Top stories

The article discusses System76's decision to implement age verification on their website, explaining the reasons behind this move and the measures taken to ensure a smooth and secure experience for customers.

The article discusses the growing importance of AI in various industries and how more people are becoming AI engineers, even those without a traditional computer science background. It explores the increasing accessibility and widespread adoption of AI tools and platforms, allowing non-experts to participate in AI-related tasks and projects.

https://openai.com/index/gpt-5-4-thinking-system-card/

https://x.com/OpenAI/status/2029620619743219811

We built an ant colony simulation as an internal hiring challenge at Moment and decided to open it up publicly.

You write a program in a custom assembly-like (we call it ant-ssembly) instruction set that controls 200 ants. Each ant can sense nearby cells (food, pheromones, home, other ants) but has no global view. The only coordination mechanism is pheromone trails, which ants can emit and sense them, but that's it. Your program runs identically on every ant.

The goal is to collect the highest percentage of food across a set of maps. Different map layouts (clustered food, scattered, obstacles) reward very different strategies. The leaderboard is live.

Grand prize is a trip to Maui for two paid for by Moment. Challenge closes March 12.

Curious what strategies people discover. We've seen some surprisingly clever emergent behavior internally.

The article discusses the potential impacts of Mastodon, a decentralized social media platform, on the future of the internet. It highlights Mastodon's growth, its role in providing an alternative to centralized platforms, and the broader implications of decentralized social media on the online ecosystem.

Original report: https://adnanthekhan.com/posts/clinejection/

The article discusses the importance of branding in the modern business landscape, highlighting how a strong brand can provide significant value and advantages over competitors. It explores the key elements that contribute to an effective brand and the strategies companies can employ to build and maintain a distinctive brand identity.

The article examines the potential impact of artificial intelligence on the labor market, exploring how AI could displace certain jobs while also creating new opportunities. It discusses the need for policies and education to help workers adapt to the changing job landscape.

LibreSprite is an open-source, cross-platform sprite editor and pixel art tool that provides a wide range of features for creating and manipulating 2D graphics, with a focus on pixel art and animation.

The article discusses Anthropic's participation in a program called the Department of War, which aims to explore the ethical and responsible development of advanced AI capabilities. The program focuses on addressing potential risks and ensuring the safe and beneficial use of these technologies.

The article discusses the importance of knowing when to stop developing software, highlighting the need to balance features, complexity, and user experience. It emphasizes that good software prioritizes simplicity, ease of use, and meeting core user needs over endless feature additions.

Stardex, a YC-backed AI company, is seeking a Customer Success Engineer to help with data migration and support for their clients. The role involves working closely with customers to ensure a smooth transition and provide ongoing technical assistance.

The article discusses the launch of a new decentralized social network called 406, which aims to provide a censorship-resistant alternative to traditional social media platforms by leveraging blockchain technology.

https://wikipediocracy.com/forum/viewtopic.php?f=8&t=14555

https://en.wikipedia.org/wiki/Wikipedia:Village_pump_(techni...

https://old.reddit.com/r/wikipedia/comments/1rllcdg/megathre...

The article reveals that the U.S. Customs and Border Protection (CBP) agency tapped into the online advertising ecosystem to track people's movements, raising privacy concerns about the government's use of commercial data for surveillance purposes.

This article provides a step-by-step guide on how to install and start using LineageOS, a popular open-source Android operating system, on your smartphone. It covers the installation process, necessary preparations, and tips for getting started with LineageOS on your device.

The article provides a comprehensive guide on how to apply for a job at Podsnap, covering the application process, resume tips, interview preparation, and what to expect during the hiring journey.

We needed a side project to give agentic coding a try, and created tensorspy.com together with Junie and ChatGPT 5.2.

Tensor Spy lets you quickly inspect the contents of numpy & pytorch tensors locally (your tensors are not uploaded to any servers).

This is useful to validate your deep learning data pipelines, to check which layers in your diverging model are actually going haywire, and just because it's kind of cool & a lot more convenient for one-off inspections than loading things up in python.

If you work with diffusion models, inspecting the latent space can be quite informative: you want some "noise" in there but it should probably be fairly smooth for your LDM (Latent Diffusion Model) to be able to target it well.

Also, if you haven't looked at your data, it's probably not what you think it is ;)

Basic stats are auto-computed, and any inf/nan values are both counted and rendered with contrasting colors, to help you quickly identify issue hotspots.

The site is free, and our broad intention is to keep it that way (we run a bunch of pro-bono little utility sites in addition to our commercial ones, they're all linked on the about page).

Would love to hear your thoughts, I'm sure there are some stats or utility features we missed, so please give it a spin and let us know!

---

Agentic coding is a brave new world. Three years ago, after the initial rush of ChatGPT's launch, I commented to some friends that "we're standing on the beach and the water just receded". The tsunami is really hitting now. As in: this project took about 2 weeks, and not only would we not have done it without agentic coding, it would have taken months using "traditional methods". With agentic coding, adding .pt/.pth support was basically a single request. And it just worked. Time to adapt yet again.

This article delves into the technical details of hardware hotplug events on Linux, exploring the kernel internals and the various subsystems involved in handling these dynamic device changes, providing a comprehensive understanding of the underlying mechanisms.

The article explores the history, health benefits, and culinary uses of citrus fruits, such as oranges, lemons, and limes. It delves into the origins and global spread of these versatile fruits, their nutritional profiles, and their diverse applications in cooking, baking, and beverages.

The article explores the concept of the 'Ship of Theseus,' a philosophical thought experiment about identity and change. It discusses how this ancient puzzle relates to modern software development, the challenges of maintaining and updating complex systems over time, and the importance of balancing continuity and transformation.

Hi HN!

I'm the author of an Elixir Agent Framework called Jido. We reached our 2.0 release this week, shipping a production-hardened framework to build, manage and run Agents on the BEAM.

Jido now supports a host of Agentic features, including:

- Tool Calling and Agent Skills - Comprehensive multi-agent support across distributed BEAM processes with Supervision - Multiple reasoning strategies including ReAct, Chain of Thought, Tree of Thought, and more - Advanced workflow capabilities - Durability through a robust Storage and Persistence layer - Agentic Memory - MCP and Sensors to interface with external services - Deep observability and debugging capabilities, including full stack OTel

I know Agent Frameworks can be considered a bit stale, but there hasn't been a major release of a framework on the BEAM. With a growing realization that the architecture of the BEAM is a good match for Agentic workloads, the time was right to make the announcement.

My background is enterprise engineering, distributed systems and Open Source. We've got a strong and growing community of builders committed to the Jido ecosystem. We're looking forward to what gets built on top of Jido!

Come build agents with us!

A U.S. judge has ordered the government to begin refunding over $130 billion in tariffs collected on imports during the Trump administration's trade war with China, a significant ruling that could have far-reaching implications for U.S. trade policy.

This article explores a technique for remotely unlocking an encrypted hard disk by leveraging a local proxy and a remote server. The author demonstrates how to set up this system and discusses the potential security implications and considerations around implementing such a solution.

Title: Show HN: PageAgent, A GUI agent that lives inside your web app

Hi HN,

I'm building PageAgent, an open-source (MIT) library that embeds an AI agent directly into your frontend.

I built this because I believe there's a massive design space for deploying general agents natively inside the web apps we already use, rather than treating the web merely as a dumb target for isolated bots.

Currently, most AI agents operate from external clients or server-side programs, effectively leaving web development out of the AI ecosystem. I'm experimenting with an "inside-out" paradigm instead. By dropping the library into a page, you get a client-side agent that interacts natively with the live DOM tree and inherits the user's active session out of the box, which works perfectly for SPAs.

To handle cross-page tasks, I built an optional browser extension that acts as a "bridge". This allows the web-page agent to control the entire browser with explicit user authorization. Instead of a desktop app controlling your browser, your web app is empowered to act as a general agent that can navigate the broader web.

I'd love to start a conversation about the viability of this architecture, and what you all think about the future of in-app general agents. Happy to answer any questions!

The article provides information on how to acquire and install the TeX Live distribution, a comprehensive TeX system for various operating systems. It guides users through the download and installation process, including options for different installation methods and platforms.

The article explores the challenges of interpreting data, highlighting how data alone does not speak for itself and requires thoughtful analysis and contextualization. It emphasizes the importance of understanding the inherent biases and limitations of data to draw meaningful conclusions.

Hi HN! We're Gobhanu and Saatvik (brothers), building Vela (https://tryvela.ai) - AI agents that handle multi-party, multi-channel scheduling.

Scheduling is a constraint satisfaction problem disguised as email! It’s easy when it’s two people, one timezone, one channel. But it becomes a constraint satisfaction problem when inputs are unstructured natural language across multiple communication channels, constraints change mid-solve, and the objective function includes social dynamics that don't exist formally anywhere.

What if scheduling just happened? For example: a recruiter sends one message, and every interview across five candidates, three hiring managers, and two time zones gets booked, confirmed, and updated automatically. No links, no back-and-forth, no one spending hours with 20 emails. Everyone just gets the right invite at the right time, on whatever channel they actually use. That's what we built Vela to do.

You loop in Vela into your emails, SMS, WhatsApp, Slack, phone or integrate into an ATS etc and it takes over: reads context, checks calendars, proposes times, follows up when people ghost, and rebooks when things shift.

One of our first customers is a staffing firm that searched for a scheduling solution for almost eight years. Their coordinators manage hundreds of candidate-client interviews where each side needs separate email threads, separate Zoom accounts to avoid double-booking links, and calendar invites connecting parties who never directly communicate. A client reschedules one interview and it cascades into four others. A candidate responds on SMS to a thread that started on email. Vela solved this in just 10 minutes of onboarding.

The hardest part has been the data problem. Scheduling behavior varies enormously across populations. C-suite folks respond to email within hours and expect formal 3-option proposals. Truck drivers applying for logistics roles respond to SMS at odd hours from shared devices with "y tm wrks." The failure mode isn't parsing -- it's applying the wrong interaction pattern for the wrong segment and watching the conversation die. We've been building behavioral datasets from thousands of real interactions: response latency by role, channel preference by demographic, follow-up timing curves, how many options to propose before you hit decision paralysis. This data doesn't exist anywhere.

The core agent challenge is state across channels. When someone responds on SMS to a thread that started in email, Vela needs to unify identity, merge context, and continue without losing information. Phone numbers don't map cleanly to emails, people use nicknames on text, shared devices mean the responder might not be who you reached out to. Temporal NLU is its own problem -- "next Friday" means different things on Monday versus Thursday. We extract structured constraints from natural language and resolve against calendar state. When ambiguity can't be resolved, Vela asks -- but deciding when to ask versus infer depends on the stakes of getting it wrong.

We're live with paying enterprise customers and every client still surfaces edge cases that surprise us. Case studies on our site (https://tryvela.ai/case-studies/). You can check out a demo here: https://www.youtube.com/watch?v=MzUOjSG5Uvw.

We'd love feedback from anyone who's worked on multi-agent coordination, conversational AI across channels, or constraint satisfaction in messy real-world domains. Looking forward to your comments!