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Hacker News! We're Filip, Stavros, and Vivek from Terminal Use (https://www.terminaluse.com/). We built Terminal Use to make it easier to deploy agents that work in a sandboxed environment and need filesystems to do work. This includes coding agents, research agents, document processing agents, and internal tools that read and write files.
Here's a demo: https://www.youtube.com/watch?v=ttMl96l9xPA.
Our biggest pain point with hosting agents was that you'd need to stitch together multiple pieces: packaging your agent, running it in a sandbox, streaming messages back to users, persisting state across turns, and managing getting files to and from the agent workspace.
We wanted something like Cog from Replicate, but for agents: a simple way to package agent code from a repo and serve it behind a clean API/SDK. We wanted to provide a protocol to communicate with your agent, but not constraint the agent logic or harness itself.
On Terminal Use, you package your agent from a repo with a config.yaml and Dockerfile, then deploy it with our CLI. You define the logic of three endpoints (on_create, on_event, and on_cancel) which track the lifecycle of a task (conversation). The config.yaml contains details about resources, build context, etc.
Out of the box, we support Claude Agent SDK and Codex SDK agents. By support, we mean that we have an adapter that converts from the SDK message types to ours. If you'd like to use your own custom harness, you can convert and send messages with our types (Vercel AI SDK v6 compatible). For the frontend, we have a Vercel AI SDK provider that lets you use your agent with Vercel's AI SDK, and have a messages module so that you don't have to manage streaming and persistence yourself.
The part we think is most different is storage.
We treat filesystems as first-class primitives, separate from the lifecycle of a task. That means you can persist a workspace across turns, share it between different agents, or upload / download files independent of the sandbox being active. Further, our filesystem SDK provides presigned urls which makes it easy for your users to directly upload and download files which means that you don't need to proxy file transfer through your backend.
Since your agent logic and filesystem storage are decoupled, this makes it easy to iterate on your agents without worrying about the files in the sandbox: if you ship a bug, you can deploy and auto-migrate all your tasks to the new deployment. If you make a breaking change, you can specify that existing tasks stay on the existing version, and only new tasks use the new version.
We're also adding support for multi-filesystem mounts with configurable mount paths and read/write modes, so storage stays durable and reusable while mount layout stays task-specific.
On the deployment side, we've been influenced by modern developer platforms: simple CLI deployments, preview/production environments, git-based environment targeting, logs, and rollback. All the configuration you need to build, deploy & manage resources for your agent is stored in the config.yaml file which makes it easy to build & deploy your agent in CI/CD pipelines.
Finally, we've explicitly designed our platform for your CLI coding agents to help you build, test, & iterate with your agents. With our CLI, your coding agents can send messages to your deployed agents, and download filesystem contents to help you understand your agent's output. A common way we test our agents is that we make markdown files with user scenarios we'd like to test, and then ask Claude Code to impersonate our users and chat with our deployed agent.
What we do not have yet: full parity with general-purpose sandbox providers. For example, preview URLs and lower-level sandbox.exec(...) style APIs are still on the roadmap.
We're excited to hear any thoughts, insights, questions, and concerns in the comments below!"},"title":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["vercel"],"value":"Launch HN: Terminal Use (YC W26) \u2013 Vercel for filesystem-based agents"}},"_tags":["story","author_filipbalucha","story_47311657","launch_hn"],"author":"filipbalucha","children":[47311500,47312128,47312283,47312288,47312840,47313240,47313285,47313804,47314137,47314936,47314939,47315094,47315389,47316087,47316119,47316291,47317238,47317915,47317981,47318540,47318617,47318940,47319030,47319691,47320329,47322018,47323981,47327914,47328382,47328793,47441813,47478031],"created_at":"2026-03-09T16:53:52Z","created_at_i":1773075232,"num_comments":72,"objectID":"47311657","points":115,"story_id":47311657,"story_text":"Hello Hacker News! We're Filip, Stavros, and Vivek from Terminal Use (https://www.terminaluse.com/). We built Terminal Use to make it easier to deploy agents that work in a sandboxed environment and need filesystems to do work. This includes coding agents, research agents, document processing agents, and internal tools that read and write files.
Here's a demo: https://www.youtube.com/watch?v=ttMl96l9xPA.
Our biggest pain point with hosting agents was that you'd need to stitch together multiple pieces: packaging your agent, running it in a sandbox, streaming messages back to users, persisting state across turns, and managing getting files to and from the agent workspace.
We wanted something like Cog from Replicate, but for agents: a simple way to package agent code from a repo and serve it behind a clean API/SDK. We wanted to provide a protocol to communicate with your agent, but not constraint the agent logic or harness itself.
On Terminal Use, you package your agent from a repo with a config.yaml and Dockerfile, then deploy it with our CLI. You define the logic of three endpoints (on_create, on_event, and on_cancel) which track the lifecycle of a task (conversation). The config.yaml contains details about resources, build context, etc.
Out of the box, we support Claude Agent SDK and Codex SDK agents. By support, we mean that we have an adapter that converts from the SDK message types to ours. If you'd like to use your own custom harness, you can convert and send messages with our types (Vercel AI SDK v6 compatible). For the frontend, we have a Vercel AI SDK provider that lets you use your agent with Vercel's AI SDK, and have a messages module so that you don't have to manage streaming and persistence yourself.
The part we think is most different is storage.
We treat filesystems as first-class primitives, separate from the lifecycle of a task. That means you can persist a workspace across turns, share it between different agents, or upload / download files independent of the sandbox being active. Further, our filesystem SDK provides presigned urls which makes it easy for your users to directly upload and download files which means that you don't need to proxy file transfer through your backend.
Since your agent logic and filesystem storage are decoupled, this makes it easy to iterate on your agents without worrying about the files in the sandbox: if you ship a bug, you can deploy and auto-migrate all your tasks to the new deployment. If you make a breaking change, you can specify that existing tasks stay on the existing version, and only new tasks use the new version.
We're also adding support for multi-filesystem mounts with configurable mount paths and read/write modes, so storage stays durable and reusable while mount layout stays task-specific.
On the deployment side, we've been influenced by modern developer platforms: simple CLI deployments, preview/production environments, git-based environment targeting, logs, and rollback. All the configuration you need to build, deploy & manage resources for your agent is stored in the config.yaml file which makes it easy to build & deploy your agent in CI/CD pipelines.
Finally, we've explicitly designed our platform for your CLI coding agents to help you build, test, & iterate with your agents. With our CLI, your coding agents can send messages to your deployed agents, and download filesystem contents to help you understand your agent's output. A common way we test our agents is that we make markdown files with user scenarios we'd like to test, and then ask Claude Code to impersonate our users and chat with our deployed agent.
What we do not have yet: full parity with general-purpose sandbox providers. For example, preview URLs and lower-level sandbox.exec(...) style APIs are still on the roadmap.
We're excited to hear any thoughts, insights, questions, and concerns in the comments below!","title":"Launch HN: Terminal Use (YC W26) \u2013 Vercel for filesystem-based agents","updated_at":"2026-04-04T20:14:25Z"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"leerob"},"title":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["vercel"],"value":"Vercel Edge Middleware: Dynamic at the speed of static"},"url":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["vercel"],"value":"https://vercel.com/blog/vercel-edge-middleware-dynamic-at-the-speed-of-static"}},"_tags":["story","author_leerob","story_31909622"],"author":"leerob","children":[31909908,31910037,31910062,31910401,31910471,31910874,31911379,31912613,31912781],"created_at":"2022-06-28T16:15:00Z","created_at_i":1656432900,"num_comments":48,"objectID":"31909622","points":113,"story_id":31909622,"title":"Vercel Edge Middleware: Dynamic at the speed of 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Pricing","updated_at":"2026-02-18T07:06:12Z","url":"https://metacast.app/blog/engineering/postmortem-llm-bots-image-optimization"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"hazebooth"},"title":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["vercel"],"value":"Reverse Engineering Vercel's BotID"},"url":{"matchLevel":"none","matchedWords":[],"value":"https://www.nullpt.rs/reversing-botid"}},"_tags":["story","author_hazebooth","story_44422356"],"author":"hazebooth","children":[44424626,44426482,44426489],"created_at":"2025-06-30T12:19:45Z","created_at_i":1751285985,"num_comments":18,"objectID":"44422356","points":109,"story_id":44422356,"title":"Reverse Engineering Vercel's BotID","updated_at":"2026-09-13T18:48:33Z","url":"https://www.nullpt.rs/reversing-botid"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"mschoening"},"title":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["vercel"],"value":"Request logging and web vitals for Vercel apps"},"url":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["vercel"],"value":"https://www.axiom.co/vercel"}},"_tags":["story","author_mschoening","story_31402935"],"author":"mschoening","children":[31403156,31403215,31403687,31403818,31403922,31404873],"created_at":"2022-05-16T20:45:08Z","created_at_i":1652733908,"num_comments":16,"objectID":"31402935","points":96,"story_id":31402935,"title":"Request logging and web vitals for Vercel apps","updated_at":"2025-06-30T22:36:15Z","url":"https://www.axiom.co/vercel"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"craigkerstiens"},"title":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["vercel"],"value":"Vercel acquires Splitbee to expand first-party analytics"},"url":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["vercel"],"value":"https://vercel.com/blog/vercel-acquires-splitbee"}},"_tags":["story","author_craigkerstiens","story_33334104"],"author":"craigkerstiens","children":[33335622],"created_at":"2022-10-25T18:04:54Z","created_at_i":1666721094,"num_comments":2,"objectID":"33334104","points":85,"story_id":33334104,"title":"Vercel acquires Splitbee to expand first-party analytics","updated_at":"2024-09-20T12:22:53Z","url":"https://vercel.com/blog/vercel-acquires-splitbee"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"mariuz"},"title":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["vercel"],"value":"Powering a Serverless Web: Vercel Joins AWS Marketplace"},"url":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["vercel"],"value":"https://vercel.com/blog/vercel-joins-aws-marketplace"}},"_tags":["story","author_mariuz","story_35471912"],"author":"mariuz","children":[35482116,35482129,35482156,35482167,35482422,35482988,35483020,35484049,35485007,35485835],"created_at":"2023-04-06T17:54:52Z","created_at_i":1680803692,"num_comments":53,"objectID":"35471912","points":77,"story_id":35471912,"title":"Powering a Serverless Web: Vercel Joins AWS Marketplace","updated_at":"2024-09-20T13:51:05Z","url":"https://vercel.com/blog/vercel-joins-aws-marketplace"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"Onavo"},"title":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["vercel"],"value":"Vercel/fun: \u0192un \u2013 Local serverless function \u03bb development runtime"},"url":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["vercel"],"value":"https://github.com/vercel/fun"}},"_tags":["story","author_Onavo","story_42406070"],"author":"Onavo","children":[42426148,42427993,42429315,42429525,42429664,42429688,42429700,42431223,42434267],"created_at":"2024-12-13T05:08:58Z","created_at_i":1734066538,"num_comments":25,"objectID":"42406070","points":77,"story_id":42406070,"title":"Vercel/fun: \u0192un \u2013 Local serverless function \u03bb development runtime","updated_at":"2025-11-02T05:22:19Z","url":"https://github.com/vercel/fun"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"windsor"},"story_text":{"matchLevel":"none","matchedWords":[],"value":"Hey HN! We are Windsor and Cathy of Dedalus Labs (https://www.dedaluslabs.ai/), a cloud platform for developers to build agentic AI applications. Our SDK allows you to connect any LLM to any MCP tools \u2013 local or hosted by us. No Dockerfiles or YAML configs required.
Here\u2019s a demo: https://youtu.be/s2khf1Monho?si=yiWnZh5OP4HQcAwL&t=11
Last October, I was trying to build a stateful code execution sandbox in the cloud that LLMs could tool-call into. This was before MCP was released, and let\u2019s just say it was super annoying to build\u2026 I was thinking to myself the entire time \u201cWhy can\u2019t I just pass in `tools=code_execution` to the model and just have it\u2026work?
Even with MCP, you\u2019re stuck running local servers and handwiring API auth and formatting across OpenAI, Anthropic, Google, etc. before you can ship anything. Every change means redeploys, networking configs, and hours lost wrangling AWS. Hours of reading docs and wrestling with cloud setup is not what you want when building your product!
Dedalus simplifies this to just one API endpoint, so what used to take 2 weeks of setup can take 5 minutes. We allow you to upload streamable HTTP MCP servers to our platform. Once deployed, we offer OpenAI-compatible SDKs that you can drop into your codebase to use MCP-powered LLMs. The idea is to let anyone, anywhere, equip their LLMs with powerful tools for function calling.
The code you write looks something like this:
python\n client = Dedalus()\n runner = DedalusRunner(client)\n \n result = runner.run(\n input=prompt,\n tools=[tool_1, tool_2],\n mcp_servers=["author/server-1\u201d, \u201cauthor/server-2\u201d],\n model=["openai/gpt-4.1\u201d, \u201canthropic/claude-sonnet-4-20250514\u201d], # Defaults to first model in list\n stream=True,\n )\n stream_sync(result) # Streams result, supports tool calling too\nThere are still a bunch of issues in the MCP landscape, no doubt. One big one is authentication (we joke that the \u201cS\u201d in MCP stands for \u201csecurity\u201d). MCP servers right now are expected to act as both the authentication server and the resource server. That is too much to ask of server writers. People just want to expose a resource endpoint and be done.
Still, we are bullish on MCP. Current shortcomings are not irrecoverable, and we expect future amendments to resolve them. We think that useful AI agents are bound to be habitual tool callers, and MCP is a pretty decent way to equip models with tools.
We aren\u2019t quite yet at the stateful code execution sandbox that I wanted last October, but we\u2019re getting there! Shipping secure and stateful MCP servers is high on our priority list, and we\u2019ll be launching our auth solution next month. We\u2019re also working on an MCP marketplace, so people can monetize their tools, while we handle billing and rev-share.
We\u2019re big on open sourcing things and have these SDKs so far (MIT licensed):
https://github.com/dedalus-labs/dedalus-sdk-python
https://github.com/dedalus-labs/dedalus-sdk-typescript
https://github.com/dedalus-labs/dedalus-sdk-go
https://github.com/dedalus-labs/dedalus-openapi
We would love feedback on what you guys think are the biggest barriers that keep you from integrating MCP servers or using tool calling LLMs into your current workflow.
Thanks HN!"},"title":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["vercel"],"value":"Launch HN: Dedalus Labs (YC S25) \u2013 Vercel for Agents"}},"_tags":["story","author_windsor","story_45054040","launch_hn"],"author":"windsor","children":[45054846,45056361,45056543,45056721,45057085,45062834],"created_at":"2025-08-28T16:22:02Z","created_at_i":1756398122,"num_comments":15,"objectID":"45054040","points":77,"story_id":45054040,"story_text":"Hey HN! We are Windsor and Cathy of Dedalus Labs (https://www.dedaluslabs.ai/), a cloud platform for developers to build agentic AI applications. Our SDK allows you to connect any LLM to any MCP tools \u2013 local or hosted by us. No Dockerfiles or YAML configs required.
Here\u2019s a demo: https://youtu.be/s2khf1Monho?si=yiWnZh5OP4HQcAwL&t=11
Last October, I was trying to build a stateful code execution sandbox in the cloud that LLMs could tool-call into. This was before MCP was released, and let\u2019s just say it was super annoying to build\u2026 I was thinking to myself the entire time \u201cWhy can\u2019t I just pass in `tools=code_execution` to the model and just have it\u2026work?
Even with MCP, you\u2019re stuck running local servers and handwiring API auth and formatting across OpenAI, Anthropic, Google, etc. before you can ship anything. Every change means redeploys, networking configs, and hours lost wrangling AWS. Hours of reading docs and wrestling with cloud setup is not what you want when building your product!
Dedalus simplifies this to just one API endpoint, so what used to take 2 weeks of setup can take 5 minutes. We allow you to upload streamable HTTP MCP servers to our platform. Once deployed, we offer OpenAI-compatible SDKs that you can drop into your codebase to use MCP-powered LLMs. The idea is to let anyone, anywhere, equip their LLMs with powerful tools for function calling.
The code you write looks something like this:
python\n client = Dedalus()\n runner = DedalusRunner(client)\n \n result = runner.run(\n input=prompt,\n tools=[tool_1, tool_2],\n mcp_servers=["author/server-1\u201d, \u201cauthor/server-2\u201d],\n model=["openai/gpt-4.1\u201d, \u201canthropic/claude-sonnet-4-20250514\u201d], # Defaults to first model in list\n stream=True,\n )\n stream_sync(result) # Streams result, supports tool calling too\nThere are still a bunch of issues in the MCP landscape, no doubt. One big one is authentication (we joke that the \u201cS\u201d in MCP stands for \u201csecurity\u201d). MCP servers right now are expected to act as both the authentication server and the resource server. That is too much to ask of server writers. People just want to expose a resource endpoint and be done.
Still, we are bullish on MCP. Current shortcomings are not irrecoverable, and we expect future amendments to resolve them. We think that useful AI agents are bound to be habitual tool callers, and MCP is a pretty decent way to equip models with tools.
We aren\u2019t quite yet at the stateful code execution sandbox that I wanted last October, but we\u2019re getting there! Shipping secure and stateful MCP servers is high on our priority list, and we\u2019ll be launching our auth solution next month. We\u2019re also working on an MCP marketplace, so people can monetize their tools, while we handle billing and rev-share.
We\u2019re big on open sourcing things and have these SDKs so far (MIT licensed):
https://github.com/dedalus-labs/dedalus-sdk-python
https://github.com/dedalus-labs/dedalus-sdk-typescript
https://github.com/dedalus-labs/dedalus-sdk-go
https://github.com/dedalus-labs/dedalus-openapi
We would love feedback on what you guys think are the biggest barriers that keep you from integrating MCP servers or using tool calling LLMs into your current workflow.
Thanks HN!","title":"Launch HN: Dedalus Labs (YC S25) \u2013 Vercel for Agents","updated_at":"2026-03-05T22:33:19Z"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"blinky88"},"title":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["vercel"],"value":"NuxtLabs is joining Vercel"},"url":{"matchLevel":"none","matchedWords":[],"value":"https://nuxtlabs.com/"}},"_tags":["story","author_blinky88","story_44499860"],"author":"blinky88","children":[44499875,44500602,44502505,44502657,44503072,44503237,44503338,44503452,44503463,44503526,44503673,44503856,44504602,44505292,44505608],"created_at":"2025-07-08T13:40:12Z","created_at_i":1751982012,"num_comments":79,"objectID":"44499860","points":76,"story_id":44499860,"title":"NuxtLabs is joining Vercel","updated_at":"2026-01-23T22:14:16Z","url":"https://nuxtlabs.com/"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"migueloller"},"title":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["vercel"],"value":"Vercel \u2013 $102M to Continue Building the Next Web, Together"},"url":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["vercel"],"value":"https://vercel.com/blog/series-c-102m-continue-building-the-next-web"}},"_tags":["story","author_migueloller","story_27605100"],"author":"migueloller","children":[27605356,27606065,27608958,27610389,27610446,27611250,27611780,27612526,27613734,27614052,27614275,27622460],"created_at":"2021-06-23T14:41:42Z","created_at_i":1624459302,"num_comments":18,"objectID":"27605100","points":75,"story_id":27605100,"title":"Vercel \u2013 $102M to Continue Building the Next Web, Together","updated_at":"2024-09-20T08:49:41Z","url":"https://vercel.com/blog/series-c-102m-continue-building-the-next-web"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"alexey2020"},"title":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["vercel"],"value":"Vercel Serverless Functions vs. Cloudflare Workers"},"url":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["vercel"],"value":"https://moiva.io/blog/vercel-serverless-functions-vs-cloudflare-workers"}},"_tags":["story","author_alexey2020","story_26580102"],"author":"alexey2020","children":[26583997,26584577,26585911,26586279,26587155,26595261,26609617],"created_at":"2021-03-25T13:39:45Z","created_at_i":1616679585,"num_comments":35,"objectID":"26580102","points":74,"story_id":26580102,"title":"Vercel Serverless Functions vs. Cloudflare Workers","updated_at":"2024-09-20T08:15:41Z","url":"https://moiva.io/blog/vercel-serverless-functions-vs-cloudflare-workers"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"Vamshi2506"},"story_text":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["vercel"],"value":"Vercel is just a layer on top of AWS, and they charge 100-200% more than what AWS charges. So, why do people pay for it when they can use AWS directly?"},"title":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["vercel"],"value":"Ask HN: Why are people paying so much for Vercel?"}},"_tags":["story","author_Vamshi2506","story_41031912","ask_hn"],"author":"Vamshi2506","children":[41032050,41032163,41032166,41032170,41032189,41032192,41032210,41032212,41032225,41032242,41032271,41032351,41032360,41032383,41032410,41032446,41032451,41032464,41032473,41032484,41032651,41032671,41032735,41032747,41032759,41032764,41032788,41032906,41033445,41033534,41034243,41034360,41034572,41036110,41036134,41036163,41036256,41037160,41040166,41045890],"created_at":"2024-07-22T08:09:43Z","created_at_i":1721635783,"num_comments":100,"objectID":"41031912","points":69,"story_id":41031912,"story_text":"Vercel is just a layer on top of AWS, and they charge 100-200% more than what AWS charges. So, why do people pay for it when they can use AWS directly?","title":"Ask HN: Why are people paying so much for Vercel?","updated_at":"2025-10-21T05:00:22Z"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"todsacerdoti"},"title":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["vercel"],"value":"Vercel's AI Accelerator"},"url":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["vercel"],"value":"https://vercel.com/blog/vercel-ai-accelerator"}},"_tags":["story","author_todsacerdoti","story_36327911"],"author":"todsacerdoti","children":[36329832,36329841,36329865,36330151,36330176,36330524,36330719,36330790,36331077,36331328,36331585],"created_at":"2023-06-14T16:00:03Z","created_at_i":1686758403,"num_comments":34,"objectID":"36327911","points":69,"story_id":36327911,"title":"Vercel's AI Accelerator","updated_at":"2026-06-24T17:18:33Z","url":"https://vercel.com/blog/vercel-ai-accelerator"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"tosh"},"title":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["vercel"],"value":"Mistral's mixtral-8x7B-32kseqlen on Vercel"},"url":{"matchLevel":"none","matchedWords":[],"value":"https://twitter.com/rauchg/status/1733540995546190048"}},"_tags":["story","author_tosh","story_38584179"],"author":"tosh","children":[38584894,38585131,38586602,38586606,38588053,38591025,38592034,38598165,38598167],"created_at":"2023-12-09T18:13:40Z","created_at_i":1702145620,"num_comments":30,"objectID":"38584179","points":67,"story_id":38584179,"title":"Mistral's mixtral-8x7B-32kseqlen on Vercel","updated_at":"2024-09-20T15:54:28Z","url":"https://twitter.com/rauchg/status/1733540995546190048"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"tobihrbr"},"story_text":{"matchLevel":"none","matchedWords":[],"value":"Hey HN! We're Wen and Tobias, and we're building Metorial (https://metorial.com), an integration platform that connects AI agents to external tools and data using MCP.
The Problem: While MCP works great locally (e.g., Cursor or Claude Desktop), server-side deployments are painful. Running MCP servers means managing Docker configs, per-user OAuth flows, scaling concurrent sessions, and building observability from scratch. This infrastructure work turns simple integrations into weeks of setup.
Metorial handles all of this automatically. We maintain an open catalog of ~600 MCP servers (GitHub, Slack, Google Drive, Salesforce, databases, etc.) that you can deploy in three clicks. You can also bring your own MCP server or fork existing ones.
For OAuth, just provide your client ID and secret and we handle the entire flow, including token refresh. Each user then gets an isolated MCP server instance configured with their own OAuth credentials automatically.
What makes us different is that our serverless runtime hibernates idle MCP servers and resumes them with sub-second cold starts while preserving the state and connection. Our custom MCP engine is capable of managing thousands of concurrent connections, giving you a scalable service with per-user isolation. Other alternatives either run shared servers (security issues) or provision separate VMs per user (expensive and slow to scale).
Our Python and TypeScript SDKs let you connect LLMs to MCP tools in a single function call, abstracting away the protocol complexity. But if you want to dig deep, you can just use standard MCP and our REST API (https://metorial.com/api) to connect to our platform.
You can self-host (https://github.com/metorial/metorial) or use the managed version at https://metorial.com.
So far, we see enterprise teams use Metorial to have a central integration hub for tools like Salesforce, while startups use it to cut weeks of infra work on their side when building AI agents with integrations.
Demo video: https://www.youtube.com/watch?v=07StSRNmJZ8
Our Repos: Metorial: https://github.com/metorial/metorial, MCP Containers: https://github.com/metorial/mcp-containers
SDKs: Node/TypeScript: https://github.com/metorial/metorial-node, Python: https://github.com/metorial/metorial-python
We'd love to hear feedback, especially if you've dealt with deploying MCP at scale!"},"title":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["vercel"],"value":"Show HN: Metorial (YC F25) \u2013 Vercel for MCP"},"url":{"matchLevel":"none","matchedWords":[],"value":"https://github.com/metorial/metorial"}},"_tags":["story","author_tobihrbr","story_45580771","show_hn"],"author":"tobihrbr","children":[45580772,45582404,45582590,45582921,45583016,45583463,45583676,45583994,45587685,45612326],"created_at":"2025-10-14T14:49:56Z","created_at_i":1760453396,"num_comments":25,"objectID":"45580771","points":59,"story_id":45580771,"story_text":"Hey HN! We're Wen and Tobias, and we're building Metorial (https://metorial.com), an integration platform that connects AI agents to external tools and data using MCP.
The Problem: While MCP works great locally (e.g., Cursor or Claude Desktop), server-side deployments are painful. Running MCP servers means managing Docker configs, per-user OAuth flows, scaling concurrent sessions, and building observability from scratch. This infrastructure work turns simple integrations into weeks of setup.
Metorial handles all of this automatically. We maintain an open catalog of ~600 MCP servers (GitHub, Slack, Google Drive, Salesforce, databases, etc.) that you can deploy in three clicks. You can also bring your own MCP server or fork existing ones.
For OAuth, just provide your client ID and secret and we handle the entire flow, including token refresh. Each user then gets an isolated MCP server instance configured with their own OAuth credentials automatically.
What makes us different is that our serverless runtime hibernates idle MCP servers and resumes them with sub-second cold starts while preserving the state and connection. Our custom MCP engine is capable of managing thousands of concurrent connections, giving you a scalable service with per-user isolation. Other alternatives either run shared servers (security issues) or provision separate VMs per user (expensive and slow to scale).
Our Python and TypeScript SDKs let you connect LLMs to MCP tools in a single function call, abstracting away the protocol complexity. But if you want to dig deep, you can just use standard MCP and our REST API (https://metorial.com/api) to connect to our platform.
You can self-host (https://github.com/metorial/metorial) or use the managed version at https://metorial.com.
So far, we see enterprise teams use Metorial to have a central integration hub for tools like Salesforce, while startups use it to cut weeks of infra work on their side when building AI agents with integrations.
Demo video: https://www.youtube.com/watch?v=07StSRNmJZ8
Our Repos: Metorial: https://github.com/metorial/metorial, MCP Containers: https://github.com/metorial/mcp-containers
SDKs: Node/TypeScript: https://github.com/metorial/metorial-node, Python: https://github.com/metorial/metorial-python
We'd love to hear feedback, especially if you've dealt with deploying MCP at scale!","title":"Show HN: Metorial (YC F25) \u2013 Vercel for MCP","updated_at":"2026-04-07T08:39:08Z","url":"https://github.com/metorial/metorial"}],"hitsPerPage":50,"nbHits":5844,"nbPages":20,"page":0,"params":"query=Vercel&tags=story&hitsPerPage=50&advancedSyntax=true&analyticsTags=backend","processingTimeMS":5,"processingTimingsMS":{"_request":{"queue":231,"roundTrip":17},"afterFetch":{"format":{"highlighting":1,"total":1}},"fetch":{"query":2,"scanning":1,"total":4},"total":5},"query":"Vercel","serverTimeMS":238}