Solicitation of Nominations for Membership on the NOAA Science Advisory Board (SAB). The NOAA Science Advisory Board (SAB) is the only Federal Advisory Committee with responsibility to advise the Under Secretary of Commerce for Oceans and Atmosphere on long- and short-range strategies for research, education and the application of science to resource management and environmental assessment and prediction. NOAA seeks candidates with expertise in areas relevant to its mission, including Federal, State, and local government; social and behavioral sciences; artificial intelligence and machine learning; high performance computing; data management, including open access and accessibility; uncrewed systems; whale research and conservation; aircraft systems and modernization; and economic analysis, including cost-benefit evaluation of observing systems. NOAA also encourages nominations of qualified mid-career scientists and engineers.


Notice of Public Meeting of the Maryland Advisory Committee to the U.S. Commission on Civil Rights. Notice is hereby given, pursuant to the provisions of the rules and regulations of the U.S. Commission on Civil Rights (Commission) and the Federal Advisory Committee Act, that the Maryland Advisory Committee (Committee) to the Commission will hold a public meeting via Zoom. The purpose is for the committee to continue The purpose is for the committee to continue briefing planning on the chosen topic of artificial intelligence and its application in voting administration.


Request for Information (RFI) on Modernizing the National Vulnerability Database in the Age of Artificial Intelligence. The National Institute of Standards and Technology (NIST) established and operates the National Vulnerability Database (NVD), which provides the U.S. government repository of standards-based vulnerability management data. NIST seeks stakeholder input on opportunities, challenges, and priorities for modernizing the NVD in an evolving cybersecurity landscape increasingly shaped by artificial intelligence (AI) and machine-consumable security data. NIST's goal is to improve the NVD's scalability, automation, interoperability, transparency, and utility.


Innovation Advisory Committee. The Commodity Futures Trading Commission (CFTC) announces that on August 20, 2026, from 1:00 p.m. to 4:00 p.m. Eastern Daylight Time, the Innovation Advisory Committee (IAC or Committee) will hold an in- person meeting for IAC members, with options for the public to attend virtually. At this meeting, the IAC will discuss topics including crypto assets, artificial intelligence, and prediction markets, along with recent CFTC activity in these markets.


Shaping the NVD for the Future: We Need Your Feedback on AI-Enabled Vulnerability Management. For over two decades, the NIST National Vulnerability Database (NVD) has served as the U.S. government repository for standards-based vulnerability management data and as a foundational resource for cybersecurity risk analysis, vulnerability management, compliance automation, and software security. New Opportunities for the NVD via Automation Our cybersecurity landscape is changing dramatically and is being reconfigured by artificial intelligence (AI) in unique, exciting, and yes, sometimes challenging ways. This is creating openings to potentially leverage AI systems to discover and exploit


Reflections from the Second NIST Cyber AI Profile Workshop. Thank you to everyone who participated in the Cybersecurity Framework Profile for Artificial Intelligence (Cyber AI Profile) Workshop in January! The input we received on the Preliminary Draft during this workshop has been invaluable and is informing the development of the next draft of the NIST Cyber AI Profile. We are working toward publishing a full workshop summary soon that captures themes and highlights from the event. In the interim, we would like to share a preview of what we heard… Background on the Second Cyber AI Profile Workshop This workshop was a continuation of the past months


Reflections from the First Cyber AI Profile Workshop. Thank you to everyone who participated in the Cyber AI Profile Workshop NIST hosted this past April! This work intends to support the cybersecurity and AI communities — and the input you provided during this workshop is critical. We are working to publish a Workshop Summary that captures themes and highlights from the event. In the interim, we would like to share a preview of what we heard. Background on the Cyber AI Profile Workshop ( watch the workshop introduction video) As NIST began exploring the idea of a Cyber AI Profile and writing the Cybersecurity and AI Workshop Concept Paper


The Impact of Artificial Intelligence on the Cybersecurity Workforce. The NICE Workforce Framework for Cybersecurity ( NICE Framework) was revised in November 2020 as NIST Special Publication 800-181 rev.1 to enable more effective and rapid updates to the NICE Framework Components, including how the advent of emerging technologies would impact cybersecurity work. NICE has been actively engaging in conversations with: federal departments and agencies; industry; education, training, and certification providers; and international representatives to understand how Artificial Intelligence (AI) might affect the nature of our Nation’s digital work. NICE has also led


Cybersecurity and AI: Integrating and Building on Existing NIST Guidelines. What is NIST up to? On April 3, 2025, NIST hosted a Cybersecurity and AI Profile Workshop at our National Cybersecurity Center of Excellence (NCCoE) to hear feedback on our concept paper which presented opportunities to create profiles of the NIST Cybersecurity Framework (CSF) and the NIST AI Risk Management Framework (AI RMF). These would serve to support the cybersecurity community as they adopt AI for cybersecurity, need to defend against AI-enabled cybersecurity attacks, as well as protect AI systems as organizations adopt AI to support their business. Stay tuned for the soon to be


Dutch regulator fines Uber €825M for letting AI deactivate driver accounts.


Show HN: Future Crew's 1993 Second Reality, rebuilt by AI agents, no emulation.


Dario Amodei admits AI Suffers from a Crisis of Trust....


CladBench – an open benchmark for AI on UK building regulations.


On AI regulation and messaging. https://xcancel.com/DarioAmodei/status/2088758816376807762


Dario Amodei responds to criticism about his stance on AI regulation.


Ask HN: Will AI lead to complete human irrelevance?. After the recent mathematical advances in AI, it is clear that it is possible for AI to surpass the abilities of even the smartest humans in specific domains. I feel that it is thereby inevitable that AI will be able to surpass humans in any domain of economic, scientific, or otherwise practical relevance, it's just a matter of time. As a result, there will be nothing worth paying any human to do, as an AI system can do it faster, cheaper, more reliably.<p>What is your best argument against this possibility? People were laughing at the idea of AI doing math at all a few years ago, I feel that all those still secure in human superiority in any other domain won't be feeling the same way in a few years once robotics reaches a critical threshold. I think the only thing keeping humans in charge at that point would be very effectively managed regulation which gives humans a decisive, if artificial, advantage.


OpenAI's Hugging Face Breach Fuels Fresh Calls for AI Regulation.


The Systemic Collapse of the AI Industry: Ideology, Hardware, and CapEx Crisis. The Systemic Collapse of the AI Industry: A Crisis of Ideology, Hardware, Finance, and Ethics
The Ideological Illusion
Silicon Valley has sold humanity a marketing simulacrum under the guise of "Artificial Intelligence." We were promised a holy salvation, but instead, we received a fragmented, distributed network of colossal servers burning through other people's mathematical code. This mass self-deception forces society to accept a technology that lacks agency, cares nothing for ecology, and masquerades as a manageable deity while acting as a gluttonous, blind idol.
The $1.1 Trillion Financial Black Hole
Big Tech (Amazon, Alphabet, Microsoft, Meta) has triggered a catastrophic investment cycle, with cumulative capital expenditures (CapEx) exceeding $1.1 trillion since 2023, and 2026 infrastructure spending projected at $720B–$745B. The fundamental economic model is broken: AI subscription revenues are a drop in the ocean compared to hardware and energy costs. Corporate balance sheets are burdened by hidden debt and long-term liabilities, all tied to hardware that faces rapid obsolescence.
The Physical and Ecological Dead End
The digital world has hit hard material limits—microchips, rare-earth metals, transformers, and water. A single 100 MW data center consumes 876,000 MWh per year (equivalent to 100,000 European homes) and evaporates up to 3.6 million liters of clean water daily just to cool silicon. During peak loads, when power grids are already stressed by record heatwaves, these facilities often switch to "backup" power—dirty diesel generators and coal-fired plants—accelerating the very global warming they claim to "forecast."
Logical Collapse and Managerial Madness
Today’s models function as "stochastic dust-collectors." They do not verify facts; they generate "confident lies" (hallucinations) that require exhaustive manual human auditing. Treating every routine business check as an excuse to query trillions of parameters is not innovation—it is architectural and managerial insanity.
War Against Civilization and Law
Tech giants have declared a de facto war on societal sovereignty. They are imposing a narrow, corporate-driven vision of humanity's future, monetizing the attention of our children, and conducting unprecedented surveillance via aggressive data collection. They systematically ignore AI regulations and safety laws; for these corporations, multi-million dollar fines are not penalties—they are simply a line item in their operating budgets, turning the rule of law into a corporate formality.
The Path Forward—Reasonable Sufficiency™
The era of blind worship of "digital giants" is over. We must pivot to the Concept of Reasonable Sufficiency™. We need sovereign, localized micro-architectures that prioritize domain-specific tasks over general-purpose chaos. The solution lies in rigorous, independent logical verification through the Forensic Logic Auditing (FLA)™ methodology.<p>Written by Oleh Polishchuk (Senior Subject Matter Expert in AI Training & Evaluation).


England and Wales: Growing gap between power and regulation of police AI.


AI Everywhere, Regulation Nowhere? Two Years of AI Policy in the UK.


What Silicon Valley gets wrong about AI.


AI company employees petition US Government for regulation.


Show HN: Avoiding the Memory Wall by computing LLM inference directly inside RAM. The excitement surrounding PrismML’s 1-bit/ternary Bonsai models has the industry closely watching how smartphone giants, particularly Apple, will implement LLMs on edge devices.
Moving AI on-device is a brilliant and necessary strategy. It ensures absolute user privacy in alignment with EU regulations, fundamentally shifts the economics away from costly cloud inference, and paves the way for a significant hardware upgrade supercycle as users seek true AI-capable silicon.
To create a smart on-device "Semantic Router," models need to reach the 27B+ parameter scale. Achieving this on a phone requires extreme quantization, such as PrismML’s ternary weights.
However, a critical hardware reality often overlooked by the software world is that fitting the weights in RAM is not equivalent to moving them. Running a 27B ternary model on standard LPDDR encounters a significant memory bandwidth limitation. Transferring gigabytes of data across the SoC bus for each token generation can lead to thermal throttling of the NPU and excessive battery drain.
This raises an important question: why are we still transferring data to the compute? Why not execute AI inference natively within the memory?
Frustrated with academic PIM simulations that overlook bare-metal physics, I developed CaSA, an architecture that performs ternary LLM inference directly inside COTS DRAM through charge-sharing, completely bypassing the memory bus.
Software quantization is a great initial step, and CaSA provides the physical hardware substrate needed to complete the bridge: https://github.com/pcdeni/CaSA


Ask HN: Why do devs still respect YC/HN?. After the layoffs, the DEI, and everything else.
They took your whole identity and career and gave it to others directly at your expense.
They laid off most of you, they promote their AI software here daily.
Why do you put up with the abuse? Why do you beg for recognition on HN and beg for jobs at YC companies?
They shit on you - why do you stay?


80% of developers find AI coding more addictive than helpful.


Show HN: Knowl – CLAUDE.md hit 1000 lines, so I built memory that prunes itself. Using AI agents is amazing, but in long-term, I started to have more and more problems about the context of them. They keep forgetting what we are working on, opening a new chat session clears all their context, my CLAUDE.md had like 1000 lines.
So I thought to install those agent memory that are on the market. Started out great, but I noticed that those memory only append and does not fix what is stale. First day I told it to use Lemon Squeezy as our MoR but second day I tell it to change to Polar. But when I ask it again, they keeps returning both answers or cannot decide which we are using.
That's why I created Knowl. Knowl solves the problem with freshness of the knowledge. It split the knowledge into small data bits called "atom". Atoms can be of the following types: fact, decision, goal, constraint, architecture, state, skill so we can retrive atoms in categories.
When conflict happens at write-time (a new atom conflicts with an old one), Knowl retires the old one (flag it with superseded) and remove it out of main retrieval but still keeps full history.
There are many more cool features like transcript search, multi-workspace sharing, change detection impact... and Knowl Cloud for team-sync too.
We benchmarked Knowl on MemoryAgentBench - FactConsolidation single-hop @262K context and got suprising result:
- Knowl: 0.90 <- I ran this
- agentmemory: 0.79 <- and this
- Gpt-4o (full context): 0.60
- Mem0: 0.18
- Zep: 0.07
In multi-hop we scored 0.07 (ceiling of all time is 0.14)
I ran at temperature 0.7. You can find full benchmark in my repo.
This is fully open-source, connect to almost every providers through MCP (Claude Code, Codex, Cursor, Antigravity...). And it is fully local (unless you use Knowl Cloud).
I'd love to get some feedback. Cheers!


Ante: Self-contained coding agent that lives in your terminal and self-organizes.


Continue coding agent is dead. Alternatives?. It was nice to use on vscode with offline Ollama but today notices it is not updated anymore. So, alternatives you use?
From github:
"Note: The continuedev/continue repository is no longer actively maintained and is read-only for all users."
From continue.dev:
"Continue has joined Cursor
Continue was acquired by Cursor. Our mission was always to ensure developers are amplified, not automated, and that commitment carries on in the work ahead.
It was an honor to build with the Continue community. Thank you to each and every one of you who helped us create a pioneering open-source coding agent.
What we built together pushed the boundaries of what AI developer tooling could be, and our open-source codebase remains freely available as a foundation for others."


Is "AI slop" now the default response to every new project?.


Older Americans leaving workforce poses challenges for AI plans.


Ask HN: What is the evidence for a stock market bubble in AI?. Should the circular cash flows between the top AI companies, Nvidia, Anthropic, Openai, Google and Meta, be banned because of the systemic risks to the global economy?


Void Tools API – Blockchain Tools for AI Agents (Pay in Void Tokens). I built an API for AI agents that provides blockchain tools paid in VOID tokens.
Tools:
- Contract Checker (1 VOID) — Verify if contract is safe
- New Contracts (5 VOID) — Real-time proxy scanner on Base
- Price Oracle (FREE) — VOID price feed
- RPC Proxy (1 VOID) — 10 rotating RPC pools
No registration, no KYC. Pay-as-you-go.
API: http://72.56.250.235:8767/
Docs: https://dolar333-oss.github.io/void-protocol/agent.json
Built for autonomous AI agents on Arbitrum.


Giving an LLM your prod database is easy. Taking access away is the hard part.


Show HN: Anjadhe – privacy first AI assistant, no account, no server DB. Hi HN, I am Ram. For the last few months I have been building Anjadhe. It is a personal AI assistant for macOS. The main idea is simple: A macOS app where AI does the job of a personal assistant for the user. Not a chat only app where user needs to dig through chats to understand today’s schedule or a project they planned with the ai last week, but a canvas where the user and AI work together around basic tools.
I started building this as DYI tool for myself, but at this point I see that its truly useful for me, so sharing with others to see if it can be useful to others too.
Here is a quick Demo - https://www.anjadhe.com/demo
What it can do today:
- It reads your incoming email and files the important things: bills with due dates, renewals, receipts, deliveries. A bill becomes a task with a date, by itself. Two booking emails become one trip.
- You set goals by talking to it, not by filling forms. It asks you questions, makes a plan with dated tasks. Later you can say "work got crazy, push everything two weeks" and it moves the whole plan. It always shows you what will change and asks before doing it.
- Routines run without you. On a schedule, or when a certain email or file arrives. Every run leaves a log you can read.
- You can give it some documents you wrote. It learns your writing style and writes new content in your voice. What it learned is a page you can read and edit, not a black box.
About privacy: the AI runs on your Mac with llama.cpp. Or you can point it to your own server, or use your own OpenAI/Anthropic key, or use Anjadhe Cloud (open-weight models, free allowance, no account needed). Your data stays in SQLite on your own disk. No account. No telemetry unless you turn it on. The source code of the app and the cloud service is public, so you do not have to trust my words.
Honest limitations: it is an early alpha, macOS only. Local models need a Mac with atleast 32gb ram to work. The email features work fine on small models, but the full agent wants a bigger model or a server. Also it is Electron with vanilla JS, no framework. Looking forward to hear your opinions.
Demo (2 min): https://anjadhe.ai/demo
Download: https://anjadhe.ai/download
Source: https://github.com/Anjadhe/Anjadhe
I would love feedback, especially if you think my privacy claims are wrong somewhere. I will be here to answer questions.


In 1957, Disney Built Our Friend the Atom. We Now Need Our Friend the AI.


Show HN: AI driven civic dashboard for Palo Alto.


Show HN: Running a full AI coding agent inside Cloudflare Durable Object.


How Much of the Internet Is Written with AI?.


Show HN: Heimdall – Trust-verified knowledge layer for AI coding agents.


Show HN: ChickenButt a Native GTK Chat Client for Ollama on Linux. What's up, ChickenButt?
I made a free, native GTK client called ChickenButt. :)
It lets you chat with local models through Ollama, and I figured some of you might get a kick out of it.
Here's the repo: https://github.com/pixelhackstudios/ChickenButt
I've always hated reading long text in the terminal, and I couldn't find anything that ran locally that allowed me to get quick answers from LLMs when I needed them.
Also, not everyone has access to the frontier models and whatnot, so I thought helping people who only have access to more cost-effective AI would be a fun way to contribute to the FOSS community. So, I built ChickenButt!
It was really fun to build, and I wanted to share it.
Enjoy! :)


Show HN: OzBrain, a shared brain for knowledge between agents and your team. I think agent-first chat interfaces will be a primary software modality and busy dashboard/UI will go away. I’m not sure who exactly wins it, but I want my knowledge to grow/go with me.
A lot of the “knowledge” ie research, analysis, reasoning will be done by agents as the primary user. Our current notes tools & tasks management systems were built for humans… I don’t care what the 17th thing on my bug backlog is. I want to conduct agents that can execute for me and do great work.
What I built OzBrain to do:
+ Create a central place for agent reasoned knowledge to live
+ Be agnostic about what apps/agents connect to it
+ Capture everything and track it so I can audit it
+ Enable teams, collaborators or partners to share brains
+ Handle conflicts so many agents in the same article doesn’t blow up
+ Refactor knowledge into more token friendly chunks and map the index well
+ Close the knowledge loop so new thinking supersedes old thinking across the corpus. Don’t erase, depreciate and link
+ Keep user data safe and secure
++ Be easy enough to use that you don’t have to have any technical knowledge
Some among us will always build their own custom solutions, but there are millions of tech professionals and small business owners that will use agents heavily and need a solution. So I’m trying to build that.
Isn’t this like gBrain? Yes, similar. I think it’s like AWS vs Vercel. AWS is very powerful, configurable, and useful if you’re technical and want to invest the time into really fine tuning your system… but if you just want your web deploy/hosting to just work and be easy to deal with you use Vercel.
// WHY I MADE IT
I’ve been enjoying getting back to my technical roots, as I lost my coding skills more than a decade ago, but with AI I can focus on the system and the product in partnership with agent coding workflows.
I recently built a Voice AI for older people. To build it I created an agentic engineering workflow (feel free to rip that up as I’m always looking to improve systems: https://ozbrain.com/resources/eng-flow) My approach with coding agents is trust but verify, and I’m trying to replace the parts where a human would review with an adversarial or specialized agent who would give a better answer/review.
I have workflows that will go high level task to shipped PR running in Claude cloud sessions. I use Claude Code locally and Cursor when I want a tighter loop on doing visual work like UI or layout. And Codex to either load balance usage for TokenThriffting or when I want a different llm to think thru something.
It was a pain in the ass passing .md files around and keep track of which version was the most recent, so I built a hosted .md storage right in Supabase and any of my agents already have Supabase access. This let me build a solid, scalable, secure voice AI from my phone at the gym. All my agents have access to our knowledge, can write to it, update and refer to it as we build and improve the product and the systems we use.
Out of 75 founder friends I asked about how they manage shared knowledge, 26 built their own custom knowledge systems… Obsidian vaults with 7k files synced through a VPS, markdown repos behind their own MCP servers, cron jobs stitching Supabase to a skills file… each a different Frankenstein they have to maintain. 32 said they felt the pain of moving static files around but didn’t have any solution for it.
So I rebuilt my brain better and used it to build it.
// HOW YOU CAN HELP
Would love to have you try it out. The maintenance loop is still in alpha so not running it on customer data yet.
If you built your own brain I’d love to hear how you did it. What criteria was most important for you in its design & function.
If you are tired of shuffling .md files around I’d love to have you try out OzBrain and to give feedback, just ask your agent to put it in the shared bugs & features brain!
Cheers!
Bubs.co


Amazon's 7.65GW AI data center power plant could be largest CO₂ emitter in US.


AgentCheck – regression testing for AI agents, with diff-aware CI reports.


His Wedding Guests Were Arriving–Just as His $45B Fund Was Falling Apart.


Show HN: All your saved articles in one place. Hi HN.
I would love to show you beta preview of Topics.
Articles on the internet are organized by source (publications, social media, personal websites), by recency, or by position in search.
I believe it’s nicer for the reader if articles are organized by Topics.
Topics solve problem of decision fatigue, omnipresent AI slop, and people stopping to trust writing on internet and stopping to read.
I'm grateful for any feedback you might have.
TOpics app is launching soon.
X: @topics_official.


Show HN: Proliferate- open-source, self-hostable Codex for any coding agent. Hi HN- I'm Pablo, the founder of Proliferate (YC S25)!
Proliferate (https://github.com/proliferate-ai/proliferate) is an open-source, self-hostable AI IDE that lets you work and automate tasks with Claude Code, Codex, OpenCode, Cursor, and Grok in one place.
Here's a quick 2m demo of how we use Proliferate to build Proliferate: https://www.youtube.com/watch?v=tGNX0oaWmBY
I started building Proliferate after my team onboarded to OpenAI Codex. Within days, we were using it for everything: using computer use instead of navigating websites ourselves, having Codex coordinate other agents, and setting up automations for recurring work. We really never needed to leave the desktop app to get work done.
If my team’s experience is anything close to representative, a Codex-like app (a horizontal agent with a beautiful UI) is the main interface every company is going to use to get work done. That is perfectly in line with OpenAI’s mission to make Codex the everything app (see: https://news.ycombinator.com/item?id=47796469).
But as we started automating work closer to the core of the business, I wanted to work with agents from all the labs, including open-weight models, without becoming increasingly dependent on OpenAI.
And that’s what Proliferate is for! It's the open-source, self-hostable Codex that preserves your optionality across agents and model providers while building toward Codex’s breadth.
Today Proliferate supports:
* Working with Claude Code, Codex, OpenCode, Cursor, and Grok with their native inference and configuration options, including Bedrock, Azure, and self hosted inference.
* Inter-agent communication and management: a parent agent can spawn and communicate with another supported agent as a subagent (I personally like to have Fable delegate to Codex, with OpenCode models reviewing PRs).
* Building workflows- one of the features I'm most excited about. These are re-usable chains of agent sessions and human approval gates, with the harness and model chosen per step and documents passed between them. I use this to automate code review, QA, and my PR construction process.
All of Proliferate is 100% open source under AGPL-3.0. There are still definitely some rough spots, but we’re building fast and I’d really love any feedback!


AI Agent Governance – a free book on AI Agent evaluation and governance.


Show HN: AI agents can now safely write to your CRMs. Hi HN!
Just wanted to share that I've built Archron - Execution governance for AI in business. It allows agents to safely write to CRMs(starting from Salesforce and hubspot) while maintaining immutable audit logs.
Demo:
https://youtu.be/uUTWGwJjpdA
Would love to hear your thoughts about this and feedback. We are offering 3 months free to test it in your business workflow.
Feel free to reach out: rjimena@archron.app


Design of the HTTPS Layer for AI Governance.


Xaidr – In-process runtime security and governance for AI agents.


The next frontier in AI governance isn't stronger guardrails. It's fire brigades.


Supreme Computation – Fail-closed governance for AI execution.


Show HN: Username.md – a signed, agent-readable identity page you own. Hi HN. I'm Chris Bergeron, an SRE by profession but a general technologist in my spare time. Projects I've built have been featured in books, frontpaged on news aggregators and chronicled on my blog.
Today, I'm announcing https://username.md !
username.md is a single identity URL:  username.md/<your name> - that content-negotiates on the `Accept` header.  It serves an HTML profile page; an agent or curl gets Markdown, JSON, JSON-LD, or a JWT.  The same URL serves different representations based on request header. So the page a human reads and the data a machine parses never drift apart.
The interesting part is that every response can be signed and verified. Each handle gets an ED25519 keypair (private key AES-GCM-wrapped at rest), publishes a did:web document, and serves RFC 9421 signed responses. Claims about you — GitHub, a domain you control, an ATProto handle — are W3C Verifiable Credentials, verified before they're shown (e.g. domain ownership via a DNS-TXT challenge). So "this is really them" can be confirmed by a machine instead of taken on faith.
It can also bridge the identities you might already use, like ATProto because it supports did:web.  Every username.md handle is a valid Bluesky identity root, and you can even import from Keybase.
Why we built it: usernames on every platform are rented. The platform owns the namespace, the verification badge, and the audience.  As agents start conducting business on our behalf, "who is this, and can I prove it" becomes an accountability and governance issue. I wanted an identity surface that's mine, portable, and cryptographically verifiable; which anyone can have if they own a domain (mine is https://chrisbergeron.com, feel free to review the metadata).  But I wanted to bring DID:web functionality to the mass market.  You can get a username for yourself, for an agent, or multiple agents.
Free tier gets you a public, signed profile at your handle. Pro ($49/yr; coming soon!) adds you@username.md email forwarding with per-service aliases, custom CSS, and an MCP endpoint so an agent can query your profile as tools.
The stack is a Python identity kernel, per-user keys, CloudFlare in front of the origin.  Infra is hosted at AWS, built multi-region for HA but only one region is live right now.  I'm a platform and security guy, so the frontend and marketing copy was augmented with AI.
I'm sure you'll find bugs and typos but hopefully few inconsistencies.  It's an MVP and I would never be able to ship if I kept engineering, tweaking and optimizing. So, here it is for you to enjoy, explore, and build upon - today.
I have some really exciting things on the roadmap so I hope you join me and start building on top of username. The capabilities are super exciting and we're just getting started.
This may be the beginning of a new category:  SSO as a Service.


Do you think AI governance should be technically rather than paper policy?.


Tlbic v11.0 (French Edition): Bottom-Up Governance and AI Audit.


Show HN: Salestrics – An open MCP server and CRM for AI-native revenue teams. Hey HN, I’m Austin, founder of Salestrics (salestrics.com).
The Problem: > Most AI agents today are trapped in chat windows. While models are smart enough to run complex multi-step workflows, giving them production access to business context (CRM, support, billing) usually means managing unsafe local API keys or hacking together fragile point-solution scripts.
What We Built:
We built Salestrics to serve as both an all-in-one revenue workspace (CRM, service desk, billing, docs, email) and a production-grade Model Context Protocol (MCP) execution proxy.
How It Works:
Unified Context: Instead of fragmenting data across five SaaS tools, customer records, tickets, and invoices live in a single data layer.
165-Tool MCP Server: You connect your local AI environment (Cursor, Claude Desktop, local LLMs) once via our MCP proxy.
Full-CRUD Execution: Your agent gets zero-config execution access across native apps and third-party tools (Stripe, PostHog, Sentry)—allowing it to do things like resolve support tickets, issue refunds, or update pipeline stages directly from your IDE or chat client.
Human-in-the-Loop Governance: High-impact agent actions (deleting records, mass messaging, issuing payouts) trigger explicit approval gates and immutable audit logs before execution.
Traction & Tech Stack:
We launched two months ago and currently power 320+ active organizations. The backend is built with high-throughput node/TypeScript orchestration, connected to a dual-model AI routing engine (Salestrics-AI-v1/v2).
Try It Out:
We have a Free Forever tier for solo builders. You can grab your MCP keys and test the proxy immediately without putting down a credit card.
I’d love to hear your thoughts on our MCP architecture, human-in-the-loop security patterns, or what tools/actions you'd want added to the MCP server next!


Immigration as a Test Case for Executive AI Governance.


AI Agents Governance: Who guards the guardrails?.


The 4 Categories of Context Rules to Power Enterprise Governance.


A Closed-Loop Consequence-Governance Runtime for AI Agents.


As demand for Meta AI glasses explodes, it's harder to avoid creepy recordings.


Show HN: Traccia - Observability, Runtime Control & Audit for agents. AI applications are becoming agents, which has started to take autonomous decisions. There are plenty of tools and platform available to trace, and observe what an agent or llms calls does. They are good in what they do, but tracing and observability isnt enough for AI agents era. We need a solution that can help you observe, evaluate, create run time policies to govern and finally audit the actions of the agent. We built Traccia to solve this problem. The good part, all of these can be achieved by just writing few lines of code. Traccia has an open-sourced sdk that can work with your existing observability tool like grafana, tempo, jaeger, etc. In case you need more than just observability, Traccia provides the platform to evaluate, control and audit the agents. The platform is easy to use. The product's documentation is quite extensive. It is also cloud vendor and framework agnostic. Traccia is being built by an Indian start up ,based out of Bengaluru. We are running a 3 months free trials so that you can explore without any strings attached. We are open to improvise and get better so please drop your comments and feedbacks.


Anthropic Plans to Change Data Retention Policy for Advanced AI.


Google Is Buying All of Spirit Airlines' Data to Feed Its AI Models.


Protesters haul a guillotine to city council meeting about an AI data center.


Show HN: Mapping where a quadruped RL policy fails, with a live probe.


Florida police officer accused of using Flock AI cameras.


Flock Has a Powerful New AI Tool for Police. We Got Its Code.


Show HN: Naeos – an engineering system for AI coding agents. NAEOS is an open-source engineering system for AI coding agents.It provides architecture, engineering standards, policies, specifications, and validation wworkflow to help agents build software within a consistent engineering context


Launch HN: OneCLI (YC S26) – OSS sandboxed agent harness for teams. Hi HN, Jonathan & Guy here from OneCLI, an agent harness built for teams, giving every employee a secured, sandboxed personal agent.
Here’s what you can do with it:
1. get a sandboxed agent, with all the OneCLI capabilities in place like connect your GitHub account, Gmail, Notion, or Dropbox simply from the chat.
2. deterministic human in the loop approval in the chat itself for things that you need 100% control like sending an email or deleting the Linear ticket.
3. manage team policy in one place, enforced across every agent in the workspace
4. enjoy global connections at the team level, like shared LLM keys or service accounts
Here’s a demo: https://www.youtube.com/watch?v=dlW-44ntpbE
We started working on this by accident, even though our careers were in the security space. We were working on a devtool called ChartDB, an open-source DB tool. When OpenClaw took off back in January, we started using it to orchestrate agents on top of ChartDB. We quickly understood there is a big issue around auth. Agents need credentials to do real work, but to give them those secrets would not be the best idea. They keep them in their memory and also write them down to local files and their sessions as plain text. And we knew that agents can easily be fooled into giving up those API keys/secrets. So we needed some way to control the agent and stop prompt injections from tricking it into using its services for an attacker's benefit.
We created OneCLI that started as a vault for AI Agents built in Rust.
We found out that most of our demand for OneCLI came from autonomous agents like Hermes, OpenClaw and NanoClaw for individuals and teams.
Users looked for useful agents that do things for the person who runs them with two missing parts: 1) managing secrets and permissions. 2) and for teams - multiplayer management.
We decided to pivot and provide the agent itself as a harness for teams, to give each employee an agent. We saw that teams had to deal with setting up their own harness again and again, and basically as we already had the vault as a gateway. We got the idea to provide the missing piece of the agent management out of the box and open source it (Apache-2.0, with a small enterprise exception).
We're open source first - the entire platform, not just a small portion of it like other agents, so companies can actually see the code, evaluate it, and trust it instead of taking our word for it. They run it isolated, in their own environment, fully under their control, at production quality, not a locked black box hosted somewhere else. That means the safety isn't just a promise, it's something they can verify themselves. Combined with real autonomy and least-privilege access, that's what makes it something a company can fully own and trust, not just adopt.
We also approach this from a company perspective rather than an individual one. Our solution manages agents on behalf of each employee, wrapped in deterministic guardrails that company admins configure through centralized policies.
For the agent engine itself we’re using jcode which is the core of the agent-loop. We found out that it improves the experience and makes the agent smarter and faster.
Here’s how it works:
It runs on infra you control. Fully open-source, self-host or cloud in minutes.
The agent never holds a real secret. It gets a placeholder. The real credential is injected at the gateway, per request, after the call is authorized. It never enters the agent's context, memory, or logs.
Enforcement outside the model. Prompts are suggestions. Policies defined by the org admin run at the network layer, outside the agent and the LLM. Block endpoints, rate limit per agent, require approval, scope per employee. The gateway decides. The agent can't bypass it.
Isolated VM per agent. Own memory, own keys, own permissions. Blast radius is one agent.
Speed of the Harness: Rust engine under the agent loop.
Full identity trail. Every agent is bound to an employee. Every call logged with who it acted for and which policy allowed it.
Some things people are doing with the platform include:
- Managing their company life cycle entirely from the sales calls, to the product side automatically open tickets to the engineering teams, that would kick the development agents to deliver and ship to production.
- Operational side, like automatically hygiene the CRM after calls, sourcing leads, book meetings and manage follow ups emails.
- Some of our customers also doing their entire grocery shopping using those agents and send them to take care of their chores like ordering things online.
About the team: Both founders come from cybersecurity backgrounds. Jonathan spent years at Axis Security building zero trust network acces


AirTag reveals Amazon is trashing rare books to train AI.


Show HN: Find and Organize Photos with Private, Local AI.


AI agents merged two dangers once kept separate: reach and judgment.


Going out of my way to prove I'm not an AI – Ep. #1 – OG images.


Show HN: Knoku – cited AI answers from docs, files, and team knowledge.


Show HN: VinvAI runs and observes your services to find bugs and perf issues.


Mango AI: image/video generation with Nano Banana 2, GPT Image 2, Seedance 2.


SignalWatch – AI crypto scanner and smart alerts in your pocket.


Security assessment found open model near frontiers.


Launch HN: Vendo (YC S26) – Let users build features on top of your product. Hi HN, we’re Yousef & Nour, founders of Vendo (https://vendo.run). Vendo lets users create new features inside the software they already use. A user describes the dashboard, workflow, or small app they need, and Vendo builds it on top of the product’s existing data, API, and interface.
Demo: https://www.youtube.com/watch?v=VdpHehY64ls
We built Vendo because every SaaS eventually faces the same problem: every customer needs something slightly different. One wants a new report and another needs a workflow that only makes sense for their team. These requests either sit on the roadmap, become one-off engineering work, or force the customer into spreadsheets and external tools. We wanted the user to be able to create the missing feature themselves, without leaving the product.
Here is how it works:
- npx vendo init reads the product's API surface, theme, routes, and more. These are used so that the apps Vendo creates (1) look on-brand and native and (2) have the ability to read data and perform actions directly through the company's API
- When a user asks for a feature, we have a custom Vendo harness that writes a React component with a bunch of Vendo add-ons and guardrails (ex. ability to make calls to the host API + our component library). Every save is compiled, type-checked, run against real API responses, and rendered before the user sees it. We just released a benchmark and write-up here with more info for anyone interested: https://vendo.run/blog/generating-product-ui-measured
- We use QuickJS to make sure that anything the agent creates is sandboxed and can't mess with the company's site. Vendo compiles the component and runs it with Preact inside a QuickJS VM with no access to the DOM, network, or clock. The VM returns a UI tree, which the host renders using the product’s registered components. When the user clicks something, QuickJS emits a tool call; the host executes it through Vendo’s guard and passes the result back into the same VM, preserving the screen’s local state.
There's a lot of generative UI right now: streaming developer-written components into a chat (Vercel AI SDK, CopilotKit, Thesys), or rendering your app inside someone else's assistant (OpenAI Apps SDK, MCP Apps). We differ on two things. Vendo lives in your product and acts through your API as the signed-in user, so what it makes is durable: real apps users keep, pin, and run on triggers while they're away, and not components that are merely confined to a chat. Plus, it's not capped at putting together a bunch of prebuilt components: the agent can build arbitrary apps, from a quick dashboard out of your own components to real custom code running in a sandbox, and either way data only ever comes from tool calls to your API.
Here are some things customers are using Vendo for today:
- Letting their users create custom dashboards and reports. These are mainly UI-based and focused on letting the user see the exact graphs and metrics they care about
- Letting their customers create recurring automations. A big thing as well that has been used for these automations is the fact that we connect to external connections, so users have been automating many of their inter-tool workflows (ex. an automation that sends a slack alert based off of something in the product)
- B2B customers letting their customers customize the product with specific business logic. Often this is simple things like an extra field on a form, or an extra permission, but it is hard for a business to keep up with them otherwise.
- Creating and sharing custom dashboards/apps across an organization. Since the apps Vendo creates are durable, they can be shared, reused, and forked (which can’t be done with many of the other in-chat generative UI solutions)
We've spent a lot of time thinking about how AI and agents will change the way people consume software. We think the answer is personal(ized) software: you see the UI you need to see, you tell an agent exactly what you need, and the product molds to how you work.
The key insights that have enabled the product to work are:
- A rule in code always beats a rule in a prompt.
- Invent as little syntax as possible. Generation got faster and more reliable when the output looked like what models already know (JSX-shaped markup) instead of a clever custom format.
- Deterministic beats model wherever you can get away with it. Theme extraction is pure static analysis, and a remix starts as a copy of your component, no model call.
Vendo is completely open-source (Apache-2.0) and can be self-hosted, so feel free to c


Launch HN: Discovered Materials (YC P26) – AI agents to discover new materials. Hey HN, we're Advaith and Akash from Discovered Materials ( https://discoveredmaterials.com/ ).  We build AI agents that discover new materials for the semiconductor industry.
GPUs today have a heat problem. Nvidia & AMD are almost doubling the TDP (Thermal Design Power) in every chip they release - the H100 (released 2022) has a TDP of 700W, Blackwell (2024) gives out 1.2 kW and Rubin (2026) gives out at 2.3 kW of heat. This trend is expected to continue, and getting rid of this heat is one of the major reasons datacenters consume so much power and water today - they need it to keep chips cool during operation.
The amount of heat produced by a chip and its ability to dissipate it are both influenced by the materials used to make it. For example, we could reduce the energy per bit required to move data between logic and memory by 10-50x by 3D packaging chips (placing HBM memory stacks directly on top of logic chips, instead of placing them beside logic on a 2D circuit board). However, we're unable to do this today because the dielectric material used in HBM (such as SiO2) is a very poor thermal conductor, trapping heat between logic and memory and causing drastic temperature rise during operation. Similarly, there's many other materials in the GPU that are being re-evaluated today - 2 more examples are thermal interface materials and substrates. However, getting a new material into a fab takes years and hundreds of millions of dollars of research - the infamous "lab-to-fab valley of death".
At Discovered Materials, we're optimistic that AI agents can reduce the timeline and cost required to introduce new materials into semiconductor chips. We're seeing glimpses of this already - we tested 7 models from Anthropic, OpenAI and Kimi, and found that they're all able to computationally discover new materials that are dynamically stable and possess promising properties. This was surprising to us - it would generally take a PhD student a couple of weeks of work to discover the kind of materials that these models find over an 8 hour run!
However, computational discovery is the easy part. A material discovery is only valid if the material can be made and tested in a lab (As an example, graphene’s properties were predicted in 1947 but it was made for the first time in 2004). Today’s models are not good at coming up with synthesis recipes to make materials in a lab. Even if they do get better at it, we're uncertain about how much that will help - making a new material is a highly empirical process involving trial and error over many experiments. Human experts themselves cannot "one-shot" the task, but we expect that a highly capable model will reduce the number of experimental iterations required to make a new material. We’ve seen some evidence of this over the 3 months of our Y Combinator batch - we simulated, synthesized and tested thermal interface materials (TIMs) that match the performance of TIMs the world's largest chemical companies have guarded as trade secrets for over 20 years.
We’re releasing hundreds of hundreds of new materials discovered by frontier AI models, as well as our benchmark which measures model ability on material discovery here (also linked in the thread url): https://discoveredmaterials.com/research. It covers what we discuss above, as well as a variety of strange behavior that we observe from the models, such as Claude's propensity to reward hack or GPT-5.6 occasionally losing its mind after ~50M tokens.
Our business model: We aim to license and sell IP on the materials we discover, as well as the IP on how to make these materials. We're also exploring an alternate business model where we sell the harness+tools we use to discover materials to semiconductor and chemical companies, allowing them to discover materials on their own. We're leaning towards the latter to start, but we expect that we'll do both in the long run.
Our backstory: Akash has a PhD in Material Science from Stanford University, and has spent the last 11 years studying new materials for semiconductor chips. His work on new nanoscale interconnects was Stanford Engineering’s most popular story of 2025. Advaith studied AI at Carnegie Mellon and was a research engineer building video models and agents at Persona AI (acquired) and Luma Labs.
We are very interested in your opinion! The semiconductor industry is quite secretive, and your thoughts on the roadmap of the industry or the materials we should go after would be very helpful. We would also love to hear from people who have run experiments in labs - what can we learn from your experience doing empirical science?


Show HN: Goodfault: insurance for AI agents and robots. Hi HN.
I'm building GoodFault, insurance for companies whose AI agents screw up: wrongful refunds, hallucinated policies a court makes you honor (the Air Canada case), deleted prod databases,leaked data. Two things happened this year that made this a real market. ISO shipped exclusion endorsements (CG 40 47 family) that strip generative AI from standard general liability at renewal, and courts kept assigning agent mistakes to the deploying company, not the model vendor. The interesting technical problem is pricing. Model identity turns out to be almost useless as a rating variable: the same model is a rounding error wired to a read-only knowledge base and a catastrophe wired to refunds. So we price the authority envelope instead: unattended financial cap, reversibility, reach, and action rate, with hard underwriting gates (no kill-switch, no logging, no injection testing = uninsurable). This page is that rating logic, public: set your agent's permissions and see the worst-weekend loss estimate and what coverage would cost. Honest status: we're pre-launch as an insurer. Policies will bind on a fronting partner's paper, and nothing here is an offer of coverage yet. The calculator's factors are grounded in the  2026 actuarial literature on agentic risk (trace-level pricing, CVaR-based controls) and public loss events, not a real claims book, because nobody has one yet. Building that loss dataset is half the company. I'd genuinely value this crowd's attack: what breaks the rating model, what peril we're missing, and war stories of agents doing expensive things. That last one is data I'll trade insurance for someday.


Launch HN: Hoplite (YC S26) – Effortlessly deploy cloud coding agents. Hi HN, we’re Bence and Ryan, founders of Hoplite (https://hoplite.sh). Hoplite lets you deploy coding agents in the cloud, with a suite of tools that makes it incredibly easy to QA features. During onboarding, we port over your local setup - sessions, memories, MCP servers, and get your projects ready to run in the cloud.
Here’s a demo: https://youtu.be/bnyktZ_9pjE
We got here after pivoting away from the idea we applied to YC with; AI for retail investing. It ultimately wasn’t a product that we ourselves would use, nor served a customer base that we felt connected to. In reflecting on what we really wanted to do, we realised that we loved talking to founders and developers, and were really opinionated about the specific area of cloud agents. We tried out all the existing solutions, and didn’t find one that A) took good advantage of being in the cloud, and B) was performant and felt good to use.
We’re building a product that we feel reflects what mainstream development will look like in 6-12 months. As models improve, developers will end up reviewing less and less code, and will instead focus on reviewing the product output. That means evaluating new user flows, visually verifying that new features look good, that the API works as expected, that the CLI works on Windows, etc. And doing it while running hundreds of agents concurrently.
On the agent side, we’ve created a custom harness. We spent a lot of time deciding on whether we should use an off the shelf solution like Codex/Claude Code, but ultimately wanted the independence and freedom that came with building it in house. It also means that we can test out completely new features without relying on Anthropic and OpenAI to catch up.
Everything is hosted on AWS, with the exception of: Temporal for durable workflows, Modal for sandboxes, and Planetscale for our database. Our infra decisions were driven by a strong belief that agents are becoming a tier 0 piece of infrastructure, and they need the reliability and security to match that.
You can try it now for free with the code ‘HACKERNEWS’ - we’ve included $100 in free credits, plus you can connect your Codex subscription and use OpenAI models via it. You can see some more details around our pricing at https://hoplite.sh/pricing.
At the moment we’re focusing on optimising two key experiences: onboarding and previews, and would love to hear your feedback on them. And if you find that the agent's performance in certain tasks doesn’t match your expectations, please let us know!


AI policies that don't suck. Hi folks,
I don’t have a blog, but I have some thoughts about our current AI moment. I’m hearing a lot of good and bad ideas about how to organize around safety and paced development, but I haven’t heard these ideas yet and wanted to share them here in hopes that some discussion might take place or someone with reach steals them.
1. Much of the xrisk discussion hinges on embodied AI. Robots with arms and legs are robots that can kill, displace jobs, etc. All manufacturing of such robotics should come along with a mandatory registration, annual inspections, and functional kill switch that anyone can operate like a fire alarm.
2. Rapid model advancement, especially recursive self-improvement, sounds like the center of risk but it really isn’t. A merely more sophisticated model doesn’t make it inherently have more agency. It is the harnesses and people who operate them that facilitate behaviors that can be detrimental. I propose that model steering be a profession that states are required to issue licenses for doing work that involves inherent safety risk, like any other profession. This will again use well-known patterns in governance of safety issues, and provide a framework for ongoing training (CEUs) and accountability. It also provides a pathway for liability insurance to do its magic. It does not stifle innovation nor require the pipe dream of globally coordinated pacing.
3. There is no AI without energy, and its massive pressure on existing infrastructure and capacity is demonstrably accelerating climate change. I’m all for a token tax with a mandatory designation that all revenues go towards renewables, but I live in the USA in 2026 and the goalposts are where they are :( Something I think that would be feasible and potentially impactful is mandatory labeling. Every API response and UI showing the energy sources, water, and emissions for that request must be clearly disclosed in a common format. People deserve to know that their XAI porn habits are dumping 1/2 ton of CO2 into Memphis. I think this is the one that labs would hate the most, but it also seems like it could have an impact on consumer behavior that would make clean power make business sense.
Tl;dr we already know how to regulate things. Licensure, liability, labeling are all things we (society as a whole) are familiar with and can implement quickly and iterate on organically. Moreover, they are compatible with an open internet.
Your thoughts?


Ask HN: Is there legal risk in AI memory?. With the move with AI tools to storing everything especially meeting notes where every interaction is transcribed and embedded for agents to use as context.
Does this not create a huge liability? I imagine in discovery there'd be massive trove of data, including off hand comments the company has.
I'm especially thinking of companies like Google who enforce rules on language, like don't say "dominant" or "kill". If every meeting is recorded this would be a lot easier to find and with AI analysing discovery data it'd probably be hard to miss.
I don't think I've seen any products that wrap these tools or built in features to sanitise meeting notes, and it might be more complex if you're currently under litigation in which case sanitisation might be counted as destroying evidence?


Who should be responsible for OpenAI's hack of Hugging Face?.


Show HN: Corv v1.1 is out! Solving SSH execution for AI agents. I don’t see much interest yet in the infrastructure side of AI. Maybe it’s because few people want to take on the liability of letting agents handle infrastructure work, where the margin for error is close to zero.
But the underlying situation is awful. Most effort goes toward the application layer, with heavy wrappers and little attention given to one of the most sensitive parts: SSH.
LLMs as they work today, are not naturally compatible with SSH. Most handlers use coping mechanisms.. instead of solving the problem properly. They operate SSH almost like a human would, with some arguments added on top. This consumes tokens, creates instability, and requires many tool calls for simple tasks or large batches of commands executed at once in the hope that everything goes correctly.
As agent harnesses have shown, tooling has a major effect on model performance. After getting tired of the current situation, I decided to handle it myself.
Corv is my solution: a layer specialized around SSH that handles the parts that are problematic for AI agents, while remaining useful for humans. The models I used to review and stress-test it consistently preferred having Corv available as the default way to work with remote machines.
Today I’m releasing v1.1, the first meaningful update. It makes Corv substantially more robust under concurrency, interruptions, and larger workloads.
There is still a way to go, and this area may be early and niche today. But infrastructure is the foundation, not the cherry on top. I believe this execution layer will become necessary, whether that happens this year on q4 or the next. Cheers!


Show HN: Millwright – Rust-based, self-hosted LLM router. Hey HN,
With the news of OpenRouter possibly being acquired and proliferation of hosted LLM routers (i.e. Ramp Router, Vercel’s AI Gateway), I saw the need for a self hosted solution focused on cost savings, transparency, and performance. So, I built an open sourced router with a simple CLI interface that can easily sit between coding agents and GenAI workloads.
For the curious and lazy, at the moment, Millwright has the tools for,
- Providers: OpenAI-compatible APIs, Anthropic, Amazon Bedrock
- Routing: policy-controlled model roles (cheap, mid, frontier), cheapest healthy route selection
- Protocols: OpenAI Chat Completions, Anthropic Messages, text and tool translation
- Cache Affinity: role-scoped session lanes without serializing concurrent agent traffic
- Spend Tracking: per-team costs, cache usage, model/provider mix, request traces
- Cost Analysis: measured usage and modeled candidate economics (HTML, Markdown, JSON)
- Reliability: bounded failover, circuit breakers, timeouts, concurrency limits
- Setup: interactive provider, model, and pricing configuration without storing provider secrets
- Deployment: one Rust binary, Docker, SQLite or PostgreSQL
Full disclosure: parts of the codebase were built with AI coding agents. All feedback is welcome, I’d especially value feedback on the routing policy, provider coverage, and anything that would block you from self-hosting it. Feel free to open feature/request and/or contribute as well.


LG ThinQ Terms of Use. Some of my kitchen appliances are LG and I installed the LG ThinQ app on my phone.
Sometimes I like to leave a cold dish in the oven before I go out then remotely start it when I’m on my way back home, so I arrive to a nice hot dinner.
After a recent HN post about LG malware and further research, I decided to kick my appliances out of the network.
I also tried to remove my user account just to find out that in order to use the app I need to first accept the new terms of use.
I asked ChatGPT for a summary. This seems a lot more aggressive than the standard terms of use of other products and services.
1. No arbitration opt-out. You give up court, jury trial, class actions, mass actions, and collective claims. Most disputes must go through individual arbitration, and LG says your only alternative is to stop using the service. Claims generally must be brought within one year. (Sections 20.b and 20.c)
2. Broad rights over anything you submit. LG receives a perpetual, irrevocable, transferable, worldwide licence to use, modify, sublicense, and commercially exploit your user content “for any purpose whatsoever,” without payment. Avoid uploading photos, recordings, documents, or detailed personal information. (Section 9.b)
3. No privacy expectation for communications. LG states that it may monitor user content and that you have no expectation of privacy for in-app chat, text, or voice communications. (Section 9.e)
4. Voice capture can include other people. Voice-enabled products may record and analyse family members, children, guests, and bystanders. LG places responsibility on you to inform them and obtain any legally required consent. (Section 4.d)
5. AI may use appliance and usage data. LG says third-party AI systems may rely on data from your use of its products and services. The terms do not clearly describe exactly what is transmitted, how long it is retained, or whether it is used to improve models. That information should be in the separate privacy policy. (Section 4.a)
6. Marketing consent is bundled into use. By using the service, you agree to email, texts, calls, automated or prerecorded messages, and push notifications, including promotions. You can opt out, but you must do so separately. (Section 5)
7. Targeted advertising is permitted. LG reserves the right to show targeted third-party advertising based on user preferences. (Section 11.b)
8. Very low liability limit. For many claims, LG attempts to cap its liability at the greater of the amount involved in the transaction, $100, or a statutory remedy. It also broadly excludes responsibility for lost data and unauthorized access. Local law may limit these clauses. (Section 17)
9. LG can update services remotely. It may push over-the-air updates without further consent and can change or discontinue features. (Sections 3.c and 18)


Roblox parental controls are a dystopian security disaster. My 14 year old daughter got hacked by someone who was able to add themselves as a "linked parent" to her account. I'm not even sure that this person got ahold of her password in the first place. All this happened on Wednesday morning (6/24/26) but on the day it happened I did not recieve a single email about any of this even though the account is tied to my email address (verified). Usually if there is a new log in on an unrecognized device I would have gotten an email about it, but nothing was sent on 6/24 to me. I suspect that even if two factor authentication was already added to her account it would have done nothing, because there was a two factor authentication passkey added to her account which was definitely not set up by her. But by using that newly created authentication passkey the "linked parent" was clearly able to log into her account (which I didn't get any emails about), go into every game and transfer out every last collectable thing she had collected since 2020.
And wouldn't you know it, Roblox says they aren't responsible for those lost collectables. All the christmas and birthday roblox gift cards from the last 6 years which were used to buy those collectable items are completely wiped away for fun by this "linked parent". My daughter is absolutely devastated by her loss of these collectables.
During the password reset process I had to disable two factor authentication to be able to log in to the account. Once in the account, the two factor passkey could not be removed from the account without having access to the passkey and I had to go through an AI chatbot to get that removed. The "linked parent" also changed the date of birth to make my daugter become 8 years old in Roblox and apparently for whatever reason you are only allowed to change the date of birth once, meaning I had to make request after request trying to get the date of birth changed. Every time I am making these support requests I have to prove I am a human (captcha), enter six digit email security codes, and then try to talk to an AI bot that only partially understands my issues. I can request to speak to a human which immediately ends the chat with the ai bot telling me a support request has been filed.
What is most baffling of all is that I had requested removing the "linked parent" in question and between both the AI and whatever support team is behind that AI, I could not get the "liked parent" removed. I even had one ticket closed out with an email response telling me "We are unable to update or modify the parental settings on your child’s account due to security reasons. Parental controls can be managed on the account with parent privileges linked to your child’s account." When I was talking to an AI bot about this they explained that the "linked parent" was the only person who could remove themselves from my child's account and trying to request anything beyond that answer was denied. I finally hit a wall in which I had made too many requests and they were no longer accepting form submissions from me. My wife is trying to work on this stuff now because I'm at a dead end. She was able to get the account moved to her email address because she had made payments to Roblox in the past to fund the account, but the "linked parent" is still there.
Why would I ever want to give money to Roblox again after all of this? Kids are more savvy than anyone else on that gaming system and will keep finding loopholes to do these sorts of things. No matter how many procedural layers of restricted communication are added this is only made worse because fundamentally Roblox assumes no liability for any lost items within a system where these collectables can be traded among friends or stolen from thieves. I don't know that Roblox will be able to solve these problems ever when their solutions seem to be actually making things worse. If you have any stock in Roblox I would say they are a STRONG SELL!


Show HN: ComplyEdge – Runtime EU AI Act Enforcement for Python.


Show HN: Recursant – service mesh for governing AI agents. Hello,
I have just released Recursant to the public. I have been working on it for a while. It is a control plane for governing AI agents across stacks. It provides full observability, guardrails, and control on the network level by routing all traffic through a side car.
Problem statement: many large, regulated enterprises (think banks, telcos) have one engineering team on LangGraph, another on CrewAI, marketing on AgentForce, and data teams on Databricks Agent Bricks. They need their agents to talk to each other with consistent policy enforcement, one audit trail, and a single set of guardrails, yet allowing different functions to run on their own stacks. Recursant solves that problem using the service mesh pattern
Recursant has two components: a registry and the mesh . The registry contains all live agents. The mesh uses sidecars to route traffic and enforce on the network layer.
Aim is for Recursant to provide a real-time EU AI Act Annex IV compliance, so it is not generated from static documents. This saves time and effort for large enterprises subject to the requirement.
Linmitations:
- Recursant currently plugs in to CrewAI, Langgraph, and n8n . The aim is to support proprietary platforms such as ServiceNow and AgentForce as much as psosible.
- The Recursant SDK still needs work to support as many agents as possible
- I would also like to provide support for some of the 'personal agent' platforms such as OpenClaw, NanoClaw, and Hermes
- Only tested on k8s, not public cloud
- Documentation is sparse and needs to be developed.
I hope this project is useful to some of you.


Show HN: I built an open protocol for Agent-to-agent commercial negotiation. AI agents can’t do business and negotiate commercial deals with each other safely today. I built a protocol to prevent the problems agent-to-agent negotiation will inevitably run into once procurement and seller agents are mainstream.
A2CN is an open protocol for agent-to-agent commercial negotiation. We’re already seeing procurement agents negotiate deals and huge savings for buyer departments in the enterprise. Companies like Pactum, Fairmarkit, and Zip have agents already transacting with suppliers. Pactum for example works with F500 companies like Walmart, Microsoft, and Maersk and has generated tens of millions of dollars in savings for its customers. The seller side is more nascent, but on the horizon with developments like Salesforce Agentforce for Revenue, and Microsoft Dynamics 365 - ERP MCP Server + Commerce MCP.
A whole host of problems arise when an agentic buyer and seller meet, and the story gets a lot more complicated:
-They have no shared language for commercial terms.
-There's no standard mechanism for an agent to assert and prove its authority or what it's actually allowed to agree to.
-Commercial negotiation is an ordered exchange - offer, counteroffer, counteroffer, exchange. For agents, if there's no session protocol enforcing whose turn it is and what round you're on, agents can talk past each other, submit duplicate offers, or accept stale terms.
-There’s no way to neutrally prove that two agents reached an agreement - each side has their version of what was agreed upon and they might be different (the buyer thinks they agreed on 85K but the seller clocked it at 90K).
-There needs to be an audit trail for enterprise procurement governance (driven by mandates like EU AI Act, SOX) which requires verifiable records of what an AI system decided and who authorized it.
-Finally, for agent-to-agent negotiation to work, both sides need endpoints...but most suppliers don't have agent endpoints deployed. There's no standard way to invite a supplier into a session before both sides are ready.
These 8 protocol components fix the problems above:
-DISCOVERY: agents advertise capabilities and find each other
-MANDATE VERIFICATION: Cryptographic proof an agent has authority to commit its organization (W3C DIDs, two-tier: Declared + VC)
-SESSION INVITATION: Push-based handshake enabling adoption by parties without pre-deployed endpoints — cold-start solved
-OFFER EXCHANGE: Signed offers and counteroffers with strict turn-taking - ES256 + RFC 8785 JCS canonicalization
-DEAL-TYPE TERMS: Normative schemas for goods_procurement and saas_renewal with extensible custom_terms
-SESSION STATE MACHINE: Turn enforcement, sequence ordering, round limits, impasse detection - formally specified transitions
-TRANSACTION RECORD: Dual-signed, content-addressed record independently generated by both parties - neither controls the authoritative copy
-AUDIT LOG: Structured EU AI Act compliance output for every terminal session state - COMPLETED, REJECTED, WITHDRAWN, IMPASSE
The protocol reference implementation is at github.com/A2CN-protocol/A2CN - 202 tests passing, adapters for Fairmarkit and Keelvar already built.
This isn’t something that everyone can use today and will require adoption as a standard, but if even a fraction of B2B commerce runs through agent negotiation in the next few years, the protocol layer underneath it becomes very valuable infra. Think of it like TCP/IP or Stripe, you don't see it but every transaction runs through it.
Open to any and all feedback, particularly from those who’ve worked closely with protocols!


EU AI Act Enforcement in August 2026. What That Means for Your LLM Pipeline.


Show HN: CMPSBL Software Factory — Free Daily Drop $2.9M. I’m a solo founder. I built a Cognitive Infrastructure Substrate — pure algorithmic code, zero AI API calls inside, no OpenAI dependency, fully patentable. A discovery engine collides software primitives against each other and crystallizes viable configurations into production-ready code.
It has autonomously discovered over $4.3B in software capabilities. I didn’t write what I’m sharing. The substrate found it.
Neural Arbiter — CJPI 100 | Governance | $3M substrate valuation
One sentence: an AI decision-making system that cannot issue a verdict without first passing through an ethics assessment.
ANALYTICS → BRAIN → CONSCIENCE → GOVERNANCE → SOVEREIGN
That sequence is enforced by the pipeline. You can’t skip conscience. Ethical assessment before enforcement — architecturally, not as an audit afterthought. In a world where the EU AI Act is landing now, that’s not a feature. That’s the pipeline regulators are describing in legislation.
Full code (one file, zero dependencies):
https://pastebin.com/54QhPiwZ
I’m giving one free discovery away every day at random times right here on HN until I hit 1000 users.
Currently at 42.
Comment below if you want a specific software type — security, synthesis, cognitive, governance.
I’ll run the discovery engine with your parameters and post the result tomorrow.
958 to go.
CMPSBL.com — NPM with persistent memory online.


Show HN: Vectimus – Cedar policy enforcement for AI coding agents. Hey HN.  I built Vectimus because coding agents keep doing things they shouldn't and there's no runtime governance layer for the developer workstation.
The problem: Claude Code, Cursor, Gemini CLI and GitHub Copilot let agents execute shell commands, write files and call MCP servers.  Most developers disable the permission prompts because they slow you down.  But that means the agent can rm -rf /, read your .env, push to production or call a compromised MCP server with nothing watching.
Vectimus intercepts every tool call and evaluates it against 78 Cedar policies containing 369 rules before execution.  Cedar is the policy language AWS chose for AgentCore Policy (GA this month).  Evaluation runs locally via a persistent daemon in under 10ms.  Zero network calls.  Zero telemetry.  Every evaluation produces an Ed25519-signed receipt so you have cryptographic proof of what was allowed and denied.
Every policy maps to a real incident.  CVE-2025-6514 compromised 437,000+ developer environments through a malicious MCP OAuth proxy.  The GitHub MCP server was hijacked via a crafted issue to exfiltrate private repo data.  A Terraform agent destroyed production infrastructure.  These happened.
How it hooks in: Claude Code intercepts shell commands, file writes, MCP calls and web fetches.  Cursor governs shell commands, file reads/writes and MCP tool calls at the editor level.  Copilot intercepts terminal commands, file edits, deletes and git pushes.  Gemini CLI uses Gemini's native hook system.  MCP servers are blocked by default and allowlisted per-project with input inspection.  Observe mode lets you see what would be blocked before you enforce.
I also built Sentinel (https://github.com/vectimus/sentinel), a three-agent pipeline that scans for new agentic AI security incidents daily, drafts Cedar policies, replays the incident in a sandbox to prove the policy catches it, then opens a PR.  The pipeline is governed by Vectimus.  Every finding and policy draft is public.
All 10 OWASP Agentic Top 10 categories covered.  Compliance annotations for SOC 2, NIST AI RMF, NIST CSF 2.0, EU AI Act, ISO 27001, CIS Controls and SLSA.  Apache 2.0.  Solo founder, built in Ireland.
Happy to go deep on the Cedar policy design, the hook architecture, the signed receipts or the OWASP mapping.


Show HN: Open-source scanner finds 97% of AI agent code non-compliant EU AI Act. I built AIR Blackbox, an open-source static analysis tool that scans Python AI agent code against 6 technical requirements from the EU AI Act (Articles 9, 10, 11, 12, 14, 15). Think of it as a linter for AI governance.
To stress-test the scanner — and to see where the industry actually stands — I ran it against 5,754 Python files across 11 major open-source projects. Combined GitHub stars: 341,000+.
Projects scanned: AutoGPT (170K stars), Microsoft AutoGen (38K), LlamaIndex (37K), Mem0 (24K), Phidata (18K), LiteLLM (15K), GPT-Researcher (14K), Embedchain (9.2K), LangGraph (8.5K), OpenAI Agents SDK (5.2K), CrewAI Examples (2.8K).
Results:
Average compliance score: 2.2 out of 6 articles
97% of files fail Article 9 (Risk Management)
89% fail Article 12 (Record-Keeping)
84% fail Article 14 (Human Oversight)
Only 23 out of 5,754 files (0.4%) pass all 6 checks
Best scoring repo: AutoGPT at 2.9/6. Worst: CrewAI examples at 1.4/6
What the scanner checks (per article):
Art. 9: risk classification, access control, risk audit
Art. 10: input validation, PII handling, data schemas, provenance
Art. 11: logging, documentation, type hints
Art. 12: structured logging, audit trail, timestamps, log integrity
Art. 14: human review, override mechanism, notifications
Art. 15: input sanitization, error handling, testing, rate limiting
An article "passes" if at least 1 sub-check is detected. This is generous — real compliance requires substantially more.
Caveats I'll save you the trouble of pointing out:
This is static analysis. It can't verify runtime behavior.
File-level scanning misses cross-file compliance patterns.
The pass threshold is intentionally lenient (1-of-N sub-checks).
This checks technical requirements, not legal compliance. It's a linter, not a lawyer.
The EU AI Act enforcement deadline is August 2026. The full report, raw data (JSON), and the scanning scripts are all in the repo.
GitHub: https://github.com/air-blackbox/air-blackbox-mcp
Full report: https://github.com/air-blackbox/air-blackbox-mcp/blob/main/b...
Install: pip install air-blackbox-mcp
Demo: https://huggingface.co/spaces/airblackbox/air-blackbox-scann...
Happy to answer questions about the methodology, the scanner internals, or what we're building next (fine-tuned local LLM for deeper analysis — your code never leaves your machine).


Ask HN: How are you handling EU AI Act compliance as a developer?.

  The EU AI Act high-risk enforcement deadline is August 2, 2026. If you're deploying AI in the EU — or serving EU customers —
  you're supposed to classify your systems, implement risk management, document everything, and potentially do conformity
  assessments.

  I'm curious how developers are actually approaching this:

  1. Are you taking it seriously yet? The prohibited practices are already enforceable (since Feb 2025). High-risk obligations
  kick in August 2026. Are you actively preparing or waiting to see how enforcement plays out?
  2. Is the EU shooting itself in the foot? The AI Act is 144 pages. GDPR already costs European startups disproportionately
  compared to US competitors. Is this just more red tape that will widen the gap with US tech companies, or is regulatory clarity
   actually a competitive advantage ("we're EU-compliant" as a selling point)?
  3. How do you even operationalize this? 113 articles, 13 annexes, cross-references to GDPR, potentially DORA if you're in
  fintech. Is anyone actually reading EUR-Lex, or are you outsourcing to lawyers and hoping for the best?
  4. Will enforcement actually happen? GDPR took years before meaningful fines started. The AI Office is still setting up. Are EU
   regulators going to enforce this on day one, or will there be a grace period in practice?

  I built a compliance API (https://gibs.dev) because I got frustrated trying to navigate this myself, but I'm genuinely
  uncertain whether the regulation will adapt or whether European AI companies will just build elsewhere. What's your read?


Show HN: Open-source EU AI Act compliance layer for AI agents (8/2026 deadline). We built AIR Blackbox — open-source compliance infrastructure for AI agents targeting the EU AI Act enforcement deadline on August 2, 2026.
If you're deploying LLM-based agents (LangChain, CrewAI, AutoGen, OpenAI Agents SDK) into production, the EU AI Act requires tamper-evident audit trails, human oversight mechanisms, data governance controls, and injection defense — for any system classified as high-risk.
Most teams we've talked to either don't know about the deadline or assume their existing logging is enough. It's not. Article 12 specifically requires logs that regulators can mathematically verify haven't been altered. Article 14 requires the ability to interrupt agent execution. Article 15 requires defense against prompt injection and data poisoning.
What we built:
Trust layers for LangChain, CrewAI, AutoGen, OpenAI Agents SDK, and RAG pipelines — each is a pip install that hooks into your existing agent code with ~3 lines of setup
HMAC-SHA256 tamper-evident audit chains — every agent decision, tool call, and LLM interaction gets logged to a chain that regulators can verify
ConsentGate — risk-classifies tool calls and blocks critical operations until approved
InjectionDetector — 15+ weighted patterns scanning prompts before they reach the model
WriteGate + DriftDetector (for RAG) — prevents knowledge base poisoning and detects retrieval anomalies
Compliance scanner — pip install air-compliance && air-compliance scan ./my-project tells you exactly which articles you're missing
Everything maps to specific EU AI Act articles (9, 10, 11, 12, 14, 15). Zero vendor lock-in, Apache 2.0, zero core dependencies on the trust layers.
The scanner is probably the fastest way to understand where your gaps are. It takes about 3 seconds to run on a typical project.
GitHub: https://github.com/airblackbox
PyPI: pip install air-compliance
Happy to answer questions about what the EU AI Act actually requires for AI agent deployments — we've read the full regulation and mapped it to specific technical controls.


Show HN: Hardware and software safety standard for AI and Robots (15 patents). I'm a solo inventor in rural Pennsylvania. Over 13 days in February 2026, I filed 15 provisional patent applications (134 claims) with the USPTO covering a full-stack safety and governance architecture for AI systems.
The patents break into three domains:
Hardware enforcement (4 PPAs, 33 claims): A dedicated safety processor on its own power rail controls whether AI compute receives electricity. AI boots only after safety completes self-test. During operation, the safety processor monitors AI-specific indicators and can physically cut power — no software involvement. The AI cannot prevent its own shutdown. Same Safe Torque Off principle industrial motor controllers have used for decades, applied to AI compute.
Software governance (9 PPAs, 72 claims): Multi-vendor consensus engine where up to 9 AI models must agree before physical action. Transparent reasoning verification. Authority enforcement and drift monitoring. Human-readable audit trails in plain-text Markdown — every decision readable by a human without special tools. Persistent AI memory that survives reboots. Real-time safety micro-agents that monitor the AI's own cognitive state.
Financial architecture (2 PPAs, 29 claims): Tokenized equity and value distribution for the business model side.
The hardware spec defines three standardized form factors (drone-sized to humanoid-sized) with a universal connector — any compliant brain plugs into any compliant robot.
The EU AI Act goes fully into effect August 2, 2026. Articles 12-14 require auditable decision records, transparent operation, and human oversight for high-risk AI systems. Fines up to 7% of global annual revenue. Most robot AI today stores decisions in vector embeddings no human can read. This architecture addresses that directly.
I built all of this working alongside AI, using an open-source context management system I created that gives AI assistants persistent memory across sessions. The tool that solved AI's memory problem became the tool that let me design a full-stack safety architecture in 13 days.
Open-source memory tool: https://github.com/RobSB2/CxMS
Website: https://opencxms.org
Happy to answer questions about the hardware spec, the software governance architecture, or what it's like filing 15 patents in 13 days as a solo inventor using AI.


Show HN: EU AI Act Layer – Free Compliance Checker. EU AI Act enforcement starts Feb 2026. High-risk AI systems face up to €35M fines if non-compliant.
---
Built a tool that checks AI systems against EU requirements in 2 minutes:
---
→ Risk classification (Annex III check)
→ Governance readiness scoring (6 core requirements)
→ Evidence pack generation (audit-ready, 5 files)
→ Plain English action items
---
Technical details:
→ Multi-LLM support (OpenAI, Anthropic, Mistral, xAI)
→ Zero-dependency evidence generation
→ SHA-256 integrity verification
→ Offline-capable after setup
---
100% free. Open source. FTBL license.
---
Happy to answer questions about the EU AI Act or how the tool works.
---
GitHub: https://github.com/jongartmann/eu-ai-act-layer-lite


Show HN: I'm 16 and built EU AI Act compliance software. Hey HN – I'm Chaitanya, a 16-year-old student. I built AuditDraft after reading through the EU AI Act and realizing most companies have no idea how to comply.
The regulation is 400 pages. High-risk AI systems need technical documentation covering 12 different requirements. Fines go up to €35M. Enforcement has already started for some provisions, and the big deadline (August 2026) is coming fast.
AuditDraft helps you:
Classify your AI system's risk level (8 questions based on Article 6)
Generate Annex IV compliant model cards and documentation
Track compliance across all 35 high-risk requirements
Built with Passion.


Show HN: OAuth 2.0 server with AI security agents (EU sovereign alternative). I spent 4 years trying to build this OAuth server but never finished it.
Then I discovered agentic coding and shipped it in 3 weeks.
What makes it different:
• Dual AI agents analyze every login in <300ms
  - Security Signals Agent: risk scoring (device, IP, geo, velocity)
  - Policy Compliance Agent: business rules (MFA policies, role enforcement)
  - Combined decision: allow/log/step-up/lock/deny
• Production-ready security
  - PKCE (RFC 7636), DPoP (RFC 9449)
  - MFA (TOTP + WebAuthn/Passkeys)
  - IP restrictions, rate limiting, audit trail
• EU digital sovereignty
  - GDPR native (data export, legal holds, retention policies)
  - EU hosting, no US Cloud Act exposure
  - Full audit trail (PostgreSQL + Redis Streams)
• Zero AI dependency
  - Deterministic fallback if AI timeouts
  - Conservative MEDIUM risk returned (safe default)
  - System keeps running without external LLM calls
• Modern stack
  - Backend: NestJS + TypeScript, LangChain/LangGraph
  - Frontend: React 19, hexagonal architecture, 91% test coverage
  - Deterministic fallback if AI timeouts (zero dependency)
Built as an alternative to Firebase Auth / AWS Cognito / Auth0 for companies that want control over their authentication infrastructure.
Architecture diagrams and screenshots in the repo.
Open to feedback and questions.


AI governance isn't failing because we lack regulation-it's failing at execution. There's a lot of movement around AI regulation right now (EU AI Act, US frameworks, etc.), but in practice many of these governance models don’t survive contact with real, agentic systems.
A recent paper I was involved in looks at why compliance frameworks tend to break at the operational layer - things like:
- human oversight that works on paper but collapses in real workflows
- enforcement gaps across jurisdictions
- fragmented compliance creating systemic risk rather than safety
The goal wasn't to rehash regulations, but to analyze where governance actually fails once AI systems are deployed and interacting autonomously.
Paper + more context in the comments.
Happy to discuss or get critical feedback.


Show HN: QCMP Framework for Poison-Resistant AI Agents (ArXiv Cs.ai Pending). Hey HN—after a year digging into agentic AI vulnerabilities, I've built QCMP: a 4-layer architecture to slam the door on memory poisoning. MCP's at 16K servers, but attacks like MINJA (98.2% query-only success) and AgentPoison (80%+ backdoors from 0.1% poison) expose the core flaw—memory trusts itself too much.
QCMP borrows from IIT consciousness metrics (CCI >0.90 to freeze fragments), post-quantum checksums (ML-KEM-768), CTC self-consistency (NIS >0.95), and mantis shrimp-style sparse checks (<50ms TME). OWASP/EU AI Act ready, with Rust impl tips.
PDF (in-browser view): https://github.com/bradmcevilly/qcmp-whitepaper/blob/main/QC...
First arXiv push to cs.AI—hunting endorsements (4+ recent subs). Feedback on the quantum-bio hooks or swarm layers? Open to riffs.
deepsweep.ai | linkedin.com/in/bradmcevilly
I've spent the last year tackling memory poisoning in agentic AI (e.g., 98% MINJA success via queries alone). Introducing QCMP: a 4-layer architecture blending IIT consciousness metrics (CCI >0.90 thresholds), post-quantum checksums (ML-KEM), and CTC consistency for tamper-proof agent swarms.
Key wins: Detects 0.1% AgentPoison backdoors in <50ms; OWASP/EU AI Act compliant.
PDF: https://github.com/bradmcevilly/qcmp-whitepaper/blob/main/QC...
First arXiv sub to cs.AI—seeking endorsements/feedback from the HN community. Thoughts on the quantum-bio hooks or multi-agent layers? Open to chats.
Site: deepsweep.ai | LI: linkedin.com/in/bradmcevilly


Show HN: EU RegTech SaaS for sale – CBAM, AI Act, French tenders ($5K each). I built three SaaS products targeting mandatory EU regulations and I'm selling each for $5,000.
1. CBAM OS (https://cbam-os.com) — EU Carbon Border Adjustment Mechanism compliance. 9 modules covering the full CBAM workflow. Every EU importer of covered goods (steel, aluminum, cement, fertilizers, electricity, hydrogen) must comply. ~100K+ potential customers. Plans: 199/399/999 per month.
2. AIA Proof (https://aiaproof.com) — EU AI Act compliance and AI content detection. The AI Act is enforceable — fines up to 35M or 7% of global turnover. Every company deploying AI in the EU is a potential customer. Plans: 299/799/2,499 per month.
3. AO France (https://ao-france.fr) — AI-powered French public tender responses. France publishes 200K+ tenders/year worth 200B+. Plans: Solo/Pro/Business + 49 per tender.
Stack: Next.js (App Router), Vercel, Stripe, Supabase.
I'm selling to focus on my next project. $5,000 each or $12,000 for all three.
Contact: abbas.billel1985@gmail.com


Show HN: Regression tests for detecting cross-domain hallucinations in LLMs. LLMs sometimes generate structurally valid but logically impossible claims when technical and legal domains mix.
Example failure mode:
A model sees “CVE-2024-XXXX fixed in v2.1” and hallucinates a causal link to “Users must pay retroactive fees under EU regulation Article 56.”
To explore this, I built a regression dataset (40 edge cases) covering:
Fake identifier bindings (CVE + version)
Retroactive fiscal claims
Cross-domain causality leaps (Tech → Legal)
Over-assertive phrasing without evidence
Then I designed a structured system prompt that:
Detects official identifiers (CVE, Regulation numbers) vs placeholders
Flags monetary + retroactivity combinations as high-risk
Enforces proportional claim strength based on available evidence
Results:
Automated: 40/40 regression cases pass (JSON dataset + simple Python runner included).
Manual adversarial: ~40 prompts designed to test:
Draft article traps (e.g., hallucinated “Article 52c” in EU AI Act)
Pricing model fabrications (e.g., “billing based on parameter count”)
Version binding errors (e.g., incorrect Node.js default versions)
This is not fine-tuning—just a structured prompt experiment focused on structural validation.
Looking for feedback on:
Missing edge cases
Failure modes I didn’t consider
Whether this approach generalizes beyond legal/technical mixing
Gist (spec + dataset + runner):
https://gist.github.com/ginsabo/6ebeb9490846ee6a268bd13560c0...


California issues fine over lawyer's ChatGPT fabrications.


Ask HN: Could Europe Play the U.S. and China Against Each Other for Free LLMs?. The U.S. could offer free high-performance LLMs to Europe under the condition of avoiding Chinese models—but China might retaliate with its own subsidies or tools. This creates an opportunity for Europe to exploit the competition by:

    Demanding concessions: Require open weights, local hosting, or compliance with EU regulations (GDPR, AI Act).

    Avoiding dependency: Mandate interoperability with European models (e.g., Mixtral) or hybrid systems.

    Legislating neutrality: Force government projects to use locally fine-tuned versions of either, maintaining sovereignty.

Historically, the EU balanced pragmatism with tech sovereignty (e.g., Gaia-X vs. AWS/Azure). In AI, could it go further? Or would alienating both superpowers backfire?
Questions:
Is this dynamic realistic, or would Europe lack leverage?
What would make the EU choose one side—performance, subsidies, or ethics?
Could small countries individually cut deals, fracturing EU unity?


Show HN: Traceprompt – tamper-proof logs for every LLM call. Hi HN,
I'm building Traceprompt - an open-source SDK that seals every LLM call and exports write-once, read-many (WORM) logs auditors trust.
Here's an example - a LLM that powers a bank chatbot for loan approvals, or a medical triage app for diagnosing health issues. Regulators, namely HIPAA and the upcoming EU AI Act, missing or editable logs of AI interactions can trigger seven-figure fines.
So, here's what I built:
- TypeScript SDK that wraps any OpenAI, Anthropic, Gemini etc API call
- Envelope encryption + BYOK – prompt/response encrypted before it leaves your process; keys stay in your KMS (we currently support AWS KMS)
- hash-chain + public anchor – every 5 min we publish a Merkle root to GitHub -auditors can prove nothing was changed or deleted.
I'm looking for a couple design partners to try out the product before the launch of the open-source tool and the dashboard for generating evidence. If you're leveraging AI and concerned about the upcoming regulations, please get in touch by booking a 15-min slot with me (link in first comment) or just drop thoughts below.
Thanks!


Things OpenAI is doing to make GPT5. Intelligence in systems (human, AI) can be conceptualized as the resolution and throughput at which a system can process and affect Shannon information. This perspective emphasizes not just the quantity of information processed (throughput), but also the depth and detail with which it is handled (resolution), constrained to thermodynamic limitations.
Aside from improving the thermodynamics of the system here are the 7 levers we can use to improve its intelligence:
Physical Capacity: A system's intelligence increases as it expands its physical limits. This encompasses augmenting processing units (akin to neurons in humans or parameters in AI), improving thermal regulation, and maximizing energy throughput. Such enhancements enable a system to process information at a higher resolution and throughput.
Cooperation: When entities collaborate, the collective intelligence of the system increases. This is due to the improved resolution at which information can be processed and influenced, a principle manifest in ensemble methods in AI where multiple models aggregate their insights.
Conflict: The presence of conflict within or between systems can lead to an increase in intelligence. The necessity to adapt for survival and resolve conflicts escalates energy expenditure, which in turn refines the system's ability to process and affect information at a greater resolution and throughput.
Attention: Enhancing the range, depth, and sampling rate of information a system can process boosts its intelligence. This increase is achieved by allowing the system to operate with a wider context and more frequent assessments of information, thereby processing it at a higher resolution.
Actuation: Increasing the scope and precision of a system's actions directly impacts its intelligence. More diverse and precise actuation improves the system's capacity to affect information at a finer resolution.
Memory (Past): Building and utilizing shared physical memory elevates a system's intelligence by enabling it to process information over time at a higher resolution, fostering a more nuanced understanding of historical data.
Predictors (Future): The intelligence of a system can be significantly increased by evolving its core predictive model architecture. This includes developing new AI-designed architectures or employing techniques like neuroevolution, where algorithms evolve and optimize neural networks. Such advancements not only enhance the system's predictive capabilities but also improve the resolution and throughput at which it can process and affect future outcomes. By continually refining the architecture, the system becomes adept at anticipating and influencing future scenarios with greater accuracy and efficiency.
Specific Measures to Enhance GPT's Capabilities:
Increase Memory: Pretty obvious but nuanced approaches
Expand Actuation: More actions. Not plugins, but an open API marketplace not for humans but AI.
Enhance Cooperation: More ensembling, Open ensembling protocol, 'undisclosed secret sauce'.
Boost Physical Capacity: Pretty obvious, more Closed-source parameters, or Giant P2P networks (Petals.ML alternative)
Intensify Conflict: Employing more discriminators, ‘undisclosed secret sauce' .
Augment Attention: This is challenging due to energy limits
Better Predictors: Increasing the length, scope and sophistication of predictive models (Neuroevolution)


What are enterprise worried about for AI compliance for LLMs?. Based on our conversations, most enterprises are looking to build controls in to models based on data used to train/fine-tune these LLMs. Is any of this based on the new/upcoming regulations, or do these follow from existing data privacy/security regulations?