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The article discusses the key features and benefits of the Go programming language, highlighting its simplicity, fast compilation, and strong standard library, which make it a popular choice for building systems, services, and tools.

I kept getting burned by “what permissions am I running with?” so I built Codeman: a thin launcher around codex that forces an explicit security level each run (read-only, orkspace-write, networked, full), with a clear confirmation panel before higher-risk modes. It also supports resuming by session UUID and optional Slack/Discord webhook notifications. Repo: https://github.com/shabo/codeman Feedback welcome: naming, UX, and whether the level breakdown matches how you actually work with Codex.

Waymo, Alphabet's self-driving car unit, is asking DoorDash drivers to close the doors of its autonomous vehicles when they pick up or drop off passengers. This is an effort to ensure the cars' sensors and systems work properly.

The article explores the role of similes in venture capital investing, comparing the process to finding life in the universe and the evolution of the universe itself. It discusses how similes can provide valuable insights and help investors identify promising opportunities.

The Museum of Modern Art (MoMA) is hosting an exhibition that explores the diverse and influential work of the celebrated American artist Georgia O'Keeffe. The exhibition provides a comprehensive look at O'Keeffe's iconic paintings, drawings, and sculptures, spanning her influential career from the 1910s to the 1970s.

The article explores the concept of an 'optimal world', where humanity's values and goals are perfectly realized. It examines various philosophical and practical considerations, including the challenges of defining and achieving such an optimal state.

The article discusses how businesses can automate the process of printing and mailing newsletters, allowing them to produce newsletters while they sleep. It highlights the benefits of automating newsletter production, such as saving time and resources, and provides tips on setting up an automated newsletter system.

The article describes an individual's efforts to create a phased array passive radar system in their kitchen, utilizing readily available software-defined radio (SDR) hardware and open-source software to detect and track aircraft in their local airspace.

This article discusses the potential of blockchain technology to revolutionize the financial sector, focusing on its ability to enhance transparency, security, and efficiency in transactions, as well as its potential to disrupt traditional banking and financial services.

The Scene Gaussian Splat Viewer is an open-source tool that allows users to visualize and interact with 3D point cloud data using Gaussian splats. It provides a user-friendly interface for exploring and analyzing point cloud scenes.

Hi fellow HNers,

I am guld and I created ROE.md (https://github.com/guld/ROE.md), a proof of concept single Markdown file that let's you raise your own custom personal OpenClaw-like AI assistant from scratch.

With a very stripped-down feature-set of course. (For now. Still alpha)

All you need is a little vibe code magic from your favorite AI coding tool. Select your favorite programming language (yes really) and hope that it has been grokked by the SOTA models. (I am looking at you Ruby programmers).

I came up with the idea last week when I first heard of the OpenClaw craze. Listening to steipete's wild stories I was wondering... if the agent can create all these skills by itself... what if we spin it further and let the agent create itself from a single Markdown file. This should work!

How to use it?

Your coding agent of choice should load the ROE.md markdown file, you answer a few questions and it builds a minimal OpenClaw-like personal assistant for you. It comes with a single build in tool (execute_bash).

This is dangerous I know, but it works.

Anyways... I tested it with OpenCode and the Kimi-2.5 model and Python selected. (OpenCode is great!) With a few questions answered and some bugs fixed the custom agent works nicely in CLI mode with a locally running gpt-oss-20b through LM Studio.

How did I create the ROE.md file?

Mostly by hand, with a very high level overview of the architecture, almost no architecture at all. I rather focused more on adding a lot of example call-responses and used first person singular to improve prompting (Thanks to a comment thread on here where postalcoder verified that first person makes the difference with Gemini)

The next few days I want to extend the ROE.md file to contain more succinct API examples to make sure it works with more languages.

- TODOs alot... Fix, Test, Repeat

Challenges:

- Getting more programming languages to work in as few prompts as possible with the big local models such as Kimi-2.5. Tested Bash, Python, Go, Ruby, and a few others but Python works best.

- Not getting to specific in pseudocode.

- Keeping its structure general enough to be applicable to most programming languages.

- Making it work with weaker local models.

For more details check out the mini-tutorial in the README (https://github.com/guld/ROE.md).

This project and the power of vibe coding brought back the enjoyment I felt when I started out writing my first line of code on an "empty piece of Windows Editor paper" more than two decades ago.

What do you think? Is vibe coding crazy cool or is it just me?

The article highlights the surge in the stock price of Chinese AI company Zhipu, showcasing the growing popularity of AI companies in China's technology market. It suggests that Zhipu has become one of the new favorites among investors in the country's AI market.

The article discusses the push by governments to introduce central bank digital currencies (CBDCs) or stablecoins despite a lack of public demand, suggesting that the motivations may be more about control and surveillance than serving the needs of citizens.

The article explores the downfall of Elon Musk's ambitious Hyperloop project, which aimed to revolutionize high-speed transportation. It examines the technical challenges, financial hurdles, and regulatory obstacles that ultimately led to the project's demise, despite Musk's grand vision and efforts.

The article explores the issue of gatekeeping in the open-source community, using the case of Scott Shambaugh as an example. It examines how power dynamics and social dynamics within open-source projects can lead to exclusion and marginalization of certain contributors, even in purportedly 'open' and inclusive spaces.

Hi HN, I've been a developer for some time now, and like many of you, I've been frustrated by the "All-or-Nothing" problem with AI coding tools.

You ask an AI to fix a bug or implement a function, and it rewrites the whole file. It changes your imports, renames your variables, or deletes comments it deems unnecessary. It’s like giving a junior developer (like me) root access to your production server just to change a config file.

So, 29 days ago, I started building Yori to solve the trust problem.

The Concept: Semantic Containers Yori introduces a syntax that acts like a firewall for AI. You define a $${ ... }$$ block inside a text file.

Outside the block (The Host): Your manual code, architecture, and structure. The AI cannot touch this. Inside the block (The Container): You write natural language intent. The AI can only generate code here.

Example: myutils.md

```cpp EXPORT: "myfile.cpp"

// My manual architecture - AI cannot change this #include "utils.h"

void process_data() { // Container: The AI is sandboxed here, but inherits the rest of the file as context $${ Sort the incoming data vector using quicksort. Filter out negative numbers. Print the result. }$$ } EXPORT: END ``` How it works: Yori is a C++ wrapper that parses these files. Whatever is within the EXPORT block and outside the containers ($${ }$$) will be copied as it is. When you run `yori myutils.md -make -series`, it sends the prompts to a local (Ollama) or cloud LLM, generates the syntax, fills the blocks, and compiles the result using your native toolchain (GCC/Clang/Python).

If compilation fails, it feeds the error back to the LLM in a retry loop (self-healing).

Why I think this matters:

1. Safety: You stop giving AI "root access" to your files.

2. Intent as Source: The prompt stays in the file. If you want to port your logic from C++ to Rust, you keep the prompts and just change the compile target.

3. Incremental Builds (to be added soon): Named containers allow for caching. If the prompt hasn't changed, you don't pay for an API call.

It’s open source (MIT), C++17, and works locally.

I’d love feedback on the "Semantic Container" concept. Is this the abstraction layer we've been missing for AI coding? Let me hear your ideas. Also, if you can't run yori.exe tell what went wrong and we see how to fix it. I opened a github issue on this. I am also working in making documentation for the project (github wiki). So expect that soon.

GitHub: https://github.com/alonsovm44/yori

Thanks!

The article discusses the importance of creating a digital garden, a personal website that serves as a hub for one's online presence and intellectual work. It emphasizes the value of owning one's digital identity and cultivating a curated online space for personal growth and knowledge sharing.

The article explores strategies for pricing and monetizing AI-powered products and services. It covers factors to consider when determining pricing, different revenue models, and techniques for optimizing monetization and driving adoption of AI offerings.

The article discusses the growing trend of flexible working arrangements, exploring the benefits and challenges for both employees and employers. It examines how the COVID-19 pandemic has accelerated the shift towards remote and hybrid work models, and the implications for the future of the workplace.

This article reveals that U.S. Immigration and Customs Enforcement (ICE) has instructed its officers to wear body cameras during certain enforcement operations, a significant policy shift that aims to increase transparency and accountability in the agency's activities.

This is something I don't understand, how will all these AI-as-a-service companies survive in the future when hardware gets better and people are able to run LLMs locally? Of course right now the rent vs. buy equation is heavily tilted towards rent, but eventually I could see people buying a desktop they keep at home, and having all their personal inference running on that one machine. Or even having inference pools to distribute load among many people.

do you think what this is possible, and what are these companies plans in that event?

Cloud Claw is an open-source cloud management tool that allows users to manage their cloud resources across multiple providers, including AWS, Azure, and Google Cloud. The project aims to simplify cloud infrastructure management by providing a unified interface and automating common tasks.

Goldman Sachs is making significant inroads in India's competitive investment banking market, capitalizing on its global reputation and diversified service offerings to attract more clients and outperform local competitors.

JoyPass is a travel technology company that offers a subscription-based platform for accessing a variety of experiences and activities at destinations around the world, providing travelers with a flexible and convenient way to explore and discover new places.

The article proposes a novel approach to training large language models that significantly improves their performance on a wide range of natural language tasks, while maintaining computational efficiency and scalability.