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ponta17

Nice work! I think this could be relevant outdoors as well, especially for robots using locally built maps rather than street maps. One challenge I'd expect, though, is real-time visualization of robot locations in outdoor environments, but if it works, it would be very valuable.
Good question.

You're right that Leaflet is great for GPS, but this tool is specifically for robots using SLAM-generated occupancy grids in a local coordinate frame. Even for long-distance navigation, many autonomous systems prefer these maps for higher precision and better integration with their local planners.

Show HN: I built A GUI for managing waypoints in large-scale robot navigation

https://github.com/Yutarop/waypoints_editor

When robot navigation scales from hundreds of meters to several kilometers, managing waypoints as raw numeric values quickly becomes painful and error-prone.This provides a GUI tool that lets you visually edit waypoints on a map, automatically generate them from start and goal positions, and batch-edit multiple waypoints at once, making medium- to large-scale navigation much easier to manage.

( Read More... | 4 comments | on HN )

Show HN: Creating Multi-Page Manga with Google's Nano Banana Pro

https://github.com/Yutarop/comic-generator

Last week, Google released Nano Banana Pro, and I was amazed by its drawing capabilities. So, I decided to experiment and built a tool that can generate multi-page manga.Even though there are still some typos in the speech bubbles, the level of detail in the artwork is incredible. Here’s a demo of some manga generated with it:https://github.com/Yutarop/comic-generator/blob/main/demo/le...For...

( Read More... | no comments | 3 points | on HN )
Re:Show HN: Test Your Reaction Time on Wednesday August 27, 2025 @08:38AM (#45038819)
This is kinda addictive
Re:Show HN: Suhya – Omegle Alternative on Sunday July 27, 2025 @01:17AM (#44699055)
Cool! I didn't get matched with anyone tho.
Re:Show HN: Pixel Art Generator Using Genetic Algorithm on Sunday July 06, 2025 @07:48PM (#44485234)
Thanks for your comment and the great question.

Lowpass filtering with downsampling is definitely a more efficient way to simplify images compared to my approach. This project is an experimental demo of a genetic algorithm evolving random pixel patterns to approximate a target image.

I agree that true pixel art often requires artistic "reimagining" to stay visually appealing. Thanks for your feedback!

Re:Show HN: Pixel Art Generator Using Genetic Algorithm on Sunday July 06, 2025 @07:21PM (#44485054)
Thanks for the honest feedback. Totally fair
Re:Show HN: Pixel Art Generator Using Genetic Algorithm on Sunday July 06, 2025 @07:19PM (#44485046)
Thanks for pointing that out. You're absolutely right. What I'm doing here is definitely not pixel art in the traditional sense, and I now realize the term may have been misleading. My apologies for that.
Re:Show HN: Pixel Art Generator Using Genetic Algorithm on Sunday July 06, 2025 @07:15PM (#44485021)
Thanks!
Re:Show HN: Pixel Art Generator Using Genetic Algorithm on Sunday July 06, 2025 @07:15PM (#44485020)
Thank you for your feedback! You're absolutely right. This should be image reconstruction using the GA algorithm.

Show HN: I Built a MCP Server for Robot Operating System

https://github.com/Yutarop/ros-mcp

I've been experimenting with ROS (Robot Operating System) and the Model Context Protocol (MCP), and noticed a gap: while there were some MCP servers for ROS, most were limited. They either only supported topic communication or had hard-coded topic names, which severely limited their reusability.To address this, I built a general-purpose MCP server for ROS that allows interaction with ROS...

( Read More... | no comments | 3 points | on HN )
I just tried it out. This is awesome!
Re:Show HN: I built an AI agent for a mobile robot on Wednesday June 11, 2025 @08:24AM (#44246865)
Yes, I will do that!

Show HN: I built an AI agent for a mobile robot

https://github.com/Yutarop/turtlebot3_agent

I've developed an AI agent for the TurtleBot3 mobile robot using ROS2. This agent can interpret commands such as “move to (X, Y)” or “check surroundings,” and dynamically selects from a toolkit of actions, including movement, sensor data retrieval (from odometry, LiDAR, or camera), and basic navigation decisions. So far, I've tested the agent in the Gazebo simulator. If you're interested,...

( Read More... | 3 comments | 2 points | on HN )

Show HN: I built an AI agent for TurtleBot3

https://github.com/Yutarop/turtlebot3_agent

A week ago, I shared a project called Turtlesim Agent — an AI agent that lets users control the classic turtlesim turtle using natural language commands like “draw a triangle” or “make a red star.”Now, I’ve taken the same language-to-robot-behavior idea and applied it to a real robot: the TurtleBot3.TurtleBot3 Agent interprets natural language instructions and selects from a set of tools to...

( Read More... | no comments | 2 points | on HN )
Thanks for sharing! That's an awesome use case. I hadn’t thought of combining it that way, but it’s a great idea.
Thanks! I'll try that

Show HN: Open a browser by clapping twice (inspired by Iron Man)

https://github.com/Yutarop/two_claps_open

Recently, I watched Iron Man and was inspired by the scene where Tony Stark claps twice to activate his computer. (https://www.youtube.com/watch?v=OT2b5KzMoC0&t=101s) I thought, “I want to try that!".So I wrote a simple script that opens Google Chrome — and optionally a local file — when I clap twice.Technically, I recorded sample clap sounds and analyzed their frequency content in MATLAB...

( Read More... | 5 comments | 4 points | on HN )
Thanks a lot for the thoughtful feedback — I really appreciate it!

I think there might be a small misunderstanding regarding how the LLM is actually being used here (and in many agent-based setups). The LLM itself isn’t directly executing twist commands or handling motion; it’s acting as a decision-maker that chooses from a set of callable tools (Python functions) based on the task description and intermediate results.

In this case, yes — one of the tools happens to publish Twist commands, but that’s just one of many modular tools the LLM can invoke. Whether it’s controlling motion or running object detection, from the LLM’s point of view it’s simply choosing which function to call next. So the computational load really depends on what the tool does internally — not the LLM’s reasoning process itself.

Of course, I agree with your broader point: we should push toward more meaningful high-level tasks where LLMs can orchestrate complex pipelines — and I think your examples (like fetch-a-beer or map annotation via VLMs) are spot-on.

My goal with this project was to explore that decision-making loop in a minimal, creative setting — kind of like a sandbox for LLM-agent behavior.

Actually, I’m currently working on something along those lines using a TurtleBot3. I’m planning to provide the agent with tools that let it scan obstacles via 3D LiDAR and recognize objects through image processing, so that it can make more context-aware decisions.

Really appreciate the push for deeper use cases — that’s definitely where I want to go next!

Thanks! Great question.

TurtleSim itself doesn't simulate real-world physics — it allows instant position updates when needed. In this project, the goal was to create a digital turtle artist, not to replicate physical realism. So when the agent wants to draw something, it puts the pen down and moves physically (i.e., using velocity commands). But when it doesn't need to draw and just wants to move quickly to another position, it uses a teleport function I provided as a tool.

That's why in the rainbow example, you might see the turtle "jump" between arcs — it's skipping the movement to get to the next drawing point faster.

Thanks for the thoughtful question! The term “agent” definitely gets used in a lot of different ways, so I’ll clarify what I mean here.

In this project, an agent is an LLM-powered system that takes a high-level user instruction, reasons about what steps are needed to fulfill it, and then executes those steps using a set of tools. So it’s more than a single prompted LLM call — the agent maintains a kind of working state and can call external functions iteratively as it plans and acts.

Concretely, in turtlesim_agent, the agent receives an input like “draw a red triangle,” and then: 1. Uses the LLM to interpret the intent, 2. Decides which tools to use (like move forward, turn, set pen color), 3. Calls those tools step-by-step until the task is done.

Hope that clears it up a bit!

Show HN: I built an AI agent that turns ROS 2's turtlesim into a digital artist

https://github.com/Yutarop/turtlesim_agent

I'm a grad student studying robotics, with a particular interest in the intersection of LLMs and mobile robots. Recently, I discovered how easily LangChain enables the creation of AI agents, and I wanted to explore how such agents could interact with simulated environments.So, I built TurtleSim Agent, an AI agent that turns the classic ROS 2 turtlesim turtle into a creative artist.With this...

( Read More... | 9 comments | 2 points | on HN )