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SmookeΒ  updated a collection about 1 month ago
Models Used in HackerNoon Publishing System
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HackerNoon/where-startups-trend
fabian339Β  updated a dataset 11 months ago
HackerNoon/tech-company-news-data-dump
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HackerNoon's activity

SmookeΒ 
posted an update 3 months ago
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Check out The AI Writing Contest with @BrightData ! https://www.contests.hackernoon.com/ai-writing-contest Cash prizes for innovative approaches to AI and LLM training. We publish blog posts, research papers, stories of side hustles, you name it.

Any story tagged #AI enters to win. Most recent stories: https://hackernoon.com/tagged/ai and RSS feed https://hackernoon.com/tagged/ai/feed

Couple of favorite recent posts we published:

Why Salesforce and Microsoft Are Battling for the Future of AI Agents https://hackernoon.com/why-salesforce-and-microsoft-are-battling-for-the-future-of-ai-agents

Decentralized AI Summit at MIT Votes OriginTrail As The Best Decentralized AI Project https://hackernoon.com/decentralized-ai-summit-at-mit-votes-origintrail-as-the-best-decentralized-ai-project

Studying is Overrated https://hackernoon.com/studying-is-overrated

Why Can’t AI Count Letters??? https://hackernoon.com/why-cant-ai-count-letters

The Paradox of AI: If It Can't Replace us, Is It Making Us Dumber? https://hackernoon.com/the-paradox-of-ai-if-it-cant-replace-us-is-it-making-us-dumber

How Does Human Memory Work? https://hackernoon.com/how-does-human-memory-work

Is AI Actually Writing Production-Ready Code? https://hackernoon.com/is-ai-actually-writing-production-ready-code

Our AI Coding Tool Went Viral, Then Everything Broke. This is What We Learned. https://hackernoon.com/our-ai-coding-tool-went-viral-then-everything-broke-this-is-what-we-learned

Startups of The Year: Meet the AI Industry https://hackernoon.com/startups-of-the-year-meet-the-ai-industry

Nobel Prize Winner Geoffrey Hinton Explores Two Paths to Intelligence in AI Lecture https://hackernoon.com/nobel-prize-winner-geoffrey-hinton-explores-two-paths-to-intelligence-in-ai-lecture

Comparing AI vs. Blockchain Hype https://hackernoon.com/comparing-ai-vs-blockchain-hype

The SaaS Apocalypse and How aI Will Give Birth to One-person Tech Giants https://hackernoon.com/the-saas-apocalypse-and-how-ai-wi
SmookeΒ 
posted an update 5 months ago
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Chomsky predicting LLMs in 1956, curated by Ryan Rhodes (Rutgers)
SmookeΒ 
posted an update 6 months ago
SmookeΒ 
posted an update 7 months ago
SmookeΒ 
posted an update 7 months ago
SmookeΒ 
posted an update 8 months ago
SmookeΒ 
posted an update 9 months ago
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NEW publishing hub for trending text model academic research papers broken down into open source technical blog posts ➑️ https://textmodels.tech
SmookeΒ 
posted an update 9 months ago
SmookeΒ 
posted an update 10 months ago
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πŸ‘‹ πŸ‘‹ πŸ‘‹ In a recent AI Time Journal Interview, I was asked: How has HackerNoon leveraged AI to enhance its platform? and what lessons have you learned about AI’s role in spam creation and prevention?

https://HackerNoon.com uses AI and machine learning to improve grammar, translate stories, generate featured images, suggest possible headlines, (and note, I would love to hear what else you think we should use AI for!) . Maybe Clippy was ahead of its time? Writers have and will continue to have smart AI assistants. AI can not and should not replace the writer, but it should and can assist the writer and editor throughout the publishing process. We are documenting our approach to editorial at https://EditingProtocol.com. With the rise of LLMs, we’ve seen an influx of AI story spam submissions. Other blogging platforms are hosting so many of these types of machine-generated stories right now. We are actively taking steps not to become a wasteland of AI’s musings. Because every story is reviewed before publication (with about half being rejected), I think our average story quality is higher than other blogging platforms. Every story submission automatically enters into plagiarism and AI writing detection. We highlight what sections are likely, possibly, and unlikely to be written by AI. It’s all about confidence levels. Ultimately, human editors make the final call in interpreting the machines’ reports.
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