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AI agents hallucinate B2B data because they have no ground truth. I built one. Nopp's Entity Graph pulls from NPPES practitioner data, 40+ state licensing boards, ODO corporate registries, and regulatory filings. Nothing scraped. Nothing inferred. Every entity is deterministically verified. The API surface I want you to stress-test: Entity Graph

POST /api/v1/identify — name, NPI, or LinkedIn URL → verified canonical entity (10 IC) GET /api/v1/people/:id — fetch a resolved person record (3 IC) POST /api/v1/people/search — filter 12M+ licensed professionals by state, city, credential (10 IC) GET /api/v1/organizations/:id — fetch an org record (3 IC)

Intelligence

POST /api/v1/company/enrich — live enrichment, falls back gracefully, never returns empty (10 IC) POST /api/v1/contact/enrich — contact-level deep enrichment (3 IC)

Signals

POST /api/v1/signals/hiring — detect hiring surges (15 IC) POST /api/v1/signals/funding — funding round detection (15 IC) POST /api/v1/signals/intent — competitor frustration + intent signals (20 IC)

Agents

POST /api/v1/agent/launch/companies — autonomous research + outreach in one call (50 IC)

Also ships as an MCP Server — connect Claude, ChatGPT, or any MCP-compatible agent directly. No API calls, no plumbing. What I want from you:

Feed /identify ambiguous or conflicting inputs — same person, two LinkedIn URLs, different name spellings Try to get /company/enrich to return empty or wrong data Test /signals/intent against a company you know well — does it catch real signals? Tell me where the schema is wrong, underdocumented, or just annoying to work with

Find a reproducible bug or a real schema issue → I'll give you free Intelligence Credits. No cap. nopp.us/api-docs | nopp.us/developers I'm the founder. I'll reply to every technical comment personally.

Comments URL: https://news.ycombinator.com/item?id=47329246

Points: 1

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Article URL: https://www.vox.com/the-highlight/480106/ai-jigsaw-puzzles-artificial-intelligence-genai

Comments URL: https://news.ycombinator.com/item?id=47329243

Points: 1

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Article URL: https://electrek.co/2026/03/09/donut-lab-solid-state-battery-self-discharge-test-vtt/

Comments URL: https://news.ycombinator.com/item?id=47329242

Points: 1

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Article URL: https://fortune.com/2026/03/10/national-debt-100-percent-of-gdp-crisis-break-glass-plan-crfb-report/

Comments URL: https://news.ycombinator.com/item?id=47329238

Points: 4

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Article URL: https://eavan.blog/posts/big-identity-sandpiles.html

Comments URL: https://news.ycombinator.com/item?id=47329229

Points: 1

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Study participants reported increased mental fatigue while using AI tools, but less burnout overall.

After 14 years in orbit, NASA's Van Allen Probe A satellite is expected to begin re-entering Earth's atmosphere on Tuesday.

Create a security compliance plan for the data center that includes various standards, audit schedules, and 2026 AI governance and sustainability reporting requirements.

I spoke to an AT&T archivist about Alexander Graham Bell's famous transmission. Even though calls have changed, the reasons behind them are still the same.

Experiencing chest pains during anxiety is common. Here's what you can do to improve symptoms.

Auditors from Assured Security Consultants only noted two low-risk issues that have since been patched.

Here are hints and the answers for the NYT Connections: Sports Edition puzzle No. 534 for Wednesday, March 11.

Article URL: https://www.payphone-project.com/iowa-payphone-defends-itself-ap-story-from-october-1984.html

Comments URL: https://news.ycombinator.com/item?id=47328104

Points: 1

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Here are hints and answers for the NYT Strands puzzle for March 11, No. 738.

Here are some hints and the answers for the NYT Connections puzzle for March 11, No. 1,004.

Here are hints and the answer for today's Wordle for March 11, No. 1,726.

Article URL: https://anybox.ltd/source-code-preview

Comments URL: https://news.ycombinator.com/item?id=47328083

Points: 1

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Article URL: https://www.williamjbowman.com/blog/2026/03/05/against-vibes-when-is-a-generative-model-useful/

Comments URL: https://news.ycombinator.com/item?id=47328071

Points: 1

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Hi all, this is an early version of a side project of mine. Would love some feedback and comments.

I like karaoke and I grew up with the Asian style karaoke with the music video behind and the karaoke lyrics at the bottom.

Sometimes I want to do a song and there is no karaoke version video like that.

A few years ago I came across ML models that cleanly separate the vocals and the instrumental music of a song. I thought of the idea to chain together ML models that can take an input music video file, extract the audio (ffmpeg), separate the tracks (ML), transcribe the lyrics (ML), burn the lyrics back with timing into the video (ffmpeg), and output a karaoke version of the video.

This is an early version of the app, Mac only so far (since I use Mac, despite it being an electron app.. I do eventually want to make a Windows build), I've only let a few friends try it. Let me know what you think!

Comments URL: https://news.ycombinator.com/item?id=47328060

Points: 1

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