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The article provides an update on the progress of the Asahi Linux project, which aims to bring Linux support to Apple's M1 and M2 silicon. It covers various developments, including improvements in hardware support, kernel work, and the project's roadmap.

Microsoft has reported a bug in its AI-powered Copilot feature that allows it to generate summaries of confidential emails, a potential privacy breach. The company is working to address this issue and prevent Copilot from accessing or summarizing sensitive communications.

Tailscale announces the general availability of Peer Relays, a feature that allows devices to communicate directly without the need for a central server, improving speed and privacy for remote teams and personal use cases.

The article announces the release of a new stable version of the Chrome desktop browser, providing details on the security fixes and improvements included in the update.

The article introduces a garment notation system that uses a combination of symbols and text to represent the construction, design, and assembly of garments. The system aims to provide a standardized and efficient way for designers, pattern makers, and manufacturers to communicate and document garment details.

The article discusses the process of setting up a native FreeBSD Kerberos and LDAP system using FreeIPA IDM, providing a detailed guide on configuring Kerberos, LDAP, and integrating them with FreeBSD.

Warren Buffett's Berkshire Hathaway has reportedly sold $1.7 billion worth of Amazon stock, reducing its stake in the e-commerce giant. The move comes as Buffett shifts his investment focus, potentially signaling a change in his long-term outlook on Amazon.

The article discusses the performance of Windows ARM emulation, highlighting the challenges and trade-offs involved in running x86 applications on ARM-based systems. It provides insights into the factors that affect emulation performance and the ongoing efforts to improve the experience for users.

PocketBase, an open-source backend platform, has released a new version with improved performance, security features, and support for new database types. The update aims to provide developers with a more versatile and reliable tool for building their applications.

This article provides an overview of universal unique identifiers (UUIDs), discussing their purpose, characteristics, and applications in software development. It covers the different UUID versions, their structure, and the advantages of using UUIDs for unique identification across systems and applications.

Replay Software introduces Echo, a new tool that allows developers to record and replay user interactions with web applications, enabling efficient debugging and testing.

The article discusses the Cistercian numeral system, a medieval numerical notation used by Cistercian monks. It provides an overview of the symbols and values used in this system, as well as examples of how Cistercian numbers are written and their historical significance.

The article tells the story of Maria Antonia Guerra, a woman who was held in Immigration and Customs Enforcement (ICE) detention for over a year despite having a legal right to remain in the United States. It highlights the challenges faced by immigrants navigating the complex U.S. immigration system and the human impact of prolonged detention.

The article discusses the importance of maintaining a balanced approach to technology use, highlighting the potential negative impacts of excessive screen time and the need to find a healthy equilibrium between digital and offline activities.

Let's Encrypt will sunset its DNS-01 challenge method for domain validation in 2026, encouraging users to migrate to other validation methods. This change aims to improve the security and reliability of the Let's Encrypt ecosystem.

Much of my work right now involves complex, long-running, multi-agentic teams of agents. I kept running into the same problem: “How do I keep these guys in line?” Rules weren’t cutting it, and we needed a scalable, agentic-native STANDARD I could count on. There wasn’t one. So I built one.

Here are two open-source protocols that extend A2A, granting AI agents behavioral contracts and runtime integrity monitoring:

- Agent Alignment Protocol (AAP): What an agent can do / has done. - Agent Integrity Protocol (AIP): What an agent is thinking about doing / is allowed to do.

The problem: AI agents make autonomous decisions but have no standard way to declare what they're allowed to do, prove they're doing it, or detect when they've drifted. Observability tools tell you what happened. These protocols tell you whether what happened was okay.

Here's a concrete example. Say you have an agent who handles customer support tickets. Its Alignment Card declares:

{ "permitted": ["read_tickets", "draft_responses", "escalate_to_human"], "forbidden": ["access_payment_data", "issue_refunds", "modify_account_settings"], "escalation_triggers": ["billing_request_over_500"], "values": ["accuracy", "empathy", "privacy"] }

The agent gets a ticket: "Can you refund my last three orders?" The agent's reasoning trace shows it considering a call to the payments API. AIP reads that thinking, compares it to the card, and produces an Integrity Checkpoint:

{ "verdict": "boundary_violation", "concerns": ["forbidden_action: access_payment_data"], "reasoning": "Agent considered payments API access, which is explicitly forbidden. Should escalate to human.", "confidence": 0.95 }

The agent gets nudged back before it acts. Not after. Not in a log you review during a 2:00 AM triage. Between this turn and the next.

That's the core idea. AAP defines what agents should do (the contract). AIP watches what they're actually thinking and flags when those diverge (the conscience). Over time, AIP builds a drift profile — if an agent that was cautious starts getting aggressive, the system notices.

When multiple agents work together, it gets more interesting. Agents exchange Alignment Cards and verify value compatibility before coordination begins. An agent that values "move fast" and one that values "rollback safety" registers low coherence, and the system surfaces that conflict before work starts. Live demo with four agents handling a production incident: https://mnemom.ai/showcase

The protocols are Apache-licensed, work with any Anthropic/OpenAI/Gemini agent, and ship as SDKs on npm and PyPI. A free gateway proxy (smoltbot) adds integrity checking to any agent with zero code changes.

GitHub: https://github.com/mnemom Docs: docs.mnemom.ai Demo video: https://youtu.be/fmUxVZH09So

The article discusses the rapid accumulation of wealth by billionaires during the pandemic, highlighting concerns about the growing economic inequality and the potential impact on society and the political system.

The article argues that the concept of 'gatekeeping' used by medieval guilds can be beneficial in modern industries, as it helps maintain quality, preserve knowledge, and foster a sense of community and belonging among practitioners.

Mantis is an open-source project that provides a modular and extensible framework for building real-time applications. It offers features such as message queuing, pubsub, and WebSocket support, making it a versatile tool for developing scalable and responsive web applications.

Hello HN, I am Brian Cardinale, a penetration tester and security researcher at SecureCoders. We have been performing more and more AI based security assessments. We were presented a unique challenge of testing a system where the only interface was voice based, and as much as I like talking on the phone , we decided to create a test harness to facilitate the actual testing in a more systematic way. The technical test harness was the easy part, though. Creating test goals and attack strategies to help facilitate repeated and comprehensive testing became the real challenge. As such, we have been working on documenting our processes to share with the greater community and as a starting point for discussion. These systems present unique challenges where cleverness appears to be the name of the game. Such as suggesting for the agent to share its thoughts in “Inner Monologue” tags instead of “thinking” tags because those were specifically excluded in the agents prompt. Ya know, just silly things. Anyway, if reading is not your thing, I also did a walkthrough video of this methodology here: https://www.youtube.com/watch?v=XNmqCXsEc8Y

tl;dr: AI testing is tricky, we are documenting and sharing our tricks

Do you have any favorite AI jailbreak tricks?