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As millions of humanoid robots enter homes, hospitals, hotels, retail, manufacturing, andpublic spaces, they'll need more than software updates—they'll need appearance maintenance. Who develops synthetic skin cosmetics?
Who restores realistic facial pigmentation?
Who creates protective coatings against UV, wear, stains, and microbes?
Who designs seasonal appearances, premium finishes, cosmetic upgrades, and personalized aesthetics?
Who certifies compatibility between cosmetic materials and different robot models? This isn't traditional cosmetics. It's thebeginning of a new discipline at the intersection of robotics, materials science, chemistry, industrial design, and AI. The industry will require:
• Specialized cosmetic formulations for synthetic skin
• Global product catalogs and compatibility databases
• Research repositories and technical standards
• Certified maintenance professionals
• Digital marketplaces connecting manufacturers and service providers
• Education, training, and certification platforms. believes we'll eventually see an entire digital ecosystem dedicated to humanoid cosmetics—where researchers, manufacturers, technicians, designers, and consumers collaborate to shape how robots look, feel, and age. Enter the digital asset of the future: The robots of tomorrow won't just be intelligent. They'll have a lifecycle of care, restoration, personalization, and cosmetic innovation. The future isn't just humanoid robotics. It's Humanoid Cosmetics. #HumanoidRobots# #Robotics# #Cosmetics# #SyntheticSkin# #MaterialsScience# #RobotDesign# #HumanRobotInteraction# #CosmeticScience# #FutureTech# #Innovation# #Automation# #DigitalTransformation# #EmergingTechnology# #TechStartups# #Industry40#

Today at #GalaxyUnpacked#, we shared new @Android and Gemini updates coming to the latest @SamsungMobile devices. You’ll be able to: ✨ Get next-generation task automation with Gemini Intelligence, now expanding to more than 40 popular apps. ✨ Manage deep projects and synthesize complex information with @Gemini_Notebook (formerly NotebookLM), which comes preinstalled on Samsung's new foldables. ✨ Take Gemini on the go with upcoming watches and glasses — including the Galaxy Watch 9 and two new intelligent eyewear frames from @_GentleMonster_ and @WarbyParker’s collections coming this fall.

📰 AI clones steal bloggers' faces, followers & money Digital copies don't just beat originals in popularity they often get the real accounts blocked American adult actress Sheri Devile (4.5M Instagram followers) keeps finding fake versions of herself. Her real page gets banned because scammers report it themselves, wanting to "become the main version of me." Journalists uncovered that this "AI identity theft" has turned into a full industry. Creators copy real women's bodies, movements, and expressions from videos, then build synthetic characters One big example: virtual model Nia Noir gained 2.7M TikTok followers before the account was deleted. Same story with 21-year-old Sierra Leone TikToker Josephine - she saw her old dance performed by an AI model with identical moves, transitions, and camera angles. The copy got way more views Now real models must compete with hyper-sexualized clones of themselves that post non-stop and do things no real person would ever agree to #AIClones# #VirtualInfluencers# #ContentTheft#

GROK 4.5 IS NOW ON THE FRONTIERCODE LEADERBOARD Cognition just launched the FrontierCode leaderboard — a new benchmark specifically designed to track which AI models are writing code you’d actually merge into production. Unlike many synthetic benchmarks, FrontierCode focuses on real-world usability, with full methodology and sample tasks publicly available. Grok 4.5 is included in the rankings alongside other top models. This is another step toward measuring what actually matters for developers: code that works, integrates cleanly, and solves real problems.

kimi k3 vs gpt 5.6 sol vs fable 5 vs grok 4.5 @Kimi_Moonshot just dropped kimi k3 – a 2.8t param native multimodal model, the first open 3t-class release. key facts: • 1m token context. stable latentmoe activating 16 of 896 experts, built on kimi delta attention (kda) and attention residuals • quantization-aware training from the sft stage onward – mxfp4 weights, mxfp8 activations. moonshot claims ~2.5x scaling efficiency over k2 • max thinking effort by default. low- and high-effort modes are "coming in updates" – there is no way to turn the thinking down today, and you feel it in every run • pricing: $0.30/mtok cache-hit input, $3.00/mtok cache-miss, $15.00/mtok output. claims >90% cache hit rate on coding workloads • benchmarks: swe marathon 42.0 (1st – fable 5: 35.0, sol: 39.0, opus 4.8: 40.0), terminal bench 2.1 88.3, browsecomp 91.2 (1st), program bench 77.8 (1st), gpqa-diamond 93.5. loses frontierswe 81.2 vs fable's 86.6, and deepswe 67.5 vs sol's 73.0 our test – 3 prompts, single-file html, @threejs, fully procedural, no assets: 1. photorealistic european roulette wheel – 37 pockets in the real sequence, mahogany clearcoat bowl, chrome turret, diamond deflectors, flick-to-spin, ball that spirals inward and settles on a mathematically real number 2. las vegas slot machine – 3 reels behind transmissive glass, drag the chrome lever to play, mechanical odometer counters modelled in 3d, coin physics on win 3. full pinball table – 6.5° tilted playfield, flipper impulse physics, spline ramps, drop targets, 6 bumpers, mechanical score reels in the backbox we ran the test on @aimlapi platform results: - cost #1# grok 4.5 – $0.30 #2# kimi k3 – $0.71 #3# gpt 5.6 sol – $2.05 #4# fable 5 – $7.69 - tokens #1# grok 4.5 – 34,241 #2# gpt 5.6 sol – 51,748 #3# fable 5 – 144,126 #4# kimi k3 – 157,999 - lines of code #1# gpt 5.6 sol – 3,054 #2# grok 4.5 – 3,047 #3# kimi k3 – 2,255 #4# fable 5 – 1,950 - generation time #1# grok 4.5 – 5.1 min #2# gpt 5.6 sol – 22.0 min #3# fable 5 – 31.5 min #4# kimi k3 – 75.6 min observations: • kimi k3 is cheap and it is slow. 75.6 minutes across three prompts against grok's 5.1. it is 2.4x grok's price and 15x grok's wall clock. the roulette took 15 min, the slot 18, the pinball 42 • it failed 2 of 3. only the roulette works. the slot machine has reel cutouts on both faces of the cabinet and the symbols face backwards – you can only read your spin by walking around to the rear of the machine. the pinball table stands vertically on its edge with the legs floating detached beside it. • 81% of kimi's output tokens are reasoning, not code. grok: 22%. you are not paying for a bigger answer, you are paying for a longer argument with itself • price per 100 shipped lines – grok $0.010, kimi $0.031, sol $0.067, fable $0.394. a 39x spread for the same three files kimi k3's code quality: upsides: • the roulette is genuinely good – procedural wood grain with real specular breakup, correct european sequence (0-32-15-19-4...), chrome turret, diamond deflectors, clean console • the pinball artwork is the best in the test – a synthwave "nova strike / deep space" field with six individually coloured neon bumper rings, a retro sun on a grid horizon, a nova burst, and a scoring legend printed on the apron. no other model printed the rules on the machine. it is a beautiful texture on a broken object • physics reasoning is real – it derived a 480hz substep for the collider, worked out ball settle conditions and termination guarantees, and checked every ramp exit vector by hand before writing any of it • it is the only model that saw the importmap trap coming. sol shipped a blank white page twice because three.js addons import the bare specifier 'three' and die without an import map downsides: • it dodged that trap on the slot by loading three.js r128 through classic script tags – a 2021 build with no working transmission. its slot glass rendered fully opaque and buried all three reels behind a white pane. the code asks for transmission: 0.93, ior: 1.5 – correct, and silently ignored by a renderer that predates the feature • after 42 minutes and 212k characters of reasoning, the pinball cabinet is not assembled. the table stands vertically on its edge like a wardrobe – the prompt asked for 6.5° from horizontal, it delivered 90°. the legs float detached in the void beside it. head-on it photographs beautifully; orbit ten degrees and it is a painted slab with four chrome rods hovering nearby • the playfield z-fights with the glass – hard black banding across the whole field as soon as you pull the camera back a note on the pinball, in fairness to kimi: nobody passed it. every model shipped broken ball physics and controls you cannot trust. it is the hardest prompt we have run and the whole field failed it, each in its own way kimi k3 reasons better than anything else here and it shows exactly where reasoning pays – physics constants, sequences, edge cases, traps the others walked into follow @thehypedotnews for 24/7 ai news, analysis and breakdowns

These features are available for Google AI Pro, Ultra and Workspace business customers. 🧍 Personal avatars are restricted to a user’s specific Google account and likeness (18+ in select regions). 🛡️ For built-in transparency, every generated clip includes an invisible SynthID digital watermark to verify that it's AI-generated. Learn more ↓

Mark Zuckerberg explains the 405B teacher-model flywheel that could make one giant AI the wrong end state "People are gonna wanna do inference directly on the 405 because it's, you know, by our estimates, it's gonna be about 50% cheaper, I think, than GPT-4o to do that directly." "Because it's open weights, the ability to take the model and distill it down to whatever size that you want, to use it for synthetic data generation, to use it as a teacher model." "Our vision is that there should be lots of different models. I think every startup out there, every enterprise, governments, they all kind of wanna have their own custom models." "Right now, as open source basically closes the gap, I think you're just gonna see this wide proliferation of models where people now have the incentive to basically customize and build and train exactly the right size model for what they're doing, train their data into it." "They're gonna have the tools to do it because of a lot of the partner integrations that the companies like Amazon are doing with AWS or Databricks or different folks like that who are building these whole suites of services for distilling and fine-tuning open models." The counterintuitive edge is that the 405B model may be most valuable as raw material, not an endpoint. The open model compresses into the right size, absorbs proprietary data, and turns one frontier release into thousands of company-specific systems. Distribution of intelligence beats centralization. - Mark Zuckerberg (@finkd), CEO of Meta, with @rowancheung