https://aimostall.com/news/ AI news briefs, tutorials, and source links from the AimostAll AI News feed. en Fri, 15 May 2026 15:01:04 +0000 https://aimostall.com/news/item/codex-is-very-good-but-it-is-still-a-very-developer-coded-interface-for--0a3f2f1c/ 0a3f2f1c7dee5e234b7ea23c7dcb6d7d2c9e09fa Fri, 15 May 2026 14:34:01 +0000 Codex is very good, but it is still a very "developer coded" interface for an everything app. And it continues the somewhat annoying AI perspective that non-coders are just not as competent and need stuff hidden from them, as opposed to requiring a different form of complexity. Ethan Mollick Developer Tools https://aimostall.com/news/item/top-stories-in-tech-today-this-startup-wants-to-sell-space-made-drugs-an-e0ba2d73/ e0ba2d73981f6750d9f3acb2650092e46ec81a28 Fri, 15 May 2026 14:31:12 +0000 Top stories in tech today: - This startup wants to sell space-made drugs - Anduril just doubled its valuation to $61B - Ray-Ban Display’s neural writing opens to everyone - NASA unveils new details on Artemis III - Quick hits on other tech news The Rundown AI AI Startups https://aimostall.com/news/item/translating-claude-s-thoughts-into-language-59549dd6/ 59549dd6495d954bebb70be90380650e8f66433d Fri, 15 May 2026 15:01:04 +0000 112K 7d ago Anthropic Foundation Models https://aimostall.com/news/item/runway-started-by-helping-filmmakers-now-it-wants-to-beat-google-at-ai-d71cb71c/ d71cb71c84585471b3c79560695cc3e8b2537b3b Fri, 15 May 2026 14:00:00 +0000 AI video generation startup Runway is betting that video generation is the path to world models. And that being an AI outsider is an advantage, not a liability. TechCrunch AI Creative AI https://aimostall.com/news/item/x-ai-plays-catch-up-with-grok-build-its-first-terminal-based-coding-agen-be47185f/ be47185ff9570269dfd848f7c23607a1df967c02 Fri, 15 May 2026 13:58:14 +0000 Elon Musk's AI company x.AI is jumping into the coding agent space with Grok Build, a new terminal-based tool. The article x.AI plays catch-up with Grok Build, its first terminal-based coding agent appeared first on The Decoder . The Decoder Developer Tools https://aimostall.com/news/item/pennsylvanians-use-town-hall-meeting-to-rail-against-data-center-boom-cea7efc3/ cea7efc38254626f6e564f3f6ddc33b6957b38f1 Fri, 15 May 2026 13:51:04 +0000 “This is a public trust and transparency issue.” Ars Technica AI Developer Tools https://aimostall.com/news/item/dependably-for-llm-agent-failures-34b67415/ 34b67415da9058269e441267d0156279d4358205 Fri, 15 May 2026 13:27:05 +0000 “Dependably for LLM agent failures” Saurabh (@sauvast) @hwchase17 Started on this and finding it awesome; also LangSmith engine sparked an idea. The "Dependabot like for LLM agent failures". LangSmith Engine gives you the smoke detector. The natural next layer is a sprinkler system; an auto-remediation with a human approval gate. A four-stage pipeline comes to mind: Classify → Patch → Eval → Shadow Trying it and will share trace results. This is a real gap in the LLMOps ecosystem; glad to see it being closed. 🔥 Will keep updated on the progress @LangChain_OSS — https://nitter.net/sauvast/status/2055273804600094741#m Harrison Chase AI Agents https://aimostall.com/news/item/1-000-days-left-anthropic-founder-96e7d502/ 96e7d5020801358a4de84ea78a910697693d03a3 Fri, 15 May 2026 13:00:08 +0000 "1,000 days left" Anthropic founder Wes Roth Foundation Models https://aimostall.com/news/item/codex-for-everyday-work-ai-agents-beyond-coding-638c2503/ 638c2503e463d7b889618ee682941ea371d339c5 Fri, 15 May 2026 14:01:04 +0000 14K 16h ago OpenAI AI Agents https://aimostall.com/news/item/inside-image-generation-s-renaissance-moment-the-openai-podcast-ep-19-430a8cec/ 430a8cec1fdee2160c40ee5fc661da04c853b1ce Fri, 15 May 2026 14:01:04 +0000 6.3K 19h ago OpenAI Creative AI https://aimostall.com/news/item/bose-lifestyle-ultra-speaker-vs-sonos-era-100-i-compared-both-models-and-24aec7f0/ 24aec7f07302378a89b6128ecf868937d5922931 Fri, 15 May 2026 13:00:00 +0000 Smart speakers are all the rage, and Bose's newcomer is a worthy competitor to the established Sonos. ZDNET AI Foundation Models https://aimostall.com/news/item/if-luigi-knows-you-there-s-a-good-chance-a-box-is-headed-to-your-desk-so-55ee537c/ 55ee537c3296277ddc837d507a6e20def683a34d Fri, 15 May 2026 12:42:45 +0000 If Luigi knows you, there’s a good chance a box is headed to your desk soon Luigi Cruz (@luigifcruz) We are confident GPU-accelerated signal processing is the future of radio astronomy. Our Stelline Developer Kit, based on @NVIDIAAI DGX Spark, lets us develop compute and networking capabilities locally before deploying to observatories. First units headed to scientists now! — https://nitter.net/luigifcruz/status/2055072006316712256#m NVIDIA AI Developer Tools https://aimostall.com/news/item/microsoft-pulls-claude-code-licenses-and-pushes-developers-back-toward-i-616dcde3/ 616dcde35ddb8890c152619f93a31a6280ff5166 Fri, 15 May 2026 12:39:55 +0000 Thousands of Microsoft developers used Anthropic's Claude Code for programming. Now the company is revoking licenses and betting on GitHub Copilot CLI. The article Microsoft pulls Claude Code licenses and pushes developers back toward its own AI tool appeared first on The Decoder . The Decoder Developer Tools https://aimostall.com/news/item/this-new-claude-skill-saves-you-from-bad-contracts-and-costs-less-than-a-56b48521/ 56b485210a934771ebcf5fd90a23627018d6bd7a Fri, 15 May 2026 12:31:53 +0000 I tested Claude for Small Business, which has 31 skills, and the contract review tool is amazing. ZDNET AI AI Policy https://aimostall.com/news/item/osaurus-brings-both-local-and-cloud-ai-models-to-your-mac-3a25ae6e/ 3a25ae6e2a9e403cdc07b25a42702487d1d4b2db Fri, 15 May 2026 12:19:48 +0000 Osaurus combines local and cloud AI models in a Mac app that keeps users’ memory, files, and tools on their own hardware. TechCrunch AI Foundation Models https://aimostall.com/news/item/arxiv-cracks-down-on-unchecked-ai-generated-content-in-research-papers-8f1a08bc/ 8f1a08bc0217eb41f115dcf28dc85077705c864d Fri, 15 May 2026 12:15:27 +0000 Arxiv, the influential preprint server where researchers worldwide publish their work before formal peer review, is tightening its rules on AI-generated content. The article Arxiv cracks down on unchecked AI-generated content in research papers appeared first on The Decoder . The Decoder Developer Tools https://aimostall.com/news/item/anthropic-just-went-after-the-44-of-u-s-gdp-that-enterprise-ai-has-mostl-fdeefce0/ fdeefce0b91777cb3456f4e125eeea713acd15ac Fri, 15 May 2026 12:14:17 +0000 Anthropic just went after the 44% of U.S. GDP that enterprise AI has mostly ignored. Claude for Small Business launched this week with 15 prebuilt agentic workflows and 15 skills connected directly into QuickBooks, PayPal, HubSpot, Canva, Docusign, Google Workspace, and Microsoft 365. It’s deployed in Claude Cowork and has no extra charge beyond existing subscriptions. Some of the use cases shared in the announcement: payroll planning, invoice chasing, month-end close, cash-flow forecasting, marketing campaign creation, tax season organizer. Anthropic also launched a 10-city free training tour with half-day AI fluency sessions for 100 SMB leaders per stop across Chicago, Tulsa, Dallas, New Jersey, Baton Rouge, Birmingham, Salt Lake City, Baltimore, San Jose, and Indianapolis. Anthropic knows SMBs adopt AI slower than enterprise, so they’re proactively creating an intelligence layer with the software they’re already using. It’s a distribution bet. (Also to whomever decided that the SMB demo should be a company with $17M in cash worried that they won’t make $65k payroll… I worry about you.) Allie K. Miller Enterprise AI https://aimostall.com/news/item/ai-skills-security-open-ai-deployment-company-zero-days-0264b68c/ 0264b68c323d2e9a39fed88f001d88b2401c5d73 Fri, 15 May 2026 15:01:04 +0000 AI skills security, Open AI Deployment Company & zero days IBM Technology AI Security https://aimostall.com/news/item/top-stories-in-ai-today-openai-s-codex-moves-beyond-the-desktop-openai-a-45650282/ 456502827a3406027862853ba7b5e8a6de6b53b4 Fri, 15 May 2026 10:30:18 +0000 Top stories in AI today: - OpenAI’s Codex moves beyond the desktop - OpenAI, Apple’s ‘deteriorating’ relationship - Automate marketing assets with ChatGPT Images 2.0 - Anthropic angers devs with new agent credit split - 4 new AI tools, community workflows, and more The Rundown AI AI Agents https://aimostall.com/news/item/claude-code-s-product-lead-talks-usage-limits-transparency-and-the-lean--e051235a/ e051235a2f2199d6626965992762faeb6c44ed48 Fri, 15 May 2026 10:30:01 +0000 "We have no grand plan," says Anthropic's Cat Wu—but that's by design. Ars Technica AI Creative AI https://aimostall.com/news/item/anthropic-frames-ai-competition-with-china-as-a-now-or-never-moment-for--04941fc1/ 04941fc164663916e59f489d533ca7149546707e Fri, 15 May 2026 10:05:51 +0000 In a policy paper, Anthropic lays out two scenarios for 2028: either the US locks in its compute lead over China, or authoritarian regimes set the rules for the AI era. The timing is no coincidence. The article Anthropic frames AI competition with China as a now-or-never moment for Washington appeared first on The Decoder . The Decoder AI Policy https://aimostall.com/news/item/1-000-days-left-anthropic-founder-7208108c/ 7208108c06975a524c36a5d5ba7831eec24eecbb Fri, 15 May 2026 15:01:04 +0000 "1,000 days left" Anthropic founder Wes Roth Foundation Models https://aimostall.com/news/item/become-an-ai-power-user-new-course-from-andrew-ng-18dac1da/ 18dac1da732345c80ee38bdf6e130368e62dde54 Fri, 15 May 2026 15:01:04 +0000 Become an AI power user 🌟 new course from Andrew Ng DeepLearning.AI Developer Tools https://aimostall.com/news/item/predictive-vs-generative-ai-how-they-work-and-when-to-use-each-069bebdb/ 069bebdb6198edf439b02a3facb5fc45b84d7e98 Fri, 15 May 2026 10:01:08 +0000 Predictive vs Generative AI: How They Work and When to Use Each IBM Technology Developer Tools https://aimostall.com/news/item/closing-time-bc25b5e5/ bc25b5e5122c198011462dbb3d1d0b17df0b9182 Fri, 15 May 2026 09:53:49 +0000 Today was closing arguments in the Musk v. Altman trial, and I almost feel bad writing about the unbelievable demolition derby I just witnessed. Steven Molo, Musk's lawyer, stumbled over his words. He at one point called Greg Brockman - a co-defendant - Greg Altman. He erroneously claimed that Musk wasn't asking for money and […] The Verge AI AI Policy https://aimostall.com/news/item/honda-s-hybrid-future-starts-with-new-accord-and-rdx-prototypes-db821b05/ db821b051cc2607b352ae425f086ef7cfc5b3ac5 Fri, 15 May 2026 09:36:16 +0000 Honda revealed prototypes of two new hybrid models, an Accord sedan and the Acura RDX SUV, during its annual business briefing this week, built on a platform that it says will begin launching next year. The RDX was announced earlier this year as Honda's first SUV to feature the next-gen version of its two-motor hybrid […] The Verge AI Foundation Models https://aimostall.com/news/item/the-us-is-using-ai-to-hunt-down-insider-trading-on-polymarket-2b08d1d6/ 2b08d1d63578b415dfa160359b94d97cc8057e04 Fri, 15 May 2026 09:30:00 +0000 The US Is Using AI to Hunt Down Insider Trading on Polymarket WIRED AI Developer Tools https://aimostall.com/news/item/how-chinese-short-dramas-became-ai-content-machines-9841e054/ 9841e05410016d29b24d5ccc5e06006ac91115f1 Fri, 15 May 2026 09:00:00 +0000 In a dimly lit bedroom, a frightened young woman is thrown onto a bed by a tall, muscular man. He grabs her hand, and flame-like vines crawl across her body, fusing with her flesh. She levitates, then drops. A dragon-shaped tattoo appears across her chest. “Two months,” the man says. “Give me an heir, or… MIT Technology Review AI Developer Tools https://aimostall.com/news/item/mira-murati-wants-her-ai-to-keep-humans-in-the-loop-8f47578a/ 8f47578a4e6f161f36598ca53ba10ead0a6a22e8 Fri, 15 May 2026 09:00:00 +0000 Mira Murati Wants Her AI to ‘Keep Humans in the Loop’ WIRED AI Foundation Models https://aimostall.com/news/item/openai-makes-its-ai-coding-assistant-codex-available-on-ios-and-android-fc458d89/ fc458d894a4b38b5f4ee2ca20c08a041de7bf8c3 Fri, 15 May 2026 08:39:26 +0000 OpenAI brings its AI coding assistant Codex to the ChatGPT app on iOS and Android. The article OpenAI makes its AI coding assistant Codex available on iOS and Android appeared first on The Decoder . The Decoder Developer Tools https://aimostall.com/news/item/best-ai-agents-for-software-development-ranked-a-benchmark-driven-look-a-74f42bd5/ 74f42bd56cee701abeb8c8b70feb41eae374ff4b Fri, 15 May 2026 08:23:01 +0000 The AI coding agent field in 2026 is more capable, more fragmented, and harder to benchmark than it looks. Claude Code leads on code quality at 87.6% SWE-bench Verified. GPT-5.5 tops Terminal-Bench at 82.7%. But the benchmark OpenAI itself declared contaminated in February 2026 is still being used to rank these tools — including by the labs publishing their own scores. The post Best AI Agents for Software Development Ranked: A Benchmark-Driven Look at the Current Field appeared first on MarkTechPost . MarkTechPost AI Agents https://aimostall.com/news/item/supertone-releases-supertonic-v3-on-device-text-to-speech-model-with-31--9f4911f2/ 9f4911f2e792a71c817a4064b88b98467b58023a Fri, 15 May 2026 07:00:49 +0000 The Seoul-based speech AI company ships its third generation of its on-device TTS engine, adding expressive tags, improved reading stability, and a 6× increase in language coverage — all while keeping the inference contract unchanged for existing integrations. The post Supertone Releases Supertonic v3: On-Device Text-to-Speech Model with 31-Language Support, Fewer Reading Failures, and Expression Tags appeared first on MarkTechPost . MarkTechPost Foundation Models https://aimostall.com/news/item/how-to-build-a-django-unfold-admin-dashboard-with-custom-models-filters--2dd50114/ 2dd50114051d0f424845ca82b2be41df7618b6d7 Fri, 15 May 2026 05:54:47 +0000 In this tutorial, we build an advanced Django-Unfold admin dashboard. We start by installing Django, Django-Unfold, and the required dependencies, then we create a fresh Django project with a shop application. We configure Unfold with a modern admin theme, custom sidebar navigation, dashboard callbacks, product badges, tabs, filters, actions, and a custom admin homepage. We […] The post How to Build a Django-Unfold Admin Dashboard with Custom Models, Filters, Actions, and KPIs appeared first on MarkTechPost . MarkTechPost Foundation Models https://aimostall.com/news/item/genetic-cubic-n-c-a-ratios-for-elementary-robotics-design-0e631643/ 0e6316430da011e8b14c1d95f58bb673a47fdd31 Fri, 15 May 2026 04:13:21 +0000 Last Updated on May 15, 2026 by Editorial Team Author(s): Greg Oliver Originally published on Towards AI. Architectural Cubic n{C/A} Ratios and Easy Shifts to Aid Robotics Design This post provides a toolbox of genetic Cubic coefficient ratios n{C/A} and n{C} ratios in Header Graph 1 applied to a depressed Cubic y=Ax³ — Cx+0 in black with Roots, Tp’s and in green the Sum Of gradients = — 3C at all possible 3 real roots (between Tp(y)’s) as presented in my recent post; Designing Polynomials Using Sum of Gradients at the Roots. Header Graph 1 Coefficient Ratios n{C/A}This article discusses genetic Cubic coefficient ratios essential for robotics design, providing efficient formulas to manipulate and shift depressed cubic functions within a coordinate system, thus aiding in various robotic applications, including movement and control mechanisms. Read the full blog for free on Medium. Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor. Published via Towards AI Towards AI AI Startups https://aimostall.com/news/item/poetiq-s-meta-system-automatically-builds-a-model-agnostic-harness-that--1fdf759f/ 1fdf759fff0e9618e8f7fb6edc9a95957b7b7960 Fri, 15 May 2026 03:38:10 +0000 Poetiq's Meta-System automatically constructed and optimized an inference harness for LiveCodeBench Pro using only Gemini 3.1 Pro — no fine-tuning, no model internals. The same harness, applied without modification to GPT 5.5 High, Kimi K2.6, Gemini 3.0 Flash, and four other models, improved every one of them. The post Poetiq’s Meta-System Automatically Builds a Model-Agnostic Harness That Improved Every LLM Tested on LiveCodeBench Pro Without Fine-Tuning appeared first on MarkTechPost . MarkTechPost Foundation Models https://aimostall.com/news/item/i-wonder-why-anthropic-thinks-2028-is-the-year-we-need-to-solidify-our-l-8b207905/ 8b207905e382ccc4797b3fbfbd3614a21ba2ca92 Fri, 15 May 2026 02:52:50 +0000 I wonder why Anthropic thinks 2028 is the year we need to solidify our lead against China🤔 Matthew Berman (@MatthewBerman) Anthropic: Chinese AI is a threat. They've correctly identified the risks, including cheap Chinese AI capturing American businesses even when it's less capable. But they completely blundered the solution: zero mention of an American open source strategy. In fact, they actively campaign AGAINST open source. 🤦‍♂️ Full breakdown of their paper from today: Video — https://nitter.net/MatthewBerman/status/2055107957562765446#m Matthew Berman Developer Tools https://aimostall.com/news/item/this-is-absolute-bs-and-an-attempted-regulatory-capture-by-anthropic-the-7edb6bb4/ 7edb6bb4cf7207b4bfc00beb4bbf23f975558112 Fri, 15 May 2026 02:23:15 +0000 This is absolute BS and an attempted regulatory capture by Anthropic. The knowledge behind CBRN attacks is already online, where do you think the models learned it from?? “Compounding the problem, labs in China often release dual-use capable models as open-weight. Once a model is open-weight, safeguards that do exist can be removed, making the model available to any state or non-state actor to use for malicious purposes, including the cyber and CBRN misuse those safeguards were built to prevent.” Matthew Berman (@MatthewBerman) Anthropic: Chinese AI is a threat. They've correctly identified the risks, including cheap Chinese AI capturing American businesses even when it's less capable. But they completely blundered the solution: zero mention of an American open source strategy. In fact, they actively campaign AGAINST open source. 🤦‍♂️ Full breakdown of their paper from today: Video — https://nitter.net/MatthewBerman/status/2055107957562765446#m Matthew Berman Developer Tools https://aimostall.com/news/item/top-20-adaboost-interview-questions-answers-part-2-of-2-d1e90d0f/ d1e90d0f9a2a55f6296a007bdb008a5b3693ff08 Fri, 15 May 2026 02:01:00 +0000 Last Updated on May 15, 2026 by Editorial Team Author(s): Shahidullah Kawsar Originally published on Towards AI. Data Scientist & Machine Learning Interview Preparation Let’s check your basic knowledge of AdaBoost. Here are 10 Q&A for your next interview. Source: This image is generated by ChatGPTThe article presents a collection of 20 interview questions and answers focused on AdaBoost, a popular machine learning algorithm. It covers various aspects of the algorithm, including its functionality, applications, and the significance of tuning parameters, while also addressing common misconceptions and the implications of model choices. Each question is answered in detail, helping candidates prepare effectively for technical interviews in the data science and machine learning fields. Read the full blog for free on Medium. Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor. Published via Towards AI Towards AI Foundation Models https://aimostall.com/news/item/the-second-scaling-law-remains-undefeated-if-you-want-better-hacking-or--6d3bd203/ 6d3bd203cb07cfc460766d4ba419604d0f3ad992 Fri, 15 May 2026 00:13:11 +0000 The Second Scaling Law remains undefeated. If you want better hacking (or math, or science, or crossword puzzle solving) out of an LLM, just add thinking tokens. There doesn't seem to be any plateau so far. Natália 🔍 (@natalia__coelho) Very important update from UK AISI. This is a meaningful change from the previous report. Here’s what the new data would look like for “Mythos Preview (new)” with $ on the x-axis: — https://nitter.net/natalia__coelho/status/2055061642736762972#m Ethan Mollick Foundation Models https://aimostall.com/news/item/agentic-ai-vs-ai-agents-what-are-the-key-differences-5ca708d5/ 5ca708d50d9c8d2feda7ba3c9ea69213ac6a20f7 Thu, 14 May 2026 23:01:00 +0000 Last Updated on May 15, 2026 by Editorial Team Author(s): Davin Convay Originally published on Towards AI. There are a lot of new terms dominating the artificial intelligence world lately, “Agentic AI” and “AI agents” being two of them. Oftentimes, they’re being used interchangeably, but the two phrases have their own distinct meanings. Organizations that understand when to deploy AI agents versus agentic ai solutions will automate intelligently while others automate blindly. The revolution isn’t just about AI doing tasks; it’s about AI pursuing goals. That difference changes everything. In this blog, we explore agentic AI vs AI agents, what makes them different, and how they will change the way we work. What is an AI Agent? An AI agent is a software program designed to perform specific tasks on behalf of users, responding to inputs with predetermined or learned behaviors. Think of AI agents as sophisticated digital assistants that excel at defined functions within established parameters. They perceive their environment through inputs, process information using programmed logic or trained models, and execute actions to achieve specific outcomes. The term “agent” implies agency, but AI agents possess limited autonomy. They operate within boundaries, following scripts, rules, or patterns learned from training data. A customer service chatbot represents a classic AI agent: it interprets queries, searches knowledge bases, and provides responses, but cannot independently decide to redesign the customer experience or proactively reach out to at-risk customers. AI agents have evolved significantly from simple rule-based systems. Modern AI agents leverage machine learning, natural language processing, and sophisticated decision trees to handle complex interactions. They can learn from experience, improving responses over time. Yet they remain fundamentally reactive, task-oriented tools waiting for activation rather than independently pursuing objectives. Examples of AI agents permeate our digital lives: ‍Chatbots and Virtual Assistants: From Siri to enterprise customer service bots, these agents respond to queries and execute simple commands. They parse language, match intents, and deliver programmed responses.‍‍ Recommendation Engines: Netflix’s content suggestions and Amazon’s product recommendations are AI agents analyzing behavior patterns to predict preferences. They excel at pattern matching but don’t independently decide to revolutionize recommendation strategies.‍‍ Robotic Process Automation (RPA) Bots: These agents automate repetitive tasks like data entry, form processing, and report generation. They follow defined workflows efficiently but cannot reimagine business processes.‍‍ Trading Bots: Algorithmic trading agents execute trades based on market signals and predetermined strategies. They react quickly to market conditions but don’t independently develop new trading philosophies.‍‍ Email Filters: Spam detection agents classify messages using learned patterns. They improve accuracy through feedback but don’t autonomously investigate new spam techniques.‍ What unites these AI agents is their fundamental characteristic: they are tools wielded by humans rather than autonomous collaborators. They augment human capabilities within defined scopes but don’t independently identify problems to solve or goals to pursue. Different Categories of AI Agents Understanding AI agent categories helps clarify why not all agents are agentic. Each category serves specific purposes, with distinct capabilities and limitations that determine their appropriate applications. Reactive Agents Reactive agents represent the simplest form, responding directly to current stimuli without memory or planning. They excel at immediate response scenarios where historical context is irrelevant. ‍Characteristics: No internal state, immediate stimulus-response, consistent behavior for identical inputs.‍‍ Examples: Basic chatbots with scripted responses, simple email autoresponders, rule-based alert systems.‍‍ Limitations: Cannot learn from experience, no context awareness, fails with complex multi-step tasks.‍‍ Use Cases: FAQ responses, simple notifications, basic data validation.‍ Proactive Agents Proactive agents anticipate needs and initiate actions without explicit user commands. They monitor conditions and trigger responses when specific criteria are met. ‍Characteristics: Environmental monitoring, threshold-based activation, predictive capabilities.‍‍ Examples: Predictive maintenance systems, inventory reorder agents, calendar scheduling assistants.‍‍ Strengths: Reduces human oversight, prevents problems before they occur, improves efficiency.‍‍ Limitations: Operates within predefined parameters, cannot adapt strategies autonomously.‍ Hybrid Agents Hybrid agents combine reactive and proactive behaviors, switching modes based on context. They respond to requests while also initiating beneficial actions. ‍Characteristics: Dual-mode operation, context-sensitive behavior, balanced autonomy.‍‍ Examples: Modern virtual assistants like Google Assistant, enterprise monitoring systems, smart home controllers.‍‍ Advantages: Versatile application, user-friendly interaction, efficient resource utilization.‍‍ Challenges: Complex design, mode-switching logic, user expectation management.‍ Specialized vs Generalist Agents The specialization spectrum determines an agent’s breadth versus depth of capabilities. ‍Specialized Agents: Excel at specific tasks with deep expertise. Example: Medical diagnosis agents trained on radiology images.‍‍ Generalist Agents: Handle diverse tasks with moderate proficiency. Example: GPT-based assistants answering various queries.‍‍ Trade-offs: Specialists offer superior performance in narrow domains. Generalists provide flexibility across multiple applications.‍ Multi-Agent Systems Multi-agent systems coordinate multiple specialized agents to achieve complex objectives. Each agent handles specific sub-tasks while communicating with others. ‍Architecture: Distributed intelligence, inter-agent communication protocols, coordinated goal pursuit.‍‍ Examples: Supply chain optimization systems, smart grid management, autonomous vehicle fleets.‍‍ Benefits: Scalability, fault tolerance, parallel processing, emergent intelligence.‍‍ Complexities: Coordination overhead, conflict resolution, communication bottlenecks.‍ Learning Agents Learning agents improve performance through experience, adapting behaviors based on feedback and outcomes. ‍Learning Mechanisms: Supervised learning from labeled data, reinforcement learning from rewards, unsupervised pattern discovery.‍‍ Examples: Recommendation systems, fraud detection agents, game-playing AI.‍‍ Evolution: From simple parameter adjustment to complex strategy development.‍‍ Limitations: Requires quality training data, can learn biases, may overfit to specific scenarios.‍ Autonomous Agents Autonomous agents operate independently within defined parameters, making decisions without human intervention. ‍Autonomy Levels: From simple script execution to complex decision-making within boundaries.‍‍ Examples: Autonomous testing bots, robotic process automation, industrial control systems.‍‍ Requirements: Robust error handling, safety constraints, performance monitoring.‍‍ Distinction: Autonomous operation doesn’t equal agentic AI; autonomy can exist without goal-setting capability.‍What is Agentic AI? Agentic AI represents a fundamental leap beyond traditional AI agents: artificial intelligence systems capable of independent goal formulation, strategic planning, and autonomous pursuit of objectives without constant human direction. While AI agents execute tasks, agentic AI owns outcomes. This distinction transforms AI from a tool into a collaborator, from an assistant into a strategic partner. The “agentic” qualifier signifies genuine agency: the capacity to act independently based on internal goals rather than […] Towards AI AI Agents https://aimostall.com/news/item/openshell-v0-0-41-agent-driven-policy-management-sandbox-resource-flags--6ef97419/ 6ef974190f45ae8e67844444215234da5814bdc3 Thu, 14 May 2026 22:50:56 +0000 OpenShell v0.0.41 🧩 agent-driven policy management 🎚️ sandbox resource flags in the CLI 🔒 custom CA support for OIDC TLS verification 📥 sandbox downloads with workspace-boundary checks 🔧 bug fixes and stability improvements Policy and resource control, directly from the shell. github.com/NVIDIA/OpenShell/… NVIDIA AI AI Policy https://aimostall.com/news/item/developers-can-now-debug-and-evaluate-ai-agents-locally-with-raindrop-s--3a495618/ 3a49561801c0e3125f5f2b4aaa8f912aa1fc58f6 Thu, 14 May 2026 22:30:51 +0000 Observability startup Raindrop AI ’s new open source, MIT Licensed " Workshop " tool, launched today, gives developers something that they've likely wanted, perhaps subconsciously, since the agentic AI era kicked off in earnest last year: a local debugger and evaluation tool specifically designed for AI agents, allowing devs to see all the traces of what their agent has been doing in a single, lightweight Structured Query Language (SQL) database file (.db) It functions as a local daemon and UI that streams every token, tool call, and decision to a local dashboard—typically hosted at localhost:5899 —the moment it occurs. By visiting their localhost, developers can then see everything their agent was up to — including mistakes or errors — and identify what went wrong, when, and ideally, discern why. It's all stored in a single .db file, which takes up relatively little memory, according to a X direct message VentureBeat received from Ben Hylak, Raindrop's co-founder and CTO (and a former Apple and SpaceX engineer). This real-time telemetry eliminates the latency of traditional polling and addresses a growing developer concern regarding the privacy of sending local traces to external servers. The tool is available for macOS, Linux, and Windows. It can be installed through a one-line shell command that automates binary placement and PATH configuration for bash, zsh, and fish shells. For developers who prefer to build from source, the repository is hosted on GitHub and utilizes the Bun runtime. The product: establishing a self-healing eval loop The platform’s standout feature is the "self-healing eval loop," which allows coding agents like Claude Code to read traces, write evals against the codebase, and fix broken code autonomously. In a practical application, if a veterinary assistant agent fails to ask necessary follow-up questions, Workshop captures the full trajectory. Claude Code then reads this trace, writes a specific eval, identifies the logic error in the prompt or code, and re-runs the agent until all assertions pass. Compatibility and ecosystem integration Workshop is compatible with a broad range of programming languages, including TypeScript, Python, Rust, and Go. It integrates with popular SDKs and frameworks such as the Vercel AI SDK, OpenAI, Anthropic, LangChain, LlamaIndex, and CrewAI. It is also designed to work seamlessly with various coding agents, including Claude Code, Cursor, Devin, and OpenCode. Licensing and community implications Workshop is released under the MIT License, ensuring it remains free and open-source for all users. This permissive licensing is intended to foster community contribution and allow enterprise users to maintain data sovereignty. Hylak noted on X that the tool was built to provide a "sane" way to debug agents locally, changing how their team and early customers build autonomous systems. To celebrate the launch, Raindrop offered limited-edition physical merchandise to users who installed the tool and executed a specific "drip" command. VentureBeat AI Developer Tools https://aimostall.com/news/item/i-d-watch-a-whole-season-of-this-5388e8c2/ 5388e8c25971fee714fd4a2ca35aee7e165e9947 Thu, 14 May 2026 22:10:25 +0000 i'd watch a whole season of this Mom (@mom_agency_) Claude's first day at Dunder Mifflin Video — https://nitter.net/mom_agency_/status/2054909371071582501#m Matthew Berman Creative AI https://aimostall.com/news/item/single-image-to-3d-editable-world-with-objects-that-are-interactive-this-0af5d100/ 0af5d100246a9c676c3e27505404eee01d55d9a8 Thu, 14 May 2026 21:55:25 +0000 Single image to 3D editable world with objects that are interactive? This looks like a lot of fun! neilson (@neilsonks) open-sourcing a 3D gen toolkit for Claude Code input image → environment, meshes, physics, lighting, & audio Video — https://nitter.net/neilsonks/status/2055013782771134479#m Matt Wolfe Creative AI https://aimostall.com/news/item/cerebras-stock-nearly-doubles-on-day-one-as-ai-chipmaker-hits-100-billio-8591362a/ 8591362adda1d30c1e973ad60712a9dbb357af2a Thu, 14 May 2026 21:38:02 +0000 Cerebras Systems , the Silicon Valley chipmaker that built the world's largest commercial AI processor, erupted onto the Nasdaq on Wednesday, opening at $350 per share — nearly double its $185 IPO price — and rocketing past a $100 billion market capitalization in its first hours of trading. The debut instantly crowned Cerebras as one of the most valuable semiconductor companies on Earth and validated a decade-long bet that the AI industry would eventually demand a fundamentally different kind of chip. The company sold 30 million shares at $185 apiece, raising $5.55 billion in what Bloomberg reported as the largest U.S. tech IPO since Uber went public in 2019 . The final pricing shattered expectations: Cerebras initially marketed shares at $115 to $125, then raised the range to $150 to $160 as investor demand surged, before ultimately pricing above even that elevated band. "This is a new beginning," Julie Choi, Senior Vice President and Chief Marketing Officer at Cerebras, told VentureBeat in an exclusive interview on the morning of the IPO. The company, she said, plans to pour its fresh capital into expanding the cloud infrastructure that has become the centerpiece of its growth strategy. "With this new capital, we're going to fill more data halls with Cerebras systems to power the world's fastest inference." The IPO caps one of the most dramatic corporate turnarounds in recent tech history. Cerebras first filed to go public in September 2024 but withdrew the effort more than a year later amid intense scrutiny over its near-total revenue dependence on a single customer in the United Arab Emirates. The company refiled in April 2026 with a radically different business profile: new partnerships with OpenAI and Amazon Web Services , a fast-growing cloud inference service , and a revenue base that had climbed 76% to $510 million in 2025 . How a dinner-plate-sized chip became the foundation of a $100 billion company To understand the frenzy, you have to understand the silicon. Cerebras builds something called the Wafer-Scale Engine , or WSE — a single processor that occupies an entire silicon wafer, the dinner-plate-sized disc from which ordinary chips are cut. The third-generation WSE-3 contains 4 trillion transistors, 900,000 compute cores, and 44 gigabytes of on-chip memory. It is 58 times larger than Nvidia's B200 "Blackwell" chip and delivers 2,625 times more memory bandwidth than the B200 package, according to the company's S-1 filing with the Securities and Exchange Commission. That bandwidth advantage matters enormously for AI inference — the process of running a trained model to generate answers. When a large language model produces text, it predicts one token at a time, and each token requires the model's entire set of weights to move from memory to compute. This work is inherently sequential and cannot be parallelized, making memory bandwidth the binding constraint on speed. Cerebras claims its architecture delivers inference responses up to 15 times faster than leading GPU-based solutions on open-source models, a figure corroborated by third-party benchmarker Artificial Analysis . "One of the architectural principles when we built the wafer was: let's keep compute closer together, so that compute elements can talk to each other at lower latency," Andy Hock, VP of Product at Cerebras, told VentureBeat. "Low latency is important to AI compute. It's a cornerstone of fast inference." The founding insight was contrarian and, for most of the company's life, commercially premature. Cerebras's founders recognized in 2015 that AI workloads were communication-bound problems — speed depended on how fast data could move between memory and compute — and that the best way to accelerate that movement was to keep everything on a single massive chip. Wafer-scale integration had been attempted and abandoned repeatedly over the semiconductor industry's 75-year history. Every previous effort had failed. Cerebras solved the problem through two key innovations detailed in its S-1 : a proprietary multi-die interconnect that stitches otherwise independent die together at the wafer level during fabrication, and a fault-tolerant architecture that routes around manufacturing defects using redundant building blocks, similar to how hyperscale data centers handle server failures. Why Cerebras is betting its future on cloud inference instead of hardware sales For most of its life, Cerebras sold hardware — massive, water-cooled AI supercomputers installed on-premises at customer facilities. That model generated $358 million in hardware revenue in 2025. But the IPO prospectus reveals a strategic pivot that will define the company's next chapter: the transition to cloud-based inference services . Cerebras launched its inference cloud in August 2024. In less than two years, cloud and other services revenue reached $151.6 million in 2025 , up 94% from $78.3 million in 2024. The company now expects this segment to comprise a significantly larger percentage of total revenue going forward, driven primarily by its enormous deal with OpenAI. "Cloud and model APIs are the preferred and natural consumption method for inference services and application developers," Hock told VentureBeat. "So that was the natural packaging and go-to-market strategy for the inference capability." Choi framed the cloud as a democratization play. "Whether that be an entrepreneurial developer, a startup, or a massive organization like OpenAI — the cloud has really made it easy for people to deploy and feel the fast inference, the value of it," she said. The economics of the transition are capital-intensive. Cerebras must lease data center space, manufacture and deploy its systems, and build software to manage capacity — all before recognizing recurring revenue. The S-1 warns bluntly that gross margins will decline in the near term as the company absorbs startup costs for cloud infrastructure. The company's gross margin already dipped to 39% in 2025 from 42.3% in 2024, driven by higher data center costs. But the demand picture appears formidable. "Every cloud system that we've deployed so far, each one gets gobbled up in capacity," Hock said. "We've been thrilled to see the demand for fast inference from Cerebras. We want to go faster to service that market." Inside the $20 billion OpenAI deal that transformed Cerebras overnight The single most consequential business relationship for Cerebras is its December 2025 agreement with OpenAI, under which OpenAI committed to purchase 750 megawatts of Cerebras inference compute capacity over the next several years. The deal is valued at more than $20 billion and includes provisions for OpenAI to purchase an additional 1.25 gigawatts of capacity, potentially bringing total deployment to 2 gigawatts. The arrangement goes far beyond a standard vendor-customer relationship. OpenAI and Cerebras are co-designing future models for future Cerebras hardware — a tight feedback loop that gives Cerebras visibility into frontier model architectures before they ship and gives OpenAI inference systems optimized for its specific workloads. The partnership moved from contract to production with remarkable speed. "After we announced the partnership, we had the first model running in like 35 days," Choi told VentureBeat. "That was Codex Spark, and the engineers over at OpenAI just were like, mind blown." Codex Spark , OpenAI's model designed for real-time coding, allows developers to turn natural-language instructions into working software in seconds using Cerebras infrastructure. Choi described a deep cultural alignment between the two companies. "Our teams truly vibe as engineers. We're on the same wavelength," she said. "There's just no amount of speed that is enough for those guys." To fund the infrastructure buildout, OpenAI advanced Cerebras a $1 billion working capital loan in January 2026, secured by a promissory note maturing no later than December 31, 2032, bearing 6% annual interest . The loan can be repaid in cash or through delivery of compute capacity. However, the S-1 discloses significant risk: if the MRA is terminated for any reason other than OpenAI's material uncured breach, OpenAI can seize control of the loan funds and demand immediate repayment. OpenAI also holds a warrant to purchase up to 33.4 million shares of Cerebras Class N common stock at an exercise price of $0.00001 per share — essentially free shares that vest as Cerebras delivers committed capacity. At the IPO opening price, the fully vested warrant would be worth approximately $11.7 billion. How the Amazon Web Services partnership could bring Cerebras chips to millions of developers In March 2026, Cerebras signed a binding term sheet with Amazon Web Services to become the first hyperscaler to deploy Cerebras systems inside its own data centers. The partnership introduces a novel architectural concept called disaggregated inference, which splits the two stages of AI inference — prefill (processing the user's prompt) and decode (generating the response) — across different hardware optimized for each task. Under this arrangement, AWS Trainium chips handle prefill , while Cerebras CS-3 systems handle decode , connected via Amazon's Elastic Fabric Adapter networking. According to the AWS press announcement in March, the approach aims to deliver an order of magnitude faster inference than what is currently available. Hock provided technical detail on why this works. "The interconnect requirements between prefill and decode systems actually aren't that high, so we can use a traditional interconnect between, say, Trainium and the wafer-scale engine and still deliver that fast time to first token and that ultra-low latency token generation," he explained. "What the Trainium wafer-scale engine combination really gives us in that disaggregated or heterogeneous inference setup is all the speed and vastly more efficiency, so we can effectively serve more tokens per unit rack space or kilowatt." The partnership provides Cerebras something it has long lacked: massive distribution. AWS serves millions of enterprise customers worldwide, and Cerebras systems deployed through Amazon Bedrock will become accessible to any developer within their existing AWS environment. "AWS has incredible reach," Hock said. "The partnership is really about bringing that fast inference capability — that sort of best-in-industry, fast inference capability delivered by wafer-scale engine and Trainium — to that broader market." The term sheet also grants AWS a warrant to purchase up to approximately 2.7 million shares of Cerebras Class N common stock at a $100 exercise price, with vesting tied to product purchases beyond the initial lease. The UAE customer concentration problem that nearly derailed the IPO — and whether it's really solved For all the excitement, Cerebras carries a risk that has haunted it since its first IPO attempt: customer concentration. In 2024, G42 — an Abu Dhabi–based technology conglomerate — accounted for 85% of Cerebras's total revenue . The company's September 2024 S-1 filing drew heavy scrutiny over this dependence, compounded by questions about export controls for advanced AI chips shipped to the UAE. Cerebras withdrew that filing . The 2025 numbers show progress but not resolution. G42's share of revenue declined to 24% , but Mohamed bin Zayed University of Artificial Intelligence ( MBZUAI ), an Abu Dhabi institution that is a related party to G42, accounted for 62% of total revenue . Together, the two UAE-linked entities still represented 86% of Cerebras's 2025 sales. The S-1 is candid about this risk , noting that MBZUAI accounted for 77.9% of accounts receivable as of December 31, 2025, and that U.S. export licenses for Cerebras systems shipped to G42 and MBZUAI require "rigorous security and compliance obligations to prevent diversion and abuse of our technology." Choi addressed the issue directly, pointing to the OpenAI and AWS deals as evidence of a broadening customer base. "Now with OpenAI and Amazon, those are the same type of deep partnerships," she told VentureBeat. "We're a deep technology company. Our technology has taken a decade to build. We go deep in how we build, and now we're going deep with two of the biggest players — the biggest AI lab, OpenAI, and the biggest cloud, AWS." Hock framed the customer evolution as a progression in market perception. "G42 caused the market to be intrigued and inspired," he said. "Nobody in the business is smarter, more credible, or has greater reach than OpenAI and AWS. And so I think OpenAI and AWS caused the market to shift from intrigued and inspired to — I'll call it curious and convinced." Still, the S-1 warns that the OpenAI MRA itself "represents a substantial portion of our projected revenues over the next several years." Cerebras's business will remain dependent on a small number of very large customers for the foreseeable future — a structural feature of the AI infrastructure market where buildouts are measured in hundreds of megawatts and billions of dollars. Can Cerebras build data centers fast enough to keep up with runaway demand? With OpenAI consuming 750 megawatts of committed capacity and AWS preparing to deploy Cerebras systems in its data centers, the question is whether Cerebras can scale its physical infrastructure quickly enough to serve everyone else. Hock acknowledged the tension. "It's a good problem to have when demand starts to outstrip supply. It doesn't mean it's an easy problem to address," he told VentureBeat. "We've got to build these extraordinary systems. We've got to procure data center space. We've got to deploy systems there. Got to stand up software to meet our customers where they are." The company is being deliberate about capacity allocation. "We're trying to be really deliberate about how we allocate capacity as it's built," Hock said. "We're working in deep partnership to service the highest-priority customers and highest-priority markets." Choi argued that the constraint actually sharpens focus. "Sometimes when you have less of something, it forces you to be very deliberate," she said. Beyond OpenAI, she named Cognition — the AI coding startup — and Block , led by Jack Dorsey, as significant customers. "Jack participated in our roadshow as well," Choi noted. "We're speeding up that entire money-bot AI experience within Cash App." The S-1 discloses that Cerebras currently operates data centers in California, Oklahoma, and Canada , with plans to expand internationally. The company executed non-cancelable data center leases in late 2025 with aggregate undiscounted future minimum payments of approximately $344 million , and in March 2026 signed a Canadian data center lease with expected minimum payments of approximately $2.2 billion over a 10-year term. The IPO proceeds — combined with $1 billion from a January 2026 Series H preferred stock round and the $1 billion OpenAI loan — give Cerebras a war chest exceeding $8 billion to fund the buildout. Whether that is enough to satisfy a market where major customers are ordering capacity measured in gigawatts remains an open question. The Nvidia shadow: what Cerebras is really up against in the AI chip wars Cerebras enters public markets into the teeth of the most competitive semiconductor environment in decades. Nvidia remains the dominant force in AI compute, controlling the vast majority of the training and inference infrastructure market. Its GPU architecture benefits from a deeply entrenched software ecosystem built around CUDA, the programming framework that has become the de facto standard for AI development. Cerebras's S-1 explicitly acknowledges this , noting that "many of our competitors benefit from competitive advantages over us, such as prominent and cutting-edge technology and software stacks designed to keep out new market entrants." But Cerebras argues the inference market is structurally different from training — and that its architecture has a fundamental advantage in the workload that matters most going forward. As AI models have shifted toward reasoning, where models perform multi-step computation during inference to think through problems, the number of tokens generated per request has exploded . Each token requires moving full model weights from memory to compute, making memory bandwidth the bottleneck. The S-1 cites Bloomberg Intelligence data projecting that Cerebras's addressable portion of the AI inference market will grow from approximately $66 billion in 2025 to $292 billion by 2029 , a 45% compound annual growth rate — significantly outpacing the 20% CAGR projected for AI training infrastructure. Nvidia has clearly taken notice of the fast-inference threat. In December 2025, Nvidia acquired Groq — a startup whose tensor streaming processor architecture more closely resembles Cerebras's approach — for $20 billion. Months later, Nvidia announced plans for Groq-based products , signaling that even the industry's dominant player recognizes the limitations of GPU architecture for latency-sensitive inference. Cerebras also competes with custom silicon developed by hyperscalers — including Google's TPUs and Amazon's Trainium chips — and a growing roster of AI cloud providers. Asked about Nvidia and Groq, Choi declined to engage. "We're feeling pretty good right now," she told VentureBeat with a smile. Revenue is surging, but the financial fine print reveals a more complicated picture The financial picture that emerges from the S-1 is one of rapid scaling with significant underlying complexity. Revenue surged from $78.7 million in 2023 to $290.3 million in 2024 to $510 million in 2025 — a more than tenfold increase over three years. The company reported GAAP net income of $237.8 million in 2025, but this figure is heavily influenced by a $363.3 million one-time gain from the extinguishment of a forward contract liability related to a preferred stock arrangement. Stripping out that gain and stock-based compensation, Cerebras's non-GAAP net loss was $75.7 million in 2025, widening from a $21.8 million non-GAAP loss in 2024. Operating losses deepened as well. Cerebras lost $145.9 million from operations in 2025, up from $101.4 million the prior year, as the company invested heavily in research and development ($243.3 million, up 54%) and sales and marketing ($70.6 million, up 237%). The company burned $10 million in operating cash flow in 2025, a sharp reversal from the $452 million of cash generated in 2024 — a year boosted by $640 million in customer deposit inflows, primarily from G42 and MBZUAI . The S-1 warns that gross margins will face near-term pressure from startup costs for cloud infrastructure, customer warrant amortization, and pass-through data center expenses. The path to this moment was anything but smooth. Cerebras shipped its first systems in 2020 and 2021 — before the market was ready. As the founders wrote in the prospectus: the company "had built something extraordinary, but the market wasn't ready." The ChatGPT moment in late 2022 changed everything. By early 2025, Cerebras's speed advantage — long a solution in search of a problem — became urgently relevant as AI coding agents, deep research tools, and real-time voice applications demanded the kind of low-latency inference that GPU clusters struggled to deliver. The S-1 describes a market where AI coding agents "barely existed in 2023" but collectively generated "billions in ARR in 2025," and where 42% of professional code is now AI-generated or assisted. What Cerebras must prove to justify a $100 billion valuation — and what happens if it can't Looking forward, Hock signaled that the current generation of hardware is just the beginning. "Wafer-scale engine three and CS-3 is not the end of the story. It's just the beginning," he told VentureBeat. "We have a multi-year technology roadmap that continues building on wafer-scale technology, accelerating performance, increasing efficiency, supporting larger scale." The S-1 confirms that Cerebras intends to expand on-chip memory and bandwidth , improve interconnect density, and leverage future process node advances — and discloses that the company has already obtained export licenses for future CS-4 systems destined for the UAE. The company also faces a web of operational risks that would test any organization, let alone one that has never operated as a public company. It depends entirely on TSMC for wafer fabrication, with no long-term supply commitment. Its data center leases stretch for years, while its inference customer contracts are often shorter-term or consumption-based, creating a mismatch between fixed costs and variable revenue. It has identified material weaknesses in its internal controls over financial reporting. And its most important customer relationship — with OpenAI — includes exclusivity provisions that restrict Cerebras from working with certain named OpenAI competitors, potentially limiting future diversification. Whether Cerebras can sustain a $100 billion-plus valuation will depend on its ability to execute against all of these challenges simultaneously: building data centers at unprecedented speed, manufacturing wafer-scale chips at scale through a single foundry, navigating export controls on its most lucrative international relationships, and competing against an Nvidia that has shown it will not cede the inference market without a fight. But Cerebras has always been built on a willingness to attempt what others said was impossible. Wafer-scale integration had stumped the semiconductor industry for its entire existence. Now a chip the size of a dinner plate — once dismissed as an engineering curiosity — powers the fastest AI inference on the planet, serves the world's leading AI lab, and just debuted on the Nasdaq to a valuation that dwarfs companies many times its age. The world, it turns out, was ready. As Hock put it to VentureBeat, recalling the journey from the lab to the trading floor: "The IPO isn't the end of the story. It's the beginning." VentureBeat AI Foundation Models https://aimostall.com/news/item/behold-the-elon-musk-jackass-trophy-e5c7e8ef/ e5c7e8ef2c5f6be83c95cb972220d523528a976c Thu, 14 May 2026 21:35:35 +0000 Yesterday, in Musk v. Altman, before the jurors came in, Sam Altman's team passed up what looked - from a distance - like a little league trophy. It was not. Yvonne Gonzalez Rogers had the lawyers read the inscription aloud for the press: "Never stop being a jackass." It's a commemoration OpenAI employees bought for […] The Verge AI AI Policy https://aimostall.com/news/item/elon-musk-s-spacexai-has-been-bleeding-staff-since-its-merger-4e62eab1/ 4e62eab1998878bff41b1b6aa6c408800364d7d0 Thu, 14 May 2026 21:30:44 +0000 More than 50 employees have reportedly left Elon Musk’s newly merged SpaceXAI since February, raising questions about burnout, leadership changes, talent poaching, and whether liquidity events weakened retention incentives. TechCrunch AI AI Startups https://aimostall.com/news/item/openai-says-codex-is-coming-to-your-phone-fbee67a6/ fbee67a68fb4bc889c77ce432c1a07da7c48c2bc Thu, 14 May 2026 20:58:55 +0000 The update gives users enhanced flexibility over how they can manage their workflows. TechCrunch AI AI Agents https://aimostall.com/news/item/ai-research-papers-are-getting-better-and-it-s-a-big-problem-for-scienti-7becd7c1/ 7becd7c180182f7aee8cdcc5109e99c33eadf086 Thu, 14 May 2026 20:57:56 +0000 Last summer, Peter Degen's postdoctoral supervisor came to him with an unusual problem: One of his papers was being cited too much. Citations are the currency of academia, but there was something unusual about these. Published in 2017, the paper had assessed the accuracy of a particular type of statistical analysis on epidemiological data and […] The Verge AI Developer Tools https://aimostall.com/news/item/sea-s-view-on-the-future-of-agentic-software-development-with-codex-55340df8/ 55340df861e2fc16e0171095e35de6d25ca828c6 Thu, 14 May 2026 20:30:00 +0000 Sea Limited's CPO explains why the company is deploying Codex across engineering teams to accelerate AI-native software development in Asia. OpenAI News AI Agents