The Agentic Rancher Experiment: Do Androids Dream of Electric Cattle? by moio

Rancher is a beast of a codebase. Let's investigate if the new 2025 generation of GitHub Autonomous Coding Agents and Copilot Workspaces can actually tame it.

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The Plan

Create a sandbox GitHub Organization, clone in key Rancher repositories, and let the AI loose to see if it can handle real-world enterprise OSS maintenance - or if it just hallucinates new breeds of Kubernetes resources!

Specifically, throw "Agentic Coders" some typical tasks in a complex, long-lived open-source project, such as:

❥ The Grunt Work: generate missing GoDocs, unit tests, and refactorings. Rebase PRs.

❥ The Complex Stuff: fix actual (historical) bugs and feature requests to see if they can traverse the complexity without (too much) human hand-holding.

❥ Hunting Down Gaps: find areas lacking in docs, areas of improvement in code, dependency bumps, and so on.

If time allows, also experiment with Model Context Protocol (MCP) to give agents context on our specific build pipelines and CI/CD logs.

Why?

We know AI can write "Hello World." and also moderately complex programs from a green field. But can it rebase a 3-month-old PR with conflicts in rancher/rancher? I want to find the breaking point of current AI agents to determine if and how they can help us to reduce our technical debt, work faster and better. At the same time, find out about pitfalls and shortcomings.

The CONCLUSION!!!

A State of the Union document was compiled to summarize lessons learned this week. For more gory details, just read on the diary below!

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GenAI-Powered Systemic Bug Evaluation and Management Assistant by rtsvetkov

Motivation

What is the decision critical question which one can ask on a bug? How this question affects the decision on a bug and why?

Let's make GenAI look on the bug from the systemic point and evaluate what we don't know. Which piece of information is missing to take a decision?

Description

To build a tool that takes a raw bug report (including error messages and context) and uses a large language model (LLM) to generate a series of structured, Socratic-style or Systemic questions designed to guide a the integration and development toward the root cause, rather than just providing a direct, potentially incorrect fix.

Goals

Set up a Python environment

Set the environment and get a Gemini API key. 2. Collect 5-10 realistic bug reports (from open-source projects, personal projects, or public forums like Stack Overflow—include the error message and the initial context).

Build the Dialogue Loop

1. Write a basic Python script using the Gemini API.
2. Implement a simple conversational loop: User Input (Bug) -> AI Output (Question) -> User Input (Answer to AI's question) -> AI Output (Next Question). Code Implementation

Socratic/Systemic Strategy Implementation

1. Refine the logic to ensure the questions follow a Socratic and Systemic path (e.g., from symptom-> context -> assumptions -> -> critical parts -> ).
2. Implement Function Calling (an advanced feature of the Gemini API) to suggest specific actions to the user, like "Run a ping test" or "Check the database logs."
3. Implement Bugzillla call to collect the
4. Implement Questioning Framework as LLVM pre-conditioning
5. Define set of instructions
6. Assemble the Tool

Resources

What are Systemic Questions?

Systemic questions explore the relationships, patterns, and interactions within a system rather than focusing on isolated elements.
In IT, they help uncover hidden dependencies, feedback loops, assumptions, and side-effects during debugging or architecture analysis.

Gitlab Project

gitlab.suse.de/sle-prjmgr/BugDecisionCritical_Question

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Update M2Crypto by mcepl

There are couple of projects I work on, which need my attention and putting them to shape:

• M2Crypto

Goal for this Hackweek

• Put M2Crypto into better shape (most issues closed, all pull requests processed)
• More fun to learn jujutsu
• Play more with Gemini, how much it help (or not).
• Perhaps, also (just slightly related), help to fix vis to work with LuaJIT, particularly to make vis-lspc working.

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SUSE Edge Image Builder MCP by eminguez

Description

Based on my other hackweek project, SUSE Edge Image Builder's Json Schema I would like to build also a MCP to be able to generate EIB config files the AI way.

Realistically I don't think I'll be able to have something consumable at the end of this hackweek but at least I would like to start exploring MCPs, the difference between an API and MCP, etc.

Goals

• Familiarize myself with MCPs
• Unrealistic: Have an MCP that can generate an EIB config file

Resources

• https://hackweek.opensuse.org/25/projects/suse-edge-image-builder-json-schema
• Anything else :)

Result

https://github.com/e-minguez/eib-mcp

I've extensively used antigravity and its agent mode to code this. This heavily uses https://hackweek.opensuse.org/25/projects/suse-edge-image-builder-json-schema for the MCP to be built.

I've ended up learning a lot of things about "prompting", json schemas in general, some golang, MCPs and AI in general :)

Example:

Generate an Edge Image Builder configuration for an ISO image based on slmicro-6.2.iso, targeting x86_64 architecture. The output name should be 'my-edge-image' and it should install to /dev/sda. It should deploy a 3 nodes kubernetes cluster with nodes names "node1", "node2" and "node3" as: * hostname: node1, IP: 1.1.1.1, role: initializer * hostname: node2, IP: 1.1.1.2, role: agent * hostname: node3, IP: 1.1.1.3, role: agent The kubernetes version should be k3s 1.33.4-k3s1 and it should deploy a cert-manager helm chart (the latest one available according to https://cert-manager.io/docs/installation/helm/). It should create a user called "suse" with password "suse" and set ntp to "foo.ntp.org". The VIP address for the API should be 1.2.3.4

Generates:

``` apiVersion: "1.0" image: arch: x86_64 baseImage: slmicro-6.2.iso imageType: iso outputImageName: my-edge-image kubernetes: helm: charts: - name: cert-manager repositoryName: jetstack

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Explore LLM evaluation metrics by thbertoldi

Description

Learn the best practices for evaluating LLM performance with an open-source framework such as DeepEval.

Goals

Curate the knowledge learned during practice and present it to colleagues.

-> Maybe publish a blog post on SUSE's blog?

Resources

https://deepeval.com

https://docs.pactflow.io/docs/bi-directional-contract-testing

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Creating test suite using LLM on existing codebase of a solar router by fcrozat

Description

Two years ago, I evaluated solar routers as part of hackweek24, I've assembled one and it is running almost smoothly.

However, its code quality is not perfect and the codebase doesn't have any testcase (which is tricky, since it is embedded code and rely on getting external data to react).

Before improving the code itself, a testsuite should be created to ensure code additional don't cause regression.

Goals

Create a testsuite, allowing to test solar router code in a virtual environment. Using LLM to help to create this test suite.

If succesful, try to improve the codebase itself by having it reviewed by LLM.

Resources

Solar router github project

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Self-Scaling LLM Infrastructure Powered by Rancher by ademicev0

Self-Scaling LLM Infrastructure Powered by Rancher

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Description

The Problem

Running LLMs can get expensive and complex pretty quickly.

Today there are typically two choices:

1. Use cloud APIs like OpenAI or Anthropic. Easy to start with, but costs add up at scale.
2. Self-host everything - set up Kubernetes, figure out GPU scheduling, handle scaling, manage model serving... it's a lot of work.

What if there was a middle ground?

What if infrastructure scaled itself instead of making you scale it?

Can we use existing Rancher capabilities like CAPI, autoscaling, and GitOps to make this simpler instead of building everything from scratch?

Project Repository: github.com/alexander-demicev/llmserverless

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What This Project Does

A key feature is hybrid deployment: requests can be routed based on complexity or privacy needs. Simple or low-sensitivity queries can use public APIs (like OpenAI), while complex or private requests are handled in-house on local infrastructure. This flexibility allows balancing cost, privacy, and performance - using cloud for routine tasks and on-premises resources for sensitive or demanding workloads.

A complete, self-scaling LLM infrastructure that:

• Scales to zero when idle (no idle costs)
• Scales up automatically when requests come in
• Adds more nodes when needed, removes them when demand drops
• Runs on any infrastructure - laptop, bare metal, or cloud

Think of it as "serverless for LLMs" - focus on building, the infrastructure handles itself.

How It Works

A combination of open source tools working together:

Flow:

• Users interact with OpenWebUI (chat interface)
• Requests go to LiteLLM Gateway
• LiteLLM routes requests to:
  • Ollama (Knative) for local model inference (auto-scales pods)
  • Or cloud APIs for fallback

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issuefs: FUSE filesystem representing issues (e.g. JIRA) for the use with AI agents code-assistants by llansky3

Description

Creating a FUSE filesystem (issuefs) that mounts issues from various ticketing systems (Github, Jira, Bugzilla, Redmine) as files to your local file system.

And why this is good idea?

• User can use favorite command line tools to view and search the tickets from various sources
• User can use AI agents capabilities from your favorite IDE or cli to ask question about the issues, project or functionality while providing relevant tickets as context without extra work.
• User can use it during development of the new features when you let the AI agent to jump start the solution. The issuefs will give the AI agent the context (AI agents just read few more files) about the bug or requested features. No need for copying and pasting issues to user prompt or by using extra MCP tools to access the issues. These you can still do but this approach is on purpose different.

Goals

1. Add Github issue support
2. Proof the concept/approach by apply the approach on itself using Github issues for tracking and development of new features
3. Add support for Bugzilla and Redmine using this approach in the process of doing it. Record a video of it.
4. Clean-up and test the implementation and create some documentation
5. Create a blog post about this approach

Resources

There is a prototype implementation here. This currently sort of works with JIRA only.

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Extended private brain - RAG my own scripts and data into offline LLM AI by tjyrinki_suse

Description

For purely studying purposes, I'd like to find out if I could teach an LLM some of my own accumulated knowledge, to use it as a sort of extended brain.

I might use qwen3-coder or something similar as a starting point.

Everything would be done 100% offline without network available to the container, since I prefer to see when network is needed, and make it so it's never needed (other than initial downloads).

Goals

1. Learn something about RAG, LLM, AI.
2. Find out if everything works offline as intended.
3. As an end result have a new way to access my own existing know-how, but so that I can query the wisdom in them.
4. Be flexible to pivot in any direction, as long as there are new things learned.

Resources

To be found on the fly.

Timeline

Day 1 (of 4)

• Tried out a RAG demo, expanded on feeding it my own data
• Experimented with qwen3-coder to add a persistent chat functionality, and keeping vectors in a pickle file
• Optimizations to keep everything within context window
• Learn and add a bit of PyTest

Day 2

• More experimenting and more data
• Study ChromaDB
• Add a Web UI that works from another computer even though the container sees network is down

Day 3

• The above RAG is working well enough for demonstration purposes.
• Pivot to trying out OpenCode, configuring local Ollama qwen3-coder there, to analyze the RAG demo.
• Figured out how to configure Ollama template to be usable under OpenCode. OpenCode locally is super slow to just running qwen3-coder alone.

Day 4 (final day)

• Battle with OpenCode that was both slow and kept on piling up broken things.
• Call it success as after all the agentic AI was working locally.
• Clean up the mess left behind a bit.

Blog Post

Summarized the findings at blog post.

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Explore LLM evaluation metrics by thbertoldi

Description

Learn the best practices for evaluating LLM performance with an open-source framework such as DeepEval.

Goals

Curate the knowledge learned during practice and present it to colleagues.

-> Maybe publish a blog post on SUSE's blog?

Resources

https://deepeval.com

https://docs.pactflow.io/docs/bi-directional-contract-testing

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pudc - A PID 1 process that barks to the internet by mssola

Description

As a fun exercise in order to dig deeper into the Linux kernel, its interfaces, the RISC-V architecture, and all the dragons in between; I'm building a blog site cooked like this:

• The backend is written in a mixture of C and RISC-V assembly.
• The backend is actually PID1 (for real, not within a container).
• We poll and parse incoming HTTP requests ourselves.
• The frontend is a mere HTML page with htmx.

The project is meant to be Linux-specific, so I'm going to use io_uring, pidfs, namespaces, and Linux-specific features in order to drive all of this.

I'm open for suggestions and so on, but this is meant to be a solo project, as this is more of a learning exercise for me than anything else.

Goals

• Have a better understanding of different Linux features from user space down to the kernel internals.
• Most importantly: have fun.

Resources

• https://github.com/mssola/pudc

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bpftrace contribution by mkoutny

Description

bpftrace is a great tool, no need to sing odes to it here. It can access any kernel data and process them in real time. It provides helpers for some common Linux kernel structures but not all.

Goals

• set up bpftrace toolchain
• learn about bpftrace implementation and internals
• implement support for percpu_counters
• look into some of the first issues
• send a refined PR (on Thu)

Resources

• https://bpftrace.org/
• https://docs.kernel.org/

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dynticks-testing: analyse perf / trace-cmd output and aggregate data by m.crivellari

Description

dynticks-testing is a project started years ago by Frederic Weisbecker. One of the feature is to check the actual configuration (isolcpus, irqaffinity etc etc) and give feedback on it.

An important goal of this tool is to parse the output of trace-cmd / perf and provide more readable data, showing the duration of every events grouped by PID (showing also the CPU number, if the tasks has been migrated etc).

An example of data captured on my laptop (incomplete!!):

          -0     [005] dN.2. 20310.270699: sched_wakeup:         WaylandProxy:46380 [120] CPU:005
          -0     [005] d..2. 20310.270702: sched_switch:         swapper/5:0 [120] R ==> WaylandProxy:46380 [120]
...
    WaylandProxy-46380 [004] d..2. 20310.295397: sched_switch:         WaylandProxy:46380 [120] S ==> swapper/4:0 [120]
          -0     [006] d..2. 20310.295397: sched_switch:         swapper/6:0 [120] R ==> firefox:46373 [120]
         firefox-46373 [006] d..2. 20310.295408: sched_switch:         firefox:46373 [120] S ==> swapper/6:0 [120]
          -0     [004] dN.2. 20310.295466: sched_wakeup:         WaylandProxy:46380 [120] CPU:004

Output of noise_parse.py:

Task: WaylandProxy Pid: 46380 cpus: {4, 5} (Migrated!!!)
        Wakeup Latency                                Nr:        24     Duration:          89
        Sched switch: kworker/12:2                    Nr:         1     Duration:           6

My first contribution is around Nov. 2024!

Goals

• add more features (eg cpuset)
• test / bugfix

Resources

• Frederic's public repository: https://git.kernel.org/pub/scm/linux/kernel/git/frederic/dynticks-testing.git/
• https://docs.kernel.org/timers/no_hz.html#testing

Progresses

isolcpus and cpusets implemented and merged in master: dynticks-testing.git commit

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Add Qualcomm Snapdragon 765G (SM7250) basic device tree to mainline linux kernel by pvorel

Qualcomm Snapdragon 765G (SM7250) (smartphone SoC) has no support in the linux kernel, nor in u-boot. Try to add basic device tree support. The hardest part will be to create boot.img which will be accepted by phone.

UART is available for smartphone :).

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early stage kdump support by mbrugger

Project Description

When we experience a early boot crash, we are not able to analyze the kernel dump, as user-space wasn't able to load the crash system. The idea is to make the crash system compiled into the host kernel (think of initramfs) so that we can create a kernel dump really early in the boot process.

Goal for the Hackweeks

1. Investigate if this is possible and the implications it would have (done in HW21)
2. Hack up a PoC (done in HW22 and HW23)
3. Prepare RFC series (giving it's only one week, we are entering wishful thinking territory here).

update HW23

• I was able to include the crash kernel into the kernel Image.
• I'll need to find a way to load that from init/main.c:start_kernel() probably after kcsan_init()
• I workaround for a smoke test was to hack kexec_file_load() systemcall which has two problems:
  1. My initramfs in the porduction kernel does not have a new enough kexec version, that's not a blocker but where the week ended
  2. As the crash kernel is part of init.data it will be already stale once I can call kexec_file_load() from user-space.

The solution is probably to rewrite the POC so that the invocation can be done from init.text (that's my theory) but I'm not sure if I can reuse the kexec infrastructure in the kernel from there, which I rely on heavily.

update HW24

• Day1
  • rebased on v6.12 with no problems others then me breaking the config
  • setting up a new compilation and qemu/virtme env
  • getting desperate as nothing works that used to work
• Day 2
  • getting to call the invocation of loading the early kernel from __init after kcsan_init()

• Day 3

  • fix problem of memdup not being able to alloc so much memory... use 64K page sizes for now
  • code refactoring
  • I'm now able to load the crash kernel
  • When using virtme I can boot into the crash kernel, also it doesn't boot completely (major milestone!), crash in elfcorehdr_read_notes()

• Day 4

  • crash systems crashes (no pun intended) in copy_old_mempage() link; will need to understand elfcorehdr...
  • call path vmcore_init() -> parse_crash_elf_headers() -> elfcorehdr_read() -> read_from_oldmem() -> copy_oldmem_page() -> copy_to_iter()

• Day 5

  • hacking arch/arm64/kernel/crash_dump.c:copy_old_mempage() to see if crash system really starts. It does.
  • fun fact: retested with more reserved memory and with UEFI FW, host kernel crashes in init but directly starts the crash kernel, so it works (somehow) \o/

update HW25

• Day 1
  • rebased crash-kernel on v6.12.59 (for now), still crashing

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