FWfwsgonzo·Dec 23, 2024
FireCracker has a jailer: https://github.com/firecracker-microvm/firecracker/blob/main...
In Show HN: Ephemeral VMs in 1 Microsecondfirecracker-microvm
firecracker
Rust2018–2026
firecracker-microvm / firecracker
firecracker
Secure and fast microVMs for serverless computing.
84Distinct people
102Mentions
0Explicit recommenders
38KGitHub stars
What to know before exploring
What it is
Secure and fast microVMs for serverless computing.
GitHub
Where it is used
No documented product use was verified in the last source check.
What people are saying
No recommendation phrase was found in the indexed comments.
No classified warning is attached to this page. Read the discussions before treating silence as approval.
Also in these discussions
- cloud-hypervisor/cloud-hypervisor · linked in 2 of the same discussions
Mentioned in the same Hacker News threads. That is not proof they are substitutes.
This week on Hacker News
Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 6:01:08 PM UTC.
No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.
Mentions over time
HN mentions by year102 dated mentions
Observed mentions: 2018-11-27 05:00:35 UTC to 2026-09-30 23:40:36 UTC.
* An incomplete calendar period is marked with an outlined bar. Its count is not directly comparable with a full period.
Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.
View mention counts and date ranges
| Period | Start (UTC) | End (exclusive, UTC) | Mentions | Submissions | Comments |
|---|---|---|---|---|---|
| 2018 | 2018-01-01 00:00:00 UTC | 2019-01-01 00:00:00 UTC | 4 | 1 | 3 |
| 2019 | 2019-01-01 00:00:00 UTC | 2020-01-01 00:00:00 UTC | 6 | 2 | 4 |
| 2020 | 2020-01-01 00:00:00 UTC | 2021-01-01 00:00:00 UTC | 10 | 0 | 10 |
| 2021 | 2021-01-01 00:00:00 UTC | 2022-01-01 00:00:00 UTC | 14 | 0 | 14 |
| 2022 | 2022-01-01 00:00:00 UTC | 2023-01-01 00:00:00 UTC | 27 | 4 | 23 |
| 2023 | 2023-01-01 00:00:00 UTC | 2024-01-01 00:00:00 UTC | 10 | 2 | 8 |
| 2024 | 2024-01-01 00:00:00 UTC | 2025-01-01 00:00:00 UTC | 15 | 0 | 15 |
| 2025 | 2025-01-01 00:00:00 UTC | 2026-01-01 00:00:00 UTC | 7 | 3 | 4 |
| 2026 (partial) | 2026-01-01 00:00:00 UTC | 2027-01-01 00:00:00 UTC | 9 | 0 | 9 |
What Hacker News says
15 sourcesThank you!
Reclaiming memory is orthogonal to unikernals. It's a concern of the VMM, and Firecracker does support this through the balloon device: https://github.com/firecracker-microvm/firecracker/blob/main...
Now, the unikernel helps us consume less memory, which is a good place to start :-)
In Prisma Postgres – Runs on bare metal and unikernelsHello, I'm the CEO of the company that built this - E2B [0]. We're building infrastructure for AI code interpreting. Companies like Perplexity are using us.
We're using Firecrackers [1] to power our sandboxes. Funnily enough, we had this repo sitting on our GitHub for about 6 months. We originally made this for one of our customers because they were running evals on the desktop-like environment with GUI for their model.
You can use PyAutoGUI [2] to control the whole environment programmatically.
The desktop-like environment is based on Linux and Xfce [3] at the moment. We chose Xfce because it's a fast and lightweight environment that's also popular and actively supported. However, this Sandbox template is fully customizable and you can create your own desktop environment.
Let me know if you have any questions.
Btw, we're hiring [4]!
[0]
Read the rest
> To add GPU support, the Google team introduced nvproxy which works using the same principles as described above for syscalls: it intercepts ioctls destined to the GPU and proxies a subset to the GPU kernel module.
This does still expose the host's kernel to a potentially malicious workload, right?
If so, could this be mitigated by (continuously) running a QEMU VM with GPUs passed through via VFIO, and running whatever Workers need within that VM?
The Debian ROCm Team faces similar challenge, we want to do CI [1] for our stack and all our dependent packages, but cannot rule out potentially hostile workloads. We spawn QEMU VMs per test (instead of the model described above) but that's because our tests must also be run against the relevant distribution's kernel and firmwares.
Incidentally, I've been monitoring the Firecracker VFIO GitHub
Read the rest
issue linked in the article. Upstream does not have a use case for and thus no resources dedicated to implement this, but there's a community meeting [2] coming up in October to discuss the future of this feature request.
[1]: https://ci.rocm.debian.net
[2]: https://github.com/firecracker-microvm/firecracker/issues/11...
What he said is true, AWS uses Rust heavily in some of AWS core systems https://aws.amazon.com/blogs/devops/why-aws-is-the-best-plac....
Some of the open source projects you can find are AWS Firecracker https://github.com/firecracker-microvm/firecracker and Cloudflare Pingora https://github.com/cloudflare/pingora
In I'm funding Ladybird because I can't fund FirefoxHey everyone!
I'm the CEO of the company that built this.
We're a company called E2B [0]. We're building and open-source [1] secure environments for running untrusted AI-generated code and AI agents. We call these environments sandboxes and they are built on top of micro VM called Firecracker [2]. We specifically decided to use Firecrackers instead of containers because of their security and ability to do snapshots.
You can think of us as giving small cloud computers to LLMs.
We recently created a dedicated SDK for building custom code interpreters in Python or JS/TS. We saw this need after a lot of our users have been adding code execution capabilities to their AI apps with our core SDK [3]. These use cases were often centered around AI data analysis so code interpreter-like behavior made sense
The way our code interpret SDK works is by
Read the rest
spawning an E2B sandbox with Jupyter Server. We then communicate with this Jupyter server through Jupyter Kernel messaging protocol [4]. We work with an LLM and AI framework. We have different examples on how to use the SDK with Llama models, Anthropic models, LangChain, LangGraph, and more in our cookbook [5].
We don't do any wrapping around LLM, any prompting, or any agent-like framework. We leave all of that to our users. We're really just a boring code execution layer that sits at the bottom. We're building for the future software that will be building another software.
Our long-term plan is to build an automated AWS for AI apps and agents where AI can build and deploy its own software while giving developers powerful observability into what's happening inside our sandboxes. With everything being open-source.
Happy to answer any questions and hear feedback!
[0] https://e2b.dev
[1] https://github.com/e2b-dev
[2] https://github.com/firecracker-microvm/firecracker
[3] https://e2b.dev/docs
[4] https://jupyter-client.readthedocs.io/en/latest/messaging.ht...
[5] https://github.com/e2b-dev/e2b-cookbook
Hey everyone! I'm the CEO of the company that built this SDK.
We're a company called E2B [0]. We're building and open-source [1] secure environments for running untrusted AI-generated code and AI agents. We call these environments sandboxes and they are built on top of micro VM called Firecracker [2]. We specifically decided to use Firecrackers instead of containers because of their security and ability to do snapshots.
You can think of us as giving small cloud computers to LLMs.
We recently created a dedicated SDK for building custom code interpreters in Python or JS/TS. We saw this need after a lot of our users have been adding code execution capabilities to their AI apps with our core SDK [3]. These use cases were often centered around AI data analysis so code interpreter-like behavior made sense
The way our code interpret SDK works is
Read the rest
by spawning an E2B sandbox with Jupyter Server. We then communicate with this Jupyter server through Jupyter Kernel messaging protocol [4]. We work with an LLM and AI framework. We have different examples on how to use the SDK with Llama models, Anthropic models, LangChain, LangGraph, and more in our cookbook [5].
We don't do any wrapping around LLM, any prompting, or any agent-like framework. We leave all of that to our users. We're really just a boring code execution layer that sits at the bottom. We're building for the future software that will be building another software.
Our long-term plan is to build an automated AWS for AI apps and agents where AI can build and deploy its own software while giving developers powerful observability into what's happening inside our sandboxes. With everything being open-source.
Happy to answer any questions and hear feedback!
[0] https://e2b.dev
[1] https://github.com/e2b-dev
[2] https://github.com/firecracker-microvm/firecracker
[3] https://e2b.dev/docs
[4] https://jupyter-client.readthedocs.io/en/latest/messaging.ht...
[5] https://github.com/e2b-dev/e2b-cookbook
Very cool project! I like that you support REST API. That's something we don't have yet.
We actively decided to use Firecracker microVMs [0] instead of containers for hardened security.
> making sure someone doesn’t use your infrastructure to DDOS other folks
Agreed. And crypto mining!
> The pain point of all of this is dependencies
Would love to learn more. Can you elaborate?
Another challenge is how to make the whole product cost efficient for our users. You don't want them to pay for idle sandboxes running on E2B but only when they actually need them for serverless execution. Firecracker and its snapshots are really helpful with this.
[0] https://github.com/firecracker-microvm/firecracker
In Show HN: Open-source SDK for creating custom code interpreters with any LLMHey everyone! I'm the CEO of the company that built this SDK.
We're a company called E2B [0]. We're building and open-source [1] secure environments for running untrusted AI-generated code and AI agents. We call these environments sandboxes and they are built on top of micro VM called Firecracker [2]. We specifically decided to use Firecrackers instead of containers because of their security and ability to do snapshots.
You can think of us as giving small cloud computers to LLMs.
We recently created a dedicated SDK for building custom code interpreters in Python or JS/TS. We saw this need after a lot of our users have been adding code execution capabilities to their AI apps with our core SDK [3]. These use cases were often centered around AI data analysis so code interpreter-like behavior made sense
The way our code interpret SDK works is
Read the rest
by spawning an E2B sandbox with Jupyter Server. We then communicate with this Jupyter server through Jupyter Kernel messaging protocol [4]. Here's how we added code interpreter to the new Llama-3 models [5].
We don't do any wrapping around LLM, any prompting, or any agent-like framework. We leave all of that to our users. We're really just a boring code execution layer that sits at the bottom. We're building for the future software that will be building another software.
Our long-term plan is to build an automated AWS for AI apps and agents where AI can build and deploy its own software while giving developers powerful observability into what's happening inside our sandboxes. With everything being open-source.
Happy to answer any questions and hear feedback!
[0] https://e2b.dev/
[1] https://github.com/e2b-dev
[2] https://github.com/firecracker-microvm/firecracker
[3] https://e2b.dev/docs
[4] https://jupyter-client.readthedocs.io/en/latest/messaging.ht...
[5] https://github.com/e2b-dev/e2b-cookbook/blob/main/examples/l...
Hi, I'm the CEO of the company that built this SDK.
We're a company called E2B [0]. We're building and open-source [1] secure environments for running untrusted AI-generated code and AI agents. We call these environments sandboxes and they are built on top of micro VM called Firecracker [2].
You can think of us as giving small cloud computers to LLMs.
We recently created a dedicated SDK for building custom code interpreters in Python or JS/TS. We saw this need after a lot of our users have been adding code execution capabilities to their AI apps with our core SDK [3]. These use cases were often centered around AI data analysis so code interpreter-like behavior made sense
The way our code interpret SDK works is by spawning an E2B sandbox with Jupyter Server. We then communicate with this Jupyter server through Jupyter Kernel messaging prot
Read the rest
ocol [4].
We don't do any wrapping around LLM, any prompting, or any agent-like framework. We leave all of that on users. We're really just a boring code execution layer that sats at the bottom that we're building specifically for the future software that will be building another software. We work with any LLM. Here's how we added code interpreter to Claude [5].
Our long-term plan is to build an automated AWS for AI apps and agents.
Happy to answer any questions and hear feedback!
[0] https://e2b.dev/
[1] https://github.com/e2b-dev
[2] https://github.com/firecracker-microvm/firecracker
[3] https://e2b.dev/docs
[4] https://jupyter-client.readthedocs.io/en/latest/messaging.ht...
[5] https://github.com/e2b-dev/e2b-cookbook/blob/main/examples/c...
Hi, I'm one of the authors of this SDK.
We're a company called E2B [0]. We're building and open-source [1] secure environments for running untrusted AI-generated code and AI agents. We call these environments sandboxes and they are built on top of micro VM called Firecracker [2].
You can think of us as giving small cloud computers to LLMs.
We recently created a dedicated SDK for building custom code interpreters in Python or JS/TS. We saw this need after a lot of our users have been adding code execution capabilities to their AI apps with our core SDK [3].
These use cases were often centered around AI data analysis so code interpreter-like behavior made sense
The way our code interpret SDK works is by spawning an E2B sandbox with Jupyter Server. We then communicate with this Jupyter server through Jupyter Kernel messaging protocol [4].
We
Read the rest
don't do any wrapping around LLM, any prompting, or any agent-like framework. We leave all of that on users. We're really just a boring code execution layer that sats at the bottom that we're building specifically for the future software that will be building another software.
We work with any LLM. Here's how we added code interpreter to Claude [5].
Our long-term plan is to build an automated AWS for AI apps and agents.
Happy to answer any questions and hear feedback!
[0] https://e2b.dev/
[1] https://github.com/e2b-dev
[2] https://github.com/firecracker-microvm/firecracker
[3] https://e2b.dev/docs
[4] https://jupyter-client.readthedocs.io/en/latest/messaging.ht...
[5] https://github.com/e2b-dev/e2b-cookbook/blob/main/examples/c...
As far as I know, Fly uses Firecracker for their VMs. I've been following Firecracker for a while now (even using it in a project), and they don't support GPUs out of the box (and have no plan to support it [1]).
I'm curious to know how Fly figured their own GPU support with Firecracker. In the past they had some very detailed technical posts on how they achieved certain things, so I'm hoping we'll see one on their GPU support in the future!
[1]: https://github.com/firecracker-microvm/firecracker/issues/11...
In Fly.io has GPUs nowFirecracker has often been recommended (here on HN and elsewhere) for a VM-for container-execution model : https://github.com/firecracker-microvm/firecracker
In Leaky Vessels flaws allow hackers to escape Docker, runc containersI pass through a GPU and USB hub to a VM running on a machine in the garage. An optical video cable and network compatible USB extender brings the interface to a different room making it my primary “desktop” computer (and an outdated laptop as a backup device). Doesn’t get more silent and cool than this. Another VM on the garage machine gets a bunch of hard drives passed through to it.
That said, hardware passthrough/VFIO is likely out of the current realistic scope for this project. VM boot times can be optimized if you never look for hardware to initialize in the first place. Though they are still likely initializing a network interface of some sort.
“MicroVM” seems to be a term used when as much as possible is stripped from a VM, such as with https://github.com/firecracker-microvm/firecracker
In MotorOS: a Rust-first operating system for x64 VMs