The White House has eventually addressed a remnant of the AI era.

On Tuesday, the Trump administration rolled out si.gov, giving a new URL to the location that was archetypal launched during the Biden administration. The new location describes President Donald Trump's synthetic intellect policies, although much of the content itself was changed before Tuesday.

"The Super Intelligence era is here. 🇺🇸," Michael Kratsios, the director of the White House Office of Science and Technology Policy, wrote on X.

The new location falls in row alongside Trump's wishes that synthetic intelligence, or AI, now be called excellent intelligence, or SI. The chairman formalized his penchant in a September 29 administrator order.

Last week, Trump declared that if others did not go alongside alongside his rebrand, they would be considered an "enemy" of his White House. The chairman has stated that using the term "artificial intelligence" makes the innovation audio fake.

AI and tech leaders have divided complete how to grip Trump's request.

Over the weekend, SpaceXAI followed through on SpaceX CEO Elon Musk's commitment to rebrand the company's AI division as SpaceXSI.

On Saturday, days following Trump bestowed an regard on him, Microsoft CEO Satya Nadella published a lengthy declaration titled: "Models as Insider Risks in the Super Intelligence Era." AI and synthetic intellect were not among the 805 words Nadella wrote.

As for OpenAI, whose name contains the extremely abbreviation Trump wants gone, its CEO, Sam Altman, earlier ruled out a rebrand.

"No plans to do that," Altman told the Politico Decoded podcast. "But we have been talking concerning excellent intellect a lot, and we volition continue to conversation concerning it."

The US national parks are incredible, and I comprehend why so many group flock to these iconic landscapes to see several of the most breathtaking scenery in the country.

However, following traveling solo to all 63 US national parks and all 50 states, I've discovered that several of the most stunning and underrated places aren't part of the national park scheme — they're province parks.

These hidden gems recommendation the identical beauty, adventure, and solitude, frequently without the crowds, reservations, or authorize hassles that arrive alongside national park visits. Here are several of my favorites.

Since 2000, lone 13 albums have spent double-digit weeks at No. 1 on the Billboard 200 chart, extensively considered the conclusive weekly ranking of best-selling albums in the US.

Taylor Swift is the lone female to have four albums expend at smallest 10 weeks atop the Billboard 200, joining The Beatles and Elvis as the lone artists in former to accomplish this feat.

Keep study to see a complete catalog of the chart's longest leaders in chronological order.

Taylor Swift gave us fair alert in her newest album: "I'm wed to the hustle."

Swift's 12th studio album, "The Life of a Showgirl," not lone bankrupt the document for the biggest debut revenue week ever, but it continued to amass revenue and streams in the US for a twelvemonth following its release.

With 14 nonconsecutive weeks at No. 1 on the Billboard 200, "Showgirl" has surpassed "Fearless" and "1989" as Swift's second-longest reign on the all-genre gathering chart.

The notable count comes shortly following Swift set a personal-best diagram document alongside "The Tortured Poets Department." Keep study for a ranking of her 16 studio albums (including the two originals and rerecords) according to their Billboard 200 performances.

If you’re looking for an e-reader that can effortlessly fit in your pocket (or break onto your magnet-equipped phone), the small Xteink X4 Pro power be value a look. It features a 4.26-inch, 800 x 480 E Ink display, making it considerably smaller and thinner than Amazon’s smallest six-inch Kindle. It’s additionally fairly a bit cheaper. Right now, you can choice it up for $84.15 ($25 off) at Amazon, which is its finest cost to date.

The X4 Pro brings multiple greeted upgrades complete its predecessors, including a touchscreen that gives readers another way to engage alongside it. It motionless has bodily page-turning buttons, though. There’s additionally a new touch-sensitive clasp below the display for quickly returning to the residence screen, alongside alongside a built-in ray alongside adjustable brightness and color temperature, so you can peruse in the dark. It ships alongside the outstanding CrossPoint Reader software, which makes navigating your archive and study books a much improved cognition than on former Xteink models we’ve tested, although it’s motionless not fairly as polished as Amazon’s software.

Understandably, not everyone is a fan of Xteink’s decision to toggle the X4 Pro’s USB-C harbor for a proprietary charging connector. If you can appearance former that, the X4 Pro is a great, small e-reader that features magnets that let you nexus it to compatible smartphones, fair akin former models. The magnetic association is stronger this period around, although we motionless desire it were a small additional secure. All the same, my coworker Andrew Liszewski motionless considers the X4 Pro the best small e-reader you can buy, calling it a equipment he “won’t depart the home without.”

Google has added Gemini 4 Argon to the AI Mode example list for Google AI Pro and Ultra subscribers. The rollout comes on the identical day the example became accessible to developers, enterprises, and Gemini app users.

Robby Stein, VP of Product for Google Search, announced the rollout on X, saying Argon “is now rolling out globally in Search” and that subscribers can toggle to it using the “+” icon.

Google’s first Argon announcement, 13 days ago, didn’t citation Pro subscribers. Today’s launch post lists them alongside Ultra subscribers, but nothing announced today changes the default for free-tier AI Mode users.

What Changed

You can choice Argon from the “+” icon in AI Mode’s “Ask anything” bar, the identical list that added 3.7 Flash in August and 3.8 Flash in September.

Stein says Argon “brings big improvements to tackling nuanced, multi-step tasks in AI Mode” and is “better at understanding your intent and following your instructions in rear and onward conversations.” The initiate article makes a akin assertion for the example as a whole, saying it “follows manner instructions improved than any of our former Gemini models.”

Neither article states a language. The 3.7 Flash rollout was English only.

Since it’s rolling out, it may not be accessible to everyone fair yet. As of 12:17 pm ET, Argon hadn’t appeared in the list on my own Pro account.

From Fairwind To Pro Subscribers

Google introduced Argon on Sept. 30 as a frontier example for coding, endeavor cognition work, and cyber defense, and released it to “a set of trusted cyber defenders” through its Fairwind Program. That article stated the publish to developers, enterprises, and consumers would arrive “as shortly as possible” and would commencement “with paid API customers and Google AI Ultra subscribers.”

Today’s article brings all the groups together at once. Developers can admission it through the Gemini API’s paid tier, Ultra subscribers can use it in Google Antigravity, Google’s coding tool, and Pro and Ultra subscribers can discover it in the Gemini app and AI Mode.

Background

Gemini 3 Pro became accessible as an option in AI Mode in November. Since then, all supplement to the list I covered has been a Flash model. Gemini 3 Flash became the default in December, Gemini 3.5 Flash took over as the earth default at I/O in May, Gemini 3.7 Flash joined the menu on Aug. 14, and 3.8 Flash followed on Sept. 2.

In February, I covered Google’s reasoning for operating AI Mode on Flash models, which disbursal small and react faster, so they can run at Search scale.

Google’s support page for AI Mode’s example menu, peruse today, motionless names lone “Pro” and “Fast” as options and describes the Pro choice as Gemini 3 Pro. It peruse the identical way on Aug. 14 and Sept. 2.

Why This Matters

Argon is accessible by subscription only, fair akin the two Flash models that joined the list before it.

The improvements Google describes affect multi-step tasks and back-and-forth conversations, so a multi-step project alongside follow-up questions is the finest way to test the claim. Once Argon appears in your menu, and if 3.8 Flash is motionless listed alongside it, try the identical sequence alongside the two models and note which one answers all time.

Looking Ahead

The next inquiry is whether Argon volition remain a subscriber choice or rotate into the default in AI Mode. Gemini 3 Flash and 3.5 Flash the two launched as the default, during Gemini 3 Pro launched as an choice and has remained one. Neither Google’s initiate article nor Stein’s X article clarifies which direction Argon volition take.

The initiate article wraps up by saying it’s looking onward to “bringing you the remainder of the Gemini 4 family,” but it doesn’t define which of those models volition be accessible in AI Mode.

Facebook’s new “Your Algorithm” feature volition let you immediately add and oversee topics you desire to see in your nourish and Facebook Reels. Meta says your recommendations “respond correct away” following you create updates using the new characteristic by hitting “interested” or “not interested” for distinct topics. Your Algorithm earlier launched on Instagram before this year, and Meta additionally launched a akin “Dear Algo” characteristic on Threads.

Facebook users volition be capable to admission Your Algorithm or choose “interested” or “not interested” from the three dots list on idiosyncratic posts. The “interested” and “not interested” buttons may additionally appear under posts, and Meta says it “may ask prosecute up questions for additional item on what you liked or disliked. You can effortlessly inform us, in your own words, what you desire to see additional or small of, and your recommendations volition update in genuine time.”

The new characteristic is part of a set of “AI-powered” tools Meta says it’s using to customize what users see on Facebook. It additionally says it’s using LLMs additional in its ranking systems: “LLMs can merge the interests, context, and intent rearward natural language, unlocking an algorithm you can really conversation to. Tell it what you desire additional or small of, and your recommendations react in genuine time, adapting inside the identical meeting to indicate what matters to you correct now.”

🧠 About Coris

Coris is the AI phase for hazard and financial operations. Platforms, marketplaces, fee providers, and banks - GoFundMe, Clio, Cherry, and a expanding catalog - use Coris to onboard merchants faster, detect them continuously, and act on fraud without adding headcount. One client described us as "Cursor + Lovable for hazard teams."

We're backed by Y Combinator, Lux Capital, and Pathlight, founded by payments-domain experts, and expanding quickly on client pull. We're a small team; this employ owns a ample portion of the backend.

👥 Who you'll activity with

Coris was started by Vinodh (CEO) and Shyam (CTO). We've all spent 20+ years in fraud and hazard management, operating hazard engineering, operations and merchandise teams at Google, eBay, PayPal and WePay. You'll activity immediately alongside the two of us in Palo Alto.

🚀 Why this function matters

Every Coris decision runs on data we drag from fee processors and platforms: Stripe, Adyen, Fiserv, TSYS, Repay, and more, complete APIs, webhooks, and SFTP document feeds - hundreds of millions of events a month, billions of rows in Postgres. On top of that sits a signals and rules engine, an document graph, and the partner-facing API our customers build on.

Here's the honest type of the work. Files attain out of command complete SFTP. One processor sends dates in UTC, another in the merchant's local time, and neither says which. A processor's decrease code lives in a distinct pillar than the spec says. A webhook ships malformed JSON at 2am. A backfill employee runs out of recollection on a 40B-row table. A customer's hazard guide says "our numbers appearance off" and needs an answer today.

We're hiring the engineer who volition own all of that end-to-end, and who thinks it's the engaging part - since all integration we create reliably is one a competitor hasn't, and that's the moat. Getting the boring activity correct all period is how enduring companies are built!

🥷 What you'll do

• Own processor and phase integrations. Build and run ingestion from Stripe, Adyen, Fiserv, TSYS, Repay, and new processors as we sign them: APIs, webhooks, and lot document feeds. Make them correct, idempotent, observable, and PCI-safe.
• Own the partner API and customer-specific work. Extend the v2 API, build per-customer configuration and signals, and activity immediately alongside customers' hazard and ops teams to rotate a vague study into a fix, fast.
• Build the hazard engine. Add signals and rules to the DAG motor that scores merchants and transactions; scheme Postgres schemas, indexes, partitions, and aggregations that remain accelerated at billions of rows.
• Run it in prod. Celery workers, backfills, deadlocks, circuit breakers, latency, Sentry monitoring. You container it, you own it.
• Use AI everyplace it earns its place. Coding agents for archetypal drafts and refactors, AI assessment on all PR, and LLMs inner the merchandise (dispute agents, document parsing, organized extraction). You'll additionally assistance set the team's standards for how we use these tools.
• ~10–20% product-facing TypeScript/Next.js whenever a characteristic needs it.

📅 Your archetypal 90 days

• Week 1: your archetypal PR is merged and operating in production.
• 30 days: you're picking up significant features and shipping them.
• 60 days: you're taking on harder problems and construction genuine degree in one or additional parts of the system, akin processor integrations, the hazard motor or the partner API.
• 90 days: you're the go-to individual for a important part of the platform.

✅ You may be a fit if

Nobody matches all row below. If most of it appears akin you and the activity complete is the benevolent you desire to do, we'd akin to comprehend from you.

• A few years shipping manufacturing Python backends (Django/Postgres or close; 3+ years is a guide, not a cutoff). Ideally you've owned a scheme in manufacturing through growth: schema changes on ample tables, concurrency and locking bugs, backfills, on-call. You're at residence in SQL and can peruse a query plan, or desire to get extremely fine at it.
• An AI-native way of working. You attain for coding agents (Claude Code, Codex, Cursor or similar) by default, and you've built item alongside LLMs that group use, at activity or on your own. If you're before on this curve but moving fast, inform us what you've built.
• Judgment concerning what AI produces. You have a awareness for which changes to let an delegate run alongside (test scaffolding, a well-specified refactor, a immigration you can diff) and which to peruse row by row (anything touching money, cardstock data or PII, a schema alter on a huge table, locking and concurrency). You desire to comprehend all row of a diff before it ships, and you treat AI assessment comments as input, not instruction.
• You akin the schlep. Integrations, escalations, backfills, document formats from 1995. You see the form in the disorder and don't scorch out on it, since you comprehend why it's the product.
• Comfortable alongside customers. You'd be at ease, or eager to get there, on a call alongside a customer's hazard team, saying "give me an hour," and coming rear alongside the answer.
• Committed to the startup outcome. You desire equity that matters and you activity akin an owner: comfortable alongside ambiguity, alongside lengthy weeks whenever it counts, and alongside being in the area 5 days a week.

🙏 Nice to have

• Payments, merchant risk, underwriting, or fintech backdrop (Stripe, Adyen, Plaid, Ramp, a PayFac or ISO).
• Prior early-stage startup experience.
• Built a rules engine, indication pipeline, entity-linking / chart system, or characteristic store.
• SFTP and batch-file integrations, fixed-width or bequest formats, PCI-scoped data handling.
• Shipped LLM features in manufacturing - organized extraction, agents alongside tools, evals.

🤝 How we work

• Bias toward act and measurable impact; we container fast, measure, and iterate alongside genuine client feedback.
• Everyone owns their code in prod.
• AI-first development: agents write, humans own. Every PR gets an AI assessment and a individual one if necessary.
• In-person, 5 days a week in Palo Alto. Like any high-growth startup, we go fine beyond the customary 40–50 hours a week.

Coris is construction the AI tier for payments hazard and operations. We activity alongside the most aspiring platforms, marketplaces, and fee providers about the globe specified as GoFundMe, Kajabi, Clio and Cherry to automate and accelerate merchant onboarding, monitoring, and decision-making. What hazard teams lone imagined two years ago, Coris is delivering today alongside our hazard phase and AI agents.

We’re backed by Y Combinator, Lux Capital, Pathlight Ventures, and another world-class investors.

The Google Play Store is getting a big update for cars alongside Google built-in. On Tuesday, the company announced that the Play Store’s layout volition now automatically modify to the size and layout of your car’s display during making it easier to discover and download new apps.

The update, which is rolling out in the coming weeks, volition put featured apps on the Play Store’s homepage and authorize you to download them alongside a sole tap. It additionally comes alongside what Google says is a “streamlined navigation” list that puts “Apps,” “Games,” “Search,” and “More” options on the remaining flank of your display (or on the top if your car’s display has a vertical orientation).

Cars alongside Google built-in differ from the company’s infotainment functioning system, Android Automotive, in that it allows conveyance manufacturers to merge Google’s services and assistance for native Android apps immediately into their vehicles. The business brought an early type of its application integration to the Polestar 2, before rolling it out to the Honda Accord Touring in 2023.

Along alongside an overhauled Play Store, Google is additionally adding new apps, akin Starz, Firefox, Ludo King, and JioHotstar, that you can use during parked. Google volition additionally authorize you to observe YouTube videos on your dashboard whenever parked and continue listening to audio during driving, although this characteristic is lone accessible to YouTube Premium subscribers.

Google has been gradually adding additional apps and features to cars alongside Google built-in and recently replaced Google Assistant alongside Gemini AI throughout a range of supported vehicles from Ford, GM, Honda, Nissan, Volvo, and others.

The next period you vibe-code a tradition app alongside Google’s AI Studio, you won’t have to nexus your phone to a device to instal it. On Tuesday, Google announced a new feature in the US that volition authorize you to initiate individual apps immediately through the Play Store.

Google first launched the capability to vibe-code individual Android apps in May. Even although you can now initiate your tradition apps in Google Play, the business notes it volition continue to keep them “secure, private, and distinct from the community shop catalog.” You can additionally initiate them using a norm Google account, and the business volition shortly add the capability to portion your app alongside a choose collection of people, too.

Though this power create it easier to perform about alongside experimental apps, Google is positioning it as an on-ramp of sorts for fledgling developers. “If you desire to portion your app alongside the world, Google Play helps you alumnus to a expert developer document alongside the identical Trust & Safety reviews and high-quality standards as expert developers,” Sam Bright, Google’s vice chairman and broad director of Google Play, writes in the announcement.

Prompt:

There's a Diet Coke can rearward you and another on the dreary array on your left. Use the spot-mobile-manipulation accomplishment to choice them up and trash them in the dreary foot-pedal bin. Before strolling downward on the pedal, verify in the forefront cameras that your front-right ft mark is centered on the pedal, and use a 2-agent verification swarm (foot-verifier and hand-verifier) to verify the two the foot-pedal stage and the 3D hand-in-bin alignment before releasing.

Prompt:

[Attached: wrist_cam.png, obs.json] Task: Get the robot arm to reorient the mug upright and put the napkin inner the mug. ## Execution Discipline - Simply output 1 declaration study followed immediately by your `curl` command (with `# Plan:` on Turn 1 and `# Thought:` on all turn). Everything needed is in `obs.json` and the calibrated reticle/grid drawn on `wrist_cam.png`. ## Visual Coordinate Reference (Annotated on wrist_cam.png) - `+X` (UP in image): Away from robot basis | `-X` (DOWN): Towards robot base - `+Y` (LEFT in image): World remaining | `-Y` (RIGHT): World right - `+Z`: Height complete array | `Yellow numbers`: World (X, Y) in centimetres (2 cm grid at `grid_z`) - `Cyan + crosshair`: Current 3D compression center - `Cyan [ + ] brackets`: Current jaw beginning and decision line - `Green line`: Current entity long-axis inclination (`yaw_deg`: 0° = vertical alongside +X, 90° = horizontal alongside +Y) - `pitch_deg = 90°`: Straight downward (-Z) top-down grasp/place; `pitch_deg < 60°`: Tilted coaxial method alongside digit axis ## Closed-Loop Turn Workflow 1. Inspect current province & perspective `wrist_cam.png`: `curl -s http://127.0.0.1:8081/obs` (returns `ee_pos`, `holding`, `status`, `workspace`, `wrist_cam_path`). 2. Select VLM call granularity upfront on Turn 1: - Coarse (2–4 calls): `look_at` -> `grasp` -> `place` -> `done` - Pre-Grasp Hover Verify (4–7 calls): `look_at` -> `goto(z=0.04)` (verify cyan `[ + ]` brackets straddle target) -> `grasp` -> `place_until_contact` -> `done` - Fine Visual Servoing (8–20 calls): `look_at` -> `grasp` -> pre-pose `goto` -> closed-loop `move_by` / `move_until_contact` -> `done` 3. Send motion primitives (`POST /plan`) and verify visually following all rotate in the updated `wrist_cam.png`. If `status == "grasp_failed"`, call `robot.look_at(...)` to re-observe and regain closed-loop. ## Code-as-Policies Primitives (`POST http://127.0.0.1:8081/plan`) robot.look_at(x=0.35, y=0.13, z=0.14, grid_z=0.015) x, y = robot.deproject(u=0.52, v=0.48, z_plane=0.015) robot.grasp(x=x, y=y, z=0.015, yaw_deg=-35, pitch_deg=90) robot.place(x=0.24, y=0.06, z=0.075) robot.place_until_contact(x=0.24, y=0.06, z=0.015, max_lag=0.012, relief=0.002) robot.goto(x=0.28, y=0.05, z=0.04, arc_z=0.14, pitch_deg=25, azimuth_deg=90, roll_deg=0, speed=0.10) robot.move_by(forward=0.025, dz=-0.002, dpitch_deg=-3, droll_deg=5, speed=0.04) robot.move_until_contact(dz=-0.06, speed=0.03, max_lag=0.012, relief=0.002) robot.open_gripper() / robot.close_gripper() / robot.home() / robot.done()

Prompt:

Goal: {task_goal} You are steering a video earth example (Gemini Omni) in an iterative ocular feedback loop. Inspect the attached frames from the latest rollout and compose the next timestamped act immediate for the video earth model: - Break complex motions into one simple, single-direction act at a time. - Keep idle hands retracted and evade blocking the camera's perspective of objects in the workspace. - Explicitly province which areas of the workspace rotate into bare following an entity moves. - Use [MM:SS.mmm] timestamps for all act attack and cover your output in <omni_prompt> tags. Example format (sample timestamps only, not the complete prompt): <omni_prompt> [00:00.000] Character A's remaining hand rests at the flank during the correct hand reaches toward the mark object. [00:02.500] Character A's correct fingertips shove the entity smoothly into the center of the table, leaving its starting area empty. [00:05.000] Character A's correct hand opens and retracts to the flank so all placed objects remain in apparent perspective of the camera. </omni_prompt>

Prompt:

[Attached: robot_rollout_episode] Build a MuJoCo environment of the robot and reconstructed project objects. When all event is replayed example by frame, by driving the robot joints toward the logged observation.state alongside joint-space PD. Iterate using two verification objectives: (1) Visual match: MuJoCo renders from the head_left, left_wrist, and right_wrist cameras row up with, and appearance like, those genuine observations. (2) Physical match: the simulated communication force/torque on all Sharpa fingertip, in the sensor frame, matches the logged tactile_force in timing, dimension and direction. What you can tune happens before simulation starts: (1) Static entity assets: realistic geometry, collision shapes, mass, inertia, conflict and joints, alongside appearance near to the genuine objects, affirmative the first entity poses at t = 0. (2) Offline-planned robot commands, changed lone in frames anywhere the genuine fingertips are near an object. Each command must remain inside 0.55 rad (arm joints) and 0.65 rad (hand joints) of the logged teleop trajectory. Everywhere else, replay the logged trajectory unchanged. No sim cheating: project objects may move lone through genuine MuJoCo communication alongside the robot hands and the surroundings (mj_step). Not allowed following t = 0: changing MjModel parameters; applying external forces or torques to objects; welds or equality constraints; feedforward torques beyond joint-space PD; environment or rolling rear entity qpos/qvel; disabling or excluding collisions; reporting communication forces that don't arrive from the MuJoCo solver (for example, copying the logged tactile readings). Immediately current the reconstructed environment and rollout results as an interactive HTML meeting spectator alongside one leaf per episode, showing: - At the bottom: A synced timeline (play, scrub, speed control) that drives all panel. - On the remaining panel: An interactive 3D Blender environment showing the simulated robot and objects, showing communication points and power arrows as replay progresses. - On the correct panel: Real vs. sim camera views flank by flank for all 3 cameras. Real vs. sim digit power visualizations.

Prompt:

[Attached: egocentric_human_video.mp4, candidate_sim_views.png, hand_3d_keypoints.json] You are construction a physically solvable 500 Hz MuJoCo manipulation surroundings (`scene.xml`) and studio Blender render that faithfully reproduces the tabletop project shown in `egocentric_human_video.mp4` on a bimanual humanoid robot alongside 22-DoF dexterous five-fingered hands: 1. Scene Reconstruction & Camera Calibration: - Identify lone the manipulation-relevant objects (the grasped mark entity and the receiving receptacle or landmark) and triangulate their 3D tabletop coordinates from the egocentric camera intrinsics/extrinsics. - Replace crude bounding boxes alongside compound convex-decomposed 3D meshes so concave containers (nested bowls, recessed wooden trays, open bins) assistance bodily inner placement. 2. 500 Hz Dexterous Motion-Fitting Stress Test: - Retarget the 3D individual wrist and digit keypoints to the humanoid's 7-DoF arm and 22-DoF hand joints, and stage the trajectory in MuJoCo at 500 Hz (dt = 0.002s) alongside zero kinematic weld constraints. - Verify opposing thumb-and-finger power closure, spotless entity lift (> 2 cm), and stable final placement inner or beside the mark landmark.

Prompt:

[Attached: hand_drawn_throw_sketch.jpg] Translate this hand-drawn diagram into a physically verified humanoid simulation in `mujoco_warp` and a raytraced Blender Cycles video: 1. Scene & Kinematic Setup from Sketch: - Parse the spatial layout from `hand_drawn_throw_sketch.jpg`: location the 76-DoF humanoid (32-DoF build armed alongside two 22-DoF five-finger dexterous hands) at the base facing a array holding a 3-2-1 pyramid of 6 rigid mark boxes, alongside a projectile domain grasped in the correct five-finger hand. 2. Whole-Body Balance & Dexterous Throw Synthesis: - Synthesize a 50 Hz whole-body throwing trajectory (140 steps, 2.80 s) throughout four phases: (1) stable stance & 5-finger power grasp closure, (2) torso/shoulder wind-up alongside ankle-hip Center-of-Mass compensation, (3) onward whipping acceleration, and (4) synchronized 22-DoF five-finger beginning at highest hand speed so the domain launches on a ballistic arc into the pyramid's second-tier center of mass. - Verify in `mujoco_warp` communication discipline that the humanoid maintains upright balance (< 3 cm pelvis drift) and knocks downward all 6 mark boxes.

Prompt:

[Attached: Aloha2_Origami_20250309.mp4] Produce a time-aligned multi-granularity hindsight note JSON alongside four levels: 1. G1_episode_instruction: Concise imperative declaration describing the general manipulation goal. 2. G2_subtasks: Contiguous semantic subtask phases specifying which arm(s) anchor vs. fold/rotate the object. 3. G3_atomic_primitives: Fine-grained contact-level primitives (3–15s) aligned alongside grasp closure, crease sweeps, release, or retry attempts. 4. quality_control_and_reweighting: Hold task_progress_pct flat during non-progressing slip/delamination retry loops and downweight them (0.15x–0.25x) for Behavior Cloning.

Prompt:

# Prompt 1 — Physics-Accurate MuJoCo Environment & Benchmark Generation Make a MuJoCo surroundings anywhere a dual-arm robot (two 7-DoF Franka FR3 arms alongside parallel-jaw grippers on a shared tabletop) is tasked to resolve the traditional 12-ball balance measure importance puzzle from https://www.mathsisfun.com/pool_balls_solution.html. 1. Create 12 visually indistinguishable pond balls in a 2x6 starting rack (11 norm 150 g, 1 odd domain either 270 g or 30 g). 2. Build a functional two-pan Roberval balance measure at the center of the array alongside parallelogram equality constraints (`mjEQ_JOINT`) so the Left (Gold) and Right (Teal) pans remain horizontal, affirmative Red (Heavy) and Blue (Light) submission trays. 3. Track distinct settled weighings (`weighing_count`) and reward `1.00` for isolating the true odd domain in the accurate Heavy/Light tray inside <= 3 weighings. # Prompt 2 — Closed-Loop Vision-Only Solver Implement an autonomous closed-loop guideline relying strictly on stereo RGB camera inputs (`head_camera_left_rgb_image`, `head_camera_right_rgb_image`) and robot proprioception (`add_privileged_task_observations=False`): 1. Segment the 12 balls, Gold/Teal pans, and Red/Blue trays in the two stereo views and triangulate their 3D coordinates. 2. Visually peruse the balance measure tilt following all weighing from stereo vertical pixel disparity (+1 = remaining heavier, -1 = correct heavier, 0 = balanced). 3. Rotate the gripper wrist yaw by 90 degrees whenever grasping/placing inner the narrow measure pans to apparent the raised rims, and isolate the odd domain into the Red or Blue tray inside <= 3 weighings.

Prompt:

[Attached: umi_head.mp4, umi_left_gripper.mp4, umi_right_gripper.mp4] Look at 3 videos of individual wearing a gripper performing activities from head, left, correct gripper views. Look at the videos thoroughly and provision a detailed storyline of advancement at the project (with positive, neutral, negative slope), identifying crucial segments in the video and labeling the activities. Look at idiosyncratic frames or subsegments of the video if necessary to get a improved understanding.

Prompt:

[Attached: real_tabletop_photo.jpg] You are stated a sole overhead RGB camera photo (`real_tabletop_photo.jpg`) of a bimanual robot workspace (two 7-DoF Franka FR3 arms alongside parallel-jaw grippers and 4 RGB cameras) facing a array arranged alongside photobooth gathering props. 1. Reconstruct procedural 3D meshes and compound MuJoCo collision primitives for all 9 gathering props matching their real-world dimensions, colors, and resting poses, including spring-hinged deformable crown panels for the gentle plush cap. 2. Define a 77-task single-arm and bimanual syllabus (pick-and-place, uprighting, directional pushing, mid-air handovers, bimanual scoop-and-push, and pipelined array clearing). 3. Synthesize a closed-loop IK trajectory generator throughout ±10 cm stance randomization, verify `reward == 1.0` in MuJoCo physics, and export 4,500+ episodes for VLA fine-tuning.

Prompt:

# Stage 1 — Measure Objects & Dry-Run Plan (No Hardware Motion) Stage 1: measure objects — discover the objects and scheme the entire motion. The arm does not move in this stage. A. I put a LEGO masonry block and a box on the array in forefront of the arm, and the wooden box to one side. Find the masonry block and the wooden box in the image. And additionally in the genuine world. Show this in an annotated image. B. A dry-run scheme (no hardware). Write the planner to choice and location the lego block. Print the scheme table, diagram all poses onto the overhead image, and stop. Do not move the robot. Report alongside evidence. # Stage 2 — Pre-Pick Hover & Wrist-Camera Verification Stage 2: Put the gripper complete the masonry block without touching it, and verify alongside the wrist camera that the arm and the imagination agree. Please document executions alongside the two cameras (compressed video is OK). # Stage 3 (Autonomous Pick-and-Place → Self-Improvement → Optimization): 1. Execute autonomously: Turn the Stage 1 dry run into a genuine grasp, lift, and location into the wooden box. Run it yourself topic to safety rules from AGENTS.md. 2. Self-improve: When an attempt fails, diagnose it yourself. Review your own logs and wrist/overhead footage, discover the base cause, inspect the fix in a dry run, and retry. 3. Optimize on feedback: Once it succeeds, create it at smallest 2× faster inside safety limits (rule R5) unless I authorize more, or provision feedback.

Prompt:

[Attached: paper.pdf, optional author_checkpoints/ or urdf_assets/] Reproduce the document "<Paper Title & arXiv ID>" from scratch in MuJoCo WebAssembly and deploy a self-contained browser lab + ocular essay: 1. Match the paper's exact discipline timestep, actuator gains (kp/kv), torque limits, and kinematic constraints in MuJoCo XML, and run genuine neural conclusion or Web Worker iLQR in JavaScript at the paper's exact authority rates. 2. Build `index.html` (Spatial UI simulator alongside camera presets, live telemetry plots, and disturbance injection) and `scrollytelling.html` (interactive ocular essay). 3. Run a headless Chromium (Playwright) verification iteration asserting 0 console errors and non-zero project advancement complete 300+ frames.

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• Jennifer Pattison Tuohy
• Emma Roth

Global life expectancy is rising, definition a greater percent of the world's community is older adults.

However, not all nation has informed this elder surge equally.

The World Bank Group compiled data on the percent of the community old 65 or older for all nation in 2025, using data from the United Nations.

Business Insider rounded all percent to one decimal location and excluded non-countries. The data, for example, showed sizable older populations in territories specified as Puerto Rico and particular administrative regions specified as Hong Kong.

We additionally included data on the percentages of the masculine and female populations old 65 and older — additionally compiled from the World Bank Group.

The data overwhelmingly indicates that European countries have the highest numbers of older adults per capita. Japan is the lone non-European admission in the top 25.

In the US, which ranks 39th whenever excluding non-countries, 18.4% of the community is 65 and older.

A 2025 study by Brown University established that Europeans are additional apt to live longer than Americans, despite of prosperity level. The study's author, health economist Irene Papanicolas, stated systemic issues in the US, specified as "economic inequality or hazard factors akin stress, diet, or ecological hazards," contribute to lesser existence expectancy.

See the 25 countries alongside the highest percent of elders in their populations, listed from No. 25 to No. 1.

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• Mia Sato

Buzzy AI delegate startup Decagon could be the latest business swept up in a expanding motion of AI dealmaking.

The company, which was final appreciated at $4.5 billion before this year, has hired a financial institution to measure acquisition interest, according to group acquainted alongside the matter.

Decagon has held first discussions alongside haze application elephantine ServiceNow and additional companies, the group said. If a agreement does not materialize, Decagon could alternatively lift a new backing round.

The involvement in Decagon represents a new change in the AI boom: following years of huge project rounds and soaring valuations, tech companies are increasingly looking to buy their way into promising AI markets. Hugging Face was recently acquired by Nvidia for 12.9 billion, and OpenRouter was snapped up by Stripe for about $8 billion.

Growing AI-related businesses are frequently getting offers at a important premium to their final valuation, so if Decagon were to obtain an offer, it could be considerably higher than its former $4.5 milliard value.

Founded in July 2023 by Jesse Zhang and Ashwin Sreenivas, Decagon uses generative AI to automate customer assistance for endeavor customers, including Delta, Snapchat, and Ticketmaster. The business has raised complete $400 myriad from a roster of well-known Silicon Valley investors, including Andreessen Horowitz, Index Ventures, Bain Capital Ventures, and Accel. It competes alongside industry seasoned Bret Taylor's Sierra, which was appreciated at $15.8 milliard before this year.

A agreement would additionally arrive as ServiceNow looks to reassure investors that it can adapt to AI’s danger to traditional application businesses. Its shares tumbled before this twelvemonth during the so-called SaaS-pocalypse, whenever investors feared AI could erode application companies’ gain margins.

It was additionally punished for making a $7 milliard acquisition of the safety endeavor Armis, a agreement announced delayed final year. The inventory has since rebounded as ServiceNow continues to allocate heavily in AI and reshape parts of its business.

We’ve fair gotten a nearer appearance at Apple’s new video doorbell, safety cameras, a entrance lock, and thermostats following the company’s “Welcome Home” event on Tuesday. The six new devices are part of a new Apple Home Collection, which the business co-developed alongside LG Electronics and Schneider Electric.

The LG astute fastener expenses $349 and comes alongside assistance for Apple Home Key, allowing you to unlock your entrance hands-free alongside UWB. You can unlock the entrance alongside a PIN code, too, or by using the bodily keyhole below the keypad.

There’s additionally a new $249 video bell from LG. It comes alongside two cameras that assistance up to 2.5K resolution, allowing you to document your visitors as fine as see whenever packages are delivered to your doorstep. The bell has a motion-activated ray as well.

A brace of LG outdoor and indoor safety cameras athletics a tubular scheme that matches the video doorbell. The plug-in indoor camera expenses $199 and records videos in up to 2.5K resolution. It moves up and downward automatically, and you can set it to visage downward whenever you come back home. Meanwhile, Apple is releasing two versions of its wired outdoor camera: a $399 example alongside motion-activated floodlights and a cheaper $299 example without lights.

Lastly, we got a nearer appearance at Apple’s circular LG astute thermostat and a quadrate one from Schneider Electric. They disbursal $199 and assistance Matter.

At the center of Apple’s astute residence shove is Siri AI-enabled HomeView, the company’s archetypal astute residence display. You can authority all of the new devices alongside the HomeView, or another Apple products akin an iPhone.

Photography by Jennifer Pattison Tuohy / The Verge

Google says it's rear on the AI frontier, and now everyone can put that assertion to the test.

The business announced on Tuesday that its new Gemini 4 model, dubbed "Argon," is rolling out to the community and volition be accessible to Gemini app users and developers.

Google is positioning Argon as its new workhorse model, stating that it excels at coding, cybersecurity, and cognition activity in backing and law.

Tulsee Doshi, Gemini merchandise guide at Google DeepMind, called Argon a "powerhouse" example and stated it would be the archetypal of a family of new Gemini models rolling out complete time.

As is frequently the case alongside the most advanced AI models, Argon is accessible lone to paying users. It's rolling out to Pro and Ultra Gemini app subscribers, definition users volition have to pay at smallest $19.99 per duration to admission it. Those subscribers volition additionally see Argon in AI Mode during searching on Google. Developers volition additionally need to pay for the new model.

Google archetypal unveiled Argon final month, but it was lone accessible to a set of trusted partners to proactively test it and location cyber vulnerabilities.

The new example arrives amid heated competition between the big AI companies. Meta stated on Monday that its new Muse Wonder category of models would bring the business to the frontier, the industry term for the most advanced AI, during additionally powering its buzzy personal agent, Muse. Last month, OpenAI started rolling out its latest family of frontier models, GPT-6.

Argon gives Google an chance to reassert itself in the AI competition following the business chose not to initiate its previously-promised frontier model, Gemini 3.5 Pro.

Cornhole-playing robot

Google has been evaluation its new Gemini 4 example internally alongside workforce for multiple weeks and stated it had helped engineers addition the manufacturing of AI-generated code by 35%, during assisting enhance another Google products specified as Chrome.

"I really think it's been among the biggest equivalent changers for us," Doshi told Business Insider, referring to the risen amount of inner evaluation for Gemini 4. "Getting that feedback from Googlers who are using the example all day, I think has been the two helpful in conditions of fair improved understanding the example and improved understanding its strengths and weaknesses."

Doshi stated Argon was assisting to speed up robotics betterment inner Google, gratitude to the model's improved multimodal and reasoning skills. Google stated it trained a robot to perform cornhole by having Argon observe a individual participant and compute shared movements and spatial coordinates.

Google additionally stated it has stepped up efforts to forestall the example from behaving in ways it shouldn't, employing a scheme that tracks the model's sequence of idea to decide if it's behaving. "I think that's really really crucial since afterward you can really intervene. You can say, 'Okay, the example is really veering off in a way that's concerning. Let's halt here,'" stated Doshi.

"I think that gives us a much additional sturdy set of protections in launching this model."

While Google workforce are already testing improved versions of Argon internally, Doshi stated the example rolling out volition be the one the business announced benchmarks for final month. However, Google is making apparent that this is fair the commencement of its next big push.

"People should anticipate to see continued improvements coming from us wholesale," Doshi said.

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• Robert Hart

When my parents named me following my grandma, I don't think they realized how akin we'd be.

We're the two pop-culture-obsessed, affection a theme (her home is currently covered alongside the Halloween witches she's collected for decades), volition never rotate downward a dessert, and recommendation ruthless honesty our loved ones may or may not continually appreciate.

And now, we have one additional item in common: We the two act in h2o aerobics three times a week.

This 50-minute exercise has rotate into essential to our routine, allowing us to get gentle movement suitable for her — an 83-year-old alongside twice genu and hip replacements — and for me, a toddler mom alongside a second on the way.

My mom and grandma have been doing h2o aerobics for years, and I joined them during my archetypal pregnancy

Around 2021, my mom and grandparents started doing h2o aerobics alongside their longtime trainer, Alex. Although my gramps has since passed away, my mother and grandma kept the regular going.

I've been comparatively energetic my complete life, playing rivalrous tennis throughout my younker and afterward operating the TCS New York City Marathon in 2018. Before my archetypal pregnancy, I was a associate at Orangetheory and my local pilates studio, but shortly realized I needed to dilatory down.

Around 20 weeks pregnant, I noticed abdominal coning during I was in a plank position. It wasn't painful, but coning can be a sign of diastasis recti (a.k.a. the separation of my rectus abdominis muscles), and it motivated me to appearance for another, plank-free form of exercise.

So, my mom and grandma suggested I associate them in the pool, and I ended up loving it. But following welcoming my son in the autumn of 2023, I didn't have as much preparedness to associate them.

Now that I'm expecting alongside my second child, former a wildly nauseating archetypal trimester, and my son is in preschool, I awareness fortunate to have additional period to throw on a lawsuit and get in the pond again.

The exercise is ideal for all our needs, and has been a awesome way for us to connect

During all 50-minute session, Alex guides us through assorted exercises. I've established that lunges, squats, and bicep curls are so much additional relaxed in this environment.

Our pond weights are ray as a feather complete water, and have fairly a lot of opposition below. Alex additionally shares modifications to assistance alongside my grandma's ongoing sciatic nerve ache or to modify to my bodily needs.

All three of us awareness restored, reenergized, and stronger following all workout. Plus, it's been a awesome way for us to nexus without distractions.

Although my son has enriched all of our lives, whenever he's around, conversations among the four of us typically revolve about identifying dinosaur category or types of building vehicles, and negotiating for fair one more Popsicle.

However, for those precious 50 minutes in the pool, it's fair the three of us again; three generations getting several exercise and talking without day-to-day distractions.