nbb
firecracker
nbb | firecracker | |
---|---|---|
48 | 75 | |
808 | 24,127 | |
0.5% | 1.2% | |
7.8 | 9.9 | |
18 days ago | 1 day ago | |
Clojure | Rust | |
Eclipse Public License 1.0 | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
nbb
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Embeddable Common Lisp 23.9.9
The SCI/babashka clojure interpreter might be a good fit, if you're ok with a lisp.
It's mature and fully sandboxed.
https://github.com/babashka/nbb
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create-helix-app: project templates with Helix and more
To try it out, run npx create-helix-app in your terminal. It is powered by Nbb, Ink, and Helix itself!
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Releasing Longdown: Convert longform markdown files to outline format used by Logseq
Thanks for building! May also want to share in #extension-news in discord to reach more users. Fwiw, you might be able to write the whole script without the need for compilation with https://github.com/babashka/nbb. You may also be interested in https://github.com/logseq/nbb-logseq as a fair amount of logseq core is scriptable
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Administrative Scripting with Julia
I wish there was something elaborated for scripts that run on Node. I've been using nbb[1] for scripting, and although it all runs through Node.js, it is fast and quick to prototype scripts. The best part is in CI I can simply `npx nbb path/to/script.cljs`. Things get clunky if I want to use anything about of the Node stdlib though, since then you need the dreaded node_modules folder around.
[1] https://github.com/babashka/nbb
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I'm considering moving from Clojure to Common Lisp
For clojure I just found for babashka it seems someone natively compiled jsoup with graalvm and exposed (minimal functionality from it) as a babashka pod, or a possibility would be use nbb like babashka for node. But if racket has the libraries you need and you don't need js/jvm ecosystem than I'm sure it'll be great also
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Is anyone using Shadow on the backend ?
There are some folks using nbb on the backend as well: https://github.com/babashka/nbb, e.g. in AWS Lambdas or via the sitefox framework: https://github.com/chr15m/sitefox. Don't expect stellar performance from nbb since it's interpreted CLJS rather than compiled (as you have with shadow-cljs) but for small scoped projects and fast prototyping it might be ok.
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What's the best lisp to js compiler
https://github.com/babashka/nbb (babashka for nodejs)
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nbb: I'm confused how to include dependencies from Clojars
I tried reproducing this example from the nbb documentation.
- nbb, scripting for Clojure on Node.js, turns 1.0!
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i am so ANGRY with Clojure community
If you don't want to deal with the tooling but want to practice the language, have a look at https://github.com/babashka/nbb
firecracker
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Lambda Internals: Why AWS Lambda Will Not Help With Machine Learning
This architecture leverages microVMs for rapid scaling and high-density workloads. But does it work for GPU? The answer is no. You can look at the old 2019 GitHub issue and the comments to it to get the bigger picture of why it is so.
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Show HN: Add AI code interpreter to any LLM via SDK
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 protocol [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...
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Fly.it Has GPUs Now
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...
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MotorOS: a Rust-first operating system for x64 VMs
I 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
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Virtual Machine as a Core Android Primitive
According to their own FAQ it is indeed: https://github.com/firecracker-microvm/firecracker/blob/main...
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Sandboxing a .NET Script
What about microVMs like firecracker?
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We Replaced Firecracker with QEMU
Dynamic memory management - Firecracker's RAM footprint starts low, but once a workload inside allocates RAM, Firecracker will never return it to the host system. After running several workloads inside, you end up with an idling VM that consumes 32 GB of RAM on the host, even though it doesn't need any of it.
Firecracker has a balloon device you can inflate (ie: acquire as much memory inside the VM as possible) and then deflate... returning the memory to the host.
https://github.com/firecracker-microvm/firecracker/blob/main...
- I'm looking for a virtual machine that prioritizes privacy and does not include tracking or telemetry.
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Neverflow: Set of C macros that guard against buffer overflows
Very few things in those companies are being written in Rust, and half of those projects chose Rust around ideological reasons rather than technical, with plenty of 'unsafe' thrown in for performance reasons
https://github.com/firecracker-microvm/firecracker/search?q=...
The fact that 'unsafe' even exists in Rust means it's no better than C with some macros.
Don't get me wrong, Rust has it's place, like all the other languages that came about for various reasons, but it's not going to gain wide adoption.
Future of programming consists of 2 languages - something like C that has a small instruction set for adopting to new hardware, and something that is very high level, higher than Python with LLM in the background. Everything in the middle is fodder.
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Do you use Rust in your professional career?
https://github.com/firecracker-microvm/firecracker is the one that comes to mind, but most of these are internal.
What are some alternatives?
babashka - Native, fast starting Clojure interpreter for scripting
cloud-hypervisor - A Virtual Machine Monitor for modern Cloud workloads. Features include CPU, memory and device hotplug, support for running Windows and Linux guests, device offload with vhost-user and a minimal compact footprint. Written in Rust with a strong focus on security.
babashka-sql-pods - Babashka pods for SQL databases
bottlerocket - An operating system designed for hosting containers
clojure - The Clojure programming language
gvisor - Application Kernel for Containers
deps.clj - A faithful port of the clojure CLI bash script to Clojure
libkrun - A dynamic library providing Virtualization-based process isolation capabilities
dbcore - Generate applications powered by your database.
krunvm - Create microVMs from OCI images
integrant - simplified integrant
deno - A modern runtime for JavaScript and TypeScript.