It cuts both ways: the employee can walk out during the trial period for reasons such as feeling like they didn't fit in, or the work being different from what they imagined. But if they merely find a better-paying job elsewhere, they cannot invoke the trial period but have to give notice in the usual way.
I would assume that your local audiologist or music instrument store will know what the U.S. equivalent to these is. It seems to me that Elacin's biggest market is musicians who want a comfortable pair of earplugs with a flat frequency response.
However, what destroys my sleep is the light from early morning, streetlights, and the neighbor's porch light. Unfortunately, our bedroom faces southeast and features French doors that open onto an east-facing three-season porch, allowing sunlight to stream in. Yeah, I've got curtains everywhere, and I have room-darkening curtains on order. If those don't work, the next step is putting solar panels over my bedroom windows. I figure if I'm going to keep light out, I might as well put it to work some other way.
As an experiment, I'm using my car camping mattress in my office, which is the quietest room in the house, and I'm blocking the light from the windows with curtains and cardboard. So far, it's the best sleep I've had in years. There's a bit of domestic disharmony now, but hopefully my partner and I can work out a compromise on light-blocking curtains and keeping them fucking shut.
https://www.elacin.com/your-perfect-fit/leisure/relax-sleep/
https://bioears.co.uk/products/bioears-ear-plugs
Very effective, but eventually they made me just focus on my tinnitus.
I now live in a quieter place and use some white noise from a speaker - ocean sounds.
https://www.elacin.com/your-perfect-fit/leisure/relax-sleep/
Currently I use Ozlo Sleepbuds which are not quite as comfortable and a little finicky to operate, but I like the masking noise.
If you let it run in the "write my code for me" mode, and ask it to fix some mistake it made, it will always add more code, never remove any. In my experience, in the end the code just ends up so brittle that the LLM will soon get stuck at a point that it never manages to overcome some mistake, no matter how many times it tries.
Has anyone managed to solve this?
When the model has the wrong solution in its context, it will use it when generating new code, and my feeling is that it doesn't handle the idea of "negative example" very well. Instead, delete the bad code and give it positive examples of the right approach.
Recent benchmark on unseen 2025 Math Olympiad shows none of the models can problem solve . They all accidentally or on purpose had prior solutions in the training set.