3. You use the act of writing code to think about a given problem, and by so doing not only produce a better code, but also gain a deeper understanding of the problem itself - in combination a better product all around.
If your product is used by humans, then it needs to be tested by humans - this cannot be automated. Those humans can be your QA people, or your customers. Perhaps your customers are happy to be testers, perhaps not.
Unit tests are very expensive and return little value. Conversely, a (manual?) 'smoke test' is very cheap and returns great value - the first thing you do when updating a server for example is to check it still responds (and nothing has gone wrong in the deployment process), takes 2 seconds to do, prevents highly embarrassing downtime due to
a misconfigured docker pull or whatever.
If you think smoke testing is cheap, try doing it across concurrent releases and multiple envs, then see how fast the "just check it manually" step gets dropped when nobody owns it. Manual QA finds weird edge cases, but if that's all you have, regression will eat you alive.
Smoke tests and unit tests do different jobs, and calling unit tests low value is backwards unless your code almost never changes. If you skip automated tests because they are "expensive", you are betting release quality on hope and a lot of repeat work.
Unit tests very roughly double the amount of effort required to make any meaningful change to your codebase. They are also require maintenance same as ordinary code - but the customer does not care in the slightest whether or not they pass. On the other side, they can only really tell you about low level bugs that you already expected, they cannot surface system level bugs - the actual hard bugs that cause problems for you and your customers.
Then there is the danger of thinking that green=all good, an example of 'automation bias' where we learn to trust the automation even as things go wrong.
As makers, it is also tempting to believe that [all] problems can be solved by making something (i.e. code), but actually many problems are not of that nature, and cannot be solved in that way.
Sorry yes. If an LLM written unit test fails, then it has to be determined whether the test was wrong or the code was wrong. This is an expense in human oversight, unless of course we believe that LLMs will get it right at a high enough rate that they can be left to code everything themselves completely automatically.
Sad to see you being downvoted, but you're exactly right. Well, almost - if you can afford to invest in a good integration test suite, that can catch many errors without requiring a human to regression-test every time.
At the same time, many quality attributes can't really be automatically tested, so automation shouldn't try to replace manual testing, it should be used to augment it.
My only hope is that it is such a disaster that it is effectively an extinction level event for this current technoscene (along the lines of the Permian–Triassic extinction event and others).
Then we can get back to the unglamorous, boring, thankless task of delivering business value to paying clients, and the public discourse will no longer be polluted by our inane witterings.
In the grand scale of things, a computer is not much more than a fancy brick. Certainly it is much closer to a brick than to a human. So the question is more 'why should this particularly fancy brick have abilities that so far we have only encountered in humans?'
I would like a personal assistant on my phone that, based on my usual routine and my exact position, can tell me (for example) which bus will get me home the quickest off the ferry, whether the bridge is clogged with traffic, do I need an umbrella? what's probably missing from my fridge, time to top up transit pass, did I tap in? etc etc. These things would appear on my lock screen when I most probably need to know them.
No email stuff, no booking things, no security problems.
Indeed I have a bunch of apps that do most of these things, but it's the seamless integration I'm looking for - which may not need much AI at all (especially of the LLM kind), just some well directed machine learning and UI integration.
I read this as the aspirational dream of computers actually doing what you want. Yes, you can absolutely spend a bunch of time to build out the personal automation that will proactively inform you of relevant events. Yet, that is likely to be a lot of finicky messing around that may be pretty fragile and dependent upon N APIs staying fixed.
Sounds like there is need for decent singular interface for bunch of expert systems. Sadly I think everyone is so deep into locking their own thing down from others that this will never happen.
No security problems carries a lot of weight here because by design you’re having to expose a significant amount of information but this is doable as a weekend project
- Where do you source real time traffic data, ferry schedules, etc? Google APIs get you part of the way there but you'd need to crawl public transit sites for the rest.
- How do you keep track of what went into the fridge, what was consumed/thrown away?
- How do you track real world events like buying a physical pass?
In an alternative reality Apple didn't absolutely shit the bed on AI and made this possible. Sadly they've shown they are woefully behind and have utterly useless people leading divisions they shouldn't have been allowed anywhere near.
> Thousands of FLASH Charging stations have already been installed in China, and BYD has committed to a global rollout that will include an initial wave of FLASH Chargers in Europe. Further details on the plans, and how they will support the Z9GT's arrival, will be revealed in due course.
With 5 minute charging, suddenly conventional gas stations can be used for EVs just as they are for ICE. Nice thing about 'plugging in' as opposed to 'filling up' is that a charging car can be left completely unattended (while you go to pay, get a coffee, or whatever).
Seems that the technological barriers have been overcome, now we just need to build out the infrastructure - which could be as simple as retooling existing gas stations. No need to electrify every parking space or such like.
Yes, retooling gas stations is the way to go. Already happening in Norway where stations now show the price of kWh in addition to gas and diesel prominently on signs by the road. Charging is just a different kind of pump.
Charging stations don't need all the environmental equipment that gas stations have to catch oil and gas run off. I guess we could convert old gas stations that don’t need extensive environmental cleanup, but building new ones just to charge EVs is huge overkill when you just need some space in a parking lot.
a key difference between a gas station and a parking lot is that people only park at a gas station to get gas (and have a rest, get dinner, bathroom break etc), so, assuming quick charging, you need less chargers as they will spend less time not charging a fully charged car left parked on them. Gas station staff can ensure charging spots are utilized correctly as they do now for the pumps.
Also everyone understands how gas stations work, so it is easier to slip EVs into the social fabric.
That's a good point. Charging stations benefit from being a service station too though, with amenities and a cafe etc, since people want something to do while they charge. So a gas station is a better candidate than a parking lot when decisions are made for where to place the new charging infrastructure. Lots of other factors too of course.
I prefer grocery stores because I can run in and grab something quickly. The only problem is that charging is often too quick to do any real shopping, so a smaller convenience store makes sense also.
Excel is 'free-at-point-of-use', i.e. once you've paid for it, to use it doesn't cost anymore. But LLMs do cost per use (unless we all go to local models). Either this cost is billed directly, or some sort of bundling occurs with 'fair use' limits.
Excel is deterministic, yes scary spaghetti-fied spreadsheets are routinely constructed, but, for example, sorting a result column somewhere can be done with a bit of poking in the right place. LLMs have a tendency to dangerously change many things if the prompting is a bit wrong (and even if it is a bit right).
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