Stripe to Acquire AI Router OpenRouter for ~$8B
Stripe 以约 80 亿美元收购 AI 路由平台 OpenRouter ⭐️ 9.0/10

Stripe has agreed to acquire OpenRouter, an AI model routing platform, at a price reportedly just above $8 billion according to Reuters, though the official price was not disclosed. This marks a significant convergence of payment infrastructure and AI token economics. By uniting Stripe's dollar flows with OpenRouter's token flows, the deal could reshape how AI services are monetized and paid for. OpenRouter is known for routing queries to multiple large language models and charging based on token usage. The acquisition reportedly exceeds $8 billion, but neither company has officially confirmed the exact figure.

rss · AI Will(@FinanceYF5) · Aug 20, 03:38

Background: Stripe is a major online payment processing company. OpenRouter is a platform that aggregates various AI models and provides a unified API, letting developers choose among models and pay per token. The combined entity would integrate financial transactions with the growing AI application ecosystem.

Tags: #Stripe, #OpenRouter, #AI Infrastructure, #M&A, #Payments

OpenAI Teases AI That Can Do Anything in Your Browser
OpenAI 预告：可在浏览器中完成任何事的 AI ⭐️ 9.0/10

OpenAI co-founder Greg Brockman teased 'OpenAI for anything you can do in your browser,' an AI agent designed to perform any browser-based task. The post highlights Codex being used for browser, Chrome, and computer automation, from managing Gmail to configuring SaaS tools. This signals OpenAI's push from chat assistants to autonomous agents that complete multi-step tasks in real software environments. If realized, it could reshape daily digital work, enabling users to offload tedious browser workflows to AI. The tweet is short on technical specifics and no product name or release date was provided. The quoted list focuses on real-world chores such as closing accounting books, clearing LinkedIn queues, setting Stripe Radar rules, and browser-based QA on every pull request.

rss · Greg Brockman(@gdb) · Aug 20, 14:44

Background: Large language models are increasingly being wrapped in 'agent' frameworks that let them operate computers, browse the web, and use software. OpenAI's Codex is an AI system originally aimed at coding tasks, but the quoted user demonstrates it can be applied to general browser automation. This teaser suggests OpenAI is targeting a broader consumer-facing 'do anything in the browser' capability.

Tags: #AI, #OpenAI, #Browser Automation, #Agent, #LLM

GLM-5.3 (Max) shifts Pareto frontier in Code Arena WebDev benchmark
GLM-5.3（Max）在 Code Arena WebDev 基准测试中推动帕累托前沿 ⭐️ 8.0/10

GLM-5.3 (Max) by Z.ai scored 1597 points on the Code Arena: WebDev benchmark, ranking #2 among open models and #8 overall. At $3.65/M, it is on par with Qwen3.8 (Max) at $5.00/M and outperforms Gemini-3.7-flash-high ($2.86/M) and Deepseek-v4-flash-high ($1.10/M). This result shows that open-weights models can achieve top-tier coding performance at a competitive price, pushing the Pareto frontier of cost-performance. It strengthens the open AI ecosystem and may pressure both proprietary and other open model providers to improve efficiency. GLM-5.3 is a 743B-parameter base model further post-trained for coding and cyber defense. The open-weights release is still pending, meaning the #2 open-model ranking is provisional until the weights become available.

rss · Arena.ai(@lmarena_ai) · Aug 20, 23:27

Background: Code Arena: WebDev is a benchmark that evaluates large language models on web development tasks, including code generation and debugging. A Pareto frontier in this context represents the best achievable trade-off between cost and performance across models. Z.ai (formerly Zhipu AI) is known for the GLM series of models, which have consistently competed with leading open and closed models.

Tags: #LLM, #Benchmarks, #GLM, #AI, #Code Generation

Feng Ji shares 10 game dev principles after Black Myth: Zhong Kui trailer
冯骥发布游戏开发十条工作原则 ⭐️ 8.0/10

Feng Ji (Yuka), producer of the upcoming Black Myth: Zhong Kui, released a new gameplay trailer alongside a set of ten working principles for game development, covering self-resonance, audience alignment, and emphasizing highlights over flaws. These principles offer rare, candid insight from one of China's most influential game developers, providing practical guidance that extends beyond games to product and content strategy. They also reflect the philosophy of '知难而进' (advance despite difficulty) that helped Game Science achieve global recognition with Black Myth: Wukong. The ten principles include: move yourself first, then find external overlap; discover highlights before fixing flaws; prefer playable versions over documentation; treat fun as both goal and baseline; beauty has no fixed form; increase density when out of options; benchmark against the best while aiming to lead; value the difficult; and recognize that action validates principles. Feng Ji also recalled adding '知难而进' to the company's core values in 2019.

rss · 歸藏(guizang.ai)(@op7418) · Aug 20, 04:48

Background: Feng Ji (also known as Yuka) is co-founder and producer of Game Science, the studio behind the globally successful 2024 title Black Myth: Wukong, one of China's first AAA single-player games to gain worldwide recognition. The principles were shared in the context of the newly announced Black Myth: Zhong Kui, continuing the studio's focus on Chinese mythology. The post also reflects on Game Science's early years, which Feng Ji described as '东搞西搞，投机取巧' (messing around and seeking shortcuts), contrasting with the later commitment to tackling hard problems.

Tags: #Game Development, #Product Strategy, #Content Creation, #Design Principles

OpenAI Brings ChatGPT to Apple's iMessage
OpenAI 将 ChatGPT 引入苹果 iMessage ⭐️ 8.0/10

OpenAI officially announced that ChatGPT is being integrated with Apple's iMessage, allowing users to chat with the AI assistant directly inside the Messages app. The announcement was made through a video posted on X by the official ChatGPT account. This brings ChatGPT into one of the most widely used messaging apps on iOS, making AI assistance more accessible in everyday conversations. It also signals deeper competition among AI assistants for integration into consumer communication platforms. The integration was revealed in a brief video on X, but no specific release timeline, feature details, or supported regions have been shared. More information is expected from OpenAI about how the iMessage integration will be rolled out.

rss · The Rundown AI(@TheRundownAI) · Aug 20, 18:26

Background: ChatGPT is OpenAI's conversational AI assistant that can answer questions, draft text, and help with a wide range of tasks. iMessage is Apple's built-in messaging service for iPhone, iPad, and Mac. Integrating the two would let iPhone users access ChatGPT without leaving their conversations.

Tags: #AI, #ChatGPT, #iMessage, #Apple, #Integration

Cursor turns AI agents from one-shot tasks to long-term goals
Cursor 将 AI Agent 从一次性任务升级为长期目标 ⭐️ 8.0/10

Cursor has introduced /goal, /loop, and Custom Mode, which allow AI agents to work continuously on a defined objective until it is completed, with periodic progress checks and customizable execution rules. This transforms AI coding assistants from handling isolated, one-shot prompts to autonomously pursuing longer-horizon engineering objectives, such as fixing all flaky tests or making CI pass. It could significantly reduce the need for constant human supervision and boost developer productivity. The /goal command defines the target, /loop schedules periodic progress checks, and Custom Mode enforces specific rules throughout execution. An example from the post is: "/goal 修复所有不稳定测试，让CI恢复正常" (fix all flaky tests and make CI pass again).

rss · AI Will(@FinanceYF5) · Aug 20, 03:18

Background: Cursor is an AI-powered code editor that has gained popularity for its agentic coding features. Previously, its Agent mode typically handled one-off requests; these new commands shift it toward goal-driven, long-running autonomy. This mirrors a broader industry trend of AI agents evolving from short-horizon tasks to sustained, multi-step workflows.

Tags: #Cursor, #AI Agent, #AI Coding, #Developer Tools, #Automation

Gary Marcus: OpenAI's Enterprise Report Ignores the ROI Question
Gary Marcus 指出 OpenAI 企业报告回避 ROI 问题 ⭐️ 8.0/10

Gary Marcus publicly criticized OpenAI's 69-page report on ChatGPT enterprise adoption for failing to address return on investment, citing a Fortune article that found no correlation between AI usage and revenue per employee. This criticism is significant because enterprises are making massive AI investments and need clear evidence of value; the lack of ROI discussion could influence adoption decisions and fuel broader debates about AI's economic impact. The Fortune article appeared to be published on August 13, 2026, and the tweet includes engagement metrics such as 32 likes and 2,130 views. Marcus specifically highlighted that the report 'doesn't address the biggest question: ROI'.

rss · Gary Marcus(@GaryMarcus) · Aug 20, 22:28

Background: OpenAI has been aggressively promoting ChatGPT for enterprise use, and this 69-page report appears to be part of that effort. However, critics argue that without demonstrating a clear return on investment, enterprise adoption may be driven by hype rather than proven economic benefit. The Fortune article referenced seems to have analyzed data and found no correlation between AI use and revenue per employee, adding fuel to the skepticism.

Tags: #AI, #OpenAI, #ROI, #Enterprise Adoption, #AI Economics

Jim Fan: GEN-1.5's Success Rooted in Repetitive Human Data
Jim Fan：GEN-1.5 的成功源于人类重复动作数据 ⭐️ 8.0/10

In a recent post, Jim Fan explained that GEN-1.5's impressive performance stems from naturally repetitive motions in human-collected data, specifically symmetric patterns (e.g., driving paired bolts) and recovery behaviors after fumbles. He argues that keeping these 'failed' first halves in training data enables in-context improvement at test time. This analysis provides a simple but powerful recipe for scaling robot manipulation data: instead of over-sanitizing demonstrations, let natural repetition and recovery serve as free training signals. It also strengthens the case for direct data collection methods like UMI over teleoperation, potentially reshaping how the robotics community collects training data. Jim Fan specifically highlights UMI (Universal Manipulation Interface), which lets a human wear a robot gripper to collect data directly, bypassing the physical-intuition loss caused by teleoperation layers (human → VR/skeletal device → robot → data). He also notes that with sufficient data, many behaviors can be zero-shot, though current demos are too simple to definitively confirm in-context learning.

rss · Jim Fan(@DrJimFan) · Aug 20, 15:48

Background: GEN-1.5 is a robot manipulation model that learns from human demonstration data. In robot learning, collecting high-quality data is a major bottleneck: teleoperation (remote control via VR or skeletal tracking) adds separation between human and robot, while UMI lets humans directly embody the robot gripper to record actions. Jim Fan's point about repetitive motions—like assembling symmetrical parts or picking up dropped objects—is that these natural repetitions act as built-in data augmentation, giving the model free examples of how to continue an action after interruption. This connects to broader trends in 'in-context learning' and zero-shot generalization in robotics.

Tags: #Robotics, #AI, #Learning from Demonstration, #Data, #Manipulation

Hackers Poison Rust 'arrayref' Crate to Distribute Infostealer Malware
黑客投毒 Rust 'arrayref' 包，分发信息窃取恶意软件 ⭐️ 8.0/10

Attackers published a malicious version of the arrayref Rust crate to slip infostealer malware into developers' builds. The poisoned crate was designed to steal sensitive information from developers who inadvertently used it. This is a supply chain attack on a widely used Rust crate, which could compromise many downstream projects and expose developers' credentials and secrets. It underscores the growing risk of open-source ecosystems being targeted to distribute malware. The malicious package was published under the same name as the legitimate arrayref crate, a common typosquatting or takeover method. Developers who fetched the tainted version could have had passwords, API keys, and other secrets exfiltrated to the attackers.

rss · Hacker News: Newest · Aug 21, 00:07

Background: Rust crates are packages distributed through the crates.io registry and are commonly used as dependencies in Rust projects. Supply chain attacks on package managers have become a frequent way for hackers to infect developers' machines because the malicious code runs automatically during the build process. Infostealers are malware designed to harvest credentials and personal data from infected systems.

Tags: #security, #supply chain, #rust, #malware, #open source

Replit and OpenAI announce long-overdue partnership, bringing GPT-5.6 Luna to free tier
Replit 与 OpenAI 宣布迟来的合作，将 GPT-5.6 Luna 引入免费版 ⭐️ 8.0/10

Replit CEO Amjad Masad announced a partnership with OpenAI, calling it long overdue, and shared a video of OpenAI CEO Sam Altman telling the story of how the two companies first met. A quoted Replit post says that Replit Free Mode will be powered by OpenAI GPT-5.6 Luna, with the goal of making intelligence accessible to everyone. The partnership could bring OpenAI's models directly into Replit's widely used cloud coding platform, making AI-assisted development more accessible to learners and independent developers. It also reflects a broader trend of AI labs and developer tooling companies forging closer alliances to embed generative AI into everyday software creation. The announcement notes that the contact predates Replit's entry into Y Combinator, with Paul Graham asking Sam Altman to recruit the startup. Specific terms, pricing, and release timing for the GPT-5.6 Luna-powered Free Mode were not provided in the post.

rss · Amjad Masad(@amasad) · Aug 20, 19:01

Background: Replit is a cloud-based development environment that lets users write, run, and deploy code from a browser, making it popular with beginners, educators, and startup teams. OpenAI is the company behind GPT models and ChatGPT, and embedding those models into coding tools could turn natural-language prompts into working software more easily. Y Combinator, Paul Graham, and Sam Altman are all prominent names in the startup world, which helps explain why the partnership feels symbolic to Replit's CEO.

Tags: #Replit, #OpenAI, #Partnership, #AI Coding, #Developer Tools

Meta unveils Muse Spark 1.2 multimodal model that turns visuals into code and guides robots.
Meta 发布 Muse Spark 1.2 多模态模型，可将视觉转化为代码并引导机器人。 ⭐️ 8.0/10

Meta announced Muse Spark 1.2, a multimodal model that supports turning visuals into working code, audio-visual understanding, and translating perception into physical action. The release includes new evals and a demo showing the model guiding a robot to navigate an unstructured environment to find a rubber duck. Muse Spark 1.2 demonstrates the convergence of perception, reasoning, and action in a single model, which could significantly impact enterprise video workflows and robotics. By showing vision-to-code and robot navigation capabilities, Meta positions itself at the forefront of multimodal AI applications. The model highlights a demo where it parses multimodal observations and calls tools to guide a robot in an unstructured environment. It also emphasizes robust audio-visual understanding aimed at video-heavy workflows common in real-world enterprise use.

rss · AI at Meta(@AIatMeta) · Aug 20, 17:06

Background: Multimodal AI models are designed to process and integrate information from multiple modalities such as text, images, audio, and video. By unifying perception and action, such models can power applications ranging from code generation from screenshots to autonomous robot navigation, making AI more applicable to real-world tasks.

Tags: #AI, #Multimodal, #Meta, #Robotics, #Model Release

OpenAI Codex Accelerates Code Migrations from Years to Weeks
OpenAI Codex 将代码迁移从数年缩短至数周 ⭐️ 8.0/10

OpenAI president Greg Brockman highlighted that Asana used Codex to finish a frontend test migration from Enzyme to React Testing Library in two calendar weeks—a project originally expected to take five more years. This is a high-profile demonstration that AI-assisted engineering can compress years-long migration projects into weeks, potentially reshaping how companies approach technical debt and legacy systems. It also signals competitive pressure on developer tooling, as AI agents become practical for large-scale codebase maintenance. The claim is based on a quoted tweet from OpenAI Developers with a link to an Asana case study on OpenAI's site, not a detailed technical report. The migration targeted frontend tests specifically, so the 'years to weeks' improvement may not generalize to every kind of code migration.

rss · Greg Brockman(@gdb) · Aug 20, 06:36

Background: Codex is OpenAI's AI software engineering agent designed to read codebases and perform coding tasks such as refactors and migrations. Enzyme and React Testing Library are both popular JavaScript testing tools for React, but they use different APIs and patterns, so migrating between them means rewriting many test files. Asana, a work management company, has a sufficiently large codebase that this frontend test migration had been expected to take five more years.

Tags: #AI-Assisted Development, #Codex, #Code Migration, #OpenAI, #Developer Tools

Pika unveils multimodal Music model with unified latent-diffusion decoder
Pika 发布多模态音乐模型，采用统一潜在扩散解码器 ⭐️ 8.0/10

Pika introduced its new Music model, which accepts text, lyrics, voice references, and music references either individually or combined. All signals are processed inside one shared latent-diffusion decoder to generate a complete track. This marks a notable advancement in AI music generation because it unifies multiple control signals in a single decoder rather than chaining separate workflows. The approach could improve coherence and creative control for musicians and content creators using generative AI. The model accepts four input modalities: text, lyrics, voice references, and music references. In its single latent-diffusion decoder, it resolves musical structure, vocal character, written direction, and the reference 'vibe' simultaneously.

rss · Pika(@pika_labs) · Aug 20, 17:01

Background: Latent diffusion is a generative AI technique that creates content by denoising compressed representations in a latent space, often used for image, video, and music generation. Most music-generation tools rely on separate pipelines or cascaded models for tasks such as lyric-to-melody or voice cloning, whereas Pika proposes a single shared decoder. This design may allow the model to better integrate all input signals and produce a more cohesive final track.

Tags: #AI music generation, #multimodal, #diffusion models, #generative AI, #Pika

JetBrains Announces Day-One Support for Go 1.27
JetBrains 宣布首日支持 Go 1.27 ⭐️ 8.0/10

JetBrains announces day-one IDE support for Go 1.27, highlighting new language features such as generic methods, promoted field names in struct composite literals, and improved function type inference, plus tooling updates like new go fix modernizers and a goroutine leak profiling profile. This matters because day-one IDE support lets Go developers immediately use new language and tooling features without waiting for plugin updates, reducing friction when upgrading. It also signals the growing importance of Go's evolving language features and profiling capabilities for production applications. Go 1.27 includes generic methods, which allow methods to have their own type parameters, and better function type inference. The go fix tool gains new modernizers, and a new profiling mode helps detect goroutine leaks.

rss · The JetBrains Blog · Aug 20, 10:19

Background: Go is a statically typed, compiled programming language developed at Google, known for its simplicity and strong concurrency support. Each release, like Go 1.27, introduces language and toolchain improvements; IDE support from JetBrains (GoLand) is often aligned with these releases. Generic methods and improved type inference are part of Go's continued evolution after generics were introduced in Go 1.18.

Tags: #Go, #Release, #JetBrains, #IDE, #Programming Language

Hacker News Roundup: Go 1.27, OpenRouter-Stripe, Android GPLv2, Malicious Rust Crate
黑客新闻精选：Go 1.27、OpenRouter 与 Stripe 合并、Android GPLv2 违规、恶意 Rust 包 ⭐️ 8.0/10

The daily Hacker News roundup for August 21, 2026, covers ten top stories, including the Go 1.27 release with generics methods, OpenRouter joining Stripe, Google's GPLv2 violation, and a malicious Rust package named arrayref removed from crates.io. This roundup surfaces high-impact developments across programming languages, AI infrastructure, and open-source security, affecting developers and companies. The Go release and OpenRouter-Stripe merger signal industry momentum, while the GPLv2 violation and malicious package highlight ongoing trust and licensing risks. Go 1.27 introduces generics methods, performance optimizations, and a new standard library package encoding/json/v2. OpenRouter, which processes over 10 trillion tokens daily and offers 400+ AI models, will retain its name, mission, and product roadmap after the Stripe merger, with the deal expected to close within weeks.

rss · HackerNews每日摘要 on SuperTechFans · Aug 21, 00:04

Background: Hacker News is a popular technology forum where the community submits and votes on links related to computing, startups, and science. The roundup aggregates the day's most-discussed items; this edition includes Go 1.27, a major release of the programming language, and OpenRouter, a marketplace and gateway for AI models.

Discussion: The included Hacker News comments for the top stories reflect a mix of practical advice and skepticism: users debate the ethics of pasting AI replies, the enterprise suitability of OpenRouter, and the technical implications of the Android GPLv2 violation.

Tags: #Hacker News, #Go, #AI, #Security, #OpenRouter

Sutton Calls Synthetic Data a 'Big Mistake' for AI Scaling
萨顿称合成数据是 AI 扩展的“大错误” ⭐️ 8.0/10

Turing Award winner Richard Sutton has publicly criticized synthetic data as a "big mistake" for scaling large language models, arguing that any simulation of the real world is "microscopic" and that human expertise acts as a bottleneck. He advocates instead for agents that learn continually from their own real-world experience rather than relying on frozen models. This challenge strikes at the dominant industry practice of using synthetic data to scale AI, and could influence research priorities and data strategies across major labs. Because Sutton is a pioneer in reinforcement learning and a Turing Award winner, his provocative claim is likely to intensify the ongoing debate about scaling laws and the most promising path toward more capable, general AI. Sutton's argument rests on the "Big World Hypothesis," the idea that the real world is infinitely complex, making any synthetic simulation negligible in comparison. His proposed alternative involves continual learning from direct experience, a sharp contrast to the current focus on static datasets and frozen model weights.

rss · The Decoder · Aug 20, 12:14

Background: Synthetic data is artificially generated data used to supplement or replace real-world data in AI training, often because high-quality real data is scarce, expensive, or raises privacy concerns. Sutton is a foundational figure in reinforcement learning, a paradigm in which agents learn by interacting with an environment and receiving rewards, rather than from passively processed static examples. The debate reflects a deeper tension between data-centric AI scaling, exemplified by giant models trained on massive datasets, and experience-driven learning that more closely mirrors how animals and humans adapt to their environment.

Tags: #synthetic data, #large language models, #reinforcement learning, #AI scaling, #Richard Sutton

Anthropic's most capable model codenamed 'Model 2' is internal-only
Anthropic 最强模型“Model 2”仅供内部使用 ⭐️ 8.0/10

Anthropic is reportedly using an unpublished AI model internally, codenamed 'Model 2.' According to The Decoder, this model is more capable than any public Claude model. This matters because it suggests Anthropic's most advanced AI may be kept out of public hands, potentially for competitive or safety reasons. It could also signal that the company's public models lag behind its internal frontier capabilities. No technical details about the model's architecture, size, or training have been disclosed. The report is attributed to anonymous sources, and Anthropic has not officially confirmed the model's existence.

rss · The Decoder · Aug 20, 10:04

Background: Anthropic is a leading AI research company best known for its Claude line of large language models. Many AI firms develop internal models that exceed their public offerings, either through research experimentation or as part of private infrastructure. This practice can create a gap between what is publicly available and what the company itself uses.

Tags: #AI, #Anthropic, #Claude, #Internal Model

Unitree IPO fueled by state-backed circular data deals, FT reports
报告称宇树科技 IPO 受国资循环数据交易推动 ⭐️ 8.0/10

Unitree Robotics surged 460 percent in its Shanghai IPO, reaching a valuation of around $50 billion. A Financial Times report reveals that much of the demand for its robots comes from state-backed training centers that buy the machines and sell the resulting data back to manufacturers, creating a circular financing scheme. This exposes a state-linked circular financing mechanism in China's AI robotics sector, raising concerns about the authenticity of demand and the transparency of IPO valuations. It echoes similar criticism leveled at Nvidia in the US, where investments in AI startups that buy its chips are seen by some as a circular scheme. Unitree's valuation reached roughly $50 billion after a 460 percent first-day gain on the Shanghai exchange. The circular model involves state-backed training centers purchasing humanoid robots and then selling the collected training data back to robot manufacturers like Unitree, inflating apparent market demand.

rss · The Decoder · Aug 20, 09:23

Background: Circular financing occurs when a company's revenue is supported by related parties that buy its products using capital that ultimately flows back to the seller, creating the illusion of strong market demand. In China, state-backed entities have been used to advance strategic AI and robotics goals. Similar concerns have been raised in the US over Nvidia's practice of investing in AI startups that use the funding to buy Nvidia chips, which some critics label as a circular scheme.

Tags: #AI, #China, #Robotics, #IPO, #Circular Financing

Terence Tao warns AI could trigger mathematics' biggest crisis since Gödel
陶哲轩警告：AI 或引发数学自哥德尔以来最大危机 ⭐️ 8.0/10

In a new essay, Terence Tao warns that AI-generated proofs could become too complex for humans to verify, potentially pushing mathematics into a crisis comparable to the foundational upheaval around 1900. He argues that a proof no human can explain should be considered incomplete. The warning challenges longstanding notions of mathematical proof and raises questions about what counts as a contribution and who did the work. It could reshape how mathematics is practiced, published, rewarded, and trusted in an AI-driven era. Tao's rule of thumb is that a proof no human can explain should be considered incomplete. He frames the coming disruption not as a crisis of mathematical truth, but as a test of the field's values.

rss · The Decoder · Aug 20, 08:49

Background: Around 1900, discoveries such as Russell's paradox and Gödel's incompleteness theorems revealed the limits of formal systems and sparked a foundational crisis in mathematics. Gödel's 1931 theorems proved that any consistent system strong enough for arithmetic contains statements that can be neither proved nor disproved. Tao now suggests AI-built proofs could pose a new kind of problem: not about whether a theorem is true, but about whether humans can comprehend and validate the reasoning.

Tags: #AI, #Mathematics, #Terence Tao, #Mathematical Proof, #Philosophy of Mathematics

📊 Run stats · Total 5m 54s · AI analysis 2m 49s · Tokens 0.51 MCY (input 0.30 / output 0.21 MCY)