add wasmedge lfx 2024-03 projects (#1270)

programs/lfx-mentorship/2024/03-Sep-Nov/project_ideas.md

@@ -142,3 +142,134 @@ Expected Outcome: Gadget developers have a way to run unit tests in different ke
   - Burak Ok (@burak-ok, burakok@microsoft.com)
 
 - Upstream Issue: https://github.com/inspektor-gadget/inspektor-gadget/issues/3193
+
+### WasmEdge
+
+#### WASM Serializer with new proposals
+
+- Description: WasmEdge provides WASM module serializer in C API level for developers to convert the loaded WASM structure back into binary format. But after supporting the `function-references`, `GC`, `relaxed-SIMD`, and `exception-handling` proposals in WasmEdge, the partitions of these proposals in serializer are not implemented yet. Thus, we would invite mentees to complete the binary format serialization with the above WASM proposals in WasmEdge.
+- Expected Outcome:
+  * Complete the serialization of the new module extensions in WASM proposals.
+  * Complete the serialization of the new instructions added in WASM proposals.
+  * Add some basic unit tests with hand-writing WASM binaries.
+- Recommended Skills: C++, WASM, git
+- Mentor(s):
+  - Yi-Ying He (@q82419, yiying@secondstate.io)
+  - Hung-Ying, Tai (@hydai, hydai@secondstate.io)
+- Upstream Issue: https://github.com/WasmEdge/WasmEdge/issues/3585
+
+#### Fix bugs found by fuzzer
+
+- Description: WasmEdge received several bug reports, which Fuzzer found. We would like to ask mentees to investigate and determine whether the issue is real, figure out solutions, or mark it as a `won't-fix` issue if it's invalid. To apply for this mentorship, you should also submit a proposal as part of the application materials. Please check the upstream issue for the detailed target list and the proposal template. 
+- Expected Outcome: At least fix/determine 60% of the mentioned issues.
+- Recommended Skills: git, C++, WebAssembly
+- Mentor(s):
+  - Hung-Ying, Tai (@hydai, hydai@secondstate.io)
+  - Yi-Ying He (@q82419, yiying@secondstate.io)
+- Upstream Issue: https://github.com/WasmEdge/WasmEdge/issues/3584
+
+#### Create an LLM app with deep understanding of a GitHub repo
+
+- Description: LLM assisted coding is one of the most promising application areas for modern AI. It will also have profound impact on open source software development. As many projects have demonstrated, "feeding" an LLM with contents from a GitHub repo will make it better at understanding coding tasks for this project. In this project, we will take a modern approach to build LLM agents based on LlamaEdge / WasmEdge, and supplement it with deep knowledge of open source projects on GitHub. The goal is for the agent to answer questions and solve problems raised by the open source community.
+- Expected Outcome:
+  * Build an automated tool to extract and process all files in a repo. That includes source code and docs.
+    * develop a GitHub bot to capture all change files and update the knowledge base in real time.
+    * generate a summary for each file (using an LLM) and supplement with its file path and other meta data.
+    * create a vector database with the summary and original text. The vector is computed from the summary to improve search efficiency.
+  * Run an LLM agent node with the RAG database from the repo.
+  * Create a GitHub bot that can read new issues and respond with either an answer or a coding suggestion based on the content inside the repo.
+  * Evaluate the answer quality.
+- Recommended Skills:
+  * Rust
+  * [LlamaEdge](https://github.com/LlamaEdge/LlamaEdge) -- see a [tutorial](https://llamaedge.com/docs/user-guide/get-started-with-llamaedge)
+  * ChatGPT and LLMs
+  * The [RAG process](https://llamaedge.com/docs/category/server-side-rag)
+- Mentor(s):
+  - Michael Yuan (@juntao, michael@secondstate.io)
+  - Hung-Ying, Tai (@hydai, hydai@secondstate.io)
+- Upstream Issue: https://github.com/WasmEdge/WasmEdge/issues/3581
+
+#### Create a Wasm-based LLM app for financial analysts
+
+- Description: We would like to develop an LLM-based financial data analytics application using open source LLMs, embedding models, the LlamaEdge application server, vector databases, and data processing tools. It will provide an open source "template" and showcase "best practices" for similar applications in this fast growing application area.
+- Expected Outcome:
+  * Create a data processing pipeline in Python or Rust to automatically
+    * collect public company’s SEC 10-Q quarterly reports and press releases. e.g., [Apple 10-Q](https://www.sec.gov/edgar/browse/?CIK=0000320193) and [Apple press release](https://www.apple.com/newsroom/2024/05/apple-reports-second-quarter-results/)
+    * generate a summary for each SEC 10-Q and press release documents using an LLM service such as [LlamaParse](https://docs.llamaindex.ai/en/stable/llama_cloud/llama_parse/) or [EYELEVEL xRay](https://dashboard.eyelevel.ai/xray/)
+    * create and continuously update a vector database with the summary and original text. The vector is computed from the summary to improve search efficiency.
+  * Create a server-side RAG app that can chat with the vector knowledge base of financial statements.
+  * Evaluate the answer quality
+  * Explore LLM function calling to incorporate real-time information and actions
+- Recommended Skills:
+  * Python
+  * [LlamaEdge](https://github.com/LlamaEdge/LlamaEdge) -- see a [tutorial](https://llamaedge.com/docs/user-guide/get-started-with-llamaedge)
+  * ChatGPT and LLMs
+  * The [RAG process](https://llamaedge.com/docs/category/server-side-rag)
+  * Rust (optional)
+- Mentor(s):
+  - Michael Yuan (@juntao, michael@secondstate.io)
+  - Hung-Ying, Tai (@hydai, hydai@secondstate.io)
+- Upstream Issue: https://github.com/WasmEdge/WasmEdge/issues/3580
+
+### Karmada
+
+#### Collect and visualize Karmada metrics
+
+- Description: Karmada dashboard now supports one-time metric retrieval, but it is difficult to observe the status of multi-clusters with one-time metric retrieval. Therefore, we would like to implement a lightweight metric collection capability to collect Karmada metrics and visualize them on the Karmada dashboard. This will allow cluster administrators to quickly get the status of the clusters and solve problems within the clusters.
+- Expected Outcome:
+- Recommended Skills:
+  - Kubernetes
+  - Go
+  - gin
+  - react
+  - sqlite
+- Mentor(s):
+  - Wenjiang Ding (@warjiang, 1096409085@qq.com)
+  - Zhen Chang (@XiShanYongYe-Chang, changzhen5@huawei.com)
+- Upstream Issue: https://github.com/karmada-io/dashboard/issues/62
+
+#### Enhance Karmada controller-manager and schedule testing coverage
+
+- Description: Karmada would like to improve the UT coverage of the code to better maintain the quality of the code and reduce the introduction of defects. Increase the UT coverage rate to 50% to 60% (currently, the UT coverage rate is [28.26%](https://app.codecov.io/gh/karmada-io/karmada) ). The entire Karmada repository is a bit large for one project, so we will split it into two projects. The current parts mainly target the `karmada-controller-manager` and `karmada-scheduler` components.
+- Expected Outcome:
+  - Increase the UT (Unit Test) coverage by more than 25% and add more than 4000 lines of code coverage in the following directories.
+```
+pkg/controllers
+pkg/dependenciesdistributor
+pkg/descheduler
+pkg/detector
+pkg/estimator
+pkg/scheduler
+pkg/resourceinterpreter
+pkg/util
+```
+- Recommended Skills:
+  - Go
+  - Cloud Native
+- Mentor(s):
+  - Zhen Chang (@XiShanYongYe-Chang, changzhen5@huawei.com)
+  - Zhuang Zhang (@zhzhuang-zju, m17799853869@163.com)
+- Upstream Issue: https://github.com/karmada-io/karmada/issues/5235
+
+####
+
+- Description: Karmada would like to improve the UT coverage of the code to better maintain the quality of the code and reduce the introduction of defects. Increase the UT coverage rate to 50% to 60% (currently, the UT coverage rate is [28.26%](https://app.codecov.io/gh/karmada-io/karmada) ). The entire Karmada repository is a bit large for one project, so we will split it into two projects. The current focus is mainly on other parts except for the `karmada-controller-manager` and `karmada-scheduler` components.
+- Expected Outcome:
+  - Increase the UT (Unit Test) coverage by more than 25% and add more than 5500 lines of code coverage except for the following directories.
+```
+pkg/controllers
+pkg/dependenciesdistributor
+pkg/descheduler
+pkg/detector
+pkg/estimator
+pkg/scheduler
+pkg/resourceinterpreter
+pkg/util
+```
+- Recommended Skills:
+  - Go
+  - Cloud Native
+- Mentor(s):
+  - Zhen Chang (@XiShanYongYe-Chang, changzhen5@huawei.com)
+  - Chaosi Pan (@chaosi-zju, chaosi@zju.edu.cn)
+- Upstream Issue: https://github.com/karmada-io/karmada/issues/5236