Jev is a decision model that fixes the output options available to an AI, similar to how RAG fixes input context. Both approaches constrain model behavior within developer-defined frames for predictability and efficiency, contrasting with agentic models that dynamically determine their own tools and context.
A play depicting two tech workers discussing an incident report. One has read a thirty-page thorough investigation and offers to share it, but the other, citing work pressures, requests only a summary instead of reading the full document. The second worker's choice to prioritize immediate work obligations over comprehensive learning becomes the metaphorical 'incident' itself.
A ChatGPT co-creator launched Jev, a lightweight decision-making model designed for multiple-choice tasks rather than text generation, which gained 31.4M views in two days. Developers quickly integrated it into browser automation, email triage, safety review systems, and other applications, with deployments at Vercel, Cloudflare, and OpenRouter within 48 hours.
TypeSafe AI released Jev, a specialized model that answers typed questions in parallel without generating text, sparking rapid adoption in browser agents, trading, and robotics. Unlike general-purpose LLMs, Jev provides calibrated probabilities and schema-constrained outputs, representing a shift toward specialized decision primitives optimized for specific tasks rather than conversational AI.
A multi-drone autonomy simulation using TypeSafe Jev as a fast reflex layer (System 1) that can request strategic advice from a reasoning model (System 2) without pausing control. The interactive dashboard lets users configure fleet size from one to twenty drones, toggle System 2 guidance, and observe real-time decision confidence, latency, and collision outcomes across independent drone navigators.
Open-alternative-jev is an open-source Python library that provides typed, calibrated decision-making using open-weights LLMs on local GPUs. It answers multiple-choice questions in a single forward pass without text generation, achieving 2.3x throughput improvement on shared-state tasks like RACE-H through token packing while maintaining accuracy through temperature scaling calibration.
Companies are legal entities without agency or moral capacity; decisions are made by people within them. To hold entities accountable for actions, one must identify and direct responsibility toward the specific individuals responsible, since people—not corporate structures—can be held accountable.
A researcher prompted AI models 78,720 times with moral dilemmas to investigate whether they share consistent values and how their moral reasoning compares across systems. The study examines how AI moral frameworks are constructed through training data, human feedback, and safety guidelines, and whether these systems exhibit consistent ethical principles when facing real-world choices.
Blast Radius 1.0 is a browser-based game where players manage a web service through eight high-pressure incidents, balancing stability, speed, budget, and trust while making real-time decisions. Each daily scenario presents the same challenge to all players, generating different outcomes based on their choices, with no account, download, or payment required.
The article describes a personal system for managing existential and life-changing questions by maintaining a dedicated file where thoughts are recorded over time as they naturally emerge, rather than attempting to force immediate answers. This approach reduces mental burden and allows insights to develop gradually through daily reflection.
Typesafe.ai's System One model is a decision-making system trained for calibrated probability predictions rather than text generation, designed to answer specific questions within structured workflows. Unlike LLMs, it provides uncertainty-aware judgments optimized against outcomes and integrates deterministic logic for tasks like refund request evaluation.
An essay exploring the philosophical tensions in AI alignment, questioning whether an AI system is truly aligned if it makes decisions that improve outcomes despite public disagreement. The author argues that genuine alignment requires preserving human agency and meaningful consent in determining what 'better' means, rather than simply optimizing for outcomes or majority approval.
Leadership must defend quality standards against AI-enabled mediocrity by refusing to accept average outputs and communicating clearly when work falls short. The standards leaders approve today become organizational norms, making quality defense essential for long-term performance, trust, and differentiation. Most people want to do good work when given constructive feedback and support.
Felix Dietze discusses applying mental models—frameworks from Farnam Street—to guide LLM and agent decision-making. While LLMs know these models conceptually, they apply them inconsistently until integrated into agentic contexts; Dietze provides a compact list of thinking tools spanning general reasoning, physics, chemistry, and biology for use in code and agent prompts.
CEO Hour is a 40-minute structured thinking practice where individuals use timed stages to clarify a work question or decision through focused writing and reflection. Participants work independently on their own paper while optionally joining others for shared momentum, with no thinking stored or shared anywhere.
Prior is a predictive intelligence platform by Prior Software Inc. that helps users forecast outcomes for sports, markets, business decisions, and personal questions by analyzing context and generating calibrated probability reports. The service offers free predictions (one per week) and paid plans ranging from $29 to $499 monthly.
Council is a decision-making skill that gathers input from five independent advisors on project questions, synthesizes their perspectives through a Chairman agent, and provides a final opinionated recommendation. It supports architectural, product, and strategic decisions across multiple AI platforms including Claude Code, Codex, and ChatGPT Work.
A framework for evaluating past decisions by examining the reasoning and information available at the time rather than outcomes alone. The article explains how hindsight bias distorts memory, why we judge wins and losses asymmetrically, and recommends time-limited reviews that treat decisions as objects of analysis rather than reflections of identity.
In August 2026, an experimenter scaled a fleet of AI agents from one to 144 across four departments in a week, achieving 106 closed work units but experiencing cognitive overload. Realizing the bottleneck was human attention rather than agent capability, they shut down half the fleet and found that fewer agents with clearer focus produced higher-quality work.