with me the first to make these arguments in 2019. everyone serious sees what i saw, in time
MACHINE LEARNING
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United Overseas Bank Uses SAS for Data-Driven Credit Decisions
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⏱️ Faster credit decisions.
— SAS Software (@SASsoftware) 5 mai 2026
💪 Stronger governance.
🌏 Expanding scale.
See how adding SAS to its toolbox helped United Overseas Bank become better equipped to deliver timely, data-driven credit decisions that support both resilience and growth. https://t.co/YRLDo4QB7P pic.twitter.com/N8RFpYHeFYFaster credit decisions. Stronger governance. Expanding scale. See how adding SAS to its toolbox helped United Overseas Bank become better equipped to deliver timely, data-driven credit decisions that support both resilience and growth. http://
2.sas.com/6014BBGxB6 -
Gemini Pro 3.1 Capabilities vs. App Interface: A Curious Discrepancy
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Alex Karp now sounding like @garymarcus.
— Gary Marcus (@GaryMarcus) 5 mai 2026
Sooner or later, everyone figures it out. https://t.co/6OqhRN46bmAlex Karp now sounding like @garymarcus
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AI Diagnosis: Experimental Success vs. Real-World Challenges
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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. -
AI Detects Hotel Amenities from Photos, Eliminating Outdated Data
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The Rapture of the Nerds motif has been discussed for decades, but an assimilative convergence of minds into an integrated optimality specced post biological agent is obviously likely now. The fascinating thing is that some people could predict this thousands of years ago
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New Book: Agentic Architectural Patterns for Multi-Agent Systems, GenAI, RAG, and LLMOps
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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, -
New Microsoft Research Paper on Long-Horizon Agent Generalization
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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
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SubQ vs Opus 4.6: massive context, cheaper, faster, threatens Claude
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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 -
Efficient Models and Happy Codexing
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we have very efficient models, especially for their capability level happy codexing