The LGTM (Page 16)

The LGTM

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Claude Code’s Local Skills Turn Agent Plugins Into Runtime Policy

Claude Code’s Local Skills Turn Agent Plugins Into Runtime Policy

Claude Code’s latest release is not the kind that wins a keynote slide. That is precisely why it matters. Version v2.1.157, published May 29, 2026, lands in the shadow of Anthropic’s bigger Opus 4.8 and dynamic-workflows push. The headline release promised large tasks, background subagents,

Entropy-Cut Sampling Makes Reasoning Models Look Less Like RL Magic and More Like Search

Entropy-Cut Sampling Makes Reasoning Models Look Less Like RL Magic and More Like Search

The lazy story about reasoning models is that intelligence lives in the weights and inference is just the receipt printer. Reasoning with Sampling pushes against that. Its Entropy-Cut Metropolis-Hastings method makes reasoning look less like a mystical property unlocked only by RL posttraining and more like a search problem where

LoRA Memory Has a Capacity Law — and a Security Footnote Nobody Should Skip

LoRA Memory Has a Capacity Law — and a Security Footnote Nobody Should Skip

LoRA adapters are usually sold as the cheap fine-tuning trick: fewer trainable parameters, lower VRAM, easier deployment, nice story for teams that cannot afford to retrain the universe every time their product changes. How LoRA Remembers? is useful because it treats adapters as something more specific and more dangerous: parametric

NSA’s MCP Security Guidance Is the Checklist the Agent Ecosystem Was Trying Not to Need

MCP just got its first serious “please stop shipping vibes into production” memo from the people whose job is noticing when flexible systems become attack surfaces. The National Security Agency’s Artificial Intelligence Security Center has published a 17-page guidance document on Model Context Protocol security, and the useful part

NVIDIA’s Model Card Generator Makes AI Governance Look Like a Build Artifact

NVIDIA’s Model Card Generator Makes AI Governance Look Like a Build Artifact

AI governance keeps failing in the least dramatic place possible: the repo. Not the policy memo, not the executive risk committee, not the glossy “responsible AI” page. The real failure mode is that a model ships with unclear training-data notes, stale evaluation claims, vague limitations, missing license context, and a

DynoSim Turns Inference Tuning Into a Cheap Inner Loop Instead of an Expensive Cluster Guess

DynoSim Turns Inference Tuning Into a Cheap Inner Loop Instead of an Expensive Cluster Guess

Inference tuning has officially entered the “you need a simulator before you touch production” phase. That is not because NVIDIA invented simulation this week. It is because modern LLM serving has accumulated enough interacting controls — tensor parallelism, prefill/decode split, routing, KV cache placement, autoscaling, cold starts, worker counts, backend