System76 on Age Verification Laws

GPL upgrades via section 14 proxy delegation

<a href="https:&#x2F;&#x2F;openai.com&#x2F;index&#x2F;gpt-5-4-thinking-system-card&#x2F;" rel="nofollow">https:&#x2F;&#x2F;openai.com&#x2F;index&#x2F;gpt-5-4-thinking-system-card&#x2F;</a><a href="https:&#x2F;&#x2F;x.com&#x2F;OpenAI&#x2F;status&#x2F;2029620619743219811" rel="nofollow">https:&#x2F;&#x2F;x.com&#x2F;OpenAI&#x2F;status&#x2F;2029620619743219811</a>

GPT-5.4

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&#x27;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&#x27;ve seen some surprisingly clever emergent behavior internally.

Show HN: Swarm – Program a colony of 200 ants using a custom assembly language

10% of Firefox crashes are caused by bitflips

Original report: <a href="https:&#x2F;&#x2F;adnanthekhan.com&#x2F;posts&#x2F;clinejection&#x2F;" rel="nofollow">https:&#x2F;&#x2F;adnanthekhan.com&#x2F;posts&#x2F;clinejection&#x2F;</a>

A GitHub Issue Title Compromised 4k Developer Machines

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Labor market impacts of AI: A new measure and early evidence

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Stardex (YC S21) is hiring customer success engineers

Good software knows when to stop

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A standard protocol to handle and discard low-effort, AI-Generated pull requests

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 &amp; 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&#x27;s kind of cool &amp; 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 &quot;noise&quot; 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&#x27;t looked at your data, it&#x27;s probably not what you think it is ;)Basic stats are auto-computed, and any inf&#x2F;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&#x27;re all linked on the about page).Would love to hear your thoughts, I&#x27;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&#x27;s launch, I commented to some friends that &quot;we&#x27;re standing on the beach and the water just receded&quot;. 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 &quot;traditional methods&quot;. With agentic coding, adding .pt&#x2F;.pth support was basically a single request. And it just worked. Time to adapt yet again.

Show HN: Tensor Spy: inspect NumPy and PyTorch tensors in the browser, no upload

CBP tapped into the online advertising ecosystem to track peoples’ movements

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Wikipedia was in read-only mode following mass admin account compromise

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Hi HN!I&#x27;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 OTelI know Agent Frameworks can be considered a bit stale, but there hasn&#x27;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&#x27;ve got a strong and growing community of builders committed to the Jido ecosystem. We&#x27;re looking forward to what gets built on top of Jido!Come build agents with us!

Show HN: Jido 2.0, Elixir Agent Framework

Judge orders government to begin refunding more than $130B in tariffs

AI and the Ship of Theseus

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Data Does Not Speak to You

Title: Show HN: PageAgent, A GUI agent that lives inside your web appHi HN,I&#x27;m building PageAgent, an open-source (MIT) library that embeds an AI agent directly into your frontend.I built this because I believe there&#x27;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&#x27;m experimenting with an &quot;inside-out&quot; 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&#x27;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 &quot;bridge&quot;. 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&#x27;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!

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

Screeching Sound of Peeling Tape

Hi HN! We&#x27;re Gobhanu and Saatvik (brothers), building Vela (<a href="https:&#x2F;&#x2F;tryvela.ai">https:&#x2F;&#x2F;tryvela.ai</a>) - 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&#x27;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&#x27;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 &quot;y tm wrks.&quot; The failure mode isn&#x27;t parsing -- it&#x27;s applying the wrong interaction pattern for the wrong segment and watching the conversation die. We&#x27;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&#x27;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&#x27;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 -- &quot;next Friday&quot; means different things on Monday versus Thursday. We extract structured constraints from natural language and resolve against calendar state. When ambiguity can&#x27;t be resolved, Vela asks -- but deciding when to ask versus infer depends on the stakes of getting it wrong.We&#x27;re live with paying enterprise customers and every client still surfaces edge cases that surprise us. Case studies on our site (<a href="https:&#x2F;&#x2F;tryvela.ai&#x2F;case-studies&#x2F;">https:&#x2F;&#x2F;tryvela.ai&#x2F;case-studies&#x2F;</a>). You can check out a demo here: <a href="https:&#x2F;&#x2F;www.youtube.com&#x2F;watch?v=MzUOjSG5Uvw" rel="nofollow">https:&#x2F;&#x2F;www.youtube.com&#x2F;watch?v=MzUOjSG5Uvw</a>.We&#x27;d love feedback from anyone who&#x27;s worked on multi-agent coordination, conversational AI across channels, or constraint satisfaction in messy real-world domains. Looking forward to your comments!