I did an experiment a few weeks back as a bit of a litmus test for a new data format my team and I have been working on. I fed the spec documentation into ChatGPT and asked it to write a file reader for it. It spat out a mostly-correct implementation almost immediately, and with some extra coaxing managed to fix the small bugs in it too.
To be fair, it's a very simple format, but it made me feel good about the quality of the documentation.
ALT | Remote (EU) | Full-time | Frontend Software Engineer
We're a small, established company specialising in subsurface instrumentation development, as well as the software needed to handle subsurface data. As of right now, we're working on a product to streamline management, delivery and visualisation of data.
Currently chasing a frontend developer with a flair for UI/UX design, and experience in Vue.js and D3. Must have a strong mathematical background.
Open to employing via company-of-record (such as remote.com), or direct B2B consulting. If you're in Luxembourg, we can employ you directly. Reach out to me at jobs <at> alt.lu.
I work in the borehole instrumentation industry, and lots of our customers get hyperspectral data from core. I'm working on a new data delivery and management platform, and I've made sure our data structures can handle hyperspectral images. Multidimensional arrays are front and centre. I too think of spectral images as "cubes".
One thing that stems from this is the question of how you visualise it. False colour mapping?
Oof, that's tough. I'm over here on a blue card, and I found that the initial application and dealing with department of immigration was actually really streamlined. Everything is very manual and personal over here, but there are a large number of public servants that are very welcoming and happy to help out.
I've noticed the umami flavour that tends to develop when cooking beer for a long time. An Irish stew with Guinness in it, or a gulas using beer as the staple liquid all develop Vegemite like flavours.
I love it, but I think a lot of non-Aussies don't recognise the similarities.
There are multiple official camping grounds and caravan parks on the route. Unfortunately wild camping isn't allowed anywhere. I know that some people still try it, and probably so long as you're somewhere off trail, camp late and leave early nobody will bother you.
It's why I switched my Model 3 to just report battery percentage instead of estimated range. Their charge usage algorithm in their route planner is very impressive. Even across 300+ km trips, I've never seen it out by more than a few percent. From what I can see, it's most affected by wind. I guess they don't input weather into the estimation model.
My experience is that real world usage gets me about 85% of the advertised range for most of the year. I average around 160 Wh/km for the driving I do, so with a pack capacity of 75 kWh, that's about 470 km. Advertised range here in the EU was 530 km.
Author here. This is just a bit of technical discussion on the first bring-up and testing of a new design of force/torque sensor that utilises a Stewart platform geometry. The ultimate goal is that it'll become a fully functional SpaceMouse style device, which at the moment are really only available from one manufacturer due to many mergers and acquisitions and some heavy patent enforcement.
I've released the design open-source, so if anyone is interested in contributing, check out the GitHub repository. Let me know if there are any questions or feedback.
These stories are great, and totally resonate with me at the moment. I'm managing a team working on a 30 year old codebase. It is in technical debt, like many codebases that old. That being said, the codebase is full of so much good stuff from hard learned lessons over 30 years, and that's where its value lies.
I'm convinced that the way forward is not to refactor or rewrite it, but to produce something new and different that draws on the 30 years of experience learned during the development of the original product. And, like Basecamp, run them side-by-side and allow users to chose what they want.
I've been working on of these for a little bit as well. I have a prototype that's working, but has a few problems I hope to solve in the next iteration.
Mine is based on a Stewart platform with strain sensors for each truss.
I implemented a well known curve simplification algorithm in Rust, and was pleasantly surprised how easy the interaction with Python was. For packaging, setuptools_rust was great, and I too used PyO3 for the bindings. I haven't tried Rumpy yet, but it looks interesting.
This is a pretty impressive project, though the results (https://www.youtube.com/watch?v=dwXs9RB6uD0) are somewhat disappointing. From what I see toward the end of the video, the image is blurry with poor colour rendition, although it is fantastically low latency.
I suspect you're currently limited by the optics you're using, lack of white-balance and low CRI lighting. Looking forward to seeing further tests!
To be fair, it's a very simple format, but it made me feel good about the quality of the documentation.