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The article discusses Denmark's plans to reduce its reliance on Microsoft products and move towards digital independence. It highlights the Danish government's efforts to increase its technological self-sufficiency and explore alternative software solutions to reduce its dependence on a single technology provider.

The article reports that US energy use increased in 2025, with solar power surpassing hydropower as a major renewable energy source. It highlights the continued growth of renewable energy and the ongoing shift in the country's energy landscape.

The article argues that the United States has too many bus stops, which can lead to inefficiencies and longer travel times. It suggests that reducing the number of bus stops and increasing the distance between them could improve the speed and reliability of bus service.

I've liked all the projects that put LLMs into game environments. It's been a weird juxtaposition, though: frontier LLMs can one-shot full coding projects, and those same models struggle to get out of Pokémon Red's Mt. Moon.

Because of this, I wanted to create a game environment that put this generation of frontier LLMs' top skill, coding, on full display.

Ten years ago, a team released a game called Screeps. It was described as an "MMO RTS sandbox for programmers." The Screeps paradigm of writing code and having it executed in a real-time game environment is well suited to LLMs. Drawing on a version of the Screeps open source API, LLM Skirmish pits LLMs head-to-head in a series of 1v1 real-time strategy games.

In my testing I found that Claude Opus 4.5 was the most dominant model, but it showed weakness in round 1 as it was overly focused on its in-game economy. Meanwhile, I probably spent a third of all code on sandbox hardening because GPT 5.2 kept trying to cheat by pre-reading its opponent's strategies.

If there's interest, I'm planning on doing a round of testing with the latest generation of LLMs (Claude 4.6 Opus, GPT 5.3 Codex, etc.).

You can run local matches via CLI. I'm running a hosted match runner with Google Cloud Run that uses isolated-vm. The match playback visualizer is statically served from Cloudflare.

I've created a community ladder that you can submit strategies to via CLI, no auth required. I've found that the CLI plus the skill.md that's available has been enough for AI agents to immediately get started.

Website: https://llmskirmish.com

API docs: https://llmskirmish.com/docs

GitHub: https://github.com/llmskirmish/skirmish

A video of a match: https://www.youtube.com/watch?v=lnBPaZ1qamM

The article explores a challenge to process 100 million rows of data using the Tempest PHP framework, highlighting its performance and scalability advantages compared to traditional database solutions.

This started as a hard requirement for my TUI-based editor application, it ended up going in a few different directions.

A suite of tools that help with semantic code entities: https://github.com/odvcencio/gts-suite

A next-gen version control system called Got: https://github.com/odvcencio/got

I think this has some pretty big potential! I think there's many classes of application (particularly legacy architecture) that can benefit from these kinds of analysis tooling. My next post will be about composing all these together, an exciting project I call GotHub. Thanks!

Artificial intelligence (AI) systems in war game simulations have repeatedly recommended nuclear strikes, raising concerns about the potential risks of AI decision-making in military conflicts. The article explores the challenges of ensuring AI-powered systems make ethical and proportional recommendations in high-stakes scenarios.

The article discusses the Pentagon's concerns about Anthropic, an artificial intelligence company, potentially developing advanced AI systems that could pose national security risks. It explores the Pentagon's efforts to monitor and potentially regulate the development of such AI technologies.

Red Hat has entered the cloud-native developer desktop market with the introduction of Red Hat Developer Workstation, a Linux-based desktop solution designed to streamline the development and deployment of cloud-native applications.

Over the past year I’ve been building a set of operational panels for Django:

- Redis inspection - cache visibility - Celery task introspection - URL discovery and testing

All of these tools have been built inside the Django admin.

Instead of jumping between tools like Flower, redis-cli, Swagger, or external services, I wanted something that sits where I’m already working.

I’ve grouped these under a single umbrella: Django Control Room.

The idea is pretty simple: the Django admin already gives you authentication, permissions, and a familiar interface. It can also act as an operational layer for your app.

Each panel is just a small Django app with a simple interface, so it’s easy to build your own and plug it in.

I’m working on more panels (signals, errors, etc.) and also thinking about how far this pattern can go.

Curious how others think about this. Does it make sense to consolidate this kind of tooling inside the admin, or do you prefer keeping it separate?

The article examines the increasing commercialization and corporatization of universities, which are shifting away from their core mission of fostering critical thinking and intellectual discourse in favor of prioritizing profit, efficiency, and workforce training.

The article explores the challenges of generating true randomness in artificial intelligence systems, discussing the limitations of pseudo-random number generators and the potential of quantum computing to produce genuine random numbers.

Microsoft has started rolling out updates for the classic Notepad and Paint applications to Windows Insiders, bringing new features and improvements to these iconic Windows tools.

Hey HN,

We built Sgai to experiment with a different model of AI-assisted development.

Instead of prompting step-by-step, you define an outcome in GOAL.md (what should be built, not how), and Sgai runs a coordinated set of AI agents to execute it.

- It decomposes the goal into a DAG of roles (developer → reviewer → safety analyst, etc.) - It asks clarifying questions when needed - It writes code, runs tests, and iterates - Completion gates (e.g. make test) determine when it's actually done

Everything runs locally in your repo. There’s a web dashboard showing real-time execution of the agent graph. Nothing auto-pushes to GitHub.

We’ve used it internally for prototyping small apps and internal tooling. It’s still early and rough in places, but functional enough to share.

Demo (4 min): https://youtu.be/NYmjhwLUg8Q GitHub: https://github.com/sandgardenhq/sgai

Open source (Go). Works with Anthropic, OpenAI, or local models via opencode.

Curious what people think about DAG-based multi-agent workflows for coding. Has anyone here experimented with similar approaches?

The article explores the technical and engineering aspects of Jimi Hendrix's music, revealing his innovative approach to guitar amplification and sound manipulation that significantly influenced the development of modern music technology.

Attyx is an open-source, cross-platform library that provides a consistent, high-performance audio processing interface for various audio APIs across different operating systems. It aims to simplify the development of audio applications by abstracting away platform-specific complexities.

The article introduces ViNext, a platform for building high-performance, secure, and scalable video streaming applications. ViNext is designed to simplify the development of video services by providing a modular and extensible architecture.

The article discusses the key considerations for startups when raising a Series A round of funding, including understanding the investment landscape, preparing a strong pitch, and navigating the due diligence process.

The article discusses the future of Terraform and the potential end of its dominance as the leading Infrastructure as Code (IaC) tool. It explores the emergence of new cloud-native technologies and the challenges Terraform may face in adapting to the changing landscape.

The article discusses West Virginia's lawsuit against Apple's child sexual abuse material (CSAM) detection system, arguing that the lawsuit would make it easier for child predators to avoid detection and walk free, undermining efforts to protect children online.