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All digests tagged Cloud Computing
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Managed Agents run coding agents in secure cloud sandboxes via a single API call, enabling scalable, multi-agent workflows for tasks like financial research and reporting.
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OpenAI unveils Codex Cloud, allowing persistent work in customizable cloud environments. New Codex CLI features include agent management and conversation forking. The episode also showcases building a retro ramen simulator game using Ultrafast.
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Is the agent harness dead? Jediah Katz explains that while models are absorbing coding skills via the data flywheel, the harness remains critical for infrastructure, security, and unlocking new, non-coding frontiers.
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The future of AI requires a shift from pull-based web search to proactive, agent-driven systems. CoreWeave and Parallel Web Systems discuss building the necessary GPU-native infrastructure to handle this massive scale.
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CoreWeave announced CoreWeave Forge, a unified platform for the entire AI loop (Run, observe, curate, improve, evaluate). Key launches include Vera Rubin NVL72 and tools like Agent Lens and Model Distillation, streamlining LLMOps.
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The future of autonomous agents lies in a neuro-symbolic approach, combining VLM adaptability with deterministic coding agents (Simulan). This infrastructure is designed for scalable cloud deployment, enabling automation in legacy enterprise systems.
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AI agents pose a 'lethal trifecta' threat: private data access, untrusted content exposure, and external communication. Secure adoption requires three guardrails: CDEs, a Model Proxy, and an Agent Firewall.
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Codex Cloud allows agents to run tasks in managed environments, eliminating the need for local infrastructure upkeep. The system supports private resource access using Tailscale and OIDC, enabling secure, scalable development.
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The Gemini Interactions API handles server-side state and long-running background jobs, replacing user/model turns with a structured timeline of steps. Agents run in a managed cloud sandbox, allowing them to execute code and share persistent environments.
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The future of AI infrastructure demands a shift from separated cloud layers to a co-designed stack. Speakers detailed the move from pull-based web search to agent-driven, push-based data consumption, emphasizing the need for GPU-native indexing and robust, holistic security.
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CoreWeave unveiled Forge, a unified platform for the AI Loop (Run, Observe, Curate, Improve, Evaluate). New hardware like the Vera Rubin NVL72 and Vera CPU accelerate the deployment of complex, agentic AI systems from concept to production.
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Running open LLMs in production requires managing the full loop: inference, data, post-training, and deployment. Nebius Token Factory provides a full-stack solution, optimizing performance using techniques like speculative decoding and KV cache offloading.
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Kimchi is an open-source coding harness that optimizes LLM usage by measuring cost per task, not cost per token. It features Ferment for autonomous, milestone-based coding and Teleport for remote, continuous agent sessions.
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OpenAI unveiled major advancements in AI agents, focusing on 'Computer Use' to achieve superhuman software operation. Key announcements include Dots, GPT-6.1, and the Decisions API.
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OpenAI unveils Dots, always-on AI agents, and ChatGPT Space for team collaboration. Key launches include UltraFast, GPT-6.1, and Codex in the Cloud, revolutionizing development workflows and AI capabilities.
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Discover how CoreWeave builds an end-to-end AI platform, integrating purpose-built GPU infrastructure, LOTA storage, and Mission Control™ for reliable, large-scale AI training and inference.
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Learn how to run powerful coding agents like Claude Code and Codex in a persistent cloud environment using a VPS and Herdr. This guide covers everything from secure SSH setup to advanced workflow integration.
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Skydio detailed its full-stack autonomy stack, demonstrating how drones are becoming critical infrastructure. The system uses World Models and VLM agents to orchestrate multiple simultaneous missions, moving beyond manual piloting to scalable, reliable operations.
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Agentic inference requires optimizing end-to-end task latency, not single-request latency. FriendliAI's cloud architecture uses Prefix Caching and Cache Aware Routing to make open-weight models economically viable for production agents.
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The inference platform supports two models: serverless (pay-per-token) and dedicated (billed per GPU hour). Optimization relies heavily on KV cache awareness and techniques like 4-bit quantization.
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