📰 AI News Daily — 26 Jan 2026

TL;DR (Top 5 Highlights)

Apple taps Google’s Gemini to supercharge Siri, signaling a major realignment in consumer AI assistants.
OpenAI plans $500B “Stargate” green data centers to meet AI’s surging power needs and grid constraints.
UNCTAD: Data centers now draw 20%+ of new greenfield investment, led by France, the US, and South Korea.
Nvidia’s new Qwen3 compresses KV cache 8× while improving reasoning—pushing state-of-the-art efficiency at inference.
APEX benchmark: Top AI agents pass only 25% of complex white-collar tasks, urging caution on enterprise deployment.

Microsoft Dayhoff (3B): Protein design model trained on 3.3B sequences on Hugging Face; accelerates wet-lab discovery by exploring novel proteins faster with open, reproducible tooling.
ByteDance Diffusion Code Model: MIT-licensed code generator claims 100× throughput over autoregressive peers and 83+ HumanEval; targets faster CI/CD and on-device coding.
LLaMA Factory: Unified toolkit to fine-tune and deploy 100+ LLMs and multimodal models; simplifies experimentation, orchestration, and production handoff for teams.
LangChain DeepAgents + Compass: New agent framework and follow-up tool improve long-horizon task reliability; reduces “lost context” and boosts auditability of agent actions.
MemOS + Manus AI: Open memory layer and fully local agent stack; keeps knowledge persistent and private, enabling compliant, offline workflows for sensitive use cases.
Anthropic Cowork: Non-coder automation for file management and daily ops; brings agentic capabilities to business users without scripting, easing adoption beyond IT.

Nvidia Qwen3 (Dynamic Memory Sparsification): 8× KV cache compression cuts inference memory and boosts reasoning—unlocking bigger contexts and cheaper deployment at scale.
Google Research (Reasoning-tuned > Instruction-tuned): Models tuned for reasoning outperform instruction-only peers on complex tasks, suggesting quality beats longer chain-of-thought alone.
Coding Performance Spread: Informal tests show Codex solving tasks far faster than some peers; reports of GPT‑5.3 “vibe coding” hint at growing coding intuition in frontier models.
Qwen3‑TTS (Local): One-click web UI with direct “voice design”; lowers latency and cost for voice apps, and improves privacy with on-device speech synthesis.
Ollama + Local Code Models: Instant local runs of Claude Code, OpenCode, and Codex; developers demo fully local coding assistants to cut API cost and boost reliability.

TTT‑Discover: Learns at inference time to expand capabilities without retraining; suggests dynamic test-time adaptation can close gaps in novel task performance.
Deep Delta Learning: Method to “forget” and rewrite features in trained networks; aids compliance, debiasing, and rapid post-hoc model updates.
RL Scaling Laws Clarified: New work refines how data, compute, and environment complexity scale RL performance—guiding more efficient training budgets.
Evolutionary Search vs RL: Evolutionary methods outperform RL on long-horizon research tasks, signaling a comeback for population-based search in open-ended domains.
Shared Weight Directions: Large study finds many neural nets reuse a small set of weight directions, hinting at universal inductive structures across architectures.
LLM Limits (Stanford): Mathematical proof shows transformers can’t reliably solve certain complex computations—motivating hybrid systems beyond pure LLM scaling.

UNCTAD Data Center Surge: Data centers now exceed 20% of new greenfield investment; France benefits from cheaper power and firms like Mistral, underscoring AI’s infrastructure race.
OpenAI “Stargate” ($500B): Massive, energy‑efficient U.S. data centers planned with community input; aims to curb power costs and stabilize grids as AI demand soars.
Apple + Google (Siri x Gemini): Apple will use Gemini to deliver a context-rich, more capable Siri—reframing the assistant landscape and Apple’s cloud AI strategy.
NCCoE AI/OT Security Standards: New U.S. guidelines for AI agents and operational tech cover identity, asset management, and resilience—setting a baseline for safer deployments.
Enterprise Security Risks (OpenAI): Vulnerability in ChatGPT Team/Enterprise invites workspace breaches; phishing campaigns impersonating OpenAI are rising—tighten email and access hygiene.
Misinformation Risk: Newsguard reports chatbots miss 90%+ of AI-generated fake videos, highlighting urgent needs for better provenance, literacy, and detection tooling.

Vision‑Language‑Action (VLA) Overview: Field guide to top VLA models clarifies capabilities and pitfalls for robotics and embodied AI projects.
Agent Robustness Playbooks: Practical recipes for resilient agents—handling tools, memory, and retries—reduce failure modes in production.
Token Cost Cutting (Up to 75%): Prompt engineering, context pruning, and smart model selection slash costs without hurting quality.
Framework Selection: When to use LangChain, LangGraph, or DeepAgents for workflows—from linear pipelines to branching, stateful graphs.
Regulated Deployments: Governance and audit patterns for healthcare/finance; balance safety, traceability, and velocity while meeting compliance.

Runway Gen‑4.5: Realistic image‑to‑video with fine-grained, sequential prompting control; elevates creative direction and ad production speed.
Cursor (Parallel Agents): Browser demo coordinating hundreds of agents in parallel—hinting at new paradigms for large-scale coding and collaboration.
Agent Showdowns: Community challenges and puzzle head‑to‑heads reveal strengths/weaknesses across models, offering informal but useful real-world benchmarks.

What Is a “Real” Agent?: Pushback on labeling simple scripts as agents; consensus moves toward durable memory, tool use, and autonomy as defining traits.
RL vs. Data Debate: Researchers spar over whether RL adds capabilities or mostly exposes base-model/data strength; implications for training strategies.
AI Adoption Gap: OpenAI flags uneven global uptake; launches education programs to expand skills, infrastructure, and responsible use in under-resourced regions.
Content Indistinguishability: As AI-made media outpaces detection, calls grow for provenance standards, watermarks, and public AI literacy.
Scaling, Robotics, and Timelines: Demis Hassabis anticipates key humanoid breakthroughs in 12–18 months; argues scaling remains potent but not singular.

Curated from 250+ RSS feeds, Twitter expert lists, Reddit, and Hacker News.