Alex Karp now sounding like @garymarcus.
— Gary Marcus (@GaryMarcus) 5 mai 2026
Sooner or later, everyone figures it out. https://t.co/6OqhRN46bm
Alex Karp now sounding like @garymarcus
. Sooner or later, everyone figures it out.
RESEARCH
-
Gemini Pro 3.1 Capabilities vs. App Interface: A Curious Discrepancy
By
–
-
Scholars Across Disciplines Researching AI and Work
By
–
no, LeCun consistently steals and some of us keep pointing it out.
-
AI Diagnosis: Experimental Success vs. Real-World Challenges
By
–
1. LLMs don’t work on their own; they need neurosymbolic harnesses, which is more or less what I send all along.
2. Hundreds of times I have said that LLMs are unreliable and that is the problem, not their power per se. But I guess you can’t or won’t read. So blocked. -
Elsevier SVP on the Right AI Tool for Researchers
By
–
What does ‘the right AI tool’ for researchers look like? Maxim Khan, SVP for Academic and Government Solutions, Elsevier, answers the question. https://
bit.ly/4n7oxSR #AI #Research #Researchers -
New Book: Agentic Architectural Patterns for Multi-Agent Systems, GenAI, RAG, and LLMOps
By
–
5- release from @PacktDataML at http://
amzn.to/3MaHy8T "Agentic Architectural Patterns for Building Multi-Agent Systems: Proven design patterns and practices for GenAI, agents, RAG, LLMOps, and enterprise-scale AI systems" Contents:
GenAI in the Enterprise: Landscape, -
Funding R&D for AI Model Benchmarking
By
–
It would also be useful for funding R&D into benchmarking models, which is currently mostly done by the labs themselves right now.
-
NIST Public AI Ability Tests as Independent Evaluator
By
–
In addition to the CAISI evaluation, it would be useful if NIST conducted public tests of AI abilities as an independent evaluator – though those obviously should not be pre-release tests & can be done when models are public. Independent testing is important & getting expensive.
-
New Microsoft Research Paper on Long-Horizon Agent Generalization
By
–
NEW paper from Microsoft Research. Nice study on long-horizon agent generalization. (bookmark it) The team runs a study where the only variable is task horizon length. They use the same decision rules, reasoning structure but different sequence length to the goal. The main
-
SubQ vs Opus 4.6: massive context, cheaper, faster, threatens Claude
By
–
Si los números de SubQ son reales, Claude tiene un problema serio.
— Nico (@nicos_ai) 5 mai 2026
SubQ vs Opus 4.6:
→ 12M tokens de contexto (Claude se rompe pasados los 200k)
→ 10 veces más barato
→ 52 veces más rápido que FlashAttention
Si esto es así, van a dejar a Claude obsoleto.
Tocará probarlo en… https://t.co/s7QbxAWn1RIf SubQ's numbers are real, Claude has a serious problem.
SubQ vs Opus 4.6:
→ 12M context tokens (Claude breaks beyond 200k)
→ 10x cheaper
→ 52x faster than FlashAttention
If this is the case, they will make Claude obsolete.
Will have to test it on -
Quadratic Attention Limits Frontier Models After 8 Years
By
–
Attention Is All You Need (2017): most cited ML paper of the decade.
— Sumanth (@Sumanth_077) 5 mai 2026
For 8 years, every frontier model has been built on quadratic attention. Process every possible word-to-word relationship. Compute explodes with context length. Accuracy degrades past 200k tokens.… https://t.co/tSJOH4A2CuAttention Is All You Need (2017): most cited ML paper of the decade. For 8 years, every frontier model has been built on quadratic attention. Process every possible word-to-word relationship. Compute explodes with context length. Accuracy degrades past 200k tokens.