So we are reinventing the docs /*/*.md directory? /s I think this is a good idea just don’t really get why would you need a tool around it

One of the things that I've been chewing on lately is the sync problem. Having a CI job that identifies places where the docs have drifted from the implementation seems pretty valuable.

Python community figured this out in 2001: https://docs.python.org/3/library/doctest.html

I don't think this is related in any way.

> Having a CI job that identifies places where the docs have drifted from the implementation seems pretty valuable.

https://docs.python.org/3/library/doctest.html

> To check that a module’s docstrings are up-to-date by verifying that all interactive examples still work as documented. To perform regression testing by verifying that interactive examples from a test file or a test object work as expected. To write tutorial documentation for a package, liberally illustrated with input-output examples. Depending on whether the examples or the expository text are emphasized, this has the flavor of “literate testing” or “executable documentation”.

Seems pretty related to me.

Not really.

> Having a CI job that identifies places where the docs have drifted from the implementation seems pretty valuable.

Testing with lat isn't about ensuring consistency of code with public API documentation. It is about:

* ensuring you can quickly analyze what tests were added / changed by looking at the English description

* ensuring you spot when an agent randomly drops or alters an important functional/regression tests

The problem with coding agents is that they produce enormous diffs, and while reading tests code is very important in practice your focus and attention drifts and you can't do thorough analysis.

This isn't a new problem though, the same thing applies to classic code reviews -- rarely coding is a bottle neck, it's getting all reviews from humans to vet the change.

Lat shifts the focus from reading test code to understanding the semantics of the test. And because instead of reviewing 2000 lines of code you can focus on reviewing only 100 lines change in lat.md you'll be able to control your tests and implementation more tightly.

For projects where code quality isn't paramount I now just glance over the code to spot anti-pattern and models failing to DRY and resorting to duplicating large swaths of code.

I am guessing as Google is vertically integrated and "actually pays" for AI infra (compared to OpenAI & Anthropic that receives hardware as partnerships) they have a more urgent incentive to reduce model sizes. Also, Google and Apple will be the first to gain from running model on-device

I can assure you OpenAI and Anthropic pay for hardware. They don’t receive it for free.

This seems to be an inference-time optimization and they are putting AI on every search result page. That seems like plenty of incentive to optimize.

Worked with a CTO that had the same rule of thumb. I quickly proved strategic testing is net positive for the business

The prophecy of the hypermedia web

I feel like I haven’t read anything about this in combination with mcp and like I am taking crazy pills: does no one remember hateoas?

Also LLMs don’t mind repeating params for each child call. Pretty neat

This is America. Like it or not.

That is so cool!!! Thank you for putting in the effort

I think if people will people know how accessible it is to run local LLMs on their device they will consider buying devices with more memory that will be able to run better models. Local LLMs in the long run are game changers

I agree. I mean mobile devices have only been getting more and more powerful.

Cool project and very clear explanation for the motivation kudos!