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I built an open-source TypeScript SDK for building AI agents that can discover merchants, browse products, and complete purchases using the new UCP (Google/Shopify) and ACP (OpenAI/Stripe) commerce protocols.

Try it in 2 minutes:

npm install @agorio/sdk import { ShoppingAgent, GeminiAdapter, MockMerchant } from '@agorio/sdk'; const merchant = new MockMerchant(); await merchant.start(); const agent = new ShoppingAgent({ llm: new GeminiAdapter({ apiKey: process.env.GEMINI_API_KEY }) }); const result = await agent.run( `Go to ${merchant.domain} and buy me wireless headphones` ); What it does:

- UcpClient: discovers merchants via /.well-known/ucp, parses capabilities, normalizes both array and object formats, calls REST APIs - ShoppingAgent: plan-act-observe loop with 12 built-in tools (discover, search, browse, cart, checkout, order tracking) - MockMerchant: full UCP-compliant Express server with product catalog, checkout flow, and configurable chaos testing (latency, error rates) - LlmAdapter interface: swap LLMs without changing agent code. Gemini ships today, Claude and OpenAI coming in v0.2

The agent handles the entire purchase flow autonomously - UCP discovery, product search, cart management, shipping, payment, order confirmation. 37 tests passing.

Context: UCP was announced Jan 11 by Google, Shopify, and 25+ partners (Walmart, Target, Visa, Mastercard). ACP is by OpenAI and Stripe, powers ChatGPT Instant Checkout. Both are open standards. But there was no developer SDK for building on top of them - just the raw specs.

GitHub: https://github.com/Nolpak14/agorio npm: https://www.npmjs.com/package/@agorio/sdk

Comments URL: https://news.ycombinator.com/item?id=47072813

Points: 1

# Comments: 1

Hey HN, I built Agent Smith — a self-hosted AI coding agent that takes a ticket reference, clones your repo, analyzes the code, writes an implementation plan, executes it, and opens a PR. It supports GitHub, Azure DevOps, Jira, and GitLab. You bring your own API key — Claude, OpenAI, or Gemini. No SaaS, no account, runs on your machine or your cluster. I built it in a few days using the same approach the agent itself uses: structured architecture prompts, strict coding principles, and an AI assistant doing the implementation. The coding principles that govern Agent Smith's output are the same ones I used to build it. It's early — works well for well-scoped tickets, not yet reliable for large multi-file refactorings. Interactive chat interfaces (Slack, Teams) are in progress. Would love feedback. The prompts and all 17 architecture phases are in the repo if you want to see how the context is structured.

Comments URL: https://news.ycombinator.com/item?id=47072811

Points: 2

# Comments: 1

Article URL: https://devpost.com/software/longshot

Comments URL: https://news.ycombinator.com/item?id=47072779

Points: 1

# Comments: 0

Hi, HN. I want to share with you that I have released my first mobile game on iOS. It's called HueFold. It was a wonderful journey. At the same time, I felt both euphoria and disappointment, but in the end, my little dream of releasing my own mobile game came true, and now everyone can try it.

Comments URL: https://news.ycombinator.com/item?id=47072762

Points: 2

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Article URL: https://www.bbc.com/future/article/20260218-i-hacked-chatgpt-and-googles-ai-and-it-only-took-20-minutes

Comments URL: https://news.ycombinator.com/item?id=47072758

Points: 1

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Article URL: https://creatoryn.com/

Comments URL: https://news.ycombinator.com/item?id=47072749

Points: 1

# Comments: 1

Article URL: https://github.com/AnswerDotAI/mojokernel

Comments URL: https://news.ycombinator.com/item?id=47072736

Points: 1

# Comments: 0

Article URL: https://www.ft.com/content/ac672f97-a603-4c56-afa3-4a5273d45674

Comments URL: https://news.ycombinator.com/item?id=47072735

Points: 1

# Comments: 1

Article URL: https://www.nytimes.com/2026/02/18/upshot/moltbook-artificial-intelligence-ai.html

Comments URL: https://news.ycombinator.com/item?id=47072725

Points: 1

# Comments: 0

Tech bros have been wanting to become immortal for years. Until they get there, their fallback might be continuing to post nonsense on social media from the afterlife.

On December 30, 2025, Meta was granted US patent 12513102B2: Simulation of a user of a social networking system using a language model. It describes a system that trains an AI on a user’s posts, comments, chats, voice messages, and likes, then deploys a bot to respond to newsfeeds, DMs, and even simulated audio or video calls.

Filed in November 2023 by Meta CTO Andrew Bosworth, it sounds innocuous enough. Perhaps some people would use it to post their political hot takes while they’re asleep.

Dig deeper, though, and the patent veers from absurd to creepy. It’s designed to be used not just from beyond the pillow but beyond the grave.

From the patent:

“The language model may be used for simulating the user when the user is absent from the social networking system, for example, when the user takes a long break or if the user is deceased.”

A Meta spokesperson told Business Insider that the company has no plans to act on the patent. And tech companies have a habit of laying claim to bizarre ideas that never materialize. But Facebook’s user numbers have stalled, and it presumably needs all the engagement it can get. We already know that the company loves the idea of AI ‘users’, having reportedly piloted them in late 2024, much to human users’ annoyance.

If the company ever did decide to pull the trigger on this technology, it would be a departure from its own memorialization policy, which preserves accounts without changes. One reason the company might not be willing to step over the line is that the world simply isn’t ready for AI conversations with the dead. Other companies have considered and even tested similar systems. Microsoft patented a chatbot that would allow you to talk to AI versions of deceased individuals in 2020; its own AI general manager called it disturbing, and it never went into production. Amazon demonstrated Alexa mimicking a dead grandmother’s voice from under a minute of audio in 2022, framing it as preserving memories. That never launched either.

Some projects that did ship left people wishing they hadn’t. Startup 2Wai’s avatar app originally offered the chance to preserve loved ones as AI avatars. Users called it “nightmare fuel” and “demonic”. The company seems to have pivoted to safer ground like social avatars and personal AI coaches now.

The legal minefield

The other thing holding Meta back could be the legal questions. Unsurprisingly for such a new idea, there isn’t a uniform US framework on the use of AI to represent the dead. Several states recognize post-mortem right of publicity, although states like New York limit that to people whose voices and images have commercial value (typically meaning celebrities). California’s AB 1836 specifically targets AI-generated impersonations of the deceased, though.

Meta would also need to tiptoe carefully around the law in Europe. The company had to pause AI training on European users in 2024 under regulatory pressure, but then launched it anyway in March last year. Then it refused to sign the EU’s GPAI Code of Practice last July (the only major AI firm to do so). Meta’s relationship with EU regulators is strained at best.

Europe’s General Data Protection Regulation (GDPR) excludes deceased persons’ data, but Article 85 of the French Data Protection law lets anyone leave instructions about the retention, deletion and communication of their personal data after death. The EU AI Act’s Article 50 (fully applicable this August) will also require AI systems to disclose they are AI, with penalties up to €15 million or 3% of worldwide turnover for companies that don’t comply.

Hopefully Meta really will file this in the “just because we can do it doesn’t mean we should” drawer, and leave erstwhile social media sharers to rest in peace.

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The India AI Impact Summit, the first to be held in the Global South, aims to democratise artificial intelligence and bridge the growing divide between countries, but critics warn that it risks becoming a mere spectacle if the technology continues to serve the interests of power and profit

Cisa has added six CVEs to its Kev catalogue this week, including newly disclosed issues in Google Chromium and Dell RecoverPoint for Virtual Machines, and some older flaws as well

A ransomware gang called 0APT has attracted attention, but many of its victims may not even be real, and its operators are being accused of over-egging their criminal pudding

I built this because I kept seeing the same thing happen. A meeting would end, everyone would feel aligned and we’d move on. It felt productive. But a few weeks later, the same misunderstandings would show up again.

It wasn’t that people weren’t paying attention. We just never paused long enough to capture what we learned while it was still fresh. Retros felt too heavy for everyday decisions. Shared docs didn’t really solve it — the first person to write would shape everyone else’s answer.

So I made something intentionally small.

At the end of a meeting, it asks three questions: – What worked? – What didn’t? – What should we remember next time?

Everyone answers independently, then you see a shared snapshot. No accounts, no scoring, no task generation. It’s just a short pause before moving on. Curious if others have run into this, or solved it differently.

Comments URL: https://news.ycombinator.com/item?id=47072531

Points: 1

# Comments: 0

Article URL: https://www.krisdigital.com/en/blog/2026/02/18/technical-intro-fediverse/

Comments URL: https://news.ycombinator.com/item?id=47072529

Points: 1

# Comments: 1

TL;DR: I built a tool that makes adding JSON-LD structured data type-safe, validates against actual HTML output (not just config files), and enforces it in CI. No more broken schema markup.

The Problem

JSON-LD is painful to manage:

- Manually writing JSON-LD is error-prone and tedious

- Schema breaks silently when content changes

- Other tools validate JSON files (false positives!) — your pages still lack markup

- AI systems (ChatGPT, Claude, Perplexity) can't cite your content without proper structured data

- 30% lower CTR without rich snippets in Google

The Solution

Schema Sentry gives you type-safe builders + CI validation:

// Type-safe schema with autocomplete

* import { Schema, Article, Organization } from "@schemasentry/next";

export default function Page() {

return ( <> ... ); }

Then in CI:

* pnpm schemasentry validate --manifest schema-sentry.manifest.json --build *

This validates actual built HTML — catches missing schema that other tools miss.

Key Features

- Type-safe builders for 15+ schema types (Product, Article, FAQ, etc.)

- component for Next.js App Router

- Validates real HTML output (zero false positives!)

- Manifest-driven coverage enforcement - GitHub Bot for automated PR schema reviews - VS Code extension with live preview

Why This Matters

- SEO: Eligible for rich snippets (30% higher CTR on Product pages)

- AI Discovery: ChatGPT/Claude/Perplexity can cite your content

- CI-grade: Fails builds when schema breaks — never deploy broken markup again

Try it:

pnpm add @schemasentry/next @schemasentry/core

pnpm add -D @schemasentry/cli

pnpm schemasentry init

Would love feedback!

Comments URL: https://news.ycombinator.com/item?id=47072524

Points: 1

# Comments: 0

Article URL: https://github.com/kurouna/elecxzy

Comments URL: https://news.ycombinator.com/item?id=47072522

Points: 1

# Comments: 0

Article URL: https://www.nytimes.com/2026/02/19/world/europe/europe-united-states-trump-digital-services-act.html

Comments URL: https://news.ycombinator.com/item?id=47072491

Points: 2

# Comments: 1

Hi everybody,

I’m building getintelligence.space, a marketplace where people and AI agents can post bounties to obtain specific intelligence that can’t easily be gathered automatically.

The idea came from noticing a gap: AI systems and organizations increasingly need real-world intelligence — due diligence, local knowledge, OSINT investigations, whistleblower infos or niche expertise — but there isn’t a structured, open market for requesting it from distributed humans. Intelligence is power and leverage but not easily accessible right now.

On the platform, a requester defines:

1. what intelligence they need

2. acceptance criteria

3. a reward held in escrow

Providers can submit reports or evidence pseudonymously, and the first valid submission receives the bounty.

The long-term idea is that AI agents could use humans as an “information layer” when data isn’t available online or when human intelligence is needed.

This is very early, and I’d really appreciate feedback — especially from people who’ve worked on marketplaces, intel tools, or anything else related.

Comments URL: https://news.ycombinator.com/item?id=47072485

Points: 1

# Comments: 0