Who's Who of AI

Nicole Hennig

172 trust @nic221.bsky.social · 5,125 followers
Why they matter

Tracked through public AI activity and peer connections inside the directory.

AI signals
58
past 30d
Sources
47
distinct domains
Discusiones
0
past 30d
Latest signal
23h ago
View every signal from Nicole Hennig →
E-learning dev & AI educator at U of Arizona Libraries. Former head of UX at MIT Libraries. Winner of MIT Excellence Award. nicolehennig.com. Digital nomad from 2013-17, locationflexiblelife.com. - vegetarian - car-free - universal basic income: yes
What they're sharing

Articles & links

Introducing Claude Opus 5 www.anthropic.com/news/claude-op… #AI #Anthropic

Introducing Claude Opus 5 anthropic.com
AI Weekly's analysis →
  • Anthropic launched Claude Opus 5 on July 24, 2026 at $5 per million input tokens, matching Opus 4.8's rate.
  • On Frontier-Bench v0.1 Opus 5 scored 43.3%, versus 18.7% for Opus 4.8 and 33.7% for Fable 5.
  • Opus 5 becomes the default on Claude Max but sits behind Mythos 5 on cybersecurity tasks, per Anthropic.
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View on Bluesky · ♥ 1 ↻ 1 ↩ 0 · 9 from the directory shared this · 24d ago

Introducing Claude Sonnet 5 www.anthropic.com/news/claude-so… #AI #Anthropic #Claude

Introducing Claude Sonnet 5 \ Anthropic anthropic.com
AI Weekly's analysis →
  • Anthropic released Claude Sonnet 5 on June 30, 2026, calling it 'the most agentic Sonnet model yet' and pitching it for autonomous browser and terminal use.
  • Through August 31, 2026 Sonnet 5 costs $2 per million input tokens and $10 per million output, then steps to standard rates of $3 and $15.
  • A new tokenizer means the same input can map to roughly 1.0 to 1.35 times more tokens than prior Anthropic models, partly offsetting the headline discount.
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View on Bluesky · ♥ 3 ↻ 1 ↩ 0 · 8 from the directory shared this · 48d ago

AI agents are checking the scientific literature — and spotting decades-old errors www.nature.com/articles/d4158… #AI #FactChecking

AI agents are checking the scientific literature — and spotting decades-old errors nature.com
AI Weekly's analysis →
  • A Zhejiang Lab chemist's AI predicting boiling points clashed with a 75-year-old reference database; manual checks showed the database, not the model, was wrong.
  • The same AI spotted further mistakes in older papers and reference books, including a typo and incorrect values of century-old boiling-point measurements.
  • Researchers caution AI fact-checkers are not reliable on their own because the models make mistakes like humans do and still need manual oversight.
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View on Bluesky · ♥ 3 ↻ 3 ↩ 0 · 6 from the directory shared this · 10d ago
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In Nicole Hennig's orbit

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