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Modal's Agent-Native Cloud: DX→AX, sandboxes, elastic inference, and 100,000 rollouts Modal CTO Akshat Bubna explains why developer experience is becoming agent experience, why agents need infra they can operate instead of YAML they have to reason through, how sandboxes turn the agent loop into something real, why elastic inference...

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16,308 görüntüleme • 3 ay önce •via X (Twitter)

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🆕Daytona’s Agent-Native Compute: 60ms sandboxes, 50K startups in 75 sec, 850K daily runs, RL/evals, CLI > MCP, & the end of localhost Daytona CEO Ivan Burazin explains why AI agents need composable computers, how Daytona pivoted from human dev environments to agent sandboxes, why bare metal and stateful snapshots matter, how RL workloads went from 0% to ~50% of usage, why Kubernetes breaks down at agent scale, and why the AI cloud may look more like Stripe than AWS.
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🆕Daytona’s Agent-Native Compute: 60ms sandboxes, 50K startups in 75 sec, 850K daily runs, RL/evals, CLI > MCP, & the end of localhost Daytona CEO Ivan Burazin explains why AI agents need composable computers, how Daytona pivoted from human dev environments to agent sandboxes, why bare metal and stateful snapshots matter, how RL workloads went from 0% to ~50% of usage, why Kubernetes breaks down at agent scale, and why the AI cloud may look more like Stripe than AWS.

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OpenAI's New Agent Stack: Superhuman Computer Use, Dots, Decisions API, UltraFast, & the AI Cloud OpenAI’s Ari Weinstein and Nikunj Handa explain why Computer Use is already becoming faster than humans at real-world tasks, how Dots gives agents their own cloud computers, why better models can now debug and recover from failures, how Decisions API went from idea to product in weeks, and why async tools, UltraFast inference, caching, compaction, and higher-level agent primitives are turning OpenAI’s API into an AI-native cloud.

Latent Space: AI News & Views

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Every agent needs its own computer: my conversation with Ivan Burazin, CEO of Daytona, about sandboxes and the emerging agent stack. 00:00 Intro 02:13 What is an AI agent sandbox? 03:17 Security risks of running agents locally 05:17 Stateful vs. stateless hyperscalers 07:04 The history of cloud IDEs and the end of localhost 09:45 Do all AI agents need a sandbox? 12:26 Sandbox use cases: RL evals & background agents 14:10 Unpacking the emerging AI Agent Stack 16:20 The unsolved problem of agent memory and learning 19:37 Where sandboxes fit in the agent harness 21:35 OpenAI, Anthropic, and agent SDKs 23:06 Ivan's founder journey: From CodeAnywhere to Daytona 26:59 GTM strategies and building developer communities 33:48 Why customer support is your best GTM strategy 35:34 Leveraging Twitter during the AI super cycle 40:50 The technical anatomy of a sandbox 41:53 Why fast spin-up speeds maximize GPU efficiency 46:09 Firecracker, QEMU, and isolation primitives 49:58 Why sandbox snapshots and state forking matter 51:40 Why Daytona built a custom scheduler from scratch 55:24 The challenge of long-running stateful sandboxes 58:10 The build your own sandbox trap 1:01:03 Why AI agents might trigger a global CPU shortage 1:02:46 The future of the AI Agent Stack
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Every agent needs its own computer: my conversation with Ivan Burazin, CEO of Daytona, about sandboxes and the emerging agent stack. 00:00 Intro 02:13 What is an AI agent sandbox? 03:17 Security risks of running agents locally 05:17 Stateful vs. stateless hyperscalers 07:04 The history of cloud IDEs and the end of localhost 09:45 Do all AI agents need a sandbox? 12:26 Sandbox use cases: RL evals & background agents 14:10 Unpacking the emerging AI Agent Stack 16:20 The unsolved problem of agent memory and learning 19:37 Where sandboxes fit in the agent harness 21:35 OpenAI, Anthropic, and agent SDKs 23:06 Ivan's founder journey: From CodeAnywhere to Daytona 26:59 GTM strategies and building developer communities 33:48 Why customer support is your best GTM strategy 35:34 Leveraging Twitter during the AI super cycle 40:50 The technical anatomy of a sandbox 41:53 Why fast spin-up speeds maximize GPU efficiency 46:09 Firecracker, QEMU, and isolation primitives 49:58 Why sandbox snapshots and state forking matter 51:40 Why Daytona built a custom scheduler from scratch 55:24 The challenge of long-running stateful sandboxes 58:10 The build your own sandbox trap 1:01:03 Why AI agents might trigger a global CPU shortage 1:02:46 The future of the AI Agent Stack

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Introducing vmux - incredibly fast, stateful cloud sandboxes for coding agents for the first time you get persistent GPU/CPU sandboxes via Modal/CF backed by Durable Objects to stream logs live, native preview URLs, and attach a real shell spin up a notebook or train nanogpt via codex - with a Modal sandbox spun up in seconds
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Introducing vmux - incredibly fast, stateful cloud sandboxes for coding agents for the first time you get persistent GPU/CPU sandboxes via Modal/CF backed by Durable Objects to stream logs live, native preview URLs, and attach a real shell spin up a notebook or train nanogpt via codex - with a Modal sandbox spun up in seconds

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Turn your AI agent into an Elastic expert. Elastic Agent Skills puts native platform expertise in your agent, built by the engineers who develop and ship Elastic. - Accurate ES|QL. - Correct Kibana dashboards. - Security and observability workflows that run correctly, first time.
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Turn your AI agent into an Elastic expert. Elastic Agent Skills puts native platform expertise in your agent, built by the engineers who develop and ship Elastic. - Accurate ES|QL. - Correct Kibana dashboards. - Security and observability workflows that run correctly, first time.

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Today we're introducing Cloud Sandboxes: same microVM isolation as Docker Sandboxes on your laptop, same CLI, always-on Docker-managed compute. Close your laptop. Keep the agent running. Move between local and cloud with one command. $250 credit for new accounts:
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Today we're introducing Cloud Sandboxes: same microVM isolation as Docker Sandboxes on your laptop, same CLI, always-on Docker-managed compute. Close your laptop. Keep the agent running. Move between local and cloud with one command. $250 credit for new accounts:

Docker

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Everything Gets Rebuilt: my conversation with Harrison Chase, CEO of LangChain about agent harnesses, evals, runtimes, sandboxes, MCP and the future of the agent stack 00:00 Intro - meet Harrison Chase - at the Chase Center for the Daytona Compute conference 01:32 What changed in agents over the last year 03:57 Why coding agents are ahead 06:26 Do models commoditize the framework layer? 08:27 Harnesses, in plain English 10:11 Why system prompts matter so much 13:11 The upside — and downside — of subagents 15:31 Why a useful agent needs a filesystem 18:13 Additional core primitives of modern agents 19:12 Skills: the new primitive 20:19 What context compaction actually means 23:02 How memory works in agents 25:16 One mega-agent or many specialized agents? 27:46 The future of MCP 29:38 Why agents need sandboxes 32:35 How sandboxes help with security 33:32 How Harrison Chase started LangChain 37:24 LangChain vs LangGraph vs Deep Agents 40:17 Why observability matters more for agents 41:48 Evals, no-code, and continuous improvement 44:41 What LangChain is building next 45:29 Where the real moat in AI lives
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Everything Gets Rebuilt: my conversation with Harrison Chase, CEO of LangChain about agent harnesses, evals, runtimes, sandboxes, MCP and the future of the agent stack 00:00 Intro - meet Harrison Chase - at the Chase Center for the Daytona Compute conference 01:32 What changed in agents over the last year 03:57 Why coding agents are ahead 06:26 Do models commoditize the framework layer? 08:27 Harnesses, in plain English 10:11 Why system prompts matter so much 13:11 The upside — and downside — of subagents 15:31 Why a useful agent needs a filesystem 18:13 Additional core primitives of modern agents 19:12 Skills: the new primitive 20:19 What context compaction actually means 23:02 How memory works in agents 25:16 One mega-agent or many specialized agents? 27:46 The future of MCP 29:38 Why agents need sandboxes 32:35 How sandboxes help with security 33:32 How Harrison Chase started LangChain 37:24 LangChain vs LangGraph vs Deep Agents 40:17 Why observability matters more for agents 41:48 Evals, no-code, and continuous improvement 44:41 What LangChain is building next 45:29 Where the real moat in AI lives

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TanStack AI runs coding agents in a sandbox now 📦 Agents: Grok build, Claude Code, Codex, OpenCode, or any ACP agent Sandboxes: Cloudflare, Docker, Local, Daytona or Vercel sandboxes Fully configurable sandboxes with skills, mcps and more! 🚀

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Docker is excited to partner with E2B, a platform for running AI-generated code in secure cloud sandboxes. Docker + E2B now gives AI agents access to real tools (GitHub, Perplexity, Browserbase) in secure sandboxes via the MCP Catalog.

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Trillions in AI compute can't buy a $10 domain name. Jeff Weinstein explains how Stripe’s new machine payments protocol will unleash autonomous agents to control real budgets. (0:00) What Is Agentic Commerce? (2:00) What Is the Machine Payments Protocol? (4:11) Why Can’t Smart Agents Buy Things Online? (7:14) How Can You Safely Give an Agent a Budget? (9:34) How Can Agents Provision Cloud Services? (12:22) Why Does Machine Payments Need Stablecoins? (14:15) Why Did Stripe Co-Found the Tempo Blockchain? (16:52) Who Are the Early Adopters of Agent Payments? (18:38) Why Are Agents Now Stripe’s Primary Customer? (20:00) How Did Stripe Atlas Reach 100,000 Startups? (23:09) Why Are AI Startups Growing 50% Faster Now? (24:59) Who Is Smarter: Patrick or John Collison?
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Trillions in AI compute can't buy a $10 domain name. Jeff Weinstein explains how Stripe’s new machine payments protocol will unleash autonomous agents to control real budgets. (0:00) What Is Agentic Commerce? (2:00) What Is the Machine Payments Protocol? (4:11) Why Can’t Smart Agents Buy Things Online? (7:14) How Can You Safely Give an Agent a Budget? (9:34) How Can Agents Provision Cloud Services? (12:22) Why Does Machine Payments Need Stablecoins? (14:15) Why Did Stripe Co-Found the Tempo Blockchain? (16:52) Who Are the Early Adopters of Agent Payments? (18:38) Why Are Agents Now Stripe’s Primary Customer? (20:00) How Did Stripe Atlas Reach 100,000 Startups? (23:09) Why Are AI Startups Growing 50% Faster Now? (24:59) Who Is Smarter: Patrick or John Collison?

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Today, I'm excited to launch @e2b_dev, release v1.0 of our SDK, and announce our $11.5M seed round! We're building infrastructure for AI code interpreting. Secure E2B Sandboxes for running AI-generated cloud. We run millions of sandboxes every month, growing fast, and have customers like Perplexity

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"if i can interface with everything through my agent, why would i go to the dashboard?" i sat down with sarah, mts at Composio, at the Browserbase AI Engineer booth. fresh off her AIE talk "dashboards are dead," we got into: - why agent experience is the next wave and how to design for agents - personal agents as the main consumer of the internet - building and open-sourcing TrustClaw, a production-ready personal agents listen to our full conversation here:

justine

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Another pattern is agent-to-agent communication, allowing Claude to coordinate and delegate tasks. Claude Managed Agents supports this with multi-agent: agents can use different models, prompts, and tools while sharing sandboxes or vault credentials.
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Another pattern is agent-to-agent communication, allowing Claude to coordinate and delegate tasks. Claude Managed Agents supports this with multi-agent: agents can use different models, prompts, and tools while sharing sandboxes or vault credentials.

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Beam (Beam) is an open source serverless platform for AI apps. Run GPU inference, background jobs, and sandboxes with ultrafast boot times and no vendor lock-in.
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Beam (Beam) is an open source serverless platform for AI apps. Run GPU inference, background jobs, and sandboxes with ultrafast boot times and no vendor lock-in.

Y Combinator

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