A tech giant has teamed up with local artists, adding vibrant colors and quirky characters in an effort to humanize its futuristic ride. A Waymo self-driving car in Los Angeles wrapped with art by Tommii Lim. His cartoon cat is a stand-in for the target Waymo customer, who enjoys kicking back and relinquishing control of the wheel.

قامت وايمو بالتعاون مع فنانين محليين، بما في ذلك تومي ليم، لتغليف سياراتها ذاتية القيادة بفن نابض بالحياة، بهدف خلق صورة أكثر إنسانية وقابلية للتواصل لمركباتها الذاتية في لوس أنجلوس. تتضمن هذه المبادرة تصاميم غريبة تت reson مع العملاء المحتملين، مما يعزز شعور الراحة والثقة في التكنولوجيا.

Waymo se ha asociado con artistas locales, incluido Tommii Lim, para envolver sus coches autónomos en arte vibrante, con el objetivo de crear una imagen más relatable y humanizada de sus vehículos autónomos en Los Ángeles. Esta iniciativa presenta diseños caprichosos que resuenan con los clientes potenciales, promoviendo un sentido de comodidad y confianza en la tecnología.

Waymo a collaboré avec des artistes locaux, dont Tommii Lim, pour habiller ses voitures autonomes d'art vibrant, dans le but de créer une image plus relatable et humanisée de ses véhicules autonomes à Los Angeles. Cette initiative présente des designs fantaisistes qui résonnent avec les clients potentiels, promouvant un sentiment de confort et de confiance dans la technologie.

Waymo has partnered with local artists, including Tommii Lim, to wrap its self-driving cars in vibrant art, aiming to create a more relatable and humanized image of its autonomous vehicles in Los Angeles. This initiative features whimsical designs that resonate with potential customers, promoting a sense of comfort and trust in the technology.

How Art Is Driving Waymo’s Feel-Good Branding

A Los Angeles-based startup has demonstrated what it calls a breakthrough in hardware development: an artificial intelligence system that designed a fully functional Linux computer in one week — a process that would typically consume nearly three months of skilled engineering labor.<a href="https://www.quilter.ai/">Quilter</a>, which has raised more than $40 million from investors including Benchmark, Index Ventures, and Coatue, used its physics-driven AI to automate the design of a two-board computer system that booted successfully on its first attempt, requiring no costly revisions. The project, internally dubbed &quot;<a href="https://quilter.ai/project-speedrun#">Project Speedrun</a>,&quot; required just 38.5 hours of human labor compared to the 428 hours that professional PCB designers quoted for the same task.The announcement also marks the first public disclosure that <a href="https://www.buildc.com/">Tony Fadell</a>, the engineer who led development of the iPod and iPhone at Apple and later founded Nest, has invested in the company and serves as an advisor.&quot;We didn&#x27;t teach Quilter to draw; we taught it to think in physics,&quot; said Sergiy Nesterenko, Quilter&#x27;s chief executive and a former SpaceX engineer, in an exclusive interview with VentureBeat. &quot;The result wasn&#x27;t a simulation — it was a working computer.&quot;<h2>Circuit board design remains the forgotten bottleneck that delays nearly every hardware product</h2>The announcement shines a light on an unglamorous but critical chokepoint in technology development: printed circuit board layout. While semiconductors and software have received enormous attention and investment, the green fiberglass boards that connect chips, memory, and components in virtually every electronic device remain stubbornly manual to design.&quot;Besides auto-routers, the technology really hadn&#x27;t changed since the early &#x27;90s,&quot; Fadell told VentureBeat. &quot;The best boards are still made by hand. You go to Apple, they&#x27;ve got the tools, and these guys are just pushing traces, checking everything, doing flood fills—and you&#x27;re like, there&#x27;s got to be a better way.&quot;The PCB design process typically unfolds in three stages. Engineers first create a schematic — a logical diagram showing how components connect. Then a specialist manually draws the physical layout in CAD software, placing components and routing thousands of copper traces across multiple layers. Finally, the design goes to a manufacturer for fabrication.That middle step — the layout — creates a persistent bottleneck. For a board of moderate complexity, the process typically consumes four to eight weeks. For sophisticated systems like computers or automotive electronics, timelines stretch to three months or longer.&quot;The timeline was always this elastic thing—they&#x27;d say, &#x27;Yeah, that&#x27;s two weeks minimum,&#x27;&quot; Fadell recalled of his experience at Apple and Nest. &quot;And we&#x27;d say, &#x27;No, no. Work day and night. It&#x27;s two weeks.&#x27; But it was always this fixed bottleneck.&quot;The consequences ripple through hardware organizations. Firmware teams sit idle waiting for physical boards to test their code. Validation engineers cannot begin debugging. Product launches slip. According to Quilter&#x27;s research, only about 10 percent of first board revisions work correctly, forcing expensive and time-consuming respins.<h2>Project Speedrun put Quilter&#x27;s AI to the test with an 843-component computer that booted on the first try</h2><a href="https://quilter.ai/project-speedrun#">Project Speedrun</a> was designed to push the technology to its limits while producing an easily understood result: a working computer that could boot Linux, browse the internet, and run applications.The system consists of two boards based on<a href="https://www.nxp.com/design/design-center/development-boards-and-designs/8MMINILPD4-EVK"> NXP&#x27;s i.MX 8M Mini</a> reference platform, a processor architecture used in automotive infotainment, industrial automation, and machine vision applications.The main system-on-module contains a quad-core ARM processor running at 1.8 gigahertz, 2 gigabytes of LPDDR4 memory, and 32 gigabytes of eMMC storage. A companion baseboard provides connectivity including Ethernet, USB, HDMI, and audio.Together, the boards incorporate 843 components and 5,141 electrical connections, or &quot;pins,&quot; routed across eight-layer circuit board stackups manufactured by Sierra Circuits in California. The minimum trace geometry reached 2 mils (two-thousandths of an inch) on the system-on-module — fine enough to require advanced high-density interconnect manufacturing techniques.Quilter&#x27;s AI completed the layout with approximately 98 percent routing coverage and zero design rule violations. Both boards passed power-on testing and successfully booted <a href="https://www.debian.org/">Debian Linux</a> on the first attempt.&quot;We made an entire computer to demonstrate that this technology works,&quot; Nesterenko said. &quot;We took something that&#x27;s typically quoted at 400 to 450 hours, automated the vast majority of it, and reduced it to about 30 to 40 hours of cleanup time.&quot;The cleanup time is work that human engineers still perform: reviewing the AI&#x27;s output, fixing any issues, and preparing final fabrication files. But even with that overhead, the total elapsed time from schematic to fabricated boards collapsed from the typical 11 weeks to a single week.<h2>Unlike ChatGPT, Quilter&#x27;s AI learns by playing billions of games against the laws of physics</h2>Quilter&#x27;s <a href="https://www.quilter.ai/product">technical approach</a> differs fundamentally from the large language models that have dominated recent AI headlines. Where systems like GPT-5 or Claude learn to predict text based on massive training datasets of human writing, <a href="https://www.quilter.ai/product/technology">Quilter&#x27;s AI</a> learns by playing what amounts to an elaborate game against the laws of physics.&quot;Language models don&#x27;t apply to us because this is not a language problem,&quot; Nesterenko explained. &quot;If you ask it to actually create a blueprint, it has no training data for that. It has no context for that.&quot;The company also rejected the seemingly obvious approach of training on examples of human-designed boards. Nesterenko cited three reasons: humans make frequent errors (explaining why most boards require revisions), the best designs are locked inside large companies unwilling to share proprietary data, and training on human examples would cap the AI&#x27;s performance at human levels.Instead, Quilter built what Nesterenko describes as a &quot;game&quot; where the AI agent makes sequential decisions — place this component here, route this trace there — and receives feedback based on whether the resulting design satisfies electromagnetic, thermal, and manufacturing constraints.&quot;What you&#x27;re really changing is not the probability of getting a very specific outcome of the model, but the probability of choosing a certain action based on that experience,&quot; Nesterenko said.The approach mirrors DeepMind&#x27;s progression with its Go-playing systems. The original <a href="https://deepmind.google/research/alphago/">AlphaGo</a> learned from human games, but its successor <a href="https://deepmind.google/research/alphazero-and-muzero/">AlphaZero</a> learned purely through self-play and ultimately surpassed human capability. Quilter harbors similar ambitions.&quot;In the long term, to come up with better designs for circuit boards than humans have ever tried to do,&quot; Nesterenko said.Fadell drew a parallel to an earlier technological transition: &quot;I remember this with assembly. You had assembly and compilers, and engineers would say, &#x27;I can&#x27;t trust the compiler. I&#x27;m going to do the loop unrolling myself.&#x27; Now very, very few people write any assembly.&quot;He expects PCB design to follow a similar arc: &quot;I hope the same thing happens with PCB design. Sure, a few people will hold out, but these tools are going to get so good that everyone else will move on.&quot;<h2>Fadell and Nesterenko spent months solving a delicate problem: how to automate design without stripping engineers of control</h2>Automating a task that skilled professionals have performed manually for decades raises an obvious question: how do engineers maintain control over designs that will ultimately ship in products where reliability matters?Fadell said he spent significant time with Nesterenko working through this tension. The solution, he said, lies in allowing users to choose their level of involvement at each stage of the process.&quot;If you&#x27;re a control freak, you can be a control freak. If you want to say &#x27;just do it for me,&#x27; you can do that too—and everything in between,&quot; Fadell said. &quot;You can walk through each phase of the design and get involved wherever you want, or let the AI handle it.&quot;The workflow breaks into three phases: setup, where engineers define constraints and requirements; execution, where the AI generates candidate layouts; and cleanup, where humans review and refine the output. Engineers can intervene at any point, adjusting constraints and regenerating designs until they&#x27;re satisfied.&quot;This is something Tony and I talk about a lot,&quot; Nesterenko said. &quot;How do we give users control while still automating most of the work?&quot;<h2>Quilter&#x27;s technology has clear boundaries: 10,000 pins and 10 gigahertz mark the current limits</h2>The technology has clear limitations. <a href="https://www.quilter.ai/">Quilter</a> currently handles boards with up to roughly 10,000 pins — sufficient for a wide range of applications but well short of the most complex designs, which can exceed 100,000 connections.Physics complexity also creates boundaries. The system handles high-speed communications up to approximately 10 gigahertz, covering typical consumer electronics and many industrial applications. But advanced systems like sophisticated radar, which can operate at 100 gigahertz, exceed current capabilities.&quot;There are boards where Quilter won&#x27;t make enough progress to make the cleanup time worthwhile,&quot; Nesterenko acknowledged. &quot;We&#x27;re just not that helpful yet with the most advanced, sophisticated designs.&quot;The company has focused initially on categories where speed matters more than extreme complexity: test fixtures, evaluation boards, design validation boards, and environmental test hardware. These boards often sit in long queues behind higher-priority production designs, delaying engineering programs.<h2>The company bets that engineers will pay the same price for a 10x speed improvement</h2>Quilter <a href="https://www.quilter.ai/pricing">prices its service by pin count</a>, matching the billing conventions that already exist when companies hire outside layout specialists. The pitch to customers is cost neutrality with a ten-fold improvement in speed.&quot;We&#x27;re going to charge you roughly the same that you would pay for the pins that you would with a person,&quot; Nesterenko said. &quot;But the reason you choose us is that we do this 10 times faster.&quot;For a company waiting three months for a board layout, receiving it in a week fundamentally changes what&#x27;s possible. Engineering teams can run multiple design experiments in parallel. Firmware developers get hardware faster. Products reach the market sooner.The company offers free access for hobbyists, students, and small businesses with less than $50,000 in revenue — a strategy to build familiarity while targeting enterprise customers for commercial revenue.<h2>The iPod creator waited years to attach his name to Quilter — until he could prove the technology actually works</h2>Fadell said he chose this moment to publicly acknowledge his investment because the <a href="https://quilter.ai/project-speedrun#">Project Speedrun</a> demonstration provides concrete evidence that the technology works.&quot;It&#x27;s not about being comfortable—I was always comfortable with the team,&quot; he said. &quot;This was about waiting until we had something you could hang your hat on. Now I can say, &#x27;I&#x27;ve used the tool. I&#x27;ve seen it.&#x27;&quot;He contrasted his approach with typical investor announcements: &quot;Every investor goes, I invested in this, it&#x27;s gonna change the world. It&#x27;s like, no, I know better. I&#x27;ve used the tool. I know people who use it. I asked my startups to use the tool.&quot;Fadell&#x27;s involvement goes beyond capital. He described email exchanges running to &quot;a dozen pages of details&quot; covering product design, user experience, enterprise sales, and technical architecture.&quot;Of all the investors I work with, Tony by far goes deepest with me on the product side,&quot; Nesterenko said.<h2>If Quilter succeeds, it could unlock a new generation of hardware startups that were never economically viable before</h2>The stakes extend far beyond one company&#x27;s product roadmap. If Quilter&#x27;s technology scales, it could fundamentally alter the economics of building physical products.Fadell argued that hardware development has historically moved slowly because each step in the process — schematic design, PCB layout, manufacturing, assembly — created friction. Other innovations have already smoothed schematic tools and manufacturing. Layout remained the stubborn holdout.&quot;Once you shrink that from weeks to hours, you can iterate so much faster because all the other friction in the chain has been reduced,&quot; Fadell said.He predicted the technology would eventually extend upstream into schematic design itself, with AI that understands both logical connections and physical constraints helping engineers avoid problems earlier in the process.At MIT, where Fadell now spends time, he encounters would-be founders who have abandoned hardware ambitions because the process seemed insurmountable.&quot;I talk to professors and startup founders, and they say, &#x27;I&#x27;m never doing hardware. It&#x27;s too hard,&#x27;&quot; he said. &quot;I hope we can make it easier for more people to jump in and try things.&quot;Industry veterans remain skeptical. Auto-routing tools — previous attempts at automation — became notorious for producing unusable results, spawning T-shirts proclaiming engineers would &quot;<a href="https://www.eevblog.com/forum/chat/chris-gammel_s-_never-trust-the-autorouter-shirt_-on-teespring/">never trust the auto-router</a>.&quot;Nesterenko has seen the skepticism dissolve in real time. He described a recent meeting with executives from a major customer who came to discuss Quilter&#x27;s capabilities. As the conversation unfolded, one executive picked up the <a href="https://quilter.ai/project-speedrun#">Project Speedrun</a> boards and began photographing them from every angle, turning them over in his hands.&quot;He was just fascinated by the fact that this is possible now,&quot; Nesterenko said.The question is no longer whether AI can design circuit boards. A working Linux computer, assembled from 843 components and booted on the first attempt, answers that definitively. The question now is what engineers will build when layout stops being the bottleneck — when hardware, as Fadell put it, finally &quot;moves at the speed of thought.&quot;On that point, Nesterenko offered a prediction. &quot;If you ask the average electrical engineer today whether automation or AI could at all help with the board of this complexity, they would say no,&quot; he said. For decades, they would have been right. As of last week, they&#x27;re not.

حققت شركة ناشئة في لوس أنجلوس تُدعى كويلتر إنجازًا كبيرًا من خلال استخدام الذكاء الاصطناعي لتصميم جهاز كمبيوتر يعمل بنظام لينكس يتكون من 843 جزءًا، حيث أكملت المهمة في أسبوع واحد فقط. عادةً ما يتطلب هذا العملية ما يقرب من ثلاثة أشهر من العمل الهندسي المهاري. وقد أسفر المشروع، المعروف باسم Project Speedrun، عن جهاز كمبيوتر تم تشغيله بنجاح في المحاولة الأولى مع الحد الأدنى من التدخل البشري.

Una startup de Los Ángeles, Quilter, ha logrado un hito significativo al utilizar inteligencia artificial para diseñar una computadora Linux completamente funcional compuesta por 843 partes, completando la tarea en solo una semana. Este proceso normalmente requiere casi tres meses de trabajo de ingeniería calificada. El proyecto, conocido como Project Speedrun, resultó en una computadora que se inició con éxito en su primer intento con mínima intervención humana.

Une startup basée à Los Angeles, Quilter, a réalisé une avancée majeure en utilisant l'intelligence artificielle pour concevoir un ordinateur Linux entièrement fonctionnel composé de 843 pièces, achevant la tâche en seulement une semaine. Ce processus nécessite généralement près de trois mois de travail d'ingénierie qualifiée. Le projet, connu sous le nom de Project Speedrun, a abouti à un ordinateur qui a réussi à démarrer dès sa première tentative avec un minimum d'intervention humaine.

A Los Angeles-based startup, Quilter, has achieved a significant milestone by using artificial intelligence to design a fully functional Linux computer consisting of 843 parts, completing the task in just one week. This process typically requires nearly three months of skilled engineering labor. The project, known as Project Speedrun, resulted in a computer that successfully booted on its first attempt with minimal human intervention.

Quilter's AI just designed an 843‑part Linux computer that booted on the first try. Hardware will never be the same.

arXiv:2512.08223v1 Announce Type: new 
Abstract: With the rise of Large Language Models (LLMs) such as GPT-3, these models exhibit strong generalization capabilities. Through transfer learning techniques such as fine-tuning and prompt tuning, they can be adapted to various downstream tasks with minimal parameter adjustments. This approach is particularly common in the field of Natural Language Processing (NLP). This paper aims to explore the effectiveness of common prompt tuning methods in 3D object detection. We investigate whether a model trained on the large-scale Waymo dataset can serve as a foundation model and adapt to other scenarios within the 3D object detection field. This paper sequentially examines the impact of prompt tokens and prompt generators, and further proposes a Scene-Oriented Prompt Pool (\textbf{SOP$^2$}). We demonstrate the effectiveness of prompt pools in 3D object detection, with the goal of inspiring future researchers to delve deeper into the potential of prompts in the 3D field.

تم تقديم ورقة جديدة بعنوان 'SOP^2: التعلم بالنقل مع مجموعة من الموجهات الموجهة للمشهد في الكشف عن الكائنات ثلاثية الأبعاد' تستكشف تطبيق تقنيات التعلم بالنقل، وخاصة ضبط الموجهات، لتحسين نماذج الكشف عن الكائنات ثلاثية الأبعاد. تدرس الدراسة قابلية التكيف لنموذج تم تدريبه على مجموعة بيانات Waymo لسيناريوهات مختلفة في هذا المجال، مقترحة مجموعة موجهات موجهة للمشهد لتحسين دقة الكشف.

Un nuevo artículo titulado 'SOP^2: Aprendizaje por transferencia con un pool de prompts orientados a la escena en la detección de objetos 3D' explora la aplicación de técnicas de aprendizaje por transferencia, especialmente el ajuste de prompts, para mejorar los modelos de detección de objetos 3D. El estudio investiga la adaptabilidad de un modelo entrenado en el conjunto de datos de Waymo a varios escenarios en este campo, proponiendo un Pool de Prompts Orientados a la Escena para mejorar la precisión de detección.

Un nouvel article intitulé 'SOP^2 : Apprentissage par transfert avec un pool de prompts orientés scène pour la détection d'objets 3D' explore l'application des techniques d'apprentissage par transfert, en particulier le réglage des prompts, pour améliorer les modèles de détection d'objets 3D. L'étude examine l'adaptabilité d'un modèle formé sur le jeu de données Waymo à divers scénarios dans ce domaine, proposant un pool de prompts orientés scène pour améliorer la précision de détection.

A new paper titled 'SOP^2: Transfer Learning with Scene-Oriented Prompt Pool on 3D Object Detection' explores the application of transfer learning techniques, particularly prompt tuning, in enhancing 3D object detection models. The study investigates the adaptability of a model trained on the Waymo dataset to various scenarios in this field, proposing a Scene-Oriented Prompt Pool to improve detection accuracy.

SOP^2: Transfer Learning with Scene-Oriented Prompt Pool on 3D Object Detection

<img width="542" height="271" src="https://insideainews.com/wp-content/uploads/2025/12/saviynt-logo-2-1-122025.png" class="webfeedsFeaturedVisual wp-post-image" alt="" style="float: right; margin-left: 5px;" link_thumbnail="" decoding="async" loading="lazy" srcset="https://insideainews.com/wp-content/uploads/2025/12/saviynt-logo-2-1-122025.png 542w, https://insideainews.com/wp-content/uploads/2025/12/saviynt-logo-2-1-122025-150x75.png 150w, https://insideainews.com/wp-content/uploads/2025/12/saviynt-logo-2-1-122025-300x150.png 300w" sizes="auto, (max-width: 542px) 100vw, 542px" />LOS ANGELES and NEW YORK, Dec. 9, 2025 – Saviynt, an identity security company, today announced a $700M Series B Growth Equity Financing at a valuation of approximately $3 billion. Funds managed by KKR, a global investment firm, led the round with participation from Sixth Street Growth and TenEleven, as well as new funding from [&#8230;]

أعلنت شركة Saviynt، المتخصصة في أمان الهوية، عن جمع 700 مليون دولار في جولة تمويل نمو من الفئة B، مع تحقيق تقييم يقارب 3 مليارات دولار. قاد الجولة صندوق KKR، وهو شركة استثمار عالمية، بمشاركة من Sixth Street Growth وTenEleven.

Saviynt, una empresa de seguridad de identidad, ha recaudado con éxito 700 millones de dólares en una ronda de financiamiento de crecimiento de Serie B, alcanzando una valoración de aproximadamente 3 mil millones de dólares. La financiación fue liderada por KKR, una firma de inversión global, con la participación de Sixth Street Growth y TenEleven.

Saviynt, une entreprise spécialisée dans la sécurité des identités, a réussi à lever 700 millions de dollars lors d'un tour de financement de croissance de série B, atteignant une valorisation d'environ 3 milliards de dollars. Le financement a été dirigé par KKR, une société d'investissement mondiale, avec la participation de Sixth Street Growth et TenEleven.

Saviynt, an identity security company, has successfully raised $700 million in a Series B Growth Equity Financing round, achieving a valuation of approximately $3 billion. The funding was led by KKR, a global investment firm, with participation from Sixth Street Growth and TenEleven.

How Art Is Driving Waymo’s Feel-Good Branding

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