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How to design governance for surveillance/law-enforcement AI: legal request types, data minimization, retention limits, and audit-ready evidence.
How to handle relationship-test prompts in AI chats: set refusal boundaries with Safe Complete, document branching rules, and validate via evaluation.
Compare RAG vs parameter updates for long-term memory, then outline validation and gating needed for recursive self-improvement loops.
GPU scarcity shifts strategy from bigger training to faster iteration and deployment, comparing mixed precision, checkpointing, and ZeRO trade-offs.
Blackstone backing for Neysa and a 20,000+ GPU plan spotlight India onshore compute tied to incentives, cost, latency.
Tight leaderboard scores can hide uncertainty and evaluation drift. Public data alone rarely confirms 3–6 month trend slowdowns.
AI coding tool choice depends on not only model quality but also tool calling, agents, and permission design shaping security and team velocity.
Serving bottlenecks shift to continuous batching, streaming, KV cache, and decoding optimizations affecting throughput, TTFT, and TBT.
Break down LLM latency into queue/compute and prefill/decode, then tune batching, KV cache limits, scheduling, and quantization.
Why AI knowledge gaps trigger hierarchy, lecturing, and withdrawal—and how to reshape talks using diffusion criteria, NVC, and MI.
Reduce family AI adoption friction with onboarding (accounts, access, recovery), safety rules, and task templates before persuasion.
How on-device AI reshapes data boundaries, and what quantization, distillation tradeoffs, and hybrid inference mean for deployment baselines.
How to route LLM requests by predicting quality and uncertainty, balancing cost and latency, with safe escalation and auditable logs.
Learn how reranking after top-K retrieval improves ranking quality in RAG, and how to evaluate gains against added latency and cost.
Perceived quality differences often come from rate limits, priority processing, context policies, and feature access—not just model strength.
Agent outcomes can hinge more on harness design—tools, permissions, runtime limits, and session/compaction rules—than on the model alone.
As AI coding tools improve, CS learning shifts from writing code to understanding, verification, design, and security.
A curated link roundup from recently collected official updates and tech news.
How combining rate limits, real-time usage tracking, and credits enables continuous access for costly models while meeting SLOs.
Split AI concerns into task automation, high-risk transparency and auditability, and TEVV safety testing for deployment decisions.
How prompt injection rides untrusted content into tool calls, and how to mitigate it with least privilege, sandboxing, fixed schemas, and output validation.
Avoid model-name anchoring by defining success criteria, output format, and failure handling, then running evals on every change.
Overview of EU DSM TDM exceptions and US Copyright Office guidance on AI training, focusing on lawful access and human contribution.
OpenAI’s GABRIEL converts qualitative text and images into measurable outputs, adding reproducible runs, batching, retries, and audit trails.