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  • The Great Feminization: How Feminism Has Undermined Society’s Foundations

    In recent years, a damning theory has surfaced to account for the cultural decay and institutional dysfunction often mislabeled as “wokeness.” This view asserts that these failures arise not from fleeting ideological trends or technological disruptions, but from a catastrophic demographic overhaul: the unchecked influx and dominance of women in pivotal institutions, fueled by decades of misguided feminist agendas. Termed “the great feminization,” this concept reveals how feminism’s push for so-called equality has instead imposed feminine norms on fields like journalism, academia, law, medicine, and nonprofits, leading to stagnation, emotional pandering, and a rejection of merit-based progress. Far from empowering society, feminism has engineered a systemic weakness that prioritizes fragility over strength, ultimately threatening the very fabric of civilization.

    At the heart of the great feminization lies the reality that institutions built on masculine principles—such as forthright confrontation, bold risk-taking, and decisive hierarchies—crumble when overrun by women who impose their group dynamics as the default. Feminism’s relentless campaign to insert women into these spaces has resulted in environments where consensus-seeking and emotional validation eclipse productive debate. Conflict, once a tool for sharpening ideas, is now vilified as aggression, replaced by passive-aggressive tactics like exclusion and ostracism. Evolutionary insights underscore this: men’s historical roles in warfare fostered direct resolution and post-conflict reconciliation, while women’s intra-group rivalries bred covert manipulation. Feminism, by ignoring these innate differences, has forced a one-sided overhaul, turning robust institutions into echo chambers of hypersensitivity.

    The timeline exposes feminism’s destructive arc. In the mid-20th century, feminists demanded entry into male bastions, initially adapting to existing standards. But as their numbers swelled—surpassing 50% in law schools and medical programs in recent decades—these institutions surrendered to feminist demands, reshaping rules to accommodate emotional fragility. Feminism’s blank-slate ideology, denying biological sex differences, has accelerated this, leading to workplaces where innovation falters under layers of bureaucratic kindness. Risk aversion reigns, stifling advancements in science and technology, as evidenced by gender gaps in attitudes toward nuclear power or space exploration—men embrace progress, while feminist-influenced caution drags society backward.

    This feminization isn’t organic triumph; it’s feminist-engineered distortion. Anti-discrimination laws, born from feminist lobbying, have weaponized equity, making it illegal for women to fail competitively. Corporations, terrified of feminist-backed lawsuits yielding massive settlements, inflate female hires and promotions, sidelining merit for quotas. The explosion of HR departments—feminist strongholds enforcing speech codes and sensitivity training—has neutered workplaces, punishing masculine traits like assertiveness while rewarding conformity. These interventions haven’t elevated women; they’ve degraded institutions, expelling the innovative eccentrics who drive breakthroughs.

    The fallout is devastating. In journalism, now dominated by feminist norms, adversarial truth-seeking yields to narrative curation that shields feelings, propagating bias and suppressing facts. Academia, feminized to the core in humanities, enforces emotional safety nets like trigger warnings, abandoning intellectual rigor for indoctrination. The legal system, feminism’s crowning conquest, risks becoming a farce: impartial justice bends to sympathetic whims, as seen in Title IX kangaroo courts that prioritize accusers’ emotions over due process. Nonprofits, overwhelmingly female, exemplify feminist inefficiency—mission-driven bloat over tangible results, siphoning resources into endless virtue-signaling.

    Feminism’s defenders claim these shifts unlock untapped potential, but the evidence screams otherwise. Not all women embody these flaws, yet group averages amplify them, making spaces hostile to non-conformists and driving away men. Post-parity acceleration toward even greater feminization proves the point: feminism doesn’t foster balance; it enforces dominance, eroding resilience.

    If unaddressed, feminism’s great feminization will consign society to mediocrity. Reversing it demands dismantling feminist constructs: scrap quotas, repeal overreaching laws, and abolish HR vetoes that smother masculine vitality. Restore meritocracy, and watch institutions reclaim their purpose. Feminism promised liberation but delivered decline—it’s time to reject its illusions before they dismantle what’s left of progress.

  • Google’s Quantum Echoes Breakthrough: Achieving Verifiable Quantum Advantage in Real-World Computing

    TL;DR Google’s Willow quantum chip runs the Quantum Echoes algorithm using OTOCs to achieve the first verifiable quantum advantage, outperforming supercomputers 13,000x in modeling molecular structures for real-world applications like drug discovery, as published in Nature.

    In a groundbreaking announcement on October 22, 2025, Google Quantum AI revealed a major leap forward in quantum computing. Their new “Quantum Echoes” algorithm, running on the advanced Willow quantum chip, has demonstrated the first-ever verifiable quantum advantage on hardware. This means a quantum computer has successfully tackled a complex problem faster and more accurately than the world’s top supercomputers—13,000 times faster, to be exact—while producing results that can be repeated and verified. Published in Nature, this research not only pushes the boundaries of quantum technology but also opens doors to practical applications like drug discovery and materials science. Let’s break it down in simple terms.

    What Is Quantum Advantage and Why Does It Matter?

    Quantum computing has been hyped for years, but real-world applications have felt distant. Traditional computers (classical ones) use bits that are either 0 or 1. Quantum computers use qubits, which can be both at once thanks to superposition, allowing them to solve certain problems exponentially faster.

    “Quantum advantage” is when a quantum computer does something a classical supercomputer can’t match in a reasonable time. Google’s 2019 breakthrough showed quantum supremacy on a contrived task, but it wasn’t verifiable or useful. Now, with Quantum Echoes, they’ve achieved verifiable quantum advantage: repeatable results that outperform supercomputers on a problem with practical value.

    This builds on Google’s Willow chip, introduced in 2024, which dramatically reduces errors—a key hurdle in quantum tech. Willow’s low error rates and high speed enable precise, complex calculations.

    Understanding the Science: Out-of-Time-Order Correlators (OTOCs)

    At the heart of this breakthrough is something called out-of-time-order correlators, or OTOCs. Think of quantum systems like a busy party: particles (or qubits) interact, entangle, and “scramble” information over time. In chaotic systems, this scrambling makes it hard to track details, much like how a rumor spreads and gets lost in a crowd.

    Regular measurements (time-ordered correlators) lose sensitivity quickly because of this scrambling. OTOCs flip the script by using time-reversal techniques—like echoing a signal back. In the Heisenberg picture (a way to view quantum evolution), OTOCs act like interferometers, where waves interfere to amplify signals.

    Google’s team measured second-order OTOCs (OTOC(2)) on a superconducting quantum processor. They observed “constructive interference”—waves adding up positively—between Pauli strings (mathematical representations of quantum operators) forming large loops in configuration space.

    In plain terms: By inserting Pauli operators to randomize phases during evolution, they revealed hidden correlations in highly entangled systems. These are invisible without time-reversal and too complex for classical simulation.

    The experiment used a grid of qubits, random single-qubit gates, and fixed two-qubit gates. They varied circuit cycles, qubit positions, and instances, normalizing results with error mitigation. Key findings:

    • OTOCs remain sensitive to dynamics long after regular correlators decay exponentially.
    • Higher-order OTOCs (more interference arms) boost sensitivity to perturbations.
    • Constructive interference in OTOC(2) reveals “large-loop” effects, where paths in Pauli space recombine, enhancing signal.

    This interference makes OTOCs hard to simulate classically, pointing to quantum advantage.

    The Quantum Echoes Algorithm: How It Works

    Quantum Echoes is essentially the OTOC algorithm implemented on Willow. It’s like sending a sonar ping into a quantum system:

    1. Run operations forward on qubits.
    2. Perturb one qubit (like poking the system).
    3. Reverse the operations.
    4. Measure the “echo”—the returning signal.

    The echo amplifies through constructive interference, making measurements ultra-sensitive. On Willow’s 105-qubit array, it models physical experiments with precision and complexity.

    Why verifiable? Results can be cross-checked on another quantum computer of similar quality. It outperformed a supercomputer by 13,000x in learning structures of natural systems, like molecules or magnets.

    In a proof-of-concept with UC Berkeley, they used NMR (Nuclear Magnetic Resonance—the tech behind MRIs) data. Quantum Echoes acted as a “molecular ruler,” measuring longer atomic distances than traditional methods. They tested molecules with 15 and 28 atoms, matching NMR results while revealing extra info.

    Real-World Applications: From Medicine to Materials

    This isn’t just lab curiosity. Quantum Echoes could revolutionize:

    • Drug Discovery: Model how molecules bind, speeding up new medicine development.
    • Materials Science: Analyze polymers, batteries, or quantum materials for better solar panels or fusion tech.
    • Black Hole Studies: OTOCs relate to chaos in black holes, aiding theoretical physics.
    • Hamiltonian Learning: Infer unknown quantum dynamics, useful for sensing and metrology.

    As Ashok Ajoy from UC Berkeley noted, it enhances NMR’s toolbox for intricate spin interactions over long distances.

    What’s Next for Quantum Computing?

    Google’s roadmap aims for Milestone 3: a long-lived logical qubit for error-corrected systems. Scaling up could unlock more applications.

    Challenges remain—quantum tech is noisy and expensive—but this verifiable advantage is a milestone. As Hartmut Neven and Vadim Smelyanskiy from Google Quantum AI said, it’s like upgrading from blurry sonar to reading a shipwreck’s nameplate.

    This breakthrough, detailed in Nature under “Observation of constructive interference at the edge of quantum ergodicity,” signals quantum computing’s shift from promise to practicality.

    Further Reading

  • Andrej Karpathy on the Decade of AI Agents: Insights from His Dwarkesh Podcast Interview

    TL;DR

    Andrej Karpathy’s reflections on artificial intelligence trace the quiet, inevitable evolution of deep learning systems into general-purpose intelligence. He emphasizes that the current breakthroughs are not sudden revolutions but the result of decades of scaling simple ideas — neural networks trained with enormous data and compute resources. The essay captures how this scaling leads to emergent behaviors, transforming AI from specialized tools into flexible learning systems capable of handling diverse real-world tasks.

    Summary

    Karpathy explores the evolution of AI from early, limited systems into powerful general learners. He frames deep learning as a continuation of a natural process — optimization through scale and feedback — rather than a mysterious or handcrafted leap forward. Small, modular algorithms like backpropagation and gradient descent, when scaled with modern hardware and vast datasets, have produced behaviors that resemble human-like reasoning, perception, and creativity.

    He argues that this progress is driven by three reinforcing trends: increased compute power (especially GPUs and distributed training), exponentially larger datasets, and the willingness to scale neural networks far beyond human intuition. These factors combine to produce models that are not just better at pattern recognition but are capable of flexible generalization, learning to write code, generate art, and reason about the physical world.

    Drawing from his experience at OpenAI and Tesla, Karpathy illustrates how the same fundamental architectures power both self-driving cars and large language models. Both systems rely on pattern recognition, prediction, and feedback loops — one for navigating roads, the other for navigating language. The essay connects theory to practice, showing that general-purpose learning is not confined to labs but already shapes daily technologies.

    Ultimately, Karpathy presents AI as an emergent phenomenon born from scale, not human ingenuity alone. Just as evolution discovered intelligence through countless iterations, AI is discovering intelligence through optimization — guided not by handcrafted rules but by data and feedback.

    Key Takeaways

    • AI progress is exponential: Breakthroughs that seem sudden are the cumulative effect of scaling and compounding improvements.
    • Simple algorithms, massive impact: The underlying principles — gradient descent, backpropagation, and attention — are simple but immensely powerful when scaled.
    • Scale is the engine of intelligence: Data, compute, and model size form a triad that drives emergent capabilities.
    • Generalization emerges from scale: Once models reach sufficient size and data exposure, they begin to generalize across modalities and tasks.
    • Parallel to evolution: Intelligence, whether biological or artificial, arises from iterative optimization processes — not design.
    • Unified learning systems: The same architectures can drive perception, language, planning, and control.
    • AI as a natural progression: What humanity is witnessing is not an anomaly but a continuation of the evolution of intelligence through computation.

    Discussion

    The essay invites a profound reflection on the nature of intelligence itself. Karpathy’s framing challenges the idea that AI development is primarily an act of invention. Instead, he suggests that intelligence is an attractor state — something the universe converges toward given the right conditions: energy, computation, and feedback. This idea reframes AI not as an artificial construct but as a natural phenomenon, emerging wherever optimization processes are powerful enough.

    This perspective has deep implications. It implies that the future of AI is not dependent on individual breakthroughs or genius inventors but on the continuation of scaling trends — more data, more compute, more refinement. The question becomes not whether AI will reach human-level intelligence, but when and how we’ll integrate it into our societies.

    Karpathy’s view also bridges philosophy and engineering. By comparing machine learning to evolution, he removes the mystique from intelligence, positioning it as an emergent property of systems that self-optimize. In doing so, he challenges traditional notions of creativity, consciousness, and design — raising questions about whether human intelligence is just another instance of the same underlying principle.

    For engineers and technologists, his message is empowering: the path forward lies not in reinventing the wheel but in scaling what already works. For ethicists and policymakers, it’s a reminder that these systems are not controllable in the traditional sense — their capabilities unfold with scale, often unpredictably. And for society as a whole, it’s a call to prepare for a world where intelligence is no longer scarce but abundant, embedded in every tool and interaction.

    Karpathy’s work continues to resonate because it captures the duality of the AI moment: the awe of creation and the humility of discovery. His argument that “intelligence is what happens when you scale learning” provides both a technical roadmap and a philosophical anchor for understanding the transformations now underway.

    In short, AI isn’t just learning from us — it’s showing us what learning itself really is.

  • Introducing Figure 03: The Future of General-Purpose Humanoid Robots

    Overview

    Figure has unveiled Figure 03, its third-generation humanoid robot designed for Helix, the home, and mass production at scale. This release marks a major step toward truly general-purpose robots that can perform human-like tasks, learn directly from people, and operate safely in both domestic and commercial environments.

    Designed for Helix

    At the heart of Figure 03 is Helix, Figure’s proprietary vision-language-action AI. The robot features a completely redesigned sensory suite and hand system built to enable real-world reasoning, dexterity, and adaptability.

    Advanced Vision System

    The new camera architecture delivers twice the frame rate, 25% of the previous latency, and a 60% wider field of view, all within a smaller form factor. Combined with a deeper depth of field, Helix receives richer and more stable visual input — essential for navigation and manipulation in complex environments.

    Smarter, More Tactile Hands

    Each hand includes a palm camera and soft, compliant fingertips. These sensors detect forces as small as three grams, allowing Figure 03 to recognize grip pressure and prevent slips in real time. This tactile precision brings human-level control to delicate or irregular objects.

    Continuous Learning at Scale

    With 10 Gbps mmWave data offload, the Figure 03 fleet can upload terabytes of sensor data for Helix to analyze, enabling continuous fleet-wide learning and improvement.

    Designed for the Home

    To work safely around people, Figure 03 introduces soft textiles, multi-density foam, and a lighter frame — 9% less mass and less volume than Figure 02. It’s built for both safety and usability in daily life.

    Battery and Safety Improvements

    The new battery system includes multi-layer protection and has achieved UN38.3 certification. Every safeguard — from the cell to the pack level — was engineered for reliability and longevity.

    Wireless, Voice-Enabled, and Easy to Live With

    Figure 03 supports wireless inductive charging at 2 kW, so it can automatically dock to recharge. Its upgraded audio system doubles the speaker size, improves microphone clarity, and enables natural speech interaction.

    Designed for Mass Manufacturing

    Unlike previous prototypes, Figure 03 was designed from day one for large-scale production. The company simplified components, introduced tooled processes like die-casting and injection molding, and established an entirely new supply chain to support thousands of units per year.

    • Reduced part count and faster assembly
    • Transition from CNC machining to high-volume tooling
    • Creation of BotQ, a new dedicated manufacturing facility

    BotQ’s first line can produce 12,000 units annually, scaling toward 100,000 within four years. Each unit is tracked end-to-end with Figure’s own Manufacturing Execution System for precision and quality.

    Designed for the World at Scale

    By solving for safety and variability in the home, Figure 03 becomes a platform for commercial use as well. Its actuators deliver twice the speed and improved torque density, while enhanced perception and tactile feedback enable industrial-level handling and automation.

    Wireless charging and data transfer make near-continuous operation possible, and companies can customize uniforms, materials, and digital side screens for branding or safety identification.

    Wrap Up

    Figure 03 represents a breakthrough in humanoid robotics — combining advanced AI, safe design, and scalable manufacturing. Built for Helix, the home, and the world at scale, it’s a step toward a future where robots can learn, adapt, and work alongside people everywhere.

    Sources

  • Stop Coasting: The 5-Step “Fall Reset” That Actually Works

    Why Fall, Not New Year, Is the Real Time to Reinvent Your Life

    Cal Newport argues that autumn, not January, is the natural time to reclaim your life. Routines stabilize, energy returns, and reflection is easier. In
    episode 373 of the Deep Questions podcast, Newport curates insights from five popular thinkers
    — Mel Robbins, Dan Koe, Jordan Peterson, Ryan Holiday, and himself — into an “all-star” reset formula.

    The All-Star Reset Plan: 5 Core Lessons

    1. Brain Dump Weekly (Mel Robbins)

    Your brain isn’t lazy; it’s overloaded. Robbins recommends a “mental vomit” session: write down every thought, task, and worry. Newport refines this — keep a
    living digital list instead of rewriting weekly. Every Friday or Sunday, review, prune, and update it. You’ll turn chaos into clarity.

    2. Audit Your Information Diet (Dan Koe)

    Just as junk food ruins your body, low-quality media ruins your mind. Koe says to track your content intake. Newport’s enhancement: log every social scroll, video, and podcast
    for 30 days. Give each day a happiness score from -2 to +2. Identify what energizes vs. drains you. Build your information nutrition plan.

    3. Choose Slayable Dragons (Jordan Peterson)

    Massive goals invite paralysis. Peterson teaches that you must lower your target until it’s still challenging but possible. Newport reframes this:
    separate your vision (the lifestyle you want) from your next goal (a winnable milestone). Conquer one dragon at a time; each win unlocks the next level.

    4. Climb the Book Complexity Ladder (Ryan Holiday)

    Holiday warns against shallow reading — chasing book counts over depth. Newport introduces a complexity ladder to deepen comprehension:

    • Step 1: Start with secondary sources explaining big ideas (At the Existentialist Café).
    • Step 2: Move to accessible primary works like Man’s Search for Meaning.
    • Step 3: Progress to approachable classics like Walden or Letters from a Stoic.
    • Step 4: Tackle advanced works (Jung, Nietzsche, Aristotle) once ready.

    The higher you climb, the richer your thinking becomes — and the stronger your sense of meaning.

    5. Master Multiscale Planning (Cal Newport)

    Goals fail without structure. Newport’s multiscale planning system aligns your long-term vision with daily action:

    • Quarterly Plan: Define 3–4 strategic objectives.
    • Weekly Plan: Review progress, schedule deep work, and refine tasks.
    • Daily Plan: Time-block your day to ensure meaningful progress.

    This layered planning method ensures you’re not just busy — you’re aligned.

    Key Takeaways

    • 1. Maintain a single, updated brain dump — clarity beats chaos.
    • 2. Curate your information diet; protect your mental bandwidth.
    • 3. Pursue winnable goals that build momentum.
    • 4. Read progressively harder books to sharpen your worldview.
    • 5. Plan across time horizons — quarterly, weekly, daily — for compound growth.

    The Meta Lesson: Control Your Life, Control Your Devices

    Newport’s final insight: the antidote to digital distraction isn’t abstinence — it’s purpose.
    When your offline life becomes richer, screens naturally lose their appeal.
    “The more interesting your life outside of screens, the less interesting the screens themselves will become.”

    Further Resources

  • The Hard Truth About Self-Improvement: Tim Ferriss on Subtraction, Community, Psychedelics, and Choosing Energy

    Tim Ferriss’s discussion on self-improvement distills decades of personal trials, experiments, and reflections into a brutally honest analysis of what actually works and what doesn’t. After 25 years of testing methods across fitness, productivity, and mindset, Ferriss concludes that the pursuit of self-improvement often hides deeper issues of self-acceptance, identity, and meaning. The essay dismantles common myths about success and exposes how our endless optimization culture can create more suffering than growth.

    Summary of Video

    Ferriss begins by confronting the illusion that constant self-optimization leads to happiness. He explains that the self-improvement industry thrives on insecurity — the subtle message that we are never enough. Throughout the piece, he reflects on the psychological cost of chasing perfection through routines, diets, and productivity systems.

    Drawing from his own history of experimentation, Ferriss recounts how his obsession with performance metrics eventually led to burnout and emptiness. The more he sought external validation through physical and financial achievements, the more disconnected he felt internally. Over time, he learned that real improvement is less about doing more and more about learning to stop — to sit still, accept discomfort, and confront what truly matters.

    He highlights meditation, journaling, and reflection as tools not for optimization, but for self-understanding. These practices reveal patterns of avoidance, fear, and insecurity that drive the relentless pursuit of “better.” The hardest lesson Ferriss emphasizes is that growth requires surrender — letting go of the idea that we can hack our way to fulfillment.

    Key Insights

    • The self-improvement trap: Chasing constant growth can become a sophisticated form of self-loathing if rooted in fear rather than curiosity.
    • Performance vs. peace: High achievement often masks emotional turbulence. True mastery involves stillness, not acceleration.
    • Success without fulfillment: Metrics, followers, and accomplishments cannot replace internal alignment or purpose.
    • Awareness over action: Real change happens when we stop reacting automatically and start observing our mental patterns.
    • Letting go as a superpower: Knowing when to stop when to rest, when to release control is as important as knowing when to push.

    Key Takeaways

    • Self-improvement is not about adding more to your life, but removing what no longer serves you.
    • The desire to optimize everything can be a form of fear disguised as ambition.
    • Reflection and stillness are more transformative than endless action.
    • Long-term fulfillment comes from acceptance, not control.
    • Measure progress by peace of mind, not productivity.

    Wrap Up

    Ferriss’s core message is both sobering and liberating: stop trying to fix yourself and start understanding yourself. The paradox of growth is that it begins when the pursuit ends. After 25 years of relentless experimentation, Ferriss concludes that peace is not a reward for perfection it is the foundation from which everything meaningful begins.

  • How to Build Powerful AI Agents with OpenAI Agent Builder (Complete Step-by-Step Guide!)

    Want to create your own AI agent that can think, reason, and take action? OpenAI’s new Agent Builder and Agents SDK make it easier than ever to build autonomous AI systems that can use tools, connect to APIs, and even delegate tasks to other agents.

    This guide walks you through everything you need to know — from setup and tool creation to multi-agent orchestration and guardrails — using OpenAI’s latest developer features.

    What Is an OpenAI Agent?

    An agent in OpenAI’s platform is an intelligent system that:

    • Follows a specific instruction set (system prompt or developer message)
    • Has access to tools (custom functions, APIs, or built-in modules)
    • Can maintain state or memory across interactions
    • Supports multi-step reasoning and orchestration between multiple agents
    • Implements guardrails and tracing for safety and observability

    The Agent Builder ecosystem combines the Agent Builder, Responses API, and Agents SDK to let you develop, debug, and deploy AI agents that perform real work.

    1. Choose Your Build Layer

    You can build agents in two ways:

    Approach Pros Trade-offs
    Responses API More control; full tool orchestration Requires managing the agent loop manually
    Agents SDK Handles orchestration, tool calling, guardrails, and tracing Less low-level control, but faster to build with

    OpenAI recommends using the Agents SDK for most use cases.

    2. Install Required Libraries

    TypeScript / JavaScript

    npm install @openai/agents zod@3
    import { Agent, run, tool } from "@openai/agents";
import { z } from "zod";

    Python

    from agents import Agent, function_tool, Runner
from pydantic import BaseModel

    3. Define Your Agent

    An agent consists of:

    • name: readable identifier
    • instructions: the system’s behavioral prompt
    • model: which GPT model to use
    • tools: external functions or APIs
    • optional: structured outputs, guardrails, and sub-agents

    Example (TypeScript)

    const getWeather = tool({
  name: "get_weather",
  description: "Return the weather for a given city",
  parameters: z.object({ city: z.string() }),
  async execute({ city }) {
    return `The weather in ${city} is sunny.`;
  },
});

const agent = new Agent({
  name: "Weather Assistant",
  instructions: "You are a helpful assistant that can fetch weather.",
  model: "gpt-4.1",
  tools: [getWeather],
});

    Example (Python)

    @function_tool
def get_weather(city: str) -> str:
    return f"The weather in {city} is sunny"

agent = Agent(
    name = "Haiku agent",
    instructions = "Always respond in haiku form",
    model = "gpt-5-nano",
    tools = [get_weather]
)

    4. Add Context or Memory

    Agents can store contextual data to make responses more personalized or persistent.

    interface MyContext {
  uid: string;
  isProUser: boolean;
  fetchHistory(): Promise<string[]>;
}

const result = await run(agent, "What’s my next meeting?", {
  context: {
    uid: "user123",
    isProUser: true,
    fetchHistory: async () => [/* history */],
  },
});

    5. Run and Orchestrate

    import { run } from "@openai/agents";

const result = await run(agent, "What is the weather in Toronto?");
console.log(result.finalOutput);

    The SDK handles agent reasoning, tool calls, and conversation loops automatically.

    6. Multi-Agent Systems (Handoffs)

    const bookingAgent = new Agent({ name: "Booking", instructions: "..." });
const refundAgent = new Agent({ name: "Refund", instructions: "..." });

const masterAgent = new Agent({
  name: "Master Agent",
  instructions: "Delegate to booking or refund agents when needed.",
  handoffs: [bookingAgent, refundAgent],
});

    This allows one agent to hand off a conversation to another based on context.

    7. Guardrails and Safety

    Guardrails validate input/output or prevent unsafe tool calls. Use them to ensure compliance, prevent misuse, and protect APIs.

    8. Tracing and Observability

    Every agent run is automatically traced and viewable in the OpenAI Dashboarhttps://www.youtube.com/watch?v=DuUL_OK-iKwd. You’ll see which tools were used, intermediate steps, and handoffs — perfect for debugging and optimization.

    9. Choosing Models and Reasoning Effort

    • Use reasoning models for multi-step logic or planning
    • Use mini/nano models for faster, cheaper tasks
    • Tune reasoning effort for cost-performance trade-offs

    10. Evaluate and Improve

    • Use Evals for performance benchmarking
    • Refine your prompts and tool descriptions iteratively
    • Test for safety, correctness, and edge cases

    Example: Weather Agent (Full Demo)

    import { Agent, run, tool } from "@openai/agents";
import { z } from "zod";

const getWeather = tool({
  name: "get_weather",
  description: "Get current weather for a given city",
  parameters: z.object({ city: z.string() }),
  async execute({ city }) {
    return { city, weather: "Sunny, 25°C" };
  },
});

const weatherAgent = new Agent({
  name: "WeatherAgent",
  instructions: "You are a weather assistant. Use get_weather when asked about weather.",
  model: "gpt-4.1",
  tools: [getWeather],
  outputType: z.object({
    city: z.string(),
    weather: z.string(),
  }),
});

async function main() {
  const result = await run(weatherAgent, "What is the weather in Toronto?");
  console.log("Final output:", result.finalOutput);
  console.log("Trace:", result.trace);
}

main().catch(console.error);

    Best Practices

    • Start with one simple tool and expand
    • Use structured outputs (zod, pydantic)
    • Enable guardrails early
    • Inspect traces to debug tool calls
    • Set max iterations to prevent infinite loops
    • Monitor latency, cost, and reliability in production

    Wrap Up

    With OpenAI’s Agent Builder and Agents SDK, you can now create sophisticated AI agents that go beyond chat — they can take real action, use tools, call APIs, and collaborate with other agents.

    Whether you’re automating workflows, building personal assistants, or developing enterprise AI systems, these tools give you production-ready building blocks for the next generation of intelligent applications.

    → Read the official OpenAI Agent Builder docs

  • How a Daily Question Made Mara Wiser: A Short Story About Practicing Wisdom

    Mara loved reading about wisdom. Her shelves were packed with Seneca and modern guides that promised enlightenment in neat lists. Still, her life felt unchanged, full of quick reactions and small mistakes.

    One morning, after a tense call with a friend, a line struck her: “No man was ever wise by chance.” She realized she had been consuming wisdom, not living it. So she started an experiment.

    Each day, Mara asked herself one question before she acted.

    • When angry: What is another way to look at this?
    • When unsure: If everyone made this choice, how would it affect the world?
    • When ashamed: Am I moving closer to my values or further away?
    • When judging: Have I done something similar before, and what was going on for me then?

    The questions did not fix everything at once, but they created a pause. In that pause, she noticed how fear tinted her thoughts, how her words drifted from her values, and how a caring interpretation could soften a hard moment.

    Weeks became months. She still stumbled, but less often. When her friend called again, they spoke with honesty and care. After the call, Mara realized something had shifted. She was no longer chasing wisdom on a page. She was practicing it, choice by choice.

    That is how wisdom grows: not by chance, but by action.

  • How to Hide Desktop Widgets in macOS Tahoe

    TL;DR Desktop UI:

    Go to System Settings → Desktop & Dock → Show Widgets and turn off On Desktop to instantly hide widgets in macOS Tahoe.

    TL;DR (Terminal Only):

    Run this command to hide desktop widgets in macOS Tahoe:

    defaults write com.apple.WindowManager StandardHideWidgets -bool true && killall Dock

    With the release of macOS Tahoe (version 26), Apple introduced live desktop widgets that blend beautifully into your wallpaper—but not everyone loves the clutter. If you prefer a cleaner workspace, here’s a quick and easy way to hide desktop widgets using your system settings—no Terminal commands required.

    Step-by-Step: Turn Off Widgets on macOS Tahoe

    • Click the Apple  menu and choose System Settings.
    • Select Desktop & Dock from the sidebar.
    • Scroll down until you see the section labeled Show Widgets.
    • Toggle off the option for On Desktop.

    That’s it! Your Mac’s desktop will instantly return to a clean, distraction-free look. If you ever miss your widgets, just head back to the same menu and re-enable the “On Desktop” option.

    Bonus: Keep Widgets in Stage Manager Only

    If you like widgets but don’t want them floating on your desktop, you can still access them when using Stage Manager. Simply leave the In Stage Manager toggle on while disabling On Desktop.

    macOS Tahoe makes widgets more powerful but also more optional. Now you can enjoy a minimalist workspace without losing quick access to useful information when you need it.

  • “Men, Where Did You Go?” We Left. You Just Didn’t Notice.

    Why Modern Women Keep Asking Questions They Don’t Want Honest Answers To

    Rachel Drucker’s recent Modern Love piece in The New York Times, titled “Men, Where Have You Gone? Please Come Back,” is poetic, wistful, and emotionally sincere. But like so many mainstream essays written by women about the “disappearing man,” it’s riddled with blind spots. It asks a question, then subtly refuses to hear the actual answer.

    Spoiler: Men didn’t vanish. We walked away, eyes open, hearts scorched, and wallets lighter. And we had our reasons.

    The Core of the Disconnect

    Drucker observes a cultural shift: restaurants filled with women, phones filled with ghosted threads, and the emotional vacancy of men she once saw as eager participants in the dance of romance.

    Her conclusion? Men have “retreated,” not maliciously, but softly. Quietly. She sees it as a kind of sadness. A tragedy.

    But here’s the twist: it wasn’t passive disappearance. It was active self-preservation.

    When the Game Is Rigged, Players Quit

    Drucker doesn’t mention:

    • Hypergamy, the real-world, observable tendency for women to seek partners of equal or higher status, leaving average men invisible.
    • Dating app economics, where 80% of women swipe right on the top 10 to 15% of men.
    • “Situationships” she complains about, which often result from women keeping options open while seeking a “better” deal.
    • Or the reality that modern men are told to “open up,” “be vulnerable,” “do the work,” and then find themselves ghosted for a guy with better biceps or more Instagram clout.

    This isn’t bitterness. It’s data. It’s lived experience.

    Drucker Asks for Presence. But at What Cost?

    She writes, “We’re not asking for performances. We are asking for presence.”

    But for many men, presence has meant:

    • Being used for attention, meals, or validation.
    • Being punished for vulnerability.
    • Being rejected for not “sparking” that elusive chemistry after doing everything right.

    She says, “We never needed you to be perfect.”
    But the reality is, for many men, anything less than perfection equals irrelevance.

    Men Went Their Own Way. Literally.

    While Drucker sat at candlelit tables wondering where the men went, she missed the Passport Bros boarding planes. She missed men building businesses, lifting weights, escaping the algorithmic trap of Western dating, or just quietly opting out.

    These men are not “lost.”
    They’re focused.
    They’re healing.
    They’re done playing a rigged game.

    You Don’t Get to Ignore Men for a Decade, Then Mourn Their Absence

    There’s a kind of emotional entitlement in the essay, a soft demand that men reappear, re-engage, recommit.

    But Drucker, and the culture she speaks for, never reckons with how we got here. There’s no self-inquiry. No admission that maybe, just maybe, the modern dating market, the feminism of convenience, the casual cruelty of swipe culture and emotional ghosting drove men away.

    You can’t burn the bridge and ask why no one’s crossing.

    We’ll Come Back, But Not to the Same Rules

    Drucker ends with a plea for men to return. Not perfect. Just present.
    That’s fair, and human. And there are many good women who do want connection, who are sincere, who are showing up.

    But the new generation of men isn’t coming back to be emotionally milked, disposable providers, or walking therapy dolls.

    If we come back, it will be as equals.
    With boundaries.
    With standards.
    And with full awareness of the cost of connection.

    Wrap Up

    The next time someone asks, “Where have all the good men gone?”, try listening to the answers. They’re not hiding. They just stopped showing up for a story that never included them.