Shortest-possible walking tour to 81,998 bars in South Korea (Score: 150+ in 4 hours)

Link: https://readhacker.news/s/6thRT
Comments: https://readhacker.news/c/6thRT

Show HN: Rowboat – Open-source IDE for multi-agent systems (Score: 150+ in 1 day)

Link: https://readhacker.news/s/6tdmr
Comments: https://readhacker.news/c/6tdmr

Hi HN! We’re Arjun, Ramnique, and Akhilesh, and we are building Rowboat (https://www.rowboatlabs.com/), an AI-assisted IDE for building and managing multi-agent systems. You start with a single agent, then scale up to teams of agents that work together, use MCP tools, and improve over time - all through a chat-based copilot.
Our repo is https://github.com/rowboatlabs/rowboat, docs are at https://docs.rowboatlabs.com/, and there’s a demo video here: https://youtu.be/YRTCw9UHRbU
It’s becoming clear that real-world agentic systems work best when multiple agents collaborate, rather than having one agent attempt to do everything. This isn’t too surprising - it’s a bit like how good code consists of multiple functions that each do one thing, rather than cramming everything into one function.
For example, a travel assistant works best when different agents handle specialized tasks: one agent finds the best flights, another optimizes hotel selections, and a third organizes the itinerary. This modular approach makes the system easier to manage, debug, and improve over time.
OpenAI’s Agents SDK provides a neat Python library to support this, but building reliable agentic systems requires constant iterations and tweaking - e.g. updating agent instructions (which can quickly get as complex as actual code), connecting tools, and testing the system and incorporating feedback. Rowboat is an AI IDE to do all this. Rowboat is to AI agents what Cursor is to code.
We’ve taken a code-like approach to agent instructions (prompts). There are special keywords to directly reference other agents, tools or prompts - which are highlighted in the UI. The copilot is the best way to create and edit these instructions - each change comes with a code-style diff.
You can give agents access to tools by integrating any MCP server or connecting your own functions through a webhook. You can instruct the agents on when to use specific tools via ‘@mentions’ in the agent instruction. To enable quick testing, we added a way to mock tool responses using LLM calls.
Rowboat playground lets you test and debug the assistants as you build them. You can see agent transfers, tool invocations and tool responses in real-time. The copilot has the context of the chat, and can improve the agent instructions based on feedback. For example, you could say ‘The agent shouldn’t have done x here. Fix this’ and the copilot can go and make this fix.
You can integrate agentic systems built in Rowboat into your application via the HTTP API or the Python SDK (‘pip install rowboat’). For example, you can build user-facing chatbots, enterprise workflows and employee assistants using Rowboat.
We’ve been working with LLMs since GPT-1 launched in 2018. Most recently, we built Coinbase’s support chatbot after our last AI startup was acquired by them.
Rowboat is Apache 2.0 licensed, giving you full freedom to self-host, modify, or extend it however you like.
We’re excited to share Rowboat with everyone here. We’d love to hear your thoughts!

YAGRI: You are gonna read it (Score: 151+ in 8 hours)

Link: https://readhacker.news/s/6thvz
Comments: https://readhacker.news/c/6thvz

Google blocked Motorola use of Perplexity AI, witness says (Score: 150+ in 9 hours)

Link: https://readhacker.news/s/6thms
Comments: https://readhacker.news/c/6thms

Show HN: My from-scratch OS kernel that runs DOOM (Score: 150+ in 6 hours)

Link: https://readhacker.news/s/6thRt
Comments: https://readhacker.news/c/6thRt

Hi there! I've been on-and-off working on TacOS for a few months, which follows some UNIX-derived concepts (exec/fork, unix-style VFS, etc) and is now able to run a port of Doom, with a fairly small amount of modifications, using my from-scratch libc. The performance is actually decent compared to what I expected. Very interested to hear your thoughts. Thank you!

Graphics livecoding in Common Lisp (Score: 150+ in 13 hours)

Link: https://readhacker.news/s/6tgMy
Comments: https://readhacker.news/c/6tgMy

The Future of MCPs (Score: 150+ in 17 hours)

Link: https://readhacker.news/s/6tgEr
Comments: https://readhacker.news/c/6tgEr

"Careless People" the book that Meta tried to suppress (🔥 Score: 150+ in 2 hours)

Link: https://readhacker.news/s/6tiAd
Comments: https://readhacker.news/c/6tiAd

Hyperwood – Open-Source Furniture (Score: 150+ in 1 day)

Link: https://readhacker.news/s/6tdef
Comments: https://readhacker.news/c/6tdef

CubeCL: GPU Kernels in Rust for CUDA, ROCm, and WGPU (Score: 150+ in 12 hours)

Link: https://readhacker.news/s/6thKd
Comments: https://readhacker.news/c/6thKd

The hidden cost of AI coding (Score: 150+ in 17 hours)

Link: https://readhacker.news/s/6tgYQ
Comments: https://readhacker.news/c/6tgYQ

First Successful Lightning Triggering and Guiding Using a Drone (Score: 150+ in 17 hours)

Link: https://readhacker.news/s/6th88
Comments: https://readhacker.news/c/6th88

I wrote to the address in the GPLv2 license notice (2022) (🔥 Score: 162+ in 1 hour)

Link: https://readhacker.news/s/6tj5s
Comments: https://readhacker.news/c/6tj5s

On loyalty to Your Employer (Score: 154+ in 5 hours)

Link: https://readhacker.news/s/6tiJh
Comments: https://readhacker.news/c/6tiJh

Mark Zuckerberg Says Social Media Is Over (Score: 152+ in 8 hours)

Link: https://readhacker.news/s/6tiAt
Comments: https://readhacker.news/c/6tiAt

I Tried to Buy an Actual Barrel of Crude Oil (2015) (❄️ Score: 151+ in 2 days)

Link: https://readhacker.news/s/6tcAE
Comments: https://readhacker.news/c/6tcAE

Launch HN: Cua (YC X25) – Open-Source Docker Container for Computer-Use Agents (Score: 151+ in 1 day)

Link: https://readhacker.news/s/6tgqM
Comments: https://readhacker.news/c/6tgqM

Hey HN, we’re Francesco and Alessandro, the creators of c/ua (https://www.trycua.com), a Docker‑style container runtime that lets AI agents drive full operating systems in lightweight, isolated VMs. Our entire framework is open‑source (https://github.com/trycua/cua), and today we’re thrilled to have our Launch HN!
Check out our demo to see it in action: https://www.youtube.com/watch?v=Ee9qf-13gho, and for more examples - including Tableau, Photoshop, CAD workflows - see the demos in our repo: https://github.com/trycua/cua.
For Computer-Use AI agents to be genuinely useful, they must interact with your system's native applications. But giving full access to your host device is risky. What if the agent's process gets compromised, or the LLM hallucinates and leaks your data? And practically speaking, do you really want to give up control of your entire machine just so the agent can do its job?
The idea behind c/ua is simple: let agents operate in a mirror of the user’s system - isolated, secure, and disposable - so users can fire-and-forget complex tasks without needing to dedicate their entire system to the agent. By running in a virtualized environment, agents can carry out their work without interrupting your workflow or risking the integrity of your system.
While exploring this idea, I discovered Apple’s Virtualization.Framework and realized it offered fast and lightweight virtualization on Apple Silicon. This led us to build a high-performance virtualization layer and, eventually, a computer-use interface that allows agents to interact with apps just like a human would - without taking over the entire system.
As we built this, we decided to open-source the virtualization core as a standalone CLI tool called Lume (Show HN here: https://news.ycombinator.com/item?id=42908061). c/ua builds on top of Lume, providing a full framework for running agent workflows inside secure macOS or Linux VMs, so your system stays free for you to use while the agent works its magic in the background.
With Cua you can build an AI agent within a virtual environment to: - navigate and interact with any application's interface; - read screen content and perform keyboard/mouse actions; - switch between applications and self-debug when needed; - operate in a secure sandbox with controlled file access. All of this occurs in a fully isolated environment, ensuring your host system, files, and sensitive data remain completely secure, while you continue using your device without interruption.
People are using c/ua to: - Bypass CryptoJS-based encryption and anti-bot measures to interact with modern web apps reliably; - Automate Tableau dashboards and export insights via Claude Desktop; - Drive Photoshop for batch image editing by prompt; - Modify 3D models in Fusion 360 with a CAD Copilot; -Extract data from legacy ERP apps without brittle screen‑scraping scripts.
We’re currently working on multi‑VM orchestration for parallel agentic workflows, Windows and Linux VM support, and episodic and long-term memory for CUA Agents.
On the open‑source side, c/ua is 100 % free under the MIT license - run it locally with any LLM you like. We’re also gearing up a hosted orchestration service for teams who want zero‑ops setup (early access sign‑ups opening soon).
We’d love to hear from you. What desktop or legacy apps do you wish you could automate? Any thoughts, feedback, or horror stories from fragile AI automations are more than welcome!

AMD Publishes Open-Source Driver for GPU Virtualization, Radeon "In the Roadmap" (Score: 151+ in 9 hours)

Link: https://readhacker.news/s/6tisT
Comments: https://readhacker.news/c/6tisT

Instant SQL for results as you type in DuckDB UI (🔥 Score: 150+ in 3 hours)

Link: https://readhacker.news/s/6tjeG
Comments: https://readhacker.news/c/6tjeG

Manufactured Consensus on X.com (🔥 Score: 154+ in 2 hours)

Link: https://readhacker.news/s/6tk3v
Comments: https://readhacker.news/c/6tk3v