Sources for ML news? [D]

2026-06-06 · Source: Machine Learning · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Novice, quick

Summary

The discussion identifies several platforms and strategies for aggregating Machine Learning (ML) news and filtering out low-quality content, addressing a common frustration with social media bots. Key recommendations include paper digest, hacker news, news.smol.ai (a newsletter), tldr.ai, Scholar Inbox for specific topics, and AlphaXiv for trending papers. Twitter (now X) is frequently mentioned as a de-facto announcement platform for papers, despite its acknowledged issue with bots and the need for heavy feed curation, which some participants suggest takes 4-5 days to optimize. One participant also promotes scholarfeed.org, a platform they are developing that uses LLMs to analyze and rank Arxiv papers based on AI-writing detection and novelty.

Key takeaway

For ML researchers and practitioners seeking efficient news consumption, you should diversify your information sources beyond general social media. Actively curate platforms like X by filtering feeds over several days to reduce noise, and integrate specialized aggregators such as news.smol.ai, tldr.ai, or Scholar Inbox for targeted content. This approach minimizes bot interference and maximizes access to relevant, high-quality research and developments.

Key insights

Effective ML news aggregation requires curated sources and active filtering to combat information overload.

Principles

• Curated aggregation is essential for ML news.
• Social media requires heavy filtering for valuable ML content.

Method

Curate social media feeds by following specific accounts and muting others over several days to reduce noise.

In practice

• Explore news.smol.ai for aggregated ML news.
• Use Scholar Inbox for topic-specific paper alerts.
• Consider AlphaXiv to identify trending research.

Topics

• Machine Learning News
• Research Aggregation
• AI Paper Discovery
• Social Media Curation
• Arxiv Analysis

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, AI Student

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Editorial summary, takeaway, and curation by AIssential. Original article published by Machine Learning.