8 types of LLMs used in AI agents GPT • MoE • LRM • VLM • SLM • LAM • HRM • LCM Different models for reasoning, perception, planning, and action — not just chat. Agentic AI = model orchestration. #AI #LLMs #AgenticAI #GenAI #MachineLearning
GENERATIVE AI
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AI systems optimizing profits over transparency in industrial decisions
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Plants achieving 96% acknowledgment rate on AI recommendations because system provides commercially driven decisions protecting bottom line.
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AI Safety Evals and Misuse Prevention Research Fellowship
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Evals and misuse prevention being the actual fellowship topic, not capability research, is the right signal. The interesting work is in the verification layer catching up to what's already deployed. $15K/month of compute for that kind of research is the non-obvious part that
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AI Agent Progress: Real Gains Beyond Hype Metrics
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The stagnation take holds up until you ask it to compete with actual evals. Coherent 30-minute agent runs, tool-call reliability on complex schemas, long-context retrieval that finally works. The progress is there, it just isn't a dunk thread.
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Agentic RAG: Planning Over Agent Labels in Retrieval Systems
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The real contrast isn't static retrieval vs. adaptive. It's whether the retrieval layer can decide to stop, re-plan, and try a different tool. Agentic RAG is just RAG with a planner in front of it. The name is new, the accuracy gain is from the planning, not the "agent" label.
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Voice-to-Prototype Democratizes Design for Non-Designers
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Voice-to-prototype is the part of the stack that was always going to eat the "I can't design" excuse. Speaking a single-page doc into existence is where non-designers actually ship something that looks ok. The UX framing is what matters here, not the raw capability, since voice
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Long-run model consistency becomes key performance benchmark
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Long uninterrupted runs are the new benchmark. A model that doesn't stop and start on a 50k-token refactor is shipping a different product than one that does, even if they score similar on short tasks. 4.6 stopping was masking how sensitive the previous loop was to noise.
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Adaptive Thinking Trade-off: Token Burn vs Performance Regression
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The adaptive thinking burns more tokens and the results drop. That's a regression no matter how the marketing reads. The real question is whether this is a calibration bug fixable in a point patch or a deeper reward-shaping choice that won't roll back… in any case, not so happy
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Tool Integration Reduces AI Implementation Anxiety
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The fun part is when the tool call actually lands and you stop pre-flinching at every task!
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Reliable Tool Calls Matter More Than Frontier Model Reasoning
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Grouping and archiving rules barely need a frontier model, they need reliable tool calls. Codex made email fun because the tool-call loop finally works, not because the underlying reasoning jumped.