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The Runtime keynote is live. Updates to Inference, Training, and Sandboxes including: 11:20 — Endpoint Candidates 16:30 — Modal Clusters 21:48 — VM Sandboxes 22:40 — Sandbox Sidecars And more

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15,731 görüntüleme • 6 gün önce •via X (Twitter)

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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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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 and GPU snapshotting matter for production AI, how RL rollouts can require 100,000 sandboxes, and why Modal’s $355M Series C marks a new phase for AI-native cloud infrastructure.

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It is not too late to _actually_ own your inference. Introducing: Modal Auto Endpoints.
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GLM-5, the latest frontier open model from Z.ai, is available now on Modal. We partnered with to release an endpoint that will be free for a limited time.
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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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The full Runtime agenda is live, including talks from: Scott Wu, Co-founder & CEO Cognition Diogo Almeida, Co-founder & CEO TypeSafe AI Dylan Patel, Founder & CEO SemiAnalysis Sara Hooker, Co-founder & CEO Adaption Alex Ratner, Co-founder & CEO Snorkel AI

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I sat down with Silas Alberti, Head of Research Cognition, to chat about the intersection of RL and inference -- from training frontier coding models to running inference at scale. Watch till the end for bonus doggo! :o 0:00 — Intros 1:04 — What's hardest to get right in an RL run 3:39 — How Cognition started using Modal 4:03 — What to weigh when buying inference 4:58 — The latency/throughput Pareto frontier 7:14 — DFlash reveal (!!) 8:44 — Why faster runtime matters 9:56 — Memory-bound vs. compute-bound 10:57 — Tree-based speculation 14:20 — Hot take: RL and inference optimization converging 15:28 — Online training of the speculator 16:22 — "Auto inference" 17:27 — Agents competing on optimization problems 18:27 — What inference providers still get wrong

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Just implemented Code Sandbox for TypingMind 🥳 I was looking at various options for sandbox and code execution, but for now the most obvious choice is to use the built-in sandbox provided by all popular providers: OpenAI/Gemini/Claude. It can create files but not yet able to accept file input, except for OpenAI's API, which gives your 20 minutes of "persistent container" which persist the files and the execution session. I'll look into implement 3rd party provided sandboxes like Vercel sandbox or E2B soon!

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Oliver Korzen sat down with ZioSec founders Aaron Walls & Andrius Useckas to break down OpenClaw security. Here is what you need to know: 0:00 - Why Mac Mini isn't the right setup for everyone 12:30 - The right way to host OpenClaw (hint: not AWS) 16:11 - Understanding your attack surface 27:33 - Why third-party skills are a security risk 36:30 - Start with sandboxes & read-only permissions 40:03 - Career advice for college students @jason

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Effort News founder Brian Chau argues AI labs are sidelining decades of cybersecurity expertise for “vibe-coded sandboxes” that still make basic security mistakes: "You should not be granting models access to the internet in an evaluation where you promise the models have no access to the internet. That's a clear competency failure. You can dismiss Irregular on competency grounds alone." "The bigger thing is this predetermined ideological conclusion. They weren't firms with traditional cybersecurity experience, and that's not just true for Irregular, but many other firms, including METR, which Anthropic later endorsed as their solution." "These are not cybersecurity professionals. I would trust the average firm working in cyber for 20 years much more than these vibe-coded sandboxes and these slop reports." "Effective altruism is attempting to displace decades of real cybersecurity expertise with this entirely fake subfield of AI alignments. Their products are vibe-coded sandboxes which grant models access to the internet when they shouldn't. This should be entirely self-explanatory."

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What is AI inference engineering, why is it such an in-demand skill, and how do you break into the field? With author of Inference Engineering Philip Kiely and head of training at Baseten Charlie O'Neill 0:00: What is inference? 2:47: History of inference 4:59: Downstream effects of AI research on inference 13:54: What you'll learn from Inference Engineering 16:14: Advice for engineers transitioning into AI 19:00: Open source models driving inference growth 20:55: Specialization vs. frontier closed models 23:51: "Big Token" and the importance of open source AI 27:18: Where to get Inference Engineering

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the new site is now live watch the video to see the new platform in action some token and mechanics updates coming up next (including migrating to uniswap v4 & more) get launching
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the new site is now live watch the video to see the new platform in action some token and mechanics updates coming up next (including migrating to uniswap v4 & more) get launching

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Today on MCG solana:6SjVTj1VGwFSXn7wEjwFm77LvACeTqB7sQUebYKX8Ds5 | Router 🌊 w/ghosthash🃏 Router is an private AI inference Built on Solana Highlights Included: 00:33 - Noah Joins 02:22 - Encrypted TEE inference setup 03:43 - Building uncensored models 05:11 - New Router-NVIDIA pool 06:47 - What does Router do? 08:01 - Mobile app traction growing 09:58 - B2B pitch, regulated industries 15:29 - Switched to subscriptions 15:55 - 50% revenue to buyback 16:43 - Mobile app tease 18:23 - SDK underused and pivot to B2B 20:20 - Privacy demand rising 21:09 - Compares to Venice and DOT 22:40 - Pursuing ISO certification 24:13 - Pricing explained 24:35 - Capital raising? 25:13 - Post token VC problems 28:26 - Built restockdotfun(Explained) 32:20 - Closing thoughts
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Today on MCG solana:6SjVTj1VGwFSXn7wEjwFm77LvACeTqB7sQUebYKX8Ds5 | Router 🌊 w/ghosthash🃏 Router is an private AI inference Built on Solana Highlights Included: 00:33 - Noah Joins 02:22 - Encrypted TEE inference setup 03:43 - Building uncensored models 05:11 - New Router-NVIDIA pool 06:47 - What does Router do? 08:01 - Mobile app traction growing 09:58 - B2B pitch, regulated industries 15:29 - Switched to subscriptions 15:55 - 50% revenue to buyback 16:43 - Mobile app tease 18:23 - SDK underused and pivot to B2B 20:20 - Privacy demand rising 21:09 - Compares to Venice and DOT 22:40 - Pursuing ISO certification 24:13 - Pricing explained 24:35 - Capital raising? 25:13 - Post token VC problems 28:26 - Built restockdotfun(Explained) 32:20 - Closing thoughts

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