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n. Control Tower now includes cost tracking and ROI dashboards to give finance teams visibility into model spend. The measurements track token consumption across providers such as OpenAI, Anthropic, and Google so customers can predict costs and tie spending to business outcomes. ServiceNow said it uses the AI Control Tower internally to manage over 1,600 AI assets and tracked half a billion dollars in cumulative AI value from internal use cases in 2025. "The number one question every CFO is aski

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and in this new world, ads don't make sense. Ads just don't pay for enough frontier tokens to make a difference, and token consumption is only going up from here. Along with this, OpenAI now offers subscription plans with even more aggressive discounts than what Anthropic offered before. You can pay OpenAI $20/month to get (seemingly) $1000+/month in tokens. This gives you a lot more "free" usage than Amp's ad-supported free tier and is a better choice if cost is your top concern. What about the

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or you to sign in with your ChatGPT account. After that, your session is cached locally. The recommended model is gpt-5.3-codex, which is the default. You can also select gpt-5.4 (OpenAI's latest omni model) or gpt-5.2-codex from the model picker. Gemini — via OAuth If you have a Google account with Gemini access, the Gemini (gemini oauth) provider lets you use goose with your existing account. Just pick Gemini when setting up goose for the first time (or changing providers). The first time you

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vice now delivers an end-to-end solution for reliable, production-grade tool calling, spanning fine-tuning through inference. Tool calls can be included in training data using the OpenAI-compatible schema. Functions are defined in a top-level tools array, and our service validates that every tool calls entry matches a declared tool, ensuring structurally correct data before training begins. At inference time, we’ve significantly improved tool call reliability to ensure the benefits of tool call

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ze it for your needs, clone the repository: Then open http://localhost:3001 and configure your API keys: OpenHands Cloud API Key - Get one from OpenHands Cloud; LLM API Key - Your OpenAI, Anthropic, or OpenHands LLM key; GitHub Token - Create one at github.com/settings/tokens with repo scope All credentials are stored locally in your browser and never sent to external servers except for the services they authenticate with. A Reference Implementation Vulnerability Fixer serves two purposes beyond

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20.1 于 2026 年 1 月 19 日发布，其开发速度可与商业产品相媲美。 对于因 Claude Code 的定价结构和使用上限而感到沮丧的开发者来说，Goose 代表了 AI 行业中日益罕见的东西：一个真正免费、无附加条件的严肃工作选项。 要理解 Goose 为何重要，你需要了解 Claude Code 的定价争议。 Anthropic 是一家由前 OpenAI 高管在旧金山创立的人工智能公司，它将 Claude Code 作为其订阅套餐的一部分提供。免费计划完全不提供任何访问权限。Pro 计划按年计费为每月 17 美元（或按月 20 美元），将用户限制为每五小时仅 10 到 40 条提示——这一限制会让严肃的开发者在几分钟的高强度工作后就耗尽额度。 Max 计划每月 100 美元和 200 美元，提供更多额度：分别为 50 到 200 条提示和 200 到 800 条提示，外加访问 Anthropic 最强大的模型 Claude 4.5 Opus。但即便是这些高级套餐也带有激怒开发者社区的限制。 7 月下旬，Anthropic 宣布了新的每周速率限制。在该系统下，Pro 用户

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eed Advantage In OnlineMind2Web, BU 1.0 agent completes tasks in 68 seconds on average. For context: Gemini 2.5 Computer Use: 225s; Claude Sonnet 4.5: 285s; Claude Sonnet 4: 295s; OpenAI Computer-Using Model: 330s We're significantly faster than other state-of-the-art models - taking on average 3 seconds per step with our strongest model. (See our full model speed and accuracy benchmark here) Seeing is Believing To put this in perspective, here's both agents executing the same task: "Find the mo

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llucinations and improve accuracy. Web search is provided as a REST API with deeper tool integrations in Ollama’s Python and JavaScript libraries. This also enables models such as OpenAI’s gpt-oss models to conduct long-running research tasks. Create an API key from your Ollama account. Install and run Ollama’s Python library Then make a request using ollama.web search: Example output JavaScript Install and run Ollama’s JavaScript library Example output Building a search agent Use Ollama’s web s

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rt through the noise to bring you a curated digest of important AI developments and practical advice for business leaders. Google adds AI image editing features in Slides and Vids OpenAI weighs encryption for temporary chats to strengthen privacy Salesforce partners report slower adoption of Agentforce in the enterprise Apple Intelligence features surface in the iOS 26 public beta, shaping user expectations at work Google adds AI image editing to Slides and Vids Summary: Google Workspace introdu

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at image understanding up to 8-10 images. By default, vLLM server accepts 1 image per request. Please pass --limit-mm-per-prompt image=10 to serve up to 10 images per request with OpenAI-compatible API. We also recommend checking out our multi-image offline inference example with Llama-4 here. Performance: With the configurations above, we observe the following output tokens/s for Scout-BF16 and Maverick-FP8: While more performance enhancements are on the way, we believe the Llama 4 models' effi

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ove such noise [3]. Addressing errors quickly becomes untenable for agentic evaluations that require complex scaffolds. For example, when using the SWE-bench coding benchmark [4], OpenAI researchers discovered that incorrect unit tests and broken environments caused many correct solutions to be rejected [5]. They paid software engineers to screen out ambiguous or unsolvable problems, resulting in SWE-bench Verified. While this new benchmark more accurately reflects model capabilities, creating i

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ished by V0 while delivering significant enhancements to performance and code complexity. 1. Optimized Execution Loop & API Server As a full-fledged continuous batching engine and OpenAI-compatible API server, vLLM’s core execution loop relies on CPU operations to manage request states between model forward passes. As GPUs are getting faster and significantly reducing model execution times, the CPU overhead for tasks like running the API server, scheduling work, preparing inputs, de-tokenizing o

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s: Separating API server and inference engine into different processes (PR 6883) Through careful profiling, we found that managing network requests and formatting the response for OpenAI protocol can consume quite a bit of CPU cycles, especially under high load with token streaming enabled. For example, Llama3 8B can generate 1 token every 13 ms under light load. This translates to the frontend needing to stream back 76 objects per second, and this demand further increases with hundreds of concu

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also reduces the interruption from long prompt processing for ongoing requests. You can install vLLM by running the following command or using our official docker image (vllm/vllm-openai): For the large Llama 405B model, vLLM supports it in several methods: FP8: vLLM runs the official FP8 quantized model natively on 8xA100 or 8xH100; Pipeline Parallelism: vLLM runs the official BF16 version on multiple nodes by placing different layers of the model on different nodes; Tensor Parallelism: vLLM ca

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inference and online serving. To use vLLM for offline inference, you can import vLLM and use the LLM class in your Python scripts: To use vLLM for online serving, you can start an OpenAI API-compatible server via: You can query the server with the same format as OpenAI API: For more ways to use vLLM, please check out the quickstart guide. Blog written by Woosuk Kwon and Zhuohan Li (UC Berkeley). Special thanks to Hao Zhang for the integration of vLLM and FastChat and for writing the correspondin

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