# AI Models AI models are programs trained on data to recognize patterns and make predictions or decisions, using algorithms and computing resources. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Snap Announces New "anticipatory" AI Service & Apps for First Consumer 'Specs' AR Glasses DevFeed: [Snap Announces New "anticipatory" AI Service & Apps for First Consumer 'Specs' AR Glasses]() Original publisher: [Read original article]() Author: Scott Hayden Published: 2026-09-16T23:40:00Z Content type: news Language: en Sources: [Road to VR]() Topics: [Artificial Intelligence](), [AI Models](), [iOS](), [iphone](), [Cloud](), [Security](), [Streaming]() Tags: [ai](), [ai-models](), [cloud](), [ios](), [iphone](), [on-device](), [security](), [streaming](), [xr-industry-news]() ### AI overview Snap announced Specs Intelligence, an anticipatory AI service for its upcoming consumer Specs AR glasses. The service is designed to work across Specs, iPhone, and Mac, using connected apps and tools to build context and surface relevant information. Snap also announced AR experiences, streaming features, Spotify integration, and partnerships including HBO Max, the NBA, and the WNBA. ### Source excerpt Snap today announced new experiences, services and partnerships for SPECS, the company's upcoming pair of consumer AR glasses. Snap's big Specs livestream today wasn't technically a launch event--they're still slated to arrive in the US, UK and France later this fall starting at $2,195--although the company did give a little more insight into what sort [...] The post Snap Announces New "anticipatory" AI Service & Apps for First Consumer 'Specs' AR Glasses appeared first on Road to VR. ## AI leaders propose embedded third-party evaluators for frontier AI safety DevFeed: [AI leaders propose embedded third-party evaluators for frontier AI safety]() Original publisher: [Read original article]() Author: Adrian Bridgwater Published: 2026-09-16T15:58:41Z Content type: news Language: en Sources: [The New Stack]() Topics: [Job](), [Artificial Intelligence](), [Frontier AI](), [anthropic](), [AI Models]() Tags: [ai](), [ai-infrastructure](), [ai-strategy](), [alignment](), [anthropic](), [developers](), [frontier-ai](), [google](), [job](), [meta](), [openai](), [safety](), [tech-careers]() ### AI overview The article examines Anthropic CEO Dario Amodei's proposal for frontier AI companies to provide embedded third-party evaluators with employee-like access. These evaluators would verify safety practices, report incidents, and assess AI models, training pipelines, and processes. ### Source excerpt The pace of frontier AI model development spurred Anthropic CEO Dario Amodei to publish an essay last weekend, calling for The post AI evaluator: The most important AI job in history? How developers might fill the proposed new job appeared first on The New Stack. ## Shopify Drops React Native for Swift and Kotlin as AI Changes Cross-Platform Development Tradeoffs DevFeed: [Shopify Drops React Native for Swift and Kotlin as AI Changes Cross-Platform Development Tradeoffs]() Original publisher: [Read original article]() Author: Bruno Couriol Published: 2026-09-16T12:45:00Z Content type: news Language: en Sources: [InfoQ]() Topics: [React Native](), [Shopify](), [Mobile](), [cross-platform](), [Kotlin](), [Swift](), [AI Models]() Tags: [ai](), [ai-coding](), [android](), [apple](), [cross-platform](), [declarative](), [development](), [kotlin](), [mobile](), [news](), [react-native](), [shopify](), [shopify-drops-react-native](), [swift](), [web-development]() ### AI overview Shopify is rewriting its flagship mobile apps in Swift and Kotlin after reassessing React Native. The decision followed the expected work to adopt React Native's New Architecture and improvements in AI coding models, which Shopify said made a clean-slate native rewrite more attractive than migration. ### Source excerpt Shopify recently announced it is abandoning React Native to rewrite its flagship apps in Swift and Kotlin. With the significant jump in the quality of AI models, Head of Mobile Mustafa Ali reassessed Shopify's commitment to React Native, estimating that the benefit/cost ratio of maintaining native codebases across mobile platforms was now above that of using an abstraction layer. By Bruno Couriol ## University of Manchester Uses NVIDIA Earth-2 to Forecast Air Pollution Across the UK DevFeed: [University of Manchester Uses NVIDIA Earth-2 to Forecast Air Pollution Across the UK]() Original publisher: [Read original article]() Author: Isha Salian Published: 2026-09-16T05:00:42Z Content type: article Language: en Sources: [NVIDIA Blog]() Topics: [Artificial Intelligence](), [AI Models](), [Supercomputing](), [DGX Spark](), [Simulation](), [Training AI Models](), [data](), [Inference](), [real-time]() Tags: [ai](), [ai-for-good](), [ai-models](), [climate](), [compute](), [dgx-spark](), [government](), [healthcare](), [inference](), [nvidia](), [open-source](), [real-time](), [science](), [supercomputing](), [training](), [uk]() ### AI overview The University of Manchester is working with NVIDIA to use Earth-2 generative AI models to forecast air pollution across the U.K. The team trained Earth-2 CorrDiff on chemistry-climate simulation data using Isambard-AI, added StormCast for time-dependent forecasts using air-quality observations, and demonstrated workflows on DGX Spark. ### Source excerpt Air pollution is a serious public health risk, contributing to an estimated 30,000 deaths in the U.K. alone last year. Data-driven insights can help -- but computing air quality with traditional chemistry-based models is expensive, which limits how detailed they can be and how regularly they can be run. David Topping, a professor in the [...] ## Chinese AI models dominate OpenRouter's US token consumption. It can now guarantee that traffic stays entirely in the US. DevFeed: [Chinese AI models dominate OpenRouter's US token consumption. It can now guarantee that traffic stays entirely in the US.]() Original publisher: [Read original article]() Author: Paul Sawers Published: 2026-09-14T13:59:33Z Content type: news Language: en Sources: [The New Stack]() Topics: [AI Models](), [Routing (disambiguation)](), [Security](), [data](), [Availability](), [hugging face](), [Nvidia](), [Nemotron](), [OpenAI]() Tags: [ai](), [ai-infrastructure](), [ai-models](), [availability](), [data](), [hugging-face](), [large-language-models](), [models](), [nemotron](), [nvidia](), [openai](), [routing](), [security]() ### AI overview OpenRouter has launched US in-region routing for business and enterprise customers. Requests sent through its US endpoint are decrypted, processed, and served entirely inside the United States, or rejected if that cannot be guaranteed. The feature addresses concerns about data location when businesses use open-weight models, including models developed in China. ### Source excerpt Everyone knows the open-weight model pitch by now: companies can download the weights, customize them, run them on infrastructure of The post Chinese AI models dominate OpenRouter's US token consumption. It can now guarantee that traffic stays entirely in the US. appeared first on The New Stack. ## MIT researchers develop a generative AI method for enforcing hard constraints in safety-critical applications DevFeed: [MIT researchers develop a generative AI method for enforcing hard constraints in safety-critical applications]() Original publisher: [Read original article]() Author: Adam Zewe | MIT News Published: 2026-09-14T04:00:00Z Content type: news Language: en Sources: [MIT AI News]() Topics: [Generative AI](), [AI Models](), [Algorithm](), [Requirements](), [Computer vision](), [Robotics]() Tags: [ai](), [ai-models](), [algorithm](), [algorithms](), [artificial-intelligence](), [computer-science-and-technology](), [computer-vision](), [diffusion-models](), [electrical-engineering-and-computer-science-eecs](), [flow-matching](), [generative-ai](), [hard-constrained-sampling](), [hardflow](), [idss](), [kaveh-alim](), [laboratory-for-information-and-decision-systems-lids](), [machine-learning](), [mechanical-engineering](), [mit-schwarzman-college-of-computing](), [navid-azizan](), [optimal-control](), [paper](), [requirements](), [research](), [robotics](), [safe-ai](), [school-of-engineering](), [trajectory-optimization](), [zeyang-li]() ### AI overview MIT researchers developed a deployment-time technique that lets pretrained generative AI models explore solutions while enforcing hard constraints on final outputs. Experiments in robotics, physical-process control, and computer vision found that the method satisfied required constraints and identified better solutions than existing techniques. ### Source excerpt The "HardFlow" algorithm could help generative AI models produce high-quality outputs that obey strict requirements when "pretty close" doesn't cut it. ## Run AI in the Browser: A Practical Guide to Transformers.js DevFeed: [Run AI in the Browser: A Practical Guide to Transformers.js]() Original publisher: [Read original article]() Author: Freek Van der Herten (freek@spatie.be) Published: 2026-09-09T14:50:26Z Content type: tutorial Language: en Sources: [freek.dev - all blogposts]() Topics: [transformers.js](), [browser](), [Artificial Intelligence](), [AI Models](), [Back end](), [API keys]() Tags: [ai](), [ai-models](), [api-keys](), [article](), [backend](), [browser](), [guide](), [internet](), [javascript](), [js](), [machine-learning](), [practical](), [run](), [transformers](), [transformers-js]() ### AI overview A practical guide to Transformers.js, covering how it runs AI models directly in the browser, available models, and the trade-offs of client-side AI compared with traditional AI providers. ### Source excerpt Transformers.js lets you run AI models directly in the browser without a backend, API keys, or an internet connection after the model is cached. The article explores how it works, which models are available, and the trade-offs of client-side AI compared to traditional AI providers. Read more ## ByteDance Reportedly Developing an AI Model for Real-Time Spatial Video DevFeed: [ByteDance Reportedly Developing an AI Model for Real-Time Spatial Video]() Original publisher: [Read original article]() Author: Scott Hayden Published: 2026-09-09T10:44:59Z Content type: news Language: en Sources: [Road to VR]() Topics: [Artificial Intelligence](), [AI Models](), [real-time](), [Google](), [Cloud](), [Streaming](), [Hardware]() Tags: [ai](), [ai-models](), [cloud](), [hardware](), [model](), [real-time](), [streaming](), [xr-industry-news]() ### AI overview ByteDance is reportedly developing an AI model for generating real-time spatial videos, based on Seedance and intended to connect the company's AI models, cloud resources, content platforms, and Pico Interactive hardware business. The report compares the model with Google's Genie, but its launch timing remains uncertain. ### Source excerpt TikTok parent ByteDance is reportedly preparing an AI model that could let you create real-time spatial videos similar to Google Genie. According to a Bloomberg report, ByteDance is readying an AI model dedicated to creating real-time spatial videos, which the company hopes will act as a "flywheel" to its various companies, including video streaming giant [...] The post TikTok Parent Reportedly Building AI Model to Rival Google's Genie for Real-Time Spatial Video appeared first on Road to VR. ## AMD Reveals Threadripper Halo Station: 96 Cores and up to 576GB of HBM3E Aimed Straight at DGX Station DevFeed: [AMD Reveals Threadripper Halo Station: 96 Cores and up to 576GB of HBM3E Aimed Straight at DGX Station]() Original publisher: [Read original article]() Author: Brian Beeler Published: 2026-09-04T16:08:49Z Content type: news Language: en Sources: [StorageReview.com]() Topics: [DGX Station](), [GPU](), [Nvidia](), [cpu](), [AI Models](), [Inference](), [Arm](), [toolchain]() Tags: [2026](), [accelerators](), [ai](), [ai-models](), [arm](), [consumer](), [cpu](), [dgx-station](), [gpu](), [toolchain](), [workstation]() ### AI overview AMD revealed the Threadripper Halo Station, a liquid-cooled workstation combining a 96-core Threadripper PRO CPU with two Instinct MI350P accelerators. AMD positions it as an answer to NVIDIA's GB300-based DGX Station, with up to four accelerators, 576GB of HBM3E, 2TB of DDR5 memory, and support for AI models exceeding one trillion parameters. The article contrasts AMD's expandable, discrete design with NVIDIA's unified-memory architecture. ### Source excerpt AMD used its IFA 2026 opening keynote to reveal the Threadripper Halo Station, a liquid-cooled workstation pairing a 96-core Threadripper PRO with a pair of Instinct MI350P accelerators, and the pitch could not be more direct: this is AMD's answer to NVIDIA's GB300 DGX Station. "This is the most powerful workstation in the world," said The post AMD Reveals Threadripper Halo Station: 96 Cores and up to 576GB of HBM3E Aimed Straight at DGX Station appeared first on StorageReview.com. ## The Pulse: tech companies move to open AI models DevFeed: [The Pulse: tech companies move to open AI models]() Original publisher: [Read original article]() Author: Gergely Orosz Published: 2026-09-03T17:00:14Z Content type: article Language: en Sources: [The Pragmatic Engineer]() Topics: [AI Models](), [Software Engineering]() Tags: [ai-models](), [automated](), [cost](), [maintenance](), [open]() ### AI overview Cost-saving efforts reportedly show that moving simpler workloads to open AI models can reduce AI bills by about 50%. The article also covers automated software maintenance experience. ### Source excerpt Cost-saving efforts reveal that moving simpler workloads to open AI models is the easiest way to save ~50% on AI bills. Also: automated software maintenance experience, and more ## Announcing General Availability of VMware Cloud Foundation 9.1.1 DevFeed: [Announcing General Availability of VMware Cloud Foundation 9.1.1]() Original publisher: [Read original article]() Author: vmwareblogs Published: 2026-09-03T13:47:18Z Content type: release Language: en Sources: [VMware Blogs]() Topics: [Cloud](), [Security](), [AI Platform](), [AI Models](), [MLOps]() Tags: [ai](), [ai-ml](), [ai-models](), [announce](), [apis](), [cloud](), [cloud-infrastructure](), [complexity](), [cost](), [cpu](), [data-privacy](), [evpn](), [governance](), [home-page](), [integrations](), [kubernetes](), [memory](), [nsx](), [private-ai-services](), [release](), [root-cause-analysis](), [security](), [troubleshooting](), [vcf-9-1](), [vcf-operations](), [vmware](), [vmware-cloud-foundation]() ### AI overview VMware announces the general availability of VMware Cloud Foundation 9.1.1. The release adds tougher security, vSAN Object Storage as a tech preview, multi-tenant AI model sharing with tenant-isolated access controls, and an AI Assistant for diagnostics, management-pack creation, and troubleshooting across infrastructure and Kubernetes clusters. ### Source excerpt Coming on the heels of a very successful VMware Explore in Vegas and the VMware Cloud Foundation (VCF) 9.1 launch in May, we're excited to announce the general availability of VCF 9.1.1. This release builds on VCF 9.1 with tougher security, vSAN Object Storage (tech preview, previously announced) and new capabilities designed to make your ... Continued The post Announcing General Availability of VMware Cloud Foundation 9.1.1 appeared first on VMware Blogs. ## A Model Portfolio for cost-efficient AI across the software development lifecycle DevFeed: [A Model Portfolio for cost-efficient AI across the software development lifecycle]() Original publisher: [Read original article]() Author: Praveen Sidda Published: 2026-09-01T07:16:01Z Content type: article Language: en Sources: [Data Science at Microsoft]() Topics: [AI Development](), [AI Models](), [Model Routing](), [sdlc](), [Agile](), [Developer experience](), [Compression](), [Requirements](), [Testing](), [Deployment](), [Code review]() Tags: [agentic-sdlc](), [ai](), [ai-cost-optimization](), [ai-models](), [code-review](), [compression](), [deployment](), [github-copilot](), [llm](), [overhead](), [requirements](), [software-development]() ### AI overview The article examines whether routing software development tasks across a portfolio of AI models can reduce costs compared with using one premium model. It reports that lower-cost models handled well-defined tasks, while premium models were reserved for architecture, implementation, and code review; context compression reduced token usage but risked losing important technical details. ### Source excerpt Image generated by AIWhat one controlled experiment taught me about matching model capability to developer work Topic: Can intelligently routing developer tasks across different AI models outperform relying on a single premium model? In this article, I put that question to the test by mapping software development lifecycle (SDLC) stages to a portfolio of AI models and comparing the outcomes. Motivation As AI becomes embedded throughout the AI-Native Development Lifecycle (AIDLC), an evolution of the traditional Software Development Lifecycle (SDLC), its cost is no longer tied to a single prompt. A single developer task can involve multiple model calls, each carrying source files, conversation history, tool definitions, and generated output. Applying the most capable model to every interaction is straightforward, but it also consumes premium model capacity on tasks that less expensive models can often complete just as effectively. This raises an important question for engineering organizations: How can teams reduce the cost of AI-assisted development without compromising quality, reliability, or the developer experience? My first instinct was to reduce token consumption. Context compression appeared to be the most direct path to lowering inference costs by shortening prompts. Although it reduced token usage, it also introduced risk. Important constraints and technical details could be lost, affecting downstream tasks. Source code, stack traces, and active instructions proved to be especially poor candidates for lossy compression. That experience shifted my focus. The objective was not to process fewer tokens, but to complete developer tasks successfully at a lower overall cost. I then experimented with model allocation. Lower-cost models handled well-defined tasks such as requirements synthesis, planning, routine test generation, deployment artifacts, and final summaries, while premium models were reserved for architecture, implementation, and code review. This appro ## ChatGPT vs. Claude vs. Grok vs. Gemini: The Best AI Across 10 Real Use Cases DevFeed: [ChatGPT vs. Claude vs. Grok vs. Gemini: The Best AI Across 10 Real Use Cases]() Original publisher: [Read original article]() Author: Peter Yang Published: 2026-08-26T15:04:55Z Content type: comparison Language: en Sources: [Behind the Craft]() Topics: [AI Models](), [ChatGPT](), [Claude](), [coding]() Tags: [ai](), [ai-models](), [chatgpt](), [claude](), [coding](), [gemini](), [vs]() ### AI overview A 26-minute tutorial compares ChatGPT, Claude, Grok, and Gemini across 10 use cases, including writing, design, coding, personal agents, and AI video. It presents the author's picks for different tasks and an overall winner. ### Source excerpt My picks for the best AI models and tools across 10 everyday tasks, plus a preview of my upcoming course for paid subscribers ## Mistral x HUMAIN DevFeed: [Mistral x HUMAIN]() Original publisher: [Read original article]() Published: 2026-08-24T16:02:41Z Content type: article Language: en Sources: [Mistral AI Blog]() Topics: [AI Strategy](), [AI Infrastructure](), [AI Models](), [Model Development](), [model-deployment](), [Localization (l10n)](), [datacenter](), [Cybersecurity]() Tags: [ai](), [ai-infrastructure](), [ai-platform](), [arabic](), [data](), [data-center](), [deployment](), [enterprise](), [europe](), [financial-services](), [manufacturing](), [model-development](), [public-sector](), [security]() ### AI overview Mistral and HUMAIN announce a strategic collaboration to advance sovereign AI in Saudi Arabia and across the Middle East. The initiative covers AI infrastructure, advanced model development, localized Arabic-capable models, and deployment of AI solutions for regulated industries. ### Source excerpt The most powerful AI platform for enterprises. Customize, fine-tune, and deploy AI assistants, autonomous agents, and multimodal AI with open models. ## How mobility gives language models a deeper understanding of place DevFeed: [How mobility gives language models a deeper understanding of place]() Original publisher: [Read original article]() Published: 2026-08-21T10:54:00Z Content type: article Language: en Sources: [The latest research from Google]() Topics: [Language models](), [AI Models](), [data](), [datasets](), [Google]() Tags: [ai](), [ai-models](), [algorithms-theory](), [artificial-intelligence](), [benchmark](), [benchmarks](), [data](), [datasets](), [earth-ai](), [embedding](), [google](), [language-models](), [machine-intelligence](), [mobility](), [model](), [models](), [points](), [research](), [vector]() ### AI overview Google Research introduces Mobility-Embedded POIs (ME-POIs), a framework that combines language-model-based text representations of places with aggregated, anonymized mobility patterns. The resulting embeddings capture both a place's identity and its changing functional activity, improving predictions such as visit intent, price level, opening hours, and busyness. ### Source excerpt Algorithms & Theory ## Raising machine-checked security benchmarks to advance hash-based SNARKs through agentic collaboration DevFeed: [Raising machine-checked security benchmarks to advance hash-based SNARKs through agentic collaboration]() Original publisher: [Read original article]() Author: Ethereum Foundation Formal Verification team Published: 2026-08-20T00:00:00Z Content type: article Language: en Sources: [Ethereum Foundation Blog]() Topics: [Formal verification](), [Lean](), [Ethereum](), [AI research agents](), [AI Models](), [Library](), [Security]() Tags: [agentic](), [ai-agents](), [collaboration](), [ethereum](), [formal-verification](), [leaderboard](), [paper](), [research](), [research-development](), [security](), [verification]() ### AI overview The Ethereum Foundation's Formal Verification team launched better.codes, an open autoresearch challenge focused on raising the machine-checked soundness bound of the Lean-formalized koalaIRS12 Reed-Solomon proximity problem toward a fixed 128-bit target. Submissions are checked by the Lean kernel and promoted proofs are shared publicly. ### Source excerpt better.codes, an open autoresearch challenge built by the Ethereum Foundation Formal Verification team in collaboration with Yukon and zkSecurity, is now live. better.codes takes a self-contained problem from the Proximity Prize research, formalized in Lean, and puts its soundness bound on a public leaderboard that anyone can push forward.... ## How AI Is Reshaping Cybersecurity for Attackers and Defenders DevFeed: [How AI Is Reshaping Cybersecurity for Attackers and Defenders]() Original publisher: [Read original article]() Published: 2026-08-17T05:30:00Z Content type: opinion Language: en Sources: [OpenAI News]() Topics: [Artificial Intelligence](), [Cybersecurity](), [OpenAI](), [Threat Research](), [Vulnerabilities](), [AI Models]() Tags: [ai](), [ai-models](), [cybersecurity](), [openai](), [openai-hugging-face-incident](), [security](), [tech-debt](), [vulnerabilities]() ### AI overview OpenAI discusses how AI is changing cybersecurity for both attackers and defenders. The article describes the OpenAI-Hugging Face incident, argues that AI can accelerate the discovery and exploitation of security weaknesses, and outlines defensive measures including stronger security fundamentals and AI-assisted vulnerability remediation. ### Source excerpt AI is reshaping cybersecurity for attackers and defenders alike. Learn how OpenAI is strengthening its defenses and what security teams can do now. ## MCP vs API DevFeed: [MCP vs API]() Original publisher: [Read original article]() Author: Roger Perkin Published: 2026-08-14T07:35:02Z Content type: article Language: en Sources: [Roger Perkin Network Automation Consultant]() Topics: [MCP vs API](), [API](), [Model Context Protocol](), [AI Models](), [Protocol (disambiguation)]() Tags: [ai-models](), [api](), [json-rpc](), [mcp](), [mcp-vs-api](), [rest-apis](), [rpc](), [vs]() ### AI overview This article explains that MCP and APIs serve different purposes. APIs connect software systems through predefined endpoints, while MCP lets AI models discover and use tools dynamically. MCP does not replace APIs; it operates on top of existing APIs as a standardized, AI-friendly translation layer. ### Source excerpt Ever since MCP became popular, I hear people saying "does MCP replace APIs?" Or do they both solve completely different problems? Firstly the facts: APIs connect software systems through predefined endpoints, while MCP enables AI models to discover and use tools dynamically. So, they are two different things, but can MCP replace APIs? More discussions ... ## Black Hat USA 2026: Will vulnerability discovery eventually decline in the AI era? DevFeed: [Black Hat USA 2026: Will vulnerability discovery eventually decline in the AI era?]() Original publisher: [Read original article]() Author: Tony Anscombe Published: 2026-08-13T14:30:00Z Content type: article Language: en Sources: [WeLiveSecurity]() Topics: [Cybersecurity](), [vulnerability](), [Artificial Intelligence](), [AI Models](), [Claude](), [anthropic](), [Linux](), [Software](), [Benchmark]() Tags: [ai](), [ai-models](), [anthropic](), [article](), [black-hat](), [business-security](), [claude](), [cybersecurity](), [linux](), [vulnerabilities]() ### AI overview The article examines whether the rapid growth of AI-assisted vulnerability discovery could eventually make software safer. It discusses research presented at Black Hat USA 2026, where AI workflows and GPT models reportedly identified hundreds to approximately 1,000 vulnerabilities, and considers the resulting strain on responsible disclosure, reporting, testing, and timely patching. ### Source excerpt And will today's surge in AI-driven vulnerability discovery eventually make tomorrow's software safer? ## Gemini 3.7 Flash now available on AI Gateway for 50% off DevFeed: [Gemini 3.7 Flash now available on AI Gateway for 50% off]() Original publisher: [Read original article]() Author: Jerilyn Zheng Published: 2026-08-13T00:00:00Z Content type: release Language: en Sources: [Vercel News]() Topics: [Artificial Intelligence](), [AI Models](), [Google](), [vercel ai sdk](), [AI-assisted coding](), [Vercel](), [API](), [web applications]() Tags: [2026](), [agentic](), [ai](), [api](), [api-keys](), [claude](), [claude-code](), [code](), [codex](), [coding](), [gemini](), [google](), [inference](), [models](), [opencode](), [performance](), [playground](), [pricing](), [routing](), [sdk]() ### AI overview Vercel announces that Google's Gemini 3.7 Flash is available through AI Gateway at 50% off until December 31, 2026. The release highlights improved reliability for software engineering and agentic work, stronger adherence when generating application code from design mocks, coding-agent integrations, a model playground, and gateway features for usage, cost, routing, retries, failover, and uptime. ### Source excerpt Gemini 3.7 Flash from Google is now available on AI Gateway for 50% off till December 31st, 2026. Gemini 3.7 Flash improves on prior Flash models at software engineering and agentic work. It resolves issues more reliably and spends less time stuck in failed agent loops, which matters on long tool-calling sequences where one derailment costs the rest of the run. It also generates desktop and web application code directly from design mocks, with closer adherence to the source design. To use Gemini 3.7 Flash, set model to google/gemini-3.7-flash in the AI SDK: To use it in a coding agent, run vercel ai-gateway coding-agents setup to connect Claude Code, Codex, OpenCode, or Pi, then select google/gemini-3.7-flash inside the agent. To try Gemini 3.7 Flash with no code, try the model in the model playground. AI Gateway provides a unified API for calling models, tracking usage and cost, and configuring retries, failover, and performance optimizations for higher-than-provider uptime. It includes built-in custom reporting, Zero Data Retention support, budgets for API keys, routing rules, and more. AI Gateway reflects provider pricing with no markup and does not charge a platform fee on inference, including on Bring Your Own Key (BYOK) requests. Read more ## Software engineering at a proprietary trading company: Optiver DevFeed: [Software engineering at a proprietary trading company: Optiver]() Original publisher: [Read original article]() Author: Gergely Orosz Published: 2026-08-11T16:17:39Z Content type: article Language: en Sources: [The Pragmatic Engineer]() Topics: [Software Engineering](), [Latency](), [AI Models](), [Hardware]() Tags: [ai-models](), [engineering](), [hardware](), [latency](), [software-engineering]() ### AI overview The article examines software engineering at Optiver, including a shift from latency-focused work toward building better AI models and ownership of the stack from applications through custom hardware. ### Source excerpt A shift from a focus on latency to building better AI models, owning the full stack from applications to building custom hardware, very different incentives to most tech companies in play, and more ## In-region inference, open models, and new European infrastructure for sovereign AI. DevFeed: [In-region inference, open models, and new European infrastructure for sovereign AI.]() Original publisher: [Read original article]() Published: 2026-08-11T12:00:27Z Content type: article Language: en Sources: [Mistral AI Blog]() Topics: [Artificial Intelligence](), [Inference](), [AI Infrastructure](), [AI Models](), [data centers](), [Cloud](), [Latency]() Tags: [ai](), [ai-infrastructure](), [ai-models](), [cloud](), [compliance](), [compute](), [data](), [data-centers](), [europe](), [inference](), [infrastructure](), [latency](), [models](), [open](), [production](), [scale]() ### AI overview Mistral outlines a sovereign AI infrastructure strategy centered on regional inference, open-model access, and long-term European compute capacity. Regional Endpoints let customers select European or US processing regions, while a Priority Tier offers committed service levels for mission-critical workloads. ### Source excerpt Mistral is bringing together the inference infrastructure, open models, and long-term commitments Europe needs to control its AI future, and setting a roadmap for the world. ## GeoPT helps AI models simulate how objects respond to physical forces DevFeed: [GeoPT helps AI models simulate how objects respond to physical forces]() Original publisher: [Read original article]() Author: Alex Shipps | MIT CSAIL Published: 2026-08-10T19:25:00Z Content type: news Language: en Sources: [MIT AI News]() Topics: [AI Models](), [Simulation](), [AI Research](), [Computer Science and Artificial Intelligence Laboratory (CSAIL)](), [Robotics]() Tags: [3-d-imaging](), [ai](), [ai-models](), [artificial-intelligence](), [computational-fluid-dynamics-cfd](), [computer-graphics](), [computer-modeling](), [computer-science-and-artificial-intelligence-laboratory-csail](), [computer-science-and-technology](), [crash-simulation](), [design](), [electrical-engineering-and-computer-science-eecs](), [geometric-pre-training](), [geopt](), [haixu-wu](), [human-computer-interaction](), [kaiming-he](), [manufacturing](), [minghao-guo](), [mit-csail](), [mit-eecs](), [mit-schwarzman-college-of-computing](), [neural-physics-simulation](), [paper](), [physics](), [physics-aware-ai](), [physics-foundation-models](), [research](), [robotics](), [school-of-engineering](), [self-supervised-learning](), [simulation](), [surrogate-modeling](), [synthetic-dynamics](), [transformer-based-simulators](), [wojciech-matusik]() ### AI overview Researchers at MIT CSAIL and Tsinghua University developed GeoPT, a pre-training approach that uses 3D simulations of mechanical interactions to help AI models learn physics more efficiently. The article reports that models using the approach reached peak performance twice as fast and trained on up to 60 percent less data than leading models. ### Source excerpt "GeoPT" helps AI models understand the basics of physics so they can simulate how objects respond to things like wind and water more efficiently and accurately. ## Why vulnerability response and deployment, not discovery, are the security bottleneck DevFeed: [Why vulnerability response and deployment, not discovery, are the security bottleneck]() Original publisher: [Read original article]() Author: Nicole Morgan Published: 2026-08-06T00:00:00Z Content type: opinion Language: en Sources: [Harness Blog]() Topics: [Vulnerabilities](), [Security](), [Artificial Intelligence](), [Large Language Model](), [AI Models]() Tags: [ai](), [ai-models](), [analysis](), [llm](), [security](), [vulnerabilities]() ### AI overview The article argues that AI-assisted vulnerability discovery is outpacing organizations' ability to triage, remediate, and deploy fixes. It presents the gap between detection and production deployment as the main security bottleneck, with remediation often taking days, weeks, or months. ### Source excerpt AI finds vulnerabilities 10x faster. Here's why response speed, not discovery, is the bottleneck keeping your organization exposed to zero-days and why security | Blog [Next page]()