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isioning code takes. Unikraft in production Each agent gets its own Unikraft micro-VM, booting in under a second. We provision them via Unikraft Cloud's REST API on dedicated bare metal machines in AWS. The sandbox receives only three env variables from the outside world: SESSION TOKEN, CONTROL PLANE URL, and SESSION ID. No AWS keys, no database credentials, no API tokens. Unikraft gives us scale-to-zero out of the box. When a sandbox is idle, the VM suspends. When the next request comes in, it

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to clone repositories, install dependencies, and build fresh filesystem snapshots — so sandboxes always start from a near-current state; Dicts manage session locks and store image metadata, providing the shared coordination layer that makes multiplayer sessions possible; Queues route prompts from any client — Slack, web, Chrome extension — into the right session, decoupling input from execution so multiple clients can feed into one session without conflicts These are what allow Inspect to suppor

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, a code terminal, and the ability to string these together to actually do stuff like researching, building, and testing your new website from scratch. Manus (recently acquired by Meta) was essentially a standalone harness that could wrap around multiple models. OpenClaw, which made big news recently, is mostly a harness that allows you to use any AI model locally on your computer. Until recently, you didn’t have to know this. The model was the product, the app was the website, and the harness w

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essing of a query into two phases, processed by different instances: First the router sends a “prefill request” (by setting max tokens=1 and by setting an empty set of KV Transfer metadata) to a prefill vLLM instance to compute the KVCache of the request. On completion, the router transfers KVCache metadata alongside a “decode request” to a decode vLLM instance. KVCache transfer is initiated through NIXL and token generation happens on the decode vLLM instance by using and extending the transfer

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er the past few weeks. Here are a few that we have found particularly useful: Agent Sandbox: Isolated execution environment for running untrusted code safely; Agent Skill Creator: Meta-skill for creating Claude agents autonomously with comprehensive skill architecture patterns; BigQuery: Expert use of the bq cli tool for querying BigQuery datasets; Tmux: Run servers and long-running tasks in the background; Web Browser: Interact with web pages via Chrome DevTools Protocol for clicking, filling f

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eck the generated proofs (LLM 1), but who checks the proof verifier? To make the proof verifier more robust and prevent it from hallucinating issues, they developed a third LLM, a meta-verifier. The meta-verifier (LLM 3) is also developed with reinforcement learning, similar to LLM 2. While the use of a meta-verifier is not required, the DeepSeek team reported that: the average quality score of the verifier’s proof analyses – as evaluated by the meta-verifier – improved from 0.85 to 0.96, while

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rameters: documentId (required, string): The ID of the document to retrieve fields (optional, string): Specific fields to return Returns: Document object with title, body content, metadata, permissions, etc. Copy salesforce.updateRecord Description: Updates a record in Salesforce Parameters: objectType (required, string): Type of Salesforce object (Lead, Contact, Account, etc.) recordId (required, string): The ID of the record to update data (required, object): Fields to update with their new va

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of offering fair measurements and comparison for the community. Thank you to Kimbo Chen, Dylan Patel, and others. We’re excited to keep refining and expanding our optimizations to unlock even greater capabilities in the weeks and months ahead! Related Posts Announcing vllm-metal: Concurrent Serving on Apple Silicon vllm-metal brings vLLM's paged, continuously batched serving stack to Apple Silicon, with flatter TTFT under concurrent agent load, batched MTP, and automatic M5 prefill acceleration.

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ut rather introduce external organization and indexing systems like file systems, inboxes, and bookmarks to retrieve relevant information on demand. Beyond storage efficiency, the metadata of these references provides a mechanism to efficiently refine behavior, whether explicitly provided or intuitive. To an agent operating in a file system, the presence of a file named test utils.py in a tests folder implies a different purpose than a file with the same name located in src/core logic/ Folder hi

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ol. Tools can consolidate functionality, handling potentially multiple discrete operations (or API calls) under the hood. For example, tools can enrich tool responses with related metadata or handle frequently chained, multi-step tasks in a single tool call. Here are some examples: Instead of implementing a list users , list events , and create event tools, consider implementing a schedule event tool which finds availability and schedules an event. Instead of implementing a read logs tool, consi

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, and forward-pass buffers such as input ids, positions, etc.); Instantiate an InputBatch object (holds CPU-side forward-pass buffers, block tables for KV-cache indexing, sampling metadata, etc.) Load model: Instantiate the model architecture; Load the model weights; Call model.eval() (PyTorch's inference mode); Optional: call torch.compile() on the model Initialize KV cache Get per-layer KV-cache spec. Historically this was always FullAttentionSpec (homogeneous transformer), but with hybrid mod

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, projects, related issue links—use the same visual language as the rest of Linear, so they blend naturally into the workflow. And we’re careful not to blur the line between issue metadata set by humans or rules and suggestions from Triage Intelligence. The UI makes that distinction clear, so you always know what came from the system and what came from your team. From there, each piece of the UI reinforces trust and transparency. Hovering over a suggestion reveals the model’s reasoning in plain

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ows, and abstractions. What's more natural for us than viewing LLMs as an extremely slow kind of unreliable computer that we program with natural language? This is a weird form of metaprogramming: we write "code" in the form of prompts that execute on the LLM to produce the actual code that runs on real CPUs. Yes, I know LLMs aren't actually computers (though there are some papers on arXiv...). The metaphor is a bit stretched. But here's the thing: as developers, we're used to encoding specifica

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ngine using the LLM API. Here is the same model being served using the LLM API. Transformers and vLLM: Inference in Action Let’s start with a simple text generation task using the meta-llama/Llama-3.2-1B model to see how these libraries stack up. Infer with Transformers The transformers library shines in its simplicity and versatility. Using its pipeline API, inference is a breeze: This approach is perfect for prototyping or small-scale tasks, but it’s not optimized for high-volume inference or

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efficiency, showing you how to leverage the power of open-source LLM inference on AMD. If you just want to see the optimal parameters, jump to the Quick Start Guide. Introduction Meta recently announced they're running 100% of their live Llama 3.1 405B model traffic on AMD MI300X GPUs, showcasing the power and readiness of AMD's ROCm platform for large language model (LLM) inference. This exciting news coincides with the release of ROCm 6.2, which brings significant improvements to vLLM support

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