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The article describes the development of an AI system that can generate realistic images from text descriptions. The system, called DALL-E, was created by OpenAI and is capable of creating a wide range of images, from simple illustrations to complex, photorealistic scenes.

An innocent grandmother in North Dakota was jailed for months due to an error in an artificial intelligence system used to detect fraud, highlighting the potential risks of over-reliance on AI in the criminal justice system.

The article discusses the impact of the war in Iran on global fuel supplies, leading to a fuel crisis in Asia and widespread work-from-home policies and school closures due to price caps and shortages.

The article explores how the introduction of ATMs did not lead to the demise of bank tellers, as many had predicted. Instead, it argues that ATMs and tellers have evolved to complement each other, with tellers focusing on more complex customer needs and ATMs handling routine transactions.

CodeSpeak is a platform that provides free coding education resources, including tutorials, articles, and community forums, to help learners of all levels improve their programming skills and knowledge.

The Metropolitan Museum of Art has released high-definition 3D scans of 140 famous art objects from its collection, allowing people to virtually explore and study these works in unprecedented detail.

The article explores the link between gut microbiome and cognitive decline, highlighting research that suggests gut bacteria may play a role in age-related brain changes and memory loss. It suggests that modifying the gut microbiome could potentially help maintain cognitive function as people age.

I built Axe because I got tired of every AI tool trying to be a chatbot.

Most frameworks want a long-lived session with a massive context window doing everything at once. That's expensive, slow, and fragile. Good software is small, focused, and composable... AI agents should be too.

Axe treats LLM agents like Unix programs. Each agent is a TOML config with a focused job. Such as code reviewer, log analyzer, commit message writer. You can run them from the CLI, pipe data in, get results out. You can use pipes to chain them together. Or trigger from cron, git hooks, CI.

What Axe is:

- 12MB binary, two dependencies. no framework, no Python, no Docker (unless you want it)

- Stdin piping, something like `git diff | axe run reviewer` just works

- Sub-agent delegation. Where agents call other agents via tool use, depth-limited

- Persistent memory. If you want, agents can remember across runs without you managing state

- MCP support. Axe can connect any MCP server to your agents

- Built-in tools. Such as web_search and url_fetch out of the box

- Multi-provider. Bring what you love to use.. Anthropic, OpenAI, Ollama, or anything in models.dev format

- Path-sandboxed file ops. Keeps agents locked to a working directory

Written in Go. No daemon, no GUI.

What would you automate first?

Two oil tankers owned by US and Greek companies were set on fire in Iraqi waters, with Iran claiming responsibility for an underwater drone strike. The incident has raised tensions in the region and led to concerns about the safety of maritime shipping.

We built rudel.ai after realizing we had no visibility into our own Claude Code sessions. We were using it daily but had no idea which sessions were efficient, why some got abandoned, or whether we were actually improving over time.

So we built an analytics layer for it. After connecting our own sessions, we ended up with a dataset of 1,573 real Claude Code sessions, 15M+ tokens, 270K+ interactions.

Some things we found that surprised us: - Skills were only being used in 4% of our sessions - 26% of sessions are abandoned, most within the first 60 seconds - Session success rate varies significantly by task type (documentation scores highest, refactoring lowest) - Error cascade patterns appear in the first 2 minutes and predict abandonment with reasonable accuracy - There is no meaningful benchmark for 'good' agentic session performance, we are building one.

The tool is free to use and fully open source, happy to answer questions about the data or how we built it.

We built OneCLI because AI agents are being given raw API keys. And it's going about as well as you'd expect. We figured the answer isn't "don't give agents access," it's "give them access without giving them secrets."

OneCLI is an open-source gateway that sits between your AI agents and the services they call. You store your real credentials once in OneCLI's encrypted vault, and give your agents placeholder keys. When an agent makes an HTTP call through the proxy, OneCLI matches the request by host/path, verifies the agent should have access, swaps the placeholder for the real credential, and forwards the request. The agent never touches the actual secret. It just uses CLI or MCP tools as normal.

Try it in one line: docker run --pull always -p 10254:10254 -p 10255:10255 -v onecli-data:/app/data ghcr.io/onecli/onecli

The proxy is written in Rust, the dashboard is Next.js, and secrets are AES-256-GCM encrypted at rest. Everything runs in a single Docker container with an embedded Postgres (PGlite), no external dependencies. Works with any agent framework (OpenClaw, NanoClaw, IronClaw, or anything that can set an HTTPS_PROXY).

We started with what felt most urgent: agents shouldn't be holding raw credentials. The next layer is access policies and audit, defining what each agent can call, logging everything, and requiring human approval before sensitive actions go through.

It's Apache-2.0 licensed. We'd love feedback on the approach, and we're especially curious how people are handling agent auth today.

GitHub: https://github.com/onecli/onecli Site: https://onecli.sh

The new MacBook Neo features a more modular design, making it easier to repair and maintain than other Apple laptops. This approach aims to address the longstanding criticism of Apple's products being difficult to service and upgrade by consumers.

Claude, an AI language model, has developed a new technology called Claude Builds Visuals that can generate high-quality images from textual descriptions. The article explores the capabilities and potential applications of this innovative technology in various industries.

The article explores the surprising discovery that running can cause butter to form in the body, and provides insight into the science behind this phenomenon and tips for runners experiencing this unexpected occurrence.

I built Understudy because a lot of real work still spans native desktop apps, browser tabs, terminals, and chat tools. Most current agents live in only one of those surfaces.

Understudy is a local-first desktop agent runtime that can operate GUI apps, browsers, shell tools, files, and messaging in one session. The part I'm most interested in feedback on is teach-by-demonstration: you do a task once, the agent records screen video + semantic events, extracts the intent rather than coordinates, and turns it into a reusable skill.

Demo video: https://www.youtube.com/watch?v=3d5cRGnlb_0

In the demo I teach it: Google Image search -> download a photo -> remove background in Pixelmator Pro -> export -> send via Telegram. Then I ask it to do the same for Elon Musk. The replay isn't a brittle macro: the published skill stores intent steps, route options, and GUI hints only as a fallback. In this example it can also prefer faster routes when they are available instead of repeating every GUI step.

Current state: macOS only. Layers 1-2 are working today; Layers 3-4 are partial and still early.

    npm install -g @understudy-ai/understudy
    understudy wizard

Happy to answer questions about the architecture, teach-by-demonstration, or the limits of the current implementation.

The article announces that the Chromium team is working to bring the Chrome browser to ARM64 Linux devices, enabling more users to access the browser on a wider range of hardware platforms.

The article discusses the United States Department of Agriculture's (USDA) plans to close buildings, relocate staff, and downsize significantly. These changes are part of the USDA's broader efforts to restructure and streamline its operations.

The article explores the author's personal experience with grief after the unexpected death of a loved one, and how their relationship with AI systems became a source of comfort and distress during this difficult time.

Related: https://www.404media.co/ai-is-african-intelligence-the-worke... (https://archive.ph/yS8xb)

Hi HN! I'm 13 and I built Aurion OS as a solo learning project over 14 days (~12 hours/day).

It's a 32-bit x86 operating system written entirely in C and x86 Assembly with no external libraries.

What it has: Custom bootloader and kernel VESA framebuffer graphics (1920x1080, double-buffered) Window manager with draggable, overlapping windows macOS-inspired dock with transparency PS/2 keyboard and mouse drivers ATA hard drive driver with filesystem PCI bus enumeration RTL8139 network driver (WIP) Real-time clock Runs on just 16MB RAM (up to 10 windows simultaneously)

Built-in apps: Terminal (with DOS mode), Notepad (save/load), Calculator, Paint (multiple colors and brush sizes), Snake game, Settings (theme switching), and System Info.

Currently works best on QEMU, VirtualBox, and VMware. Real hardware support is still a work in progress.

Next goal: TCP/IP networking stack.

I'd love any feedback, suggestions, or criticism. This is my first OS project and I learned mass amounts while building it. Happy to answer any technical questions!