a surprising paragraph in the middle of the article "The “urine-no,” a hostile antiurination surface"

In the UK, the telegraph newspaper censured for story of fictional top-earner family’s struggle to pay school fees

Wow! This is the kind of news that we want on hacker news in 2026. Amazing hacker ethos.

The article could have been better with sharing some photos taken with the new camera.

Agreed, this is some proper nice tinkering writeup that we get far too rarely now.

Lovely project! I'm a software guy who in recent years does lots of CAD for hobby projects (mainly robotics) and orders custom machined parts (lots of sheet metal construction, occasionally milled parts) along with 3D printing.

I find parametric modelling very zen. Stacking operations is very Lego-like, like stringing up pure functions. Plus I can listen to podcasts while I model, but not while I write code - it engages the brain differently.

Now that LLMs are sapping some of the joy out of programming (I use the tools, they're productive, achieving goals and delivering user value is still satisfying, etc. - but the act of writing code is just more enjoyable than prompting, so it's a tad dispiriting that it's getting harder to jusitify) I also find that I get a lot of satisfaction from doing something with my hands. In some ways it's a safer space for technical creativity.

Can highly recommend hobbies like this.

You're saying you don't want to hear how someone then stuffed AI into their camera?

Imagine a camera that uses one AI to transcribe the image the sensor picked up, and then another one to use the transcription to generate the image.

No need to imagine: https://news.ycombinator.com/item?id=36139729

Paragraphica is a lensless, sensorless camera that, when you press the shutter, compiles a bunch of data with GPS, location, time of day etc, and feeds it to an image generator to create the image.

As large language models (LLMs) transition from research prototypes to production systems, practitioners often need reliable methods to verify that model outputs satisfy required constraints. While sampling-based estimates provide an intuition of model behavior, they offer no sound guarantees. We present BEAVER, the first practical framework for computing deterministic, sound probability bounds on LLM constraint satisfaction. Given any prefix-closed semantic constraint, BEAVER systematically explores the generation space using novel token trie and frontier data structures, maintaining provably sound bounds at every iteration. We formalize the verification problem, prove soundness of our approach, and evaluate BEAVER on correctness verification, privacy verification and secure code generation tasks across multiple state of the art LLMs. BEAVER achieves 6 to 8 times tighter probability bounds and identifies 3 to 4 times more high risk instances compared to baseline methods under identical computational budgets, enabling precise characterization and risk assessment that loose bounds or empirical evaluation cannot provide.

how are you creating the IR?

this reminds me of permutation city and the question of what is time...

It reminded me of the human computer in "Three Body Problem"

Only if you can’t multiply two numbers or if you can’t monitor a digital gauge that literally shows how much it will cost.

I suspect the person above hasn’t even travelled to the US.

Yeah, if he had he would have known the gasoline is one of the few things in the US where the pricing is fully transparent.

this sounds about right - the same yardstick was used by the British to define Hindus in India - people who are neither christian nor muslim.

wondering how would literate programming be with the actual code being written by LLM? I have been searching for any tools that allow for such a setup... may be my next weekend project in a long list of projects