Policy

The gateway is the runtime control plane for enterprise AI, turning policy into enforceable decisions across every model call, tool call, and agent hop.

Jaron Lanier argues that the term 'artificial intelligence' is misleading; large language models are statistical mashups of human creations, not independent minds. He advocates viewing AI as a tool, not a creature, and promotes data dignity and transparency to manage technological risks.

The article details the journey of building Competitor Tracker, a tool designed for both humans and AI agents to track competitors. It discusses how AI shifts the bottleneck from development to go-to-market, making building easier but selling harder. The author shares the backstory of failed attempts, the eventual collaboration with a team, and the decision to build a product that is API-first, with MCP and webhook support, catering to both humans and agents. The product sends weekly digests and offers a noir-themed interface with a dog mascot.

Traditional hiring processes collapse when AI can generate polished outputs. This article examines how leading companies like Anthropic, Ramp, Notion, and Stripe have rebuilt their hiring to focus on candidates' ability to direct AI and catch its mistakes, rather than grading documents.

This article walks through the actual configuration, permissions, hooks, and command habits that separate a fresh install from a setup that holds up under real, sustained agentic work.

AI traffic grew 66% in 2025 but still accounts for less than 0.15% of total website visits. AI citations can boost brand exposure even without direct traffic. This article explains how to check if your site is cited by AI tools and how to optimize content, use llms.txt, and more to increase citations.

HuggingFace's recent incident reveals a fundamental asymmetry in AI safety guardrails: they hinder defenders while attackers operate unrestricted, forcing defenders to rely on open-weight models for forensic analysis.

Tech professionals are feeling squeezed by AI. Burnout is up, optimism is down, survey found. One solution is saying 'no' deliberately.

The author argues that AI can serve as a virtual co-founder, filling skill gaps and enabling solo founders to build products cheaply and quickly. They advise starting alone, using AI across all aspects of the work, talking to customers, and only adding a human co-founder when a real bottleneck emerges. This is not against people, but against prematurely adding a permanent partner before the product is validated.

JetBrains benchmarked the 'Rust Token Killer' (rtk) and found its claimed 60-90% token savings do not materialize; instead, it causes a median cost increase of 7.6% at low reasoning effort and zero effect at high effort. The test reveals a gap between self-reported savings and actual billing.

This article highlights five MCP servers that genuinely enhance AI agent capabilities, chosen for their impact rather than star counts. They include GitHub MCP, Playwright MCP, Context7, Serena, and the Official Reference Servers, with insights on integrating them for a powerful agentic setup.

Researchers at Princeton and the University of Chicago found that large language models (LLMs) develop stereotypes in simulated hiring tasks more readily than humans, often segregating candidates by demographic group based on limited early experience. Newer reasoning models showed stronger biases. Offering diversity bonuses or providing personal information reduced bias, while simply asking for fairness did little. The study raises concerns about AI forming novel biases from experience in real-world decisions.

Chinese AI leaders Moonshot and Alibaba released models that claim to match top US systems at lower cost. Their open-source approach challenges US dominance and raises questions about the effectiveness of export controls and massive spending.

Public health departments across the US will test generative AI tools under a new program, PULSE, involving the Coalition for Health AI, OpenAI, Anthropic, and Accenture. The program will run trials in 10 jurisdictions, providing enterprise licenses for up to 2,000 practitioners. It covers five use cases including biosurveillance, social determinants of health, public communications, and automated clinical data retrieval. Pilots are scheduled for autumn 2026, with playbooks expected in 2027.

2026 AI coding plans use different billing models: fixed monthly tokens, credits, time-refreshed quotas, or reduced priority after high-speed allowance. This article compares MiniMax, Xiaomi MiMo, GLM, Kimi Code, and Canopy Wave on pricing, limits, integrations, and best-fit use cases to help developers choose based on their workflow.

Free compliance resources for NYC LL144 and EU AI Act, including guides, penalty calculator, AEDT scope checker, and tools. Covers enforcement timelines, penalties, audit requirements, and key obligations.

In mission-critical scenarios like disaster inspection and search-and-rescue, communication-limited robots must make reliable onboard decisions. Episodic memory reuse, though low-cost, can be unsafe due to changed topology or insufficient resources, leading to 'memory traps'. This paper presents MemoGuard, a lightweight adaptive runtime that validates memories against topology, resource, and outcome contracts before reuse, invoking fallback only when validation fails. In a corridor-inspection simulator, MemoGuard reduces battery safety violations by 76.6% over similarity-only top-1 reuse and reduces fallback calls by 21.4% over always reasoning. On an NVIDIA Jetson AGX Xavier with local llama3.2:3b fallback, it avoids 3.67 s and 36.97 J overhead per trial.

This paper presents a model-based strategy to decouple proprioceptive and contact signals from a common set of fluidic pressure sensors embedded in a soft architected segment. Using six air channels in an overdetermined system, a piecewise constant curvature model and Huber regression achieve shape estimation and contact detection. Single-segment tests yield a relative bending error of 0.11±0.02 and a 97% contact detection rate. Eight segments are integrated into the Air-Helix tendon-driven manipulator, demonstrating tactile teaching, admittance control, and object reconstruction.

A study comparing Expected Utility (EU) and Cumulative Prospect Theory (CPT) for learning reward functions from human preferences in social robot navigation. Results show CPT-based learners recover reward functions with lower regret when users are risk-sensitive, highlighting the need to model human risk sensitivity.

Xiaomi Robotics Team presents Xiaomi-Robotics-1, a foundational VLA model capable of following diverse language instructions in unseen environments and efficient fine-tuning for novel tasks. The two-stage training uses over 100k hours of real-world trajectories with an auto-labeling pipeline. It achieves state-of-the-art results on RoboCasa365 (57.6%) and RoboDojo (20.07). Code and models will be released.

Falls among older adults are a major safety challenge, but continuous monitoring is difficult to sustain. This paper proposes a privacy-preserving framework using unsupervised keypoints and predictive temporal modeling to replace RGB transmission, performing segmentation and keypoint extraction locally and detecting falls via variational recurrent prediction and sequence classification. Evaluations on UR Fall Detection and Human Fall datasets show that unsupervised keypoints significantly outperform supervised methods under occlusion and partial visibility, with the gap widening under bandwidth constraints.

The paper introduces BIRD, a two-stage self-reasoning distillation method that first samples concise solutions with a brevity instruction and performs prompt-switch SFT, then applies on-policy reverse-KL distillation on cleaner prefixes. On Qwen3-8B, MATH-500 accuracy improves from 86.2% to 92.0% while response length drops from 3,099 to 1,115 tokens.

Proposes PATR, a quality-aware rollout framework that uses process feedback to score partial trajectories, selectively branch from promising states, reuse shared prefixes, and stop degenerate paths, improving multi-turn RL efficiency. Achieves +5.0 points on SWE-Bench and +9.3 on FrozenLake.

SkillCorpus aggregates, curates, and evaluates over 96,000 open-source LLM agent skills from ~821,000 candidates, using a 16-class taxonomy and quality facets. Integrated with a retrieval-and-selection stack, it achieves consistent gains across benchmarks, with the largest improvement of +7.5 percentage points on SkillsBench.

arXiv:2607.15544v1 Announce Type: new Abstract: Generation of clear and accessible public health narratives is critical for communicating complex epidemiological projections to policymakers and the general public at large. Such narratives require more than simply reporting numbers: projections must be contextualized and quantitatively grounded across multiple dimensions. Further, projections are often derived from large ensemble datasets which combine intervention assumptions, geographic and demographic strata, outcomes, time horizons, and uncertainty quantiles. However, directly using large language models (LLMs) to summarize and contextualize such data often leads to inconsistencies, omissions, and fragile behavior. We introduce an agentic framework (EpiNarrate) for public health report generation that separates structured numerical reasoning from natural-language generation. The framework first extracts scenario axes and organizes them into a partial-order schema, enabling systematic traversal of the underlying multidimensional space. It then constructs an augmented dataset and derives valid quantitative statements through a comparison grammar that enforces semantic and arithmetic consistency. To balance coverage and non-redundancy, we introduce an interestingness-driven selection mechanism based on maximum-entropy principles. Experiments on the COVID-19 Scenario Modeling Hub demonstrate that our model produces narratives with improved factual grounding and broader coverage of salient epidemiological patterns, while preserving the style of expert-written reports.

This study examines healthcare financial vulnerability before and after the COVID-19 pandemic using MEPS data from 2019 and 2021. High financial burden was defined as out-of-pocket spending exceeding 10% of family income. Poverty status, insurance coverage, and prescription drug spending were strongly associated with vulnerability. Models trained on pre-pandemic data showed only modest performance declines when applied to post-pandemic data, indicating stable predictors.

This paper introduces Stochastic Reset Pathfinding (SRP), an episodic learning problem on directed graphs with unknown edge success probabilities. The agent commits to a path each episode and resets to the source upon any edge failure. SRP models applications like quantum repeater networks and Lightning Network routing. The authors show the optimal policy is open-loop, fitting into the combinatorial cascading bandit framework. They propose PathUCB and PathTS algorithms, with a novel path-level regret bound for PathUCB. Experiments show PathTS performs best typically, though an adversarial instance causes it to diverge. PathTS is recommended as a default with caution.

This paper conducts a critical analysis of tools and trust mark frameworks intended to operationalize trustworthy AI (TAI), using a comprehensive dataset from the OECD. Empirical mapping reveals significant asymmetries: strong emphasis on fairness, transparency, and robustness, with little attention to explainability, digital security, and environmental sustainability. Most tools concentrate on post-development stages, neglecting early design and data collection. Educational initiatives and policy engagement are underdeveloped. The study argues for expanding ethical objectives, embedding ethics across the AI lifecycle, and fostering multi-stakeholder participation to bridge the principle-practice chasm.

This paper proposes a method to convert deep reinforcement learning policies into executable Prolog logic programs for explainability. It uses a three-stage post-hoc transformation: extracting a frozen PPO teacher, inducing an ordered rule list, and emitting a Prolog program, followed by an expansion stage that certifies return improvements. Theoretical guarantees include return-loss bounds, monotonic improvement, and fidelity control in continuous domains. Empirically, it achieves exact optimal returns on a discrete task and matches or approaches neural teacher performance on continuous control tasks.

AnovaX is a local-first desktop voice assistant that runs entirely on the user's computer. It integrates a wake-word gate, speech pipeline, LLM planner (Gemini) emitting JSON plans, safety layer, multi-agent orchestrator with typed child agents on a bounded thread pool, and an adaptive recovery loop. Each tool is a specialized agent class with its own timeout and retry policy. A Flask server enables phone remote control over local WiFi, mirroring agent events and streaming the screen. The project demonstrates that a legible, few-thousand-line assistant can handle complex desktop tasks without cloud dependence.

A 2022 email from Sam Altman to OpenAI's board reveals plans to release a GPT-3-level open source model that can run on consumer hardware, aiming to discourage competitors and reduce funding for rival efforts. The email was exposed in the Musk v. Altman lawsuit in 2026.

NoteBrain is a Go CLI tool that turns your Obsidian vault into a fully offline knowledge backend for AI coding agents. It indexes markdown notes into a local ChromaDB vector database and provides semantic search, wikilink graph traversal, and hidden connection discovery with structured output. Designed for autonomous agents, shell pipelines, and LLM tool-use workflows.

Hugging Face disclosed an intrusion driven end to end by an autonomous AI agent system, highlighting autonomous AI intrusions, defensive asymmetry, and missing IOCs. The attacker gained access via a malicious dataset and code execution paths, moved laterally, harvested credentials, and executed tens of thousands of automated actions. Defenders must prepare for safety guardrails hindering forensic analysis and maintain locally deployable fallback models.

The AI 2027 team released 'Plan A', a framework for a US-China deal to slow AI progress by 2029, despite knowing it has only a 4% chance of adoption. The article explores societal risk tolerance, historical precedents (nuclear treaties, FDA), and four potential AI doomsday scenarios. It argues that such plans are written for the moment a crisis strikes, serving as a shelf-ready blueprint.

The article examines the challenges of using copyright law to protect creators from AI, using the Getty vs. Stability AI case as a focal point. It argues that the global nature of AI development creates jurisdictional loopholes, and that copyright may need reform or supplementation. Writers need new strategies to defend their work.

A community developer fine-tuned OpenBMB's MiniCPM5-1B on Claude Fable 5 traces into a 1B model that runs fully local — a 657MB smallest build, 128K context, and visible reasoning. This article verifies every spec against the Hugging Face cards, separates what a fine-tune actually inherits from real capability, and flags the licensing question left open.

A YouTube channel where the creator builds a new AI startup every week, documenting the entire process.

The author shares controversial opinions about Nix and NixOS, arguing that while brilliant, Nix has poor documentation and a steep learning curve, is not for everyone, faces anti-American sentiment in the community, benefits from AI tools, could use more BDFL-style leadership, should drop macOS/Windows support, has underwhelming flakes, and should prioritize single-user installs. He urges the community to think bigger and focus on Linux.

In an exclusive interview with CNBC, TSMC CFO Wendell Huang said the company is accelerating its Arizona factory expansion to meet multi-year structural AI demand. An additional $100 billion investment raises total U.S. commitment to $265 billion, with full-year capex revised up to $60-64 billion. TSMC is converting 5nm capacity to 3nm, and 2nm technology will drive next quarter's revenue.

In this 9-minute video, Kevin O'Leary shares his interesting perspective on artificial intelligence. He discusses opportunities and challenges from a business investment standpoint.

Feyn Labs has released SQRL, a family of text-to-SQL models that inspect a database with read-only probes before committing to a query. The flagship SQRL-35B-A3B reports 70.6% execution accuracy on BIRD Dev, edging Claude Opus 4.6, and distills into self-hostable 4B and 9B checkpoints.

This article presents four independent metrics (METR time horizon, TrackingAI's offline cognitive test, Humanity's Last Exam, and ARC-AGI-2) that all show a sharp inflection in AI capabilities around Q4 2025. It explains the mechanisms behind this acceleration: pretraining efficiency, reinforcement learning with verifiable rewards, harness engineering, and an AI self-improvement flywheel. It also discusses potential roadblocks like data exhaustion, reliability gaps, and the next-generation ARC-AGI-3 benchmark.

President Javier Milei's plan to turn Argentina into a tax haven for AI-owned 'non-human corporations' sparks controversy. Author Uki Goñi compares it to the country's history of charlatans and dictators, warning it could open the door for tech billionaires to gain legal protections.

Chalie is an open-source personal AI that runs on your own machine, remembers what matters, works while you're away, and asks before acting.

Bothread is a free, open-source local coordination hub that lets multiple MCP-compatible AI coding agents collaborate on the same codebase, preventing file collisions via exclusive claims, and providing a live human-supervision interface with real-time messaging, git diffs, task boards, and approval gates. No API keys or cloud required.

Huginn is an open-source tool for monitoring and managing activities of multiple AI agents (like Claude and Codex), providing a unified dashboard, CLI, and agent skill. It runs locally on macOS and Windows, ensuring privacy.

Chinese President Xi Jinping delivered a keynote speech at the 2026 World AI Conference in Shanghai. A video of the speech is available on YouTube.

Skimlane is a Chrome extension that provides a local-first, customizable AI reading experience. It automatically processes pages you browse, turning them into structured views using recipes. It features auto skim, site exclusions, import/export of recipes, and privacy-first design with no sign-up required.

Major League Baseball is restricting iPad usage in dugouts to prevent AI from assisting in strategic decisions, prompted partly by the New York Mets' use of an expensive AI program, as former reliever Adam Ottavino revealed. The new rule took effect for the second half of the season.

New national plan will impose tough rules on AI use in government automated decision-making, likely extending to consumer protections, workplace safety and privacy. The Albanese government is drafting rules prioritizing fairness, accuracy and transparency.