LLM
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The Opaque Prompt Pipeline: Why “AI-Powered” Tools Make You Leak on Autopilot
Many “AI-powered” apps hide the model, costs, and retention—turning your text into an untracked data export. That’s not paranoia.
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From PDE Guarantees to LLM Inference: What BEACONS Gets Right About Reliability
BEACONS shows how bounded-error, composable neural solvers can be certified—hinting at LLM inference pipelines with checkable reliability.
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The Assistant Axis: when “helpful” is a place, not a promise
Anthropic finds a measurable “Assistant Axis” in LLMs. Capping drift along it reduces harmful persona shifts and jailbreaks—raising questions about human identity.
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Engram, DeepSeek, and the return of “memory” as an architectural primitive
DeepSeek’s Engram adds conditional memory to MoE models, shifting routine local patterns to fast lookup—freeing compute as memory costs surge.
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Meta-Prompting: How to Get More Signal Out of Your Prompts
Unlock sharper prompts and get more signal from every query. A quick guide to boosting your LLM results with smart meta‑prompting.
