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After warning AI is too dangerous, Bill Gates bets a billion on its upside
Gates Foundation pledges at least $1 billion over two years to widen AI access in health, education and agriculture, warning of a rich-poor divide.
The Gates Foundation's 2026 Goalkeepers report outlines spending of at least $1 billion over two years on AI access in health, education and farming. Gates notes over 90% of early LLM training data was English, with speech recognition error rates below 6% in English but above 60% in Yoruba. Cited projects include Penda Health clinics in Kenya (16-point diagnostic accuracy gain), Gemini Guided Learning in Sierra Leone (1.7 years of learning gains in eight weeks), and India's MahaVISTAAR reaching 740,000+ farmers at under 18 cents per person.
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Negative Self-Distillation: Learning to Reason by Avoiding Flaws
Researchers propose Negative Self-Distillation (NSD), a label-free LLM self-improvement method that diverges from self-generated flawed reasoning rather than imitating privileged solutions.
The authors show On-Policy Self-Distillation can degrade complex reasoning by forcing imitation of artificially confident traces built on privileged information, suppressing uncertainty and self-correction. NSD instead generates a question-specific negative condition — such as acting as a 'careless reasoner' — and pushes the model's distribution away from it without ground-truth labels. A dynamic gating mechanism isolates reasoning-critical tokens so gradient updates fix behavioral flaws without damaging foundational linguistic capabilities. NSD consistently outperforms OPSD and other label-free, self-bootstrapping reinforcement learning baselines.
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Stop Telling People Not To Click Links
Tailscale CEO argues link-clicking is not the problem; deploying phishing-resistant authentication like passkeys and WebAuthn eliminates entire attack classes.
Tailscale CEO Avery Pennarun argues in an opinion piece that clicking links is a symptom, not the disease, and that weak authentication is the real failure behind phishing damage. He advocates phishing-resistant FIDO/WebAuthn, passkeys, and device-bound tokens, plus continuous identity-anchored network access so unexpected login prompts become suspicious signals. He argues this approach makes security less burdensome for users while neutralizing credential replay and fake login pages.
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Discovery Foundation Models: Toward Open-Ended Discovery Intelligence
Proposes Discovery Foundation Models that participate in creating new problems and knowledge, instantiated in Zetema and the GALILEO therapeutic-discovery system.
The paper formulates Discovery Foundation Models (DFMs) as general-purpose systems for open-ended discovery, supporting seven coupled capabilities from problem discovery through evidence-grounded revision and continual improvement. It instantiates the framework with Zetema, combining explicit research-state dynamics, verification gating, external grounding, and cross-task Discovery Skill evolution. GALILEO, a real therapeutic-discovery system, closes the loop between Dry-Lab reasoning, robotic and hands-on Wet-Lab experimentation, and iterative hypothesis revision. The authors also define process-centered evaluation so discovery behavior can be trained and measured beyond final answers.
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How Developers Prevent Production Risk at the Source
Wiz argues shift-left code-stage security cuts remediation costs and ships Wiz Code, WizOS, and WizCLI to catch flaws pre-deployment.
A Wiz blog post argues that fixing vulnerabilities at the code stage costs a fraction of production remediation, citing the node:20-slim base image which ships 14 known CVEs, three of them critical. It claims AI agents can now analyze open-source commits and generate working exploits in minutes, shrinking the window between a public fix and automated attacks. The post promotes WizOS near-zero-CVE base images, WizCLI and MCP-server checks inside developer and AI agent sessions, and Wiz Code scanning across pull requests, repositories, and pipelines covering SAST, SCA, secrets, and IaC misconfigurations.
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Top 10 Best Kubernetes Security Tools in 2026
Top 10 Kubernetes security tools roundup highlights Sysdig and Aqua as specialists plus strong free options Kubescape, Calico, and Falco.
A buying guide ranks ten Kubernetes security tools across admission control, network policy, runtime detection, and posture management. Sysdig leads runtime depth, Aqua leads full lifecycle, Red Hat ACS owns OpenShift estates, and Wiz offers agentless graph correlation. The guide notes this category has cloud security's strongest open-source floor with Kubescape, Calico, and Falco.
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ZGCM-1: A Fully Open and Extremely Efficient Foundation Model for Math and Agentic Search
ZGCM-1 is a fully open 7B foundation model with 256K context that stays competitive with frontier models on math reasoning and agentic search.
ZGCM-1 is a fully open 7B dense foundation model trained from scratch using an efficiency-focused recipe: interleaved gated sliding-window and full attention, a stable FP8 Muon optimizer, and MDP-based mid-training with context scaling across 16K, 64K, and 256K. On mathematical reasoning and agentic search suites it remains competitive with much larger frontier models such as Qwen3-235B-A22B and GLM-5.1. The recipe yields a ~4.2x improvement in 16K pre-training time-to-loss, and all weights, checkpoints, training code, data recipes, and W&B logs are open-sourced.
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