geospatial-data-lake VS Installation

One option is to trust on first use, checksum the installation script and at least casually verify the diff each time the checksum changes[1].

Pros:

- Protects against simple hijacking.

- Reproducible as long as the installer doesn't also call out to a moving target, such as example.com/releases/latest.

Cons:

- Build breaks as soon as the installer is bumped. If it's bumped often (or just before an important release) this can cause pain.

- TOFU may not be acceptable, but of course you could review the code thoroughly before even the first use.

[1] https://github.com/linz/geostore/blob/b3cd162605109da8a3a688...

I'd recommend a project from work, Geostore[1]. Highlights:

- 100% test coverage (with some typical exceptions like `if __name__ == "__main__":` blocks)

- Randomises test sequence and inputs reproducibly

- Passes Pylint with max McCabe complexity of 6

- Passes `mypy --strict`

- Formatted using Black and isort

[1] https://github.com/linz/geostore

The current work project[1] has all of these: Pyenv, Poetry, Pytest, pytest-cov with 100% branch coverage, pre-commit, Pylint rather than Flake8, Black, mypy (with a stricter configuration than recommended here), and finally isort. These are all super helpful.

There's also a simpler template repo[2] with almost all of these.

[1] https://github.com/linz/geostore/

[2] https://github.com/linz/template-python-hello-world

As someone who's used CDK for a few months and never handcoded CF, that sounds completely correct. If you're comfortable with Python, here's a simple but non-trivial architecture you can check out: https://github.com/linz/geospatial-data-lake/blob/master/app....

"The site" is actually just their blog. The github repo at https://github.com/netbox-community/netbox is more like the real homepage and has a good presentation.

Sweet! If it ends up anything like Netbox it will be so sweet. By looking at the screenshots I am very excited to see this implemented!

NetBox Release v3.6.6 is now live (as of November 29, 2023)!

Take a look at netbox. https://netbox.dev/

Pre-trained systems dominate AI today (as deep as the P in GPT). My team and I have been researching and building alternatives given how the hallucination, blackbox and other problems don't seem solvable within* the current paradigm.

One of our first applications is a network automation solution for Netbox-- locally trained AI Agents unique to each Netbox account to predict roles of newly added devices given the current local list of devices, like a context-aware autocomplete.

Agents are lightweight by design, this particular Netbox Agent is 40-neuron, and when hooked up demo.netbox.dev and consistently gets 80%+ accuracy predicting device roles even when trained on ~60 devices only.

Try it out: https://aolabs-netbox.streamlit.app/

You can use dummy data from demo.netbox.dev. More on Netbox: https://github.com/netbox-community/netbox

We'd keen for feedback, if this is useful, how we could extend it if it is, or if it sparks other application ideas.

* Somebody has to be the pre-trainer, leaving an irreducible gap of misunderstanding between AI and its application which we are trying to diminish by adding a layer of local training.

Before upgrading, please: 1. Verify in release notes changelog if any new breaking changes might affect you. You can also review the NetBox Issues on GitHub to see if any new issues have arisen that might affect you. 2. Next, refer to the Upgrading to a new NetBox Release guide for steps to upgrade your instance.

I've been looking into some projects for some opensource software i use and noticed a few of them have empty __init__.py files (for example https://github.com/netbox-community/netbox/blob/develop/netbox/core/__init__.py)

IT wise, It's been a fair few years since I've been in a department that does asset management, we used to use GLPI with it's warts and all, which got replace with https://github.com/netbox-community/netbox