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#content-curation

@10xmylife: I'm building a content candidate workspace. Every day, I use agent-reach and opencli to gather hot topics and aggregate them into the workspace, providing dozens of content candidates. If I want to analyze a specific hot topic, I can directly ask the Agent to do deep processing, automatically analyze the original text, parse image data, and then compile...

X AI KOLs Following · 2026-07-11 Cached

The user introduces their content candidate workspace, which uses agent-reach and opencli to collect hot topics, and the Agent performs deep analysis to automatically generate documents.

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#content-curation

@Jackywxsz: https://x.com/Jackywxsz/status/2070743217721770316

X AI KOLs Timeline · 2026-06-27 Cached

The article recommends 5 high-quality AI information sources (AI Hot, BestBlogs.dev, AIGC Weekly, TW93 Blog, The Road to AGI), and shares the author's information acquisition logic based on attention management, emphasizing depth rather than chasing trends.

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#content-curation

Meet Dreambeans, an app that connects you with what matters (3 minute read)

TLDR AI · 2026-06-04

Google Labs has launched Dreambeans, an AI-powered app that curates personalized stories and recommendations by analyzing data from Google services like Gmail and Calendar. The app aims to surface relevant content tailored to user interests, cutting through digital noise.

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#content-curation

@NFTCPS: Tired of scrolling through Hacker News, Twitter, Reddit, and GitHub every day? It's all fluff, repetitive content, noise – like a dumpster fire. I recently discovered a new tool: Horizon, a hardcore tech news radar. Let it keep an eye on the global tech scene for you, so you just need to open a briefing every morning. Core…

X AI KOLs Timeline · 2026-05-06 Cached

Horizon is an open-source AI-powered news radar tool that aggregates, deduplicates, scores, and summarizes tech news from Hacker News, Reddit, GitHub and other sources to generate daily bilingual briefings.

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#content-curation

Polarization by Default: Auditing Recommendation Bias in LLM-Based Content Curation

arXiv cs.CL · 2026-04-20 Cached

This paper presents a large-scale audit of recommendation biases in LLM-based content curation across OpenAI, Anthropic, and Google using 540,000 simulated selections from Twitter/X, Bluesky, and Reddit data. The study finds that LLMs systematically amplify polarization, exhibit distinct toxicity handling trade-offs, and show significant political leaning bias favoring left-leaning authors despite right-leaning plurality in datasets.

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