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Optimize KCL LSP(language server protocol) prompt information, including the implementation of type inlayhint and optimization of hover content rendering. Currently, KCL’s hover content is in plain text format and needs to be rendered into a more beautiful style. Expected Outcome: Added type inlayhint in KCL IDE and optimize hover content render.

Barometer collects system metrics and events to aid in the monitoring of the NFVI and provide means for these metrics and events to be relayed to higher level fault management systems for enforcement and corrective actions. In addition, this information could be fed to analytics systems to enable failure prediction, and can also be used for intelligent workload placement. Barometer uses collected to collect the system metrics, and works closely with the collected community. Increasing the automated test coverage benefits collected as well as Barometer. The Barometer team has contributed several plugins to collected, and in some cases, specialized hardware is needed to test that the plugins are functional. Major work items for the Jerma release are improving the test infrastructure for collected. This project will involve automating existing tests for individual plugins. The test automation being set up will be one instance of a set of collected community CIs.

This project focuses on implementing an enhanced Model Context Protocol (MCP) and A2A communication framework for KubeStellar's Management Control Plane server. It builds upon the foundation MCP implementation to include advanced A2A coordination capabilities, distributed AI agent communication, and sophisticated context management for multi-cluster environments. Expected Outcome: A fully specified Model Context Protocol with A2A communication extensions, implementation of protocol handlers in Python for the MCP server, A2A communication framework enabling AI agent coordination, context management system for KubeStellar state information, serialization mechanisms for cluster state, protocol extension mechanisms for different AI providers, performance optimizations, and comprehensive test suite.

Build a documentation playbook detailing the specifics and resources needed for running Open Programs Offices within a university. This work will involve developing an outline of topics required, summarizing various resources, and working with team members to re-format documentation to be used in our playbook. Essential skills - Writing educational material that evolves over time - Ability to quickly learn & understand complex topics - Copywriting branding content - simplifying technical concepts for non-technical folks - Social Media/Journalism - Computing generalist Previous co-op/internship experience a plus· Experience in working with and/or contributing to Open Source Software, Open Data, Open Hardware or similar communities a plus Mentees will learn how to work within a multi-disciplinary team of developers, designers, and writers to collate and re-package technical information for a broader, more generalized audience. Essential skills - Writing educational material that evolves over time - Ability to quickly learn & understand complex topics - Copywriting branding content - simplifying technical concepts for non-technical folks - Social Media/Journalism - Computing generalist Previous co-op/internship experience a plus· Experience in working with and/or contributing to Open Source Software, Open Data, Open Hardware or similar communities a plus

Kmesh employs eBPF for load balancing. However, at present it only supports short-lived connections and offers a limited range of load balancing algorithms. Consequently, we aim to support a broader range of load balancing capabilities. - Expected Outcome: - 1.Load balancing with load-dependent connections via eBPF. - 2.Support as many load balancing algorithms as possible - 2.1 Sticky Round Robin - 2.2 Weighted Round Robin - 3.Userguide and Proposal - 4.Unit Test and E2E test

The VelaUX API server follows the Open API schema. It could auto-generate the swagger configs via CLI. When VelaUX frontend or other projects need to call these API, they must write the model code and request the API code. We can provide SDK for them to start faster. [OpenAPI generator](https://openapi-generator.tech/) could help to generate most codes. But there are still some special cases like: - Dynamic component/trait/policy/workflowsteps properties need to be generated according to CUE. - Automatically handles the user authentication process, including automatically refreshing tokens. - The API definition may be incomplete accuracy, we should check it to generate high-quality code. - Expected Outcome: The outcome of this project will be expand two more database driver for KubeVela VelaUX API server: - VelaUX APIServer TypeScript SDK - VelaUX APIServer Java SDK

ClusterDuck Protocol is a mesh communication firmware that utilizes LoRa radios and makes it easy to set up emergency networks. The current implementation only allows for information to flow in one direction. While this works well, the experience is not great for end users who need to know if help is on the way. Therefore we want to add 2-Way communication so that disaster survivors can chat with emergency response teams.

Cortex provides horizontally scalable, multi-tenant, long term storage for Prometheus metrics when used as a remote write destination, and a horizontally scalable, Prometheus-compatible query API.

Since jobflow is an important built-in orchestration engine for Volcano, it is still lack of user guidance. Please add more docs to demonstrate its installation, usage, tips and so on. - Expected Outcome: Add docs into volcano-sh/volcano/docs/user-guide and describe the usage of jobflow.

This project focuses on improving localization workflows in Kubernetes SIG Docs by strengthening visibility, prioritization, and traceability across English and localized documentation. The enhanced workflows will benefit to reducing review burden and coordination costs that arise in environments where upstream content changes and subsequent localization efforts occur asynchronously. The project also explores practical approaches to achieving measurable productivity gains by adopting AI-assisted tools within established human workflows. Mentees work with mentors in SIG Docs localization to improve existing localization review and coordination processes, such as label-based coordination and review signals implemented through Prow/CI. The project focuses on the following core areas: improving traceability between frequently changing English content and localized documentation, continuously tracking and notifying review priorities for translation updates, and maintaining visibility into localization coverage and prioritization based on document importance. All outcomes are designed to integrate naturally into existing SIG Docs GitHub workflows and practices while preserving and supporting human-led review and decision-making. - Expected Outcome: - Reusable Prow/CI automation prototypes - Extensible architecture - Best practices documentation

Reduce adoption friction by building integrations between KubeStellar and popular Kubernetes ecosystem tools. The mentee will survey early users to identify integration priorities, design integration architectures, develop working integrations with tools like GitOps platforms, Terraform, CI/CD systems, or monitoring solutions, create comprehensive documentation and examples, and validate integrations with real users. This program emphasizes software development, API integration, understanding ecosystem tools, and creating seamless user experiences. Expected Outcome: 2 production-ready integrations with popular Kubernetes tools, Integration documentation with clear setup guides for each, 2 demo videos demonstrating integration value and setup, Sample implementations and templates for common scenarios, 3 users actively adopting each integration, Submissions to relevant tool marketplaces where applicable, Integration maintenance guide for ongoing support, User feedback on integration quality and usefulness, User engagement: 6 GitHub issues filed by integration users, 4 PRs or PR reviews contributed by integration users

Policy-based control for cloud native environments.

The award-winning SERV (https://github.com/olofk/serv) is the world's smallest RISC-V CPU and is suitable for deeply embedded applications where chip area is at a premium. In a real system however, not only the CPU sets the system size and the smaller the CPU, the more important to also minimize the memory size. Adding support for the C ISA extension (compressed instructions) to SERV would enable RISC-V to reach even more use-cases on the deeply embedded side. SERV is an open source bit-serial RISC-V implementation written in Verilog with an emphasis on resource efficiency. The addition of the C ISA extension is expected to consist of changes to the instructions fetcher, an extra optional decoder stage for converting the compressed instructions into their non-compressed equivalents as well as other changes related to the PC not necessarily being word-aligned. On the infrastructure side, the relevant test suites (riscv-formal and riscv-compliance) are expected to pass and it should run the Zephyr operation OS with compressed instructions enabled. The SERV FuseSoC core description file should also be updated to allow users to conditionally select the C ISA extension at compile-time for simulation and FPGA/ASIC targets.

Leading Open Source Container Attached Storage, built using Cloud Native Architecture, simplifies running Stateful Applications on Kubernetes.

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.

Harbor is a widely adopted container registry. As one of the most widely adopted container registries, it is a critical component in modern software supply chains. This project aims to enhance its security capabilities by extending Harbor's Pluggable Scanner specification to support Runtime Behavior Profiles (also known as a Behavior of Bill, or "BoB"). While Software Bill of Materials (SBOMs) describe what an artifact contains, a BoB describes how it behaves at runtime. By integrating kubescape-node-agent as a scanner, Harbor will be able to retrieve, store, and display these runtime profiles for OCI artifacts. This allows software producers to ship secure-by-default configurations and provides consumers with a way to verify runtime behavior, detect anomalies, and report unexpected activity. This feature will create greater trust in artifacts and help users meet emerging compliance requirements, such as the EU's CyberResilience Act, by enabling active breach identification through anomaly detection. Expected Outcome: Propose and document the minimally necessary modifications to the Harbor Pluggable Scanner Spec to support the retrieval of runtime profiles. Implement a scanner adapter that integrates kubescape-node-agent with Harbor. The adapter must be able to retrieve and process SPDX-compliant Runtime Profiles (SBOBs). Extend Harbor's UI to allow users to view runtime profiles and see potential mismatches between expected and observed behavior.

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