Hacker News

Article URL: https://www.msn.com/en-us/money/other/perplexity-ai-drops-ads-implementation-plan-to-keep-user-trust/ar-AA1WBvyY

Comments URL: https://news.ycombinator.com/item?id=47072876

Points: 1

# Comments: 0

Article URL: https://github.com/fjrevoredo/mini-diarium

Comments URL: https://news.ycombinator.com/item?id=47072863

Points: 1

# Comments: 0

Article URL: https://theamericanscholar.org/the-popper-principle/

Comments URL: https://news.ycombinator.com/item?id=47072849

Points: 3

# Comments: 0

Article URL: https://mememonkeys.com/

Comments URL: https://news.ycombinator.com/item?id=47072836

Points: 1

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Article URL: https://www.llmdrugs.com

Comments URL: https://news.ycombinator.com/item?id=47072829

Points: 2

# Comments: 0

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

# Comments: 0

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

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

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

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

Points: 1

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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