semvec/pypi-packages huggingface dataset:
cdk-fck-nat 1.6.22
A NAT Gateway instance construct built on the fck-nat AMI.
License: MIT
Requires Python: ~=3.9

# CDK fck-nat

A CDK construct for deploying NAT Instances using [fck-nat](https://github.com/AndrewGuenther/fck-nat). The (f)easible (c)ost (k)onfigurable NAT!

* Overpaying for AWS Managed NAT Gateways? fck-nat.
* Want to use NAT instances and stay up-to-date with the latest security patches? fck-nat.
* Want to reuse your Bastion hosts as a NAT? fck-nat.

fck-nat offers a ready-to-use ARM and x86 based AMIs built on Amazon Linux 2023 which can support up to 5Gbps NAT traffic
on a t4g.nano instance. How does that compare to a Managed NAT Gateway?

Hourly rates:

* Managed NAT Gateway hourly: $0.045
* t4g.nano hourly: $0.0042

Per GB rates:

* Managed NAT Gateway per GB: $0.045
* fck-nat per GB: $0.00

Sitting idle, fck-nat costs 10% of a Managed NAT Gateway. In practice, the savings are even greater.

*"But what about AWS' NAT Instance AMI?"*

The official AWS supported NAT Instance AMI hasn't been updates since 2018, is still running Amazon Linux 1 which is
now EOL, and has no ARM support, meaning it can't be deployed on EC2's most cost effective instance types. fck-nat.

*"When would I want to use a Managed NAT Gateway instead of fck-nat?"*

AWS limits outgoing internet bandwidth on EC2 instances to 5Gbps. This means that the highest bandwidth that fck-nat
can support is 5Gbps. This is enough to cover a very broad set of use cases, but if you need additional bandwidth,
you should use Managed NAT Gateway. If AWS were to lift the limit on internet egress bandwidth from EC2, you could
cost-effectively operate fck-nat at speeds up to 25Gbps, but you wouldn't need Managed NAT Gateway then would you?
fck-nat.

Read more about EC2 bandwidth limits here: https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/ec2-instance-network-bandwidth.html
kickstart-ai 0.1.2
KickstartAI: a lightweight scaffolding engine that turns natural language prompts into full project structures.
License: MIT
Requires Python: >=3.9

🚀 kickstart-ai

AI-powered code scaffolding that actually respects your brain.
kickstart-ai turns a short natural-language request into a clean, debuggable project skeleton — Flask apps, FastAPI services, Streamlit dashboards, RAG demos, whatever. It generates the folder structure, starter code, configs, templates, and even validates the output so you don’t get hallucinated junk.

Not a Cursor clone. Not a “press button → production SaaS” hallucination machine.
Just a reliable launchpad that gets you from idea → working scaffold fast, while keeping you in control of the real coding.

✨ What kickstart-ai Does

Parses vague user intent

Builds a meta-prompt describing the ideal project

Uses pluggable LLM backends (OpenAI first, HF fallback)

Generates a file-tree JSON

Writes the whole project to disk

Validates imports, directories, and missing files

Warns you about errors before you even touch the code

🔧 Why It’s Useful

kickstart-ai gives you the code you would've written anyway, just faster:

Perfect for prototypes, demos, hackathon starters

Great for students who need structure but still want to code

Ideal for devs who hate boilerplate

Avoids hallucinated garbage — you get clean TODOs, not fake logic

🧠 Philosophy

kickstart-ai is a launchpad, not a replacement for developers.
It handles the boring scaffolding so you can focus on the real engineering.

🗺️ Roadmap

v0 — core scaffolding engine (done)

v1 — template packs + stronger validator

v2 — intent classifier + safety layer

v3 — plug-and-play local models (llama.cpp, GGUF)

v4 — interactive generation / iterative refinement