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I want to implement a feature for pocket that enables users to merge their existing projects, because it's not possible in the current version. Implementing a project merging feature will greatly improve the experience for pocket code users when it comes to building even more powerful applications and games. I've divided the problem into the following 5 milestones, with their subtasks to achieve: 1. Merging Projects Components with existing, simple conflicting variables protocol: 1.1 Merging Multiple Sprites 1.2 Merging Scenes 2. Implementing a Manual Conflict-Handler Interface: 2.1 User Interface for Merging Dialog: (choosing which conflict to merge, option to abort merge etc.) 2.2 Handling Local & Global Variable Conflicts 2.3 Handling Script Conflicts: View of Conflicting Scripts, Choose One 2.4 Handling Looks & Sounds Conflicts: Duplication User chooses 2.5 Handling Equal Sprites 2.6 Handling Equal Scenes 3. Merging options for UI 3.1 Abort Merge 3.2 Automatic Merge Without Conflict Search ( may not import everything) 3.3 Manual Merge ( Powerful Merge every conflict needs to be resolved) 4. Device specific configurations 4.1 Handling Screen Size & Resolution sensitive variables 4.2 Handling Screen Size & Resolution sensitive bricks 5. Merging Remix Graph 5.1 Implementing project merge history Each feature will be implemented using Kotlin or Java depending on source code files affected by changes, and each feature should be well tested.

MariaDB currently implements the JSON data type as an alias for LONGTEXT rather than as a distinct type, causing JSON columns to be misreported in schema introspection, client metadata, and the wire protocol. This breaks ORM frameworks such as Ruby on Rails ActiveRecord, which cannot identify the column as JSON and silently corrupts data. It also puts MariaDB out of compliance with the SQL:2016 standard. This project implements JSON as a fully distinct type in MariaDB's type system without changing storage so that JSON columns are correctly identified and reported at every layer: schema commands, the Information Schema, wire protocol metadata, and implicit validation. Deliverables include correct SHOW COLUMNS and SHOW CREATE TABLE output, accurate INFORMATION_SCHEMA reporting, proper wire protocol type codes, implicit JSON validation on write, correct JSON function return types, and a full MTR test suite covering all changes.

<p>This project involves advancing the performance analysis capabilities provided by the PCP integration within the pbench benchmarking and analysis framework. The problems to be worked on are:</p> <ol> <li>Modifying pbench to leverage pmlogger's remote collection capabilities and leverage PCP's archive compression feature for efficient storage.</li> <li>Enhancing pbench with the ability to enable live display of collected metrics via Grafana.</li> <li>Modify PCP to provide new pmcd agent metrics, pmlogger and pmlogconf templates tailored to recommended performance analysis data collection for target workloads (database, web server, computation, low-latency networking, etc.)</li> </ol>

Meshery integrates with a bunch of CNCF tools, and keeping those integrations stable needs proper automation. The goal of this project is to improve and expand end-to-end (E2E) test coverage using Playwright, so that the Meshery UI and workflows stay reliable even as new changes come in. I'll start by understanding the current test setup and structure, identify gaps or flaky tests, and then refactor or write new Playwright tests that are modular and clean. These tests will be fully integrated with the CI (GitHub Actions), ensuring they run with every PR and provide quick feedback. Deliverables include new E2E tests for major workflows, fixes for unstable tests, performance improvements through parallelization, updated documentation for contributors, and a more robust CI test pipeline. I'll be actively collaborating with the Meshery community throughout

<p>By opening an unknown flowgraph file in the GRC you expose your system to arbitrary python code being evaluated via the block parameters. A View-Only Mode could remedy this by first providing a view where evaluated parameter values are saved to and loaded from the file instead of being evaluated until the users chooses to trust the flowgraph. The proposed project aims to implement such a mode in a secure and convenient manner.</p>

Currently, there is no plugin in MariDB that can generate vector embeddings. If the user wishes to store vector embeddings for a document, they have to use a separate solution and then manually insert them into the database. This is inefficient and complicates related pipelines. The goal of this project is twofold: 1. Create a function plugin that implements a function to query OpenAI’s API to generate vector embeddings for a given input. 2. Implement a concept of “expensive” functions in the MariaDB server. When expensive functions are used in stored generated columns, they should not be recomputed whenever possible (for example during ALTER TABLE commands). Instead, the old values should be copied to the new table, just like with regular columns For this project to be successfully completed, the goal would be to create a PR to MariaDB’s server repository that: 1. Implements a function plugin for generating vector embeddings via API calls to OpenAI 2. Introduces and implements the concept of “expensive” functions, to avoid recomputing them whenever possible 3. Ensures the quality of the newly developed features is up to standards, via appropriate testing and documentation

When I was little, one of my favorite 2D games was Super Mario Bros. I want to share this game with many people, but not only play, I also want them to be able to learn to make this game using the Catrobat platform with a fun experience. I want to help them make this game by making a Demo Project for the Super Mario Bros game. For me, working on this project means helping many children around the world to learn programming for free in a very interesting way. I believe all children should have easy access to education. Platforms like Catrobat are a bridge to make this happen and this is what motivated me to want to enter this project

<p>UBlas is one of the most respectable linear algebra libraries but it still lacks doing the matrix computations on GPU. My proposal is to provide support for doing that through the boost.Compute technology and an api that will make it so easy to do these computations on a device from the user's choice</p>

This project goal is to implement basic vectorized filtering for ARM platforms.The bootstrap script is right when the project is build in arm platforms.So we can skip the first step.And the main task are getting familiar with vector extensions(NEON, SVE) for ARM platforms and implement vectorization on ARM I plan to spend three months to complete this project. In the first month, understand the basic code structure and the architecture of 128bit arm neon extension and SVE. In the second month, I will go to get family with vector extensions (neon, SVE) for arm platforms and implementation vectorization on arm. Continue to complete testing and bug fixes in the third month

<p>In this project, the goal is to implement algorithms for computing Similarity between Geometries using Expression Templates and Meta-programming paradigm in C++. The project will initially focus on Linestrings and will try to extend it to rings or polygons. For implementation purposes, It requires studying the different variation of Hausdroff distance and Frechet distance. After we are done with implementation part, we will generate test cases and verify those test cases and set benchmarks for different algorithms. Finally, we will integrate it with Boost.Geometry.</p>

Window functions in MariaDB are currently always materialized into temporary tables before producing any results, even for simple cases, like ranking with a limit. This prevents index usage, enforces unnecessary sorting, and introduces avoidable I/O overhead. For hybrid search queries that combine vector and full-text search using Reciprocal Rank Fusion (RRF), this limitation forces both result sets to be fully computed before any top results can be returned, significantly impacting performance. This project introduces a streaming execution path for window functions, allowing eligible queries to produce rows incrementally without materialization. Building on this, a dedicated execution node for RRF will merge vector and full-text search streams and return top results early, avoiding full computation of either source and significantly improving hybrid search efficiency.

<p>This document, submitted to the GNSS-SDR open-source software defined radio project as part of the 2018 Google Summer of Code (GSoC) program, proposes an algorithmic change to an key part of the signal processing pathway utilized by the GNSS-SDR project in order to perform outdoor positioning using a software defined radio. The first section of this document describes in detail certain techniques which represent the current state of the art for GNSS positioning, along with citations and literature supporting the effectiveness of these techniques in GNSS positioning.</p>

<p>gr-modtool is a magical script that automates the boring work involved in writing the boilerplate code, editing makefiles, creating templates, etc. so that the user can directly jump into the DSP coding.</p> <p>The aim of the project is to make the tool Py3K compatible, rewrite it as a plugin architecture, create a Python API for it, restructure its code in the favor of functional programming, upgrade its present UI, replace the XML generator by a YAML generator, and add an additional command for the conversion of XML scripts to YAML.</p>

This project aims to extend Rizin's OOP binary analysis capabilities across multiple languages. It completes support for Objective-C and Swift binaries, and introduces support for Rust and Java, focusing on vtable detection, devirtualization, and inheritance graph reconstruction. It also adds Windows Structured Exception Handling for both x86 and x64. Demanglers for Swift and D will be integrated into rz-libdemangle.

<p>Metal Renegades is meant to feel like an immersive Old West-style world. Right now, it’s just a desert, with some mountains made out of sand. I propose to expand that with more interesting terrain typical of the Old West: mesas where rock around a plateau has eroded away, canyons where rivers have cut deep into the rock, mountains made of rock instead of sand, and more. Additionally, I plan to create a realistic distribution of flora and fauna.</p>

mARine currently lacks a dynamic, reproducible environment system. This project builds a seed-based procedural generation pipeline that creates consistent marine terrain and living coral ecosystems across devices. A deterministic noise system shapes the ocean floor using fractional Brownian motion, cluster-based placement populates it with seven coral species using slope filtering and raycast confirmation, and a rule-driven simulation governs growth, bleaching, reproduction, and die-off — all emerging from agent interactions rather than scripted behavior. The same seed always reconstructs the identical environment without storing any state, making it practical for mobile AR. Deliverables include: chunk-based terrain streaming, a per-agent coral lifecycle system, URP underwater rendering (caustics, volumetric fog), and Unity Job System optimization for real-time mobile AR performance.

AIM: To develop an awesome tutorial game in Catrobat visual programming language. Playable on smartphones and smart TV via casting. Project showcases the immense abilities of Pocket Code for designing games. Interactive story depiction with easy-to-play UI. THEME: Treasure hunting has always been among the most-talked themes. Be it for movies, games, novels. It’s evergreen! The theme and characters are inspired by Spelunky with added remixes and touch-ups for more excitement. The game displays the effective use of Pocket code in creating a wide-span story game. The ability to induce interactive puzzles and mind-boggling features with simple blocks of code.

<p>The aim of this project is to advance the current implementation of radeco to output decompiled code and implement a robust type inference system.</p>

Meshery stands as the canonical cloud-native management plane, rigorously managing the lifecycle, performance, and topologies of multicloud infrastructure. Currently, Meshery manages multiple load generators (Nighthawk, Fortio, Wrk2) to execute Service Mesh Performance (SMP) benchmarks. However, the heavyweight C++ Nighthawk binaries are bundled directly inside the meshery/meshery Go backend container. This statically couples the data plane execution to the control plane, bloating the core image size and preventing independent scaling. This project resolves this architectural flaw by cleanly severing Nighthawk from the core server and wrapping it into an autonomous, standalone gRPC adapter (meshery-nighthawk). To achieve this, I will evolve the existing meshkit/meshes/meshops.proto into a generalized adapters.proto capable of routing both traditional service mesh commands and raw load-generation requests. By the end of this project, Meshery Server will dynamically discover and orchestrate Nighthawk as a first-class out-of-tree component, supported by fully overhauled CLI commands (mesheryctl perf), Helm chart deployments, and a dedicated Meshery UI Adapter chip.

<p>Signal intelligence describes the gathering of information out of intercepted radio signals with unknown origin and unknown parameters. GNU Radio has already the ability to do signal intelligence, but it is only possible through some detours. Real-time signal intelligence is hardly possible. The tool <code>gqrx</code> has some of this ability (real-time AM/FM demodulation), but there is no final solution for this problem at this time. The target of this project is to develop an easily accessible and extendible solution for this workflow including automatic signal detection and automatic modulation classification. Further deliverables are a radio service allocation database, blind synchronization methods and an automatic demodulation block.</p>

<p><a href="https://wiki.debian.org/FreedomBox/Plinth" target="_blank">Plinth</a> is a web interface to administer the functions of the FreedomBox which is a Debian based project, and the main goal of this idea is to make it available for Fedora.</p> <p>To solve this issue, I made up a scenario in this proposal, it can be divided roughly into two parts. The first one is modifying the source code module by module to convert it to RPM-based, including replacing the apt command code with the dnf command code or fit both of them, changing the Deb-based packages into RPM-based packages which play the same roles and testing after each module finished. The second part is a little scattered, including making a RPM package for Plinth and setting up a repo for it in <a href="https://copr.fedorainfracloud.org/" target="_blank">Copr</a>, testing inside a Virtual Machine, and modifying some configuration files and the documents for users to fit the changes of Plinth during this project, at the same time, a new wiki page for Plinth in Fedora is needed.</p>

<p>The project mainly concentrates on automating the module site for collecting information about modules from all over Terasology organization and display their generated information on the module site it also focuses on migrating the web presence and it will include working on frontend layout and improving the UX and UI of the module site.</p>

<p>The Shogun Toolbox is a well-established machine learning project that provides efficient algorithms implementations that can be used in a wide range of applications and with multi-language support (thanks to SWIG magic). Unfortunately, since it was built by many hands for many years, its code has become not easily maintainable or extendable and it does not use many new programming techniques and components that have appeared since the Shogun foundation. The time has come to blow some fresh air (and some new fresh code) into Shogun's depths. This project aims to correct and update the codebase and to complete the integration of many new features that will make it more modular and easy to use. My efforts will be focused on: integrate the new Tags and serialization frameworks, substitute old-style macros with brand new C++11 smart pointers, enable premature stopping of ML algorithm and, last but not least, implement a useful (and beautiful) progress bar to show a visual representation of algorithm’s execution.</p>