cloudformation-guard
firecracker
cloudformation-guard | firecracker | |
---|---|---|
20 | 75 | |
1,241 | 24,127 | |
1.5% | 1.2% | |
8.7 | 9.9 | |
10 days ago | 3 days ago | |
Rust | Rust | |
Apache License 2.0 | 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.
cloudformation-guard
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Pull Request Reporting with CDK-Validator-CFNGuard and Azure DevOps
If you now use these services to fix the infrastructure findings, a drift occurs that is not always easy to fix. It is better to check for possible problems before the actual deployment. This approach is called “Shift-Left”. This can be done with the package cdk-validator-cfnguard. It's based on the CloudFormation Guard package.
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Write AWS Config rules using cfn-guard
AWS Config rules allow you to determine if a resource is compliant or not. Previously when you wanted to do custom checks you needed to write AWS Lambda functions to validate the configuration of a resource. Since Aug 2, 2022 you have the ability to use cfn-guard rules to achieve the same.
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This is how you can test your cfn-guard rules
In my previous blog, How do you prove that your infrastructure is compliant. I explained how you can prove your infrastructure is compliant using CloudFormation Guard. But, how do you write those rules? And even more important, how do you test your rules? If you look at the repository CloudFormation Guard. You will notice that the project itself offers a testing framework. Alright! Let’s build a ruleset and write some tests for it!
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How do you prove that your infrastructure is compliant
When you use CloudFormation Guard in combination with CodeBuild Reports it makes it easier to see what rules have failed and keeps a history. When you have a solid set of compliance rules. It gives you a report that you can use to prove that the build of the infrastructure was compliant. You are also able to prevent non-compliant code rollout in production.
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Make your life easier using Makefiles
cloudformation-guard.
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Uncomplicating cloud Security — Foundations (Part 1)
AWS CloudFormation: can help with deploying compliant stacks. You can make sure that a stack is compliant by using AWS CloudFormation guard.
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OPA Rego is ridiculously confusing - best way to learn it?
See https://github.com/aws-cloudformation/cloudformation-guard
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How we use AWS Config and Security Hub for Cloud Governance
Currently, we're also exploring the brand new AWS Config rules backed by guard. Now you can write rules using guard which is a policy-as-code language. Here is some example of a Guard Rule which we are testing.
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Validating cloudFormation templates
https://github.com/aws-cloudformation/cloudformation-guard is also very useful, but more so when you want to keep your templates consistent to standards.
- AWS CloudFormation Guard
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?
cfn-python-lint - CloudFormation Linter
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.
delta - A syntax-highlighting pager for git, diff, and grep output
bottlerocket - An operating system designed for hosting containers
leaf - A versatile and efficient proxy framework with nice features suitable for various use cases.
gvisor - Application Kernel for Containers
cfn-guard-test - This tool allows you to easily run your cfn-guard tests against your cfn-guard rules.
libkrun - A dynamic library providing Virtualization-based process isolation capabilities
rust-raspberrypi-OS-tutorials - :books: Learn to write an embedded OS in Rust :crab:
krunvm - Create microVMs from OCI images
bat - A cat(1) clone with wings.
deno - A modern runtime for JavaScript and TypeScript.