The Software Package Data Exchange (SPDX) is an open source standard for communicating software bills of material information (including components, licenses, copyrights and security references). To support SPDX, an online tool has been developed which provides an easy all-in-one website to upload and parse SPDX documents for validation, comparison and conversion and search SPDX license list. This project will provide enhancements and support for the online tools including: - More automated and high-quality deployment for software updates - Removing some of the dependencies on a Python Java Bridge by moving features like license matching over to a native Python implementation - Improving performance and quality of the online tools implementation