Signals, research, and debates around general intelligence and superintelligence.
Hub content is updated incrementally.
Even with the same model alias, outputs can shift due to snapshot routing, safety behaviors, and sampling settings. Use logs and regression tests to isolate causes.
Static benchmark gains may not translate to real work quality. Covers contamination risks and a practical evaluation framework.
Agent memory shifts personal data from one-off chat to reusable records. Design deletion, expiry, and audit logs before storage.
How multi-plan switching to spread chat caps and API rate limits can clash with terms, security, and automation restrictions.
How to run long-form AI animation on existing IP with a bible, asset library, and QA loops, while managing derivative-work risks.
Tool calls become real actions. JSON validity is not enough—use strict schema checks, allowed_tools, refusal detection, and state-aware gates.
Why conversational AI sycophancy is treated as a quality/alignment risk in official docs and evals, plus practical mitigation prompts.
Examine when speed, copying, and updates translate into general intelligence, using scaling laws, g, and real-world bottlenecks.
Korean LLM adoption now hinges on training opt-in, retention exceptions, and in-region storage vs processing, not model names.
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.
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.
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.
As AI coding tools improve, CS learning shifts from writing code to understanding, verification, design, and security.
Avoid model-name anchoring by defining success criteria, output format, and failure handling, then running evals on every change.
How agent link-opening expands the attack surface, and how instruction hierarchy, URL constraints, and sandboxing reduce leakage and injection.
A curated link roundup from recently collected official updates and tech news.
Android 17 reports highlight Secure Lock Device, intrusion logging, and Identity Check expansion—reshaping lock as an OS-level security state.
OpenAI dissolved the Mission Alignment team; watch how safety ownership, RACI paths, and SSC/DSB governance appear in upcoming releases.
ZDNET tests six popular AIs with trick questions, highlighting hallucination risk and why teams need RAG, CoT, self-checks, and evaluation rules.