Measuring success in LoC is so wrong… It's like bragging that you took 10,000 steps to reach the store by walking in circles when you could have taken 500 steps if you just walked straight. The end result is the same.
AI has a tendency to generate more code than necessary. It keeps re-inventing things, and every time you ask it to add a new feature or fix something, it just keeps on piling the code. I now periodically ask AI to refactor the code by simplifying, removing unused things, factoring out, and reusing.
Graphene OS user here. Almost all of the apps I tried work fine. All the banking apps I use work. Have you tried reaching out to the app developer or the service and explaining what Graphene OS is and asking them to support it? I was able to persuade one app to do it.
EDIT: but unlike Pine, ThirdReality one comes with quad-core ARM A53 CPU; 256MB RAM; 512MB flash; and can actually do audio processing. The price is about 1.5 times higher though
So this whole thing is about romaji… Judging from your article and the comments you've been writing, you have strong feelings about it. Okay, you don't need kana to understand verb conjugation…
But what you've already encountered is that everyone keeps insisting you should learn kana as soon as possible and avoid using romaji. I'm not gonna write an essay on why that is, but if you're serious about learning Japanese, you're gonna have to learn kana, and it's one of those things you should really get out of the way early.
Don't know of any books that get it right. I don't think you can get it right the first time, it would be way too complex and technical. Most people would give up on the first lesson. Maybe better to treat it as an iterative process, which is how it's taught actually. First you learn it in some idealized, oversimplified concepts, then you go over them more deeply and pick up more nuance each time.
I’d recommend r/LearnJapanese for finding material and ways to study.
I get that: getting stuck on something, losing all interest, and just stopping altogether for a month or two. Happens to me from time to time.
Genki, for example, did a pretty awful job of explaining verbs. I bet a lot of people gave up right at that lesson. I myself have re-learned the verbs at least three times now. The first time, I learned all of them in the ます (masu) form. To conjugate, you just drop that and add another ending like ました (mashita) for past tense. That was a really bad approach :D But hey, it got me to learn 15 or so commonly used verbs.
There’s nothing wrong with trying to make sense of something new using terms and concepts you already know and that click for you. We all do it, me included.
I was speaking more broadly about learning Japanese and what I see on HN. Every six months or so, somebody discovers a clever pattern in the Japanese language. It’s almost always related to something taught in the beginning, the N5 level. And that pattern seems to have been eluding rest of us.
Don’t want to sound negative, just want to share my observations as someone who’s been actively learning Japanese for a couple of years. I much prefer how Japanese people actually teach verbs: 一段 (ichidan), literally "1-step"; 五段 (godan), literally "5 steps", plus a few exceptions sometimes called irregular verbs. It's not hard; it makes sense, just some textbooks (e.g. Genki) teach it really bad.
Learning Japanese is about learning a new way of thinking and structuring your thoughts. The more you learn, the more you realize it just doesn't fit into the English world. You can't really translate Japanese into English without losing nuance — and sometimes that nuance is important. So start early and start training your brain to think in the language, instead of trying to translate it and force it into English or some other language brackets. It won’t work; it won’t make sense; you will get stressed and confused.
I find myself leaning into the food authenticity camp. To clarify, I define “authenticity” as follows: a dish is cooked with particular ingredients in a particular way at a particular place.
I don’t mind fusion recipes or substituting with what you have. I have a fusion recipe collection I enjoy, and I substitute ingredients too. But sometimes I want to experience a certain taste that is unlike everything else around: break the pattern, have something how I remember it tasted. I don’t want to eat an “adapted” version of the dish, and it really annoys me that restaurants consistently fail.
Take, for example, pad thai and the way its simplest version is commonly cooked in Thailand today. There’s a set of ingredients commonly used to make pad thai. Some serving sides are optional and maybe uncommon (e.g., banana flower); some ingredients are adjusted individually at the table (e.g., amount of dry chili powder). But you’d always see it with garlic chives and never with mushrooms, for example. So the dish has a certain flavor profile and a feel to it you’ll remember after eating enough of them.
A pattern, and likely the source of annoyance to some, is that restaurants have a tendency to adapt foreign cuisines to the local taste as opposed to preserving the “authentic” taste. I’ll give credit: sometimes a new interesting dish gets created that even appears in the home country: California sushi roll. Oftentimes, though, it results in something that tastes like neither the original nor a distinct new dish. It kind of tastes like the “authentic” but wrong - an uncanny valley. For example, Indian restaurants in Europe tend to significantly underseason their food, making it taste bland to anyone who has tasted the real thing.
From my experience with Python, both personal and professional, I find it immature and not well-suited for large codebases. Typing should have become part of the language a long time ago; it is clear that users want it.
Take, for example, PHP… look at the features released in the last 6 or so years, starting with PHP 7, and how mature the language has become.
With the advance of AI-assisted programming, I feel like Python is always a bad choice.
I'm not that interested in the game much, but I really like how the repo with the Claude things is set up and the wording. I’ll use some of it in my projects. It’s well balanced IMO, includes all the important details, tools, and scripts, but not excessively wordy.
The article gives no mention of what exactly was done to achieve the speedup and whether or not the kernel is still able to perform the same function as before.
I’m doubtful this is a meaningful result. Kernel contains a lot of legacy code and generalizations to support different hardware etc.; removing that would result in a speedup. Next are all the mitigations for hardware vulnerabilities and attacks. If removed would give a nice speedup as well at the cost of security. And then finally, just specializing the Kernel in whatever the benchmark is measuring, making it useless as a general piece of software would also make it fast.
It would be interesting to see a study comparing languages where writing encodes sounds like English versus languages where writing encodes meaning, like Chinese. And also how a person’s visual and auditory capabilities relate to reading. Because languages like English need both I think.
I’m learning Japanese, and I’ve started learning Chinese characters, both their meanings and how to read them. Reading them feels different than English... I have a hypothesis that our brains work differently when processing symbols that encode meanings as opposed to just sounds. English requires an extra step, where characters are translated into sounds and then into words.
With Chinese characters, you are immediately looking at the meaning; you don’t need translation into sounds. This feels like a more efficient process cognitively to me, even though I have to memorize to recognize more characters.
That’s our new reality. Some people seem not to not grasp that all those AIs are just mathematical models producing the next most statistically likely token. It doesn’t feel anything, nor does it care about what it does. The difference between test and production environment is just a word. That, in contrast to a human who would typically have a voice in the back of his head “this is production DB, I need to be careful”.
I don't see any meaningful performance improvements in those paid models anymore.
They all roughly produce junior developer-level code, continue to have mental breakdowns in their “thinking” stage, occasionally hallucinate things, delete pieces of code/docs they don’t understand or don’t like, use 1.5 times the necessary words to explain things when generating docs and so on.
I'm now testing "avoid sycophancy, keep details short and focus on the facts" in my AGENTS.md files.
I wanted home assistant compatible plant watering solution that works on a solar panel and does not require being connected to the water line and is Zigbee compatible. Unfortunately, I could not find any. So I did a DIY solution: a big barrel which I manually fill with water, a 12V pump (usually sold for camper vans), some rechargeable batteries, 10W solar panel, a solar charging controller, and Tuya ZG-2002-RF switch.
I hate the tipping culture in the USA and Germany. Instead of being an extra, it feels like an obligatory surcharge I have to pay just to receive the service, good or bad. I usually don’t return to restaurants or bars that nag for tips. In those few places that I like and visit regularly, I don’t give tips. Me being a regular customer brings them more revenue than any tip I’d give otherwise.
Somehow, employers of these establishments convinced the staff that it's the customer’s fault that their wages are inadequate and that they should go after the customers to get the difference. I would much rather pay a higher price and not hear anything about the tips.
AI has a tendency to generate more code than necessary. It keeps re-inventing things, and every time you ask it to add a new feature or fix something, it just keeps on piling the code. I now periodically ask AI to refactor the code by simplifying, removing unused things, factoring out, and reusing.