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I Work in Hollywood. Everyone Who Used to Make TV Is Now Secretly Training AI

Key Takeaway: A struggling TV writer exposes how AI training gigs lure skilled professionals with promises of flexible, high-paying work, then trap them in unstable, poorly managed, emotionally exhausting “task” labor.

More Insights:

• AI training work is sold as flexible, but workers often wait unpaid, compete for scarce tasks, and get fired without notice.

• Highly skilled writers, artists, and experts are being used to improve AI systems while their own industries shrink.

• The platforms rely on secrecy, NDAs, chaotic Slack channels, vague rules, and constant performance scoring.

• Pay rates are falling fast, with some “expert” jobs dropping from $150/hour to much lower rates.

• The author argues that making AI more “human” often requires making human workers more machine-like.

Why it matters: The article reveals the hidden human cost behind AI progress: the technology may look seamless, but it is being built on precarious labor, professional desperation, and a quiet transfer of creative expertise from people to platforms.

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Digg tries again, this time as an AI news aggregator

Key Takeaway: Digg is relaunching yet again, this time as an AI-focused news aggregator that ranks stories by tracking influential voices, engagement signals, and real-time discussion on X.

More Insights:

• Digg abandoned its Reddit-style reboot after struggling with bot traffic and weak differentiation.

• The new version focuses first on AI news, with plans to expand into other topics if the model works.

• Digg ranks stories using X data, sentiment analysis, clustering, and signal detection instead of relying only on on-site engagement.

• The site highlights top AI stories, influential AI figures, companies, and politicians.

• Its biggest challenge is proving users need another news-ranking tool, especially outside AI where conversations may not center on X.

Why it matters: Digg’s pivot shows how media discovery is shifting from human-curated communities to algorithmic signal detection, but its success depends on whether it can turn social noise into real editorial value rather than just proving that influential people can make stories go viral.

Behind the Claude Frenzy That Ate Up All the Mac Minis

Key Takeaway: Apple’s Mac mini is unexpectedly emerging as a popular home base for AI agents that can automate real work, from coding and sales updates to customer service and admin tasks.

More Insights:

• OpenClaw lets users build personalized AI agents that can control apps, browsers, and workflows.

• Small-business owners are using Mac mini-powered agents to handle daily tasks like emails, marketing, coding, and supply-chain issues.

• Apple’s hardware is attractive because it is compact, energy-efficient, and capable of running some AI work locally.

• The trend is creating a new tinkerer culture around “agentic computers,” headless Macs, and custom AI frameworks.

• The upside is productivity; the risk is runaway cloud AI costs, security concerns, and agents acting with too much autonomy.

Why it matters: The Mac mini’s AI boom shows that the future of work may not arrive as one giant corporate platform, but as small, cheap, always-on machines quietly turning individuals and small businesses into AI-powered operations.

Other stuff

• The new Wild West of AI kids’ toys

• The Courtroom Circus with Elon Musk and Sam Altman

• How ChatGPT adoption broadened in early 2026

• End of Money: Elon Musk, 100 TW of Compute and the New Economics of Abundance

• Meta-Meta-Prompting: The Secret to Making AI Agents Work

• NVIDIA Founder, CEO Jensen Huang to Carnegie Mellon University Graduates: ‘Shape What Comes Next’

• Meta's embrace of AI is making its employees miserable

• So you’ve heard these AI terms and nodded along; let’s fix that

• "openai.com" was once the personal homepage of a guy named glenn

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