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The article discusses the importance of secure boot and hardware root of trust in ensuring the security of computing devices. It explains how these technologies work together to prevent unauthorized modifications and protect the integrity of the device's firmware and software.

This article presents a novel deep learning-based method for segmentation and analysis of microscopy images of cell nuclei. The proposed approach demonstrates improved performance compared to traditional techniques, making it a valuable tool for biological and medical research.

Hey HN! We launched a thing today, and built a cool demo that I'm excited to share with the community.

This tool creates AI agents easily and can handle some really technically complex work. I whipped up this rocket scientist agent in our tool in 10 minutes. I asked a couple of aerospace engineer friends what they thought, and they found it useful for their real work, which is why I want to share it here. Here's the demo https://demo.contextual.ai/ and an overview https://docs.contextual.ai/examples/rocket_science

Would love to see technical HN folks and their experience with our tool (and whether it would be useful in your work). Happy to go deep on architecture, retrieval strategies, and lessons learned.

The Doomsday Clock was moved to 100 seconds to midnight, the closest it has ever been to catastrophic disaster, reflecting increasing threats from nuclear weapons, climate change, and disruptive technologies. Experts warn that the world is now facing unprecedented global dangers that require immediate action to reduce risks and ensure a safer future.

Hi HN — I’ve been working on a small open-source experiment around runtime AI safety.

The idea is to treat AI risk not as a binary “safe/unsafe” state or post-hoc failure analysis, but as accumulated constraint strain over time — similar to how engineers think about mechanical stress.

The project defines a simple, model-agnostic signal (GV) and a lightweight runtime monitor (Sentinel) that classifies risk into bands (green/yellow/red) and suggests interventions (alert, throttle, human review).

This is an early MVP — intentionally minimal — meant to explore whether continuous, quantitative safety signals are useful before failures occur, especially for agents and LLM-based systems in production.

I’d really appreciate feedback, criticism, or pointers to prior art I should study. Repo: https://github.com/willshacklett/gvai-safety-systems

The article discusses the U.S. National Defense Strategy, which reportedly shifts focus from Europe to Greenland, reflecting a change in geopolitical priorities. The strategy is said to downgrade Europe's importance while elevating Greenland as a strategic location for the U.S.

This article explores the relationship between humor and longevity, highlighting research that suggests laughter and a positive attitude can have a positive impact on lifespan and overall health. The article examines various studies and anecdotal evidence that suggest incorporating more humor and laughter into one's life may contribute to increased longevity.

As a senior-almost-staff engineer just having turned 30, I feel pretty pessimistic about AI and how much it's going to affect the lives and careers of myself and my peers. I see people optimistic about AI for how it can impact and improve society but also at a personal level for their own careers. What should I be looking towards to not despair for the future?

DocEndorse is an AI-powered e-signature assistant that lets you prepare, send, track, and follow up on documents through natural language instead of forms and drag-and-drop editors.

You can tell it what you want (“Send this NDA to John for signature”), and it will: • Find the right document and contacts • Detect and place signing fields • Assign signer roles • Suggest titles and messages • Send requests via email, SMS, WhatsApp, or Microsoft Teams • Track status and automatically follow up with signers

It supports the full e-signature lifecycle: self-signing, sending requests, templates, reminders, resends, and real-time status updates. Follow-ups are generated automatically with tone and timing adjusted based on progress.

visit https://docendorse.com/esignature to learn more

Hey HN,

I wanted to share a project I’ve been working on called Convoxa. It’s a native iOS transcriber/summarizer. I had two main goals: keep it efficient and keep it private.

THE TECH STACK

100% Swift & SwiftUI: No heavy cross-platform wrappers or bloated dependencies.

Binary Size: The final build is only 4.8 MB.

Transcription: Uses Apple's latest speech APIs for maximum privacy and efficiency.

THE CHALLENGE: BYPASSING THE 4K CONTEXT LIMIT

The biggest technical hurdle was working with Apple’s foundation models. The default context window is capped at 4096 tokens, which is practically useless for anything over a 10-minute meeting transcript.

I ended up building a recursive chunking method to "feed" the model long-form data without losing the global context of the conversation. I use a sliding window approach where each chunk's summary informs the next, ensuring the final output doesn't "hallucinate" at the seams where the chunks meet. It’s now stable enough for long-form audio while remaining entirely on-device for supported hardware.

PRIVACY & AI MODES

On-Device: (Apple Intelligence required) - Total local processing.

Cloud: With reasoning for intelligent insights (Zero Data Retention).

I’m currently in the pre-order phase (out on Feb 3rd) and would love to get some feedback from this community on the performance and the chunking logic.

App Store: https://apps.apple.com/us/app/convoxa-ai-meeting-minutes/id6...

Hi HN,

We built Stac a server driven UI framework for Flutter.

The idea is simple: instead of shipping a new app release for every UI change, the app renders UI from JSON delivered by the backend. This allows teams to update layouts, experiment with flows, and roll out changes instantly while keeping Flutter’s performance and type safety.

Stac is open source and already used in production by startups and enterprise teams for things like:

Feature flags and experiments

Faster iteration on UI without app store delays

Sharing UI logic across multiple Flutter apps

We are currently working on better tooling, validation, and a hosted solution for managing and deploying UI schemas.

Would love feedback from people who have built or evaluated server driven UI systems. What tradeoffs or pitfalls should we be thinking more about?

Repo: https://github.com/StacDev/stac

The article presents Latent MoE, a novel AI model that improves upon existing Mixture of Experts (MoE) architectures. Latent MoE leverages a latent space representation to efficiently route inputs to specialized expert models, leading to improved performance and efficiency compared to standard MoE approaches.

The article discusses the launch of Crate, a new cloud-based platform that aims to simplify and streamline the process of managing cloud-based infrastructure and applications. Crate provides tools for deployment, monitoring, and optimization, with a focus on making cloud management more accessible and efficient for businesses.

The article provides practical tips for navigating the behavioral interview process at big tech companies. It covers strategies for preparing strong responses, managing nerves, and demonstrating key skills that interviewers look for, such as problem-solving, communication, and teamwork.

Meta CEO Mark Zuckerberg blocked attempts to restrict sexual conversations between minors and chatbots, according to a court filing. The filing reveals tensions within Meta over how to handle such sensitive issues involving minors and AI.

The article explores the benefits of using React, a popular JavaScript library for building user interfaces. It highlights React's component-based architecture, virtual DOM, and other features that make it a powerful tool for developing efficient and scalable web applications.

The article explores the vast number of potential chess games, estimated to be around 10^120, and discusses the mathematical and computational complexity involved in fully enumerating all possible game sequences.

The article reports that consumer confidence in the U.S. has plunged to a 12-year low, reflecting growing concerns about the economy and rising inflation. The Conference Board's consumer confidence index fell sharply in June, indicating a pessimistic outlook among consumers.

https://archive.is/mi308

The article provides a detailed tutorial on how to visualize the inner workings of convolutional neural networks (CNNs) using PyTorch. It covers techniques like saliency maps, class activation maps, and feature visualization, which can help understand the decision-making process of CNN models.

Texas Governor Greg Abbott proposes restricting H-1B visas for universities and technical schools, citing concerns that the program is harming American workers and that the state's higher education institutions are overusing the visas to hire foreign workers.