ESETv2 Emulator / interpreter by m.crivellari

Description

ESETv2 is an intriguing challenge developed by ESET, available on their website under the "Challenge" menu. The challenge involves an "assembly-like" language and a Python compiler that generates .evm binary files.

This is an example using one of their samples (it prints N Fibonacci numbers):

.dataSize 0
.code

loadConst 0, r1 # first
loadConst 1, r2 # second

loadConst 1, r14 # loop helper

consoleRead r3

loop:
    jumpEqual end, r3, r15

    add r1, r2, r4
    mov r2, r1
    mov r4, r2

    consoleWrite r1

    sub r3, r14, r3
    jump loop
end:
hlt

This language also supports multi-threading. It includes instructions such as createThread to start a new thread, joinThread to wait until a thread completes, and lock/unlock to facilitate synchronization between threads.

Goals

• create a full interpreter able to run all the available samples provided by ESET.
• improve / optimize memory (eg. using bitfields where needed as well as avoid unnecessary memory allocations)

Resources

• Challenge URL: https://join.eset.com/en/challenges/core-software-engineer
• My github project: https://github.com/DispatchCode/eset_vm2 (not 100% complete)

Achivements

Project still not complete. Added lock / unlock instruction implementation but further debug is needed; there is a bug somewhere. Actually the code it works for almost all the examples in the samples folder. 1 of them is not yet runnable (due to a missing "write" opcode implementation), another will cause the bug to show up; still not investigated, anyhow.

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FastFileCheck work by pstivanin

Description

FastFileCheck is a high-performance, multithreaded file integrity checker for Linux. Designed for speed and efficiency, it utilizes parallel processing and a lightweight database to quickly hash and verify large volumes of files, ensuring their integrity over time.

https://github.com/paolostivanin/FastFileCheck

Goals

• Release v1.0.0

Design overwiew:

• Main thread (producer): traverses directories and feeds the queue (one thread is more than enough for most use cases)
• Dedicated consumer thread: manages queue and distributes work to threadpool
• Worker threads: compute hashes in parallel

This separation of concerns is efficient because:

• Directory traversal is I/O bound and works well in a single thread
• Queue management is centralized, preventing race conditions
• Hash computation is CPU-intensive and properly parallelized

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FizzBuzz OS by mssola

Project Description

FizzBuzz OS (or just fbos) is an idea I've had in order to better grasp the fundamentals of the low level of a RISC-V machine. In practice, I'd like to build a small Operating System kernel that is able to launch three processes: one that simply prints "Fizz", another that prints "Buzz", and the third which prints "FizzBuzz". These processes are unaware of each other and it's up to the kernel to schedule them by using the timer interrupts as given on openSBI (fizz on % 3 seconds, buzz on % 5 seconds, and fizzbuzz on % 15 seconds).

This kernel provides just one system call, write, which allows any program to pass the string to be written into stdout.

This project is free software and you can find it here.

Goal for this Hackweek

• Better understand the RISC-V SBI interface.
• Better understand RISC-V in privileged mode.
• Have fun.

Resources

• SBI v2.0 ratified specification
• Blog post on SBI
• mssola/fbos

Results

The project was a resounding success Lots of learning, and the initial target was met.

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Add a machine-readable output to dmidecode by jdelvare

Description

There have been repeated requests for a machine-friendly dmidecode output over the last decade. During Hack Week 19, 5 years ago, I prepared the code to support alternative output formats, but didn't have the time to go further. Last year, Jiri Hnidek from Red Hat Linux posted a proof-of-concept implementation to add JSON output support. This is a fairly large pull request which needs to be carefully reviewed and tested.

Goals

Review Jiri's work and provide constructive feedback. Merge the code if acceptable. Evaluate the costs and benefits of using a library such as json-c.

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Drag Race - comparative performance testing for pull requests by balanza

Description

«Sophia, a backend developer, submitted a pull request with optimizations for a critical database query. Once she pushed her code, an automated load test ran, comparing her query against the main branch. Moments later, she saw a new comment automatically added to her PR: the comparison results showed reduced execution time and improved efficiency. Smiling, Sophia messaged her team, “Performance gains confirmed!”»

Goals

• To have a convenient and ergonomic framework to describe test scenarios, including environment and seed;
• to compare results from different tests
• to have a GitHub action that executes such tests on a CI environment

Resources

The MVP will be built on top of Preevy and K6.

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ADS-B receiver with MicroOS by epaolantonio

I would like to put one of my spare Raspberry Pis to good use, and what better way to see what flies above my head at any time?

There are various ready-to-use distros already set-up to provide feeder data to platforms like Flightradar24, ADS-B Exchange, FlightAware etc... The goal here would be to do it using MicroOS as a base and containerized decoding of ADS-B data (via tools like dump1090) and web frontend (tar1090).

Goals

• Create a working receiver using MicroOS as a base, and containers based on Tumbleweed
• Make it easy to install
• Optimize for maximum laziness (i.e. it should take care of itself with minimum intervention)

Resources

• 1x Small Board Computer capable of running MicroOS
• 1x RTL2832U DVB-T dongle
• 1x MicroSD card
• https://github.com/antirez/dump1090
• https://github.com/flightaware/dump1090 (dump1090 fork by FlightAware)
• https://github.com/wiedehopf/tar1090

Project status (2024-11-22)

So I'd say that I'm pretty satisfied with how it turned out. I've packaged readsb (as a replacement for dump1090), tar1090, tar1090-db and mlat-client (not used yet).

Current status:

• Able to set-up a working receiver using combustion+ignition (web app based on Fuel Ignition)
• Able to feed to various feeds using the Beast protocol (Airplanes.live, ADSB.fi, ADSB.lol, ADSBExchange.com, Flyitalyadsb.com, Planespotters.net)
• Able to feed to Flightradar24 (initial-setup available but NOT tested! I've only tested using a key I already had)
• Local web interface (tar1090) to easily visualize the results
• Cockpit pre-configured to ease maintenance

What's missing:

• MLAT (Multilateration) support. I've packaged mlat-client already, but I have to wire it up
• FlightAware support

Give it a go at https://g7.github.io/adsbreceiver/ !

Project links

• https://g7.github.io/adsbreceiver/
• https://github.com/g7/adsbreceiver
• https://build.opensuse.org/project/show/home:epaolantonio:adsbreceiver

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Improve Development Environment on Uyuni by mbussolotto

Description

Currently create a dev environment on Uyuni might be complicated. The steps are:

• add the correct repo
• download packages
• configure your IDE (checkstyle, format rules, sonarlint....)
• setup debug environment
• ...

The current doc can be improved: some information are hard to be find out, some others are completely missing.

Dev Container might solve this situation.

Goals

Uyuni development in no time:

• using VSCode:
  • setting.json should contains all settings (for all languages in Uyuni, with all checkstyle rules etc...)
  • dev container should contains all dependencies
  • setup debug environment
• implement a GitHub Workspace solution
• re-write documentation

Lots of pieces are already implemented: we need to connect them in a consistent solution.

Resources

• https://github.com/uyuni-project/uyuni/wiki

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Port the classic browser game HackTheNet to PHP 8 by dgedon

Description

The classic browser game HackTheNet from 2004 still runs on PHP 4/5 and MySQL 5 and needs a port to PHP 8 and e.g. MariaDB.

Goals

• Port the game to PHP 8 and MariaDB 11
• Create a container where the game server can simply be started/stopped

Resources

• https://github.com/nodeg/hackthenet

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ClusterOps - Easily install and manage your personal kubernetes cluster by andreabenini

Description

ClusterOps is a Kubernetes installer and operator designed to streamline the initial configuration and ongoing maintenance of kubernetes clusters. The focus of this project is primarily on personal or local installations. However, the goal is to expand its use to encompass all installations of Kubernetes for local development purposes.
It simplifies cluster management by automating tasks and providing just one user-friendly YAML-based configuration config.yml.

Overview

• Simplified Configuration: Define your desired cluster state in a simple YAML file, and ClusterOps will handle the rest.
• Automated Setup: Automates initial cluster configuration, including network settings, storage provisioning, special requirements (for example GPUs) and essential components installation.
• Ongoing Maintenance: Performs routine maintenance tasks such as upgrades, security updates, and resource monitoring.
• Extensibility: Easily extend functionality with custom plugins and configurations.
• Self-Healing: Detects and recovers from common cluster issues, ensuring stability, idempotence and reliability. Same operation can be performed multiple times without changing the result.
• Discreet: It works only on what it knows, if you are manually configuring parts of your kubernetes and this configuration does not interfere with it you can happily continue to work on several parts and use this tool only for what is needed.

Features

• distribution and engine independence. Install your favorite kubernetes engine with your package manager, execute one script and you'll have a complete working environment at your disposal.
  
• Basic config approach. One single config.yml file with configuration requirements (add/remove features): human readable, plain and simple. All fancy configs managed automatically (ingress, balancers, services, proxy, ...).
• Local Builtin ContainerHub. The default installation provides a fully configured ContainerHub available locally along with the kubernetes installation. This configuration allows the user to build, upload and deploy custom container images as they were provided from external sources. Internet public sources are still available but local development can be kept in this localhost server. Builtin ClusterOps operator will be fetched from this ContainerHub registry too.
• Kubernetes official dashboard installed as a plugin, others planned too (k9s for example).
• Kubevirt plugin installed and properly configured. Unleash the power of classic virtualization (KVM+QEMU) on top of Kubernetes and manage your entire system from there, libvirtd and virsh libs are required.
• One operator to rule them all. The installation script configures your machine automatically during installation and adds one kubernetes operator to manage your local cluster. From there the operator takes care of the cluster on your behalf.
• Clean installation and removal. Just test it, when you are done just use the same program to uninstall everything without leaving configs (or pods) behind.

Planned features (Wishlist / TODOs)

• Containerized Data Importer (CDI). Persistent storage management add-on for Kubernetes to provide a declarative way of building and importing Virtual Machine Disks on PVCs for

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SUSE AI Meets the Game Board by moio

Use tabletopgames.ai’s open source TAG and PyTAG frameworks to apply Statistical Forward Planning and Deep Reinforcement Learning to two board games of our own design. On an all-green, all-open source, all-AWS stack!

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Results: Infrastructure Achievements

We successfully built and automated a containerized stack to support our AI experiments. This included:

• a Fully-Automated, One-Command, GPU-accelerated Kubernetes setup: we created an OpenTofu based script, tofu-tag, to deploy SUSE's RKE2 Kubernetes running on CUDA-enabled nodes in AWS, powered by openSUSE with GPU drivers and gpu-operator
• Containerization of the TAG and PyTAG frameworks: TAG (Tabletop AI Games) and PyTAG were patched for seamless deployment in containerized environments. We automated the container image creation process with GitHub Actions. Our forks (PRs upstream upcoming):
  • https://github.com/moio/TabletopGames/tree/hackweek24
  • https://github.com/moio/PyTAG/tree/hackweek24

./deploy.sh and voilà - Kubernetes running PyTAG (k9s, above) with GPU acceleration (nvtop, below)

Results: Game Design Insights

Our project focused on modeling and analyzing two card games of our own design within the TAG framework:

• Game Modeling: We implemented models for Dario's "Bamboo" and Silvio's "Totoro" and "R3" games, enabling AI agents to play thousands of games ...in minutes!
• AI-driven optimization: By analyzing statistical data on moves, strategies, and outcomes, we iteratively tweaked the game mechanics and rules to achieve better balance and player engagement.
• Advanced analytics: Leveraging AI agents with Monte Carlo Tree Search (MCTS) and random action selection, we compared performance metrics to identify optimal strategies and uncover opportunities for game refinement .
  • more about Bamboo on Dario's site
  • more about R3 on Silvio's site (italian, translation coming)
  • more about Totoro on Silvio's site

A family picture of our card games in progress. From the top: Bamboo, Totoro, R3

Results: Learning, Collaboration, and Innovation

Beyond technical accomplishments, the project showcased innovative approaches to coding, learning, and teamwork:

• "Trio programming" with AI assistance: Our "trio programming" approach—two developers and GitHub Copilot—was a standout success, especially in handling slightly-repetitive but not-quite-exactly-copypaste tasks. Java as a language tends to be verbose and we found it to be fitting particularly well.
• AI tools for reporting and documentation: We extensively used AI chatbots to streamline writing and reporting. (Including writing this report! ...but this note was added manually during edit!)
• GPU compute expertise: Overcoming challenges with CUDA drivers and cloud infrastructure deepened our understanding of GPU-accelerated workloads in the open-source ecosystem.
• Game design as a learning platform: By blending AI techniques with creative game design, we learned not only about AI strategies but also about making games fun, engaging, and balanced.

Last but not least we had a lot of fun! ...and this was definitely not a chatbot generated line!

The Context: AI + Board Games

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