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Article URL: https://www.webdesignmuseum.org/golden-age-of-web-design

Comments URL: https://news.ycombinator.com/item?id=47068897

Points: 1

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Article URL: https://www.scientificamerican.com/article/the-quirky-geology-behind-olympic-curling-stones/

Comments URL: https://news.ycombinator.com/item?id=47068889

Points: 1

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Article URL: https://thresholdiq.app/

Comments URL: https://news.ycombinator.com/item?id=47068873

Points: 1

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Article URL: https://github.com/RightNow-AI/picolm

Comments URL: https://news.ycombinator.com/item?id=47068850

Points: 2

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Article URL: https://walletwisdom.app

Comments URL: https://news.ycombinator.com/item?id=47068833

Points: 1

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Article URL: https://twitter.com/Caust1c/status/2024296452135227607

Comments URL: https://news.ycombinator.com/item?id=47068831

Points: 2

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Article URL: https://neutree.ai/projects/openapi-to-skills

Comments URL: https://news.ycombinator.com/item?id=47068810

Points: 1

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Article URL: https://github.com/subinium/agf

Comments URL: https://news.ycombinator.com/item?id=47068793

Points: 1

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Article URL: https://www.energy.gov/science/articles/creating-heart-quantum-computer-developing-qubits

Comments URL: https://news.ycombinator.com/item?id=47068780

Points: 2

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Article URL: https://www.dropsitenews.com/p/israeli-government-surveillance-epstein-apartment-66th-street-ehud-barak

Comments URL: https://news.ycombinator.com/item?id=47068777

Points: 8

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Article URL: https://apps.microsoft.com/detail/9p4txws57pld?hl=en-US&gl=US

Comments URL: https://news.ycombinator.com/item?id=47068775

Points: 1

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Article URL: https://www.economist.com/business/2026/02/18/chinas-humanoids-are-dazzling-the-world-who-will-buy-them

Comments URL: https://news.ycombinator.com/item?id=47068742

Points: 2

# Comments: 1

Article URL: https://bigmachine.io/articles/opinion/becoming-the-wolf/

Comments URL: https://news.ycombinator.com/item?id=47068741

Points: 1

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Article URL: https://thehustle.co/news/are-reverse-recruiters-the-only-professionals-thriving-in-this-job-market

Comments URL: https://news.ycombinator.com/item?id=47068735

Points: 1

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Article URL: https://www.cleveland.com/news/2026/02/journalism-schools-are-teaching-fear-of-the-future-letter-from-the-editor.html

Comments URL: https://news.ycombinator.com/item?id=47068629

Points: 1

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Article URL: https://scripps.ucsd.edu/giving/name-new-species

Comments URL: https://news.ycombinator.com/item?id=47068623

Points: 1

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Article URL: https://github.com/microsoft/playwright-cli

Comments URL: https://news.ycombinator.com/item?id=47068611

Points: 1

# Comments: 1

Traditional analytics tracks clicks. For AI products, you need to know: what was the user trying to do, did the AI help, and did they succeed?

I built a demo of this. It ingests AI conversations and runs 3 workers (GPT-4o-mini): intent classifier, quality scorer (LLM-as-judge), and task completion detector. Results show up in a dashboard designed for PMs, not engineers.

Stack: Python SDK (zero deps, async) → FastAPI → Supabase → GPT-4o-mini workers → Next.js dashboard.

Demo with sample data (not live product, validating the concept): https://dashboard-xi-taupe-75.vercel.app

The sample data models an AI app builder. Interesting patterns: scaffolding works great (78% success), but API integrations fail 75% of the time, and users who enter bug-fix loops almost always churn.

Key design question: is the "insights layer" (auto-generated recommendations, revenue-at-risk estimates, root cause identification) valuable enough to differentiate from Langfuse/Helicone adding product analytics to their existing tracing tools?

Looking for honest feedback, especially from AI product builders.

Comments URL: https://news.ycombinator.com/item?id=47068605

Points: 1

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Article URL: https://tomtunguz.com/inference-as-compensation/

Comments URL: https://news.ycombinator.com/item?id=47068599

Points: 1

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Article URL: https://github.com/jurgen-siegel/rusty-claw

Comments URL: https://news.ycombinator.com/item?id=47068587

Points: 1

# Comments: 1