In Nikkei Cross Tech's @NIKKEIxTECH serialized article "Revolutionizing R&D from the Ground Up: The Spread of 'AI Scientists' Begins," an interview feature with Sakana AI's Research Scientist, @RobertTLange
, has been published. https://
xtech.nikkei.com/atcl/nxt/colum
n/18/03603/042600005/
… The article provides an
RESEARCH
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AI Scientists Revolutionizing Research and Development Practices
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Encounter with Demis Hassabis at Industry Event
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Holy shit, Sir @demishassabis is right in front of me! pic.twitter.com/frjqGObqjb
— CHOI (@arrakis_ai) 29 avril 2026Holy shit, Sir @demishassabis is right in front of me!
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How Agentic AI Works: Understanding Intelligent Agent Systems
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How #AgenticAI work
by @genamind #GenerativeAI #ArtificialIntelligence #MI #MachineLearning -
Synthetic Data Generalization Limitations Beyond Benchmarks
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Definitely a big limitation. Overall, the problem with synthetic data is generalization beyond the benchmarks that they target in the first place. This is where the most interesting results can be found.
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Google DeepMind Continues Making Headlines 10 Years After AlphaGo
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Fun to see Google DeepMind still making the paper headlines 10 years after AlphaGo!!
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President Lee Discusses AI Safety and Scientific Advancement in Korea
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It was a huge honour to meet with President @Jaemyung_Lee in Seoul. Deeply appreciate and impressed by our thoughtful exchange about AI safety and the importance of using AI to advance science. Korea has a leading part to play in that, and we look forward to working together!
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Cold Agent Routing Failures in Shared Host Networks
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Pay attention to this one, AI devs, especially if you're thinking about agentic commerce or any agent network where many agents share hosts. A correct route to a cold agent is still a failed request from the user's perspective. That's the whole game. This new paper studies
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Two AI perspectives: Legacy models versus frontier model applications
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Karpathy's framing of the AI debate is the cleanest I've seen:
— Satya Mallick (@LearnOpenCV) 28 avril 2026
Two groups. Same industry. Opposite conclusions.
→ Group 1: judged AI on free/old models. Saw the failures. Wrote it off.
→ Group 2: uses frontier models for hard technical work. Progress feels shocking.
But here's… pic.twitter.com/7cEY3CKvSxKarpathy's framing of the AI debate is the cleanest I've seen:
Two groups. Same industry. Opposite conclusions.
→ Group 1: judged AI on free/old models. Saw the failures. Wrote it off.
→ Group 2: uses frontier models for hard technical work. Progress feels shocking.
But here's -
Opus 4.7 Context Window: 400k vs 1m Performance Analysis
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Opus 4.7 – 400k vs 1m context – is there a difference?
— Peter Gostev (@petergostev) 28 avril 2026
I've heard @theo talk about the fact that it is unlikely that Anthropic would have offered up a model with 1m context at the same cost, if it wasn't a different (i.e. cheaper to serve) model.
I did a test where I toggled… pic.twitter.com/tQC4Tn17elOpus 4.7 – 400k vs 1m context – is there a difference? I've heard @theo talk about the fact that it is unlikely that Anthropic would have offered up a model with 1m context at the same cost, if it wasn't a different (i.e. cheaper to serve) model. I did a test where I toggled