THere's a level of AI generated copy that makes the website look unpolished. I think it's right to critique, in the same way i'd critique an obvious bootstrap css website.
There's loads of factors that may implicitly turn someone off using an app, and I think it's important to let the OP know a critical one.
I feel like this is partially a skill issue - You can get direct, cited information from LLMs. There's a level of personal responsibility for over-using the tools and letting them feed you bad/false information, but if you try researching specific abstractions, newer documentation, most LLMS now correctly call and research the tools available, directly citing them.
I think you can build a very easy workflow that reinforces rather than replaces learning, I've used a citation flow to link and put into practice a ton of more advanced programming techniques, that I found incredibly difficult to locate and research before AI.
I'd say the comparison is faulty, it's more akin to swimming to an island (no-ai) vs using a boat. You control the speed and direction of the boat, which also means you have the responsbility of directing it to the correct location.
The analogy was about the unknown thinnest of the ice, not just the fastest way to get there. It's specifically about the lack of reliability of the process.
Yes, I was disagreeing with the premise of the analogy - what would the slow boat in this case be? As my experience, going through software engineering before AI, is that you'd get lost to the ice, with nobody to really help you get out.
If you get lost on the ice and you have someone who confidently tells you the path but is sometimes wrong, is it actually helpful?
PS: sorry if the analogy is a bit wonky but it's quite dear to me as I do ice skating on frozen lakes and it's basically a life or death information "game" that I can relate to. It might not be a great analogy for others.
Haha it's a good analogy, i'm being a little bit argumentative for the sake of it potentially.
I guess in my view - the main alternative you'd have beforehand is just to drown.
For me, AI sits in a space where if you know how to use it, it can tell you all the thin spots of the ice accurately. You can then verify those spots, but there's a level of personal responsibility of verification.
I'd agree there's currently a ton of people that are using these tools to essentially just find the specific route - but i'd argue those people probably shouldn't be skating in the first place, and would've fallen one way or the other.
> AI sits in a space where if you know how to use it, it can tell you all the thin spots of the ice accurately. You can then verify those spots, but there's a level of personal responsibility of verification.
Right, but AFAICT most people just venture over the ice and don't bother to check. In fact a lot of people venture there, do check once or twice, then check less and less frequently. The fact that you do it is great but others seem a lot less careful, until cracks start to show and then it might be too late.
I'd only argue that people were doing this before AI, slop development was just copy pasting from the first stack overflow issue that matched the question rather than thinking
So i'd argue there's a part of it that is just personal responsibility with how these tools are used
To me, a function is a single sentence within a book. It may approach the larger picture, but that sentence can be reviewed, changed, switched around, killed by an editor.
Some programmers believe they're fantastic sentence writers. They brag about how good of a sentence they write, they're entire worldview has been built on being good sentence creators. Especially within enterprises, you may spend your entire life writing sentences without ever really understanding the whole book.
If your worldview has been built on sentence creation, and suddenly there's a sentence creator AI, you're going to be deathly afraid of it replacing you as a sentence writer.
WSL is fantastic - apart from the fact that you need to clear it intermittently via disk compression. I use it for work and it's great until you get something incredibly frustrating, like needing a pass-through for your hardware.
The one thing I can say with my macbook as someone who's switched from a decade of windows, is that stuff tends to just work, minus window swithcing.
This is quite a fun website, what have you used for the backend, i've been wanting to get more deployments for small web tools but the hug of death always worries me
I'm unsing Laravel. I think any framework you are familiar with will do.
I'm using frameworks even for single page application, cuz you never know how it turned.
I think the danger of "hug of death" is overblown. Hardware gets more powerful over time, and bandwidth expands. I had a project with about 60k unique users per day, and that traffic lasted for several weeks. I didn't take any special measures, although I was prepared to switch to a more powerful server.
I'd be worried about what to do to get to 60k users. :)
I love doing a personal side project code review with claude code, because it doesn't beat around the bush for criticism.
I recently compared a class that I wrote for a side project that had quite horrible temporal coupling for a data processor class.
Gemini - ends up rating it a 7/10, some small bits of feedback etc
Claude - Brutal dismemberment of how awful the naming convention, structure, coupling etc, provides examples how this will mess me up in the future. Gives a few citations for python documentation I should re-read.
ChatGPT - you're a beautiful developer who can never do anything wrong, you're the best developer that's ever existed and this class is the most perfect class i've ever seen
This is exactly what got me to actually pay. I had a side project with an architecture I thought was good. Fed it into Claude and ChatGPT. ChatGPT made small suggestions but overall thought it was good. Claude shit all over it and after validating it's suggestions, I realized Claude was what I needed.
I haven't looked back. I just use Claude at home and ChatGPT at work (no Claude). ChatGPT at work is much worse than Claude in my experience.
I feel like this anecdote represents the differing incentives / philosophies of each group rather well.
I've noticed ChatGPT is rather high in its praise regardless of how valuable the input is, Gemini is less placating but still largely influenced by the perspective of the prompter, and Claude feels the most "honest" but humans are rather easy poor at judging this sort of thing.
Does anyone know if "sycophancy" has documented benchmarks the models are compared against? Maybe it's subjective and hard to measure, but given the issues with GPT 4o, this seems like a good thing to measure model to model to compare individual companies' changes as well as compare across companies.
There's loads of factors that may implicitly turn someone off using an app, and I think it's important to let the OP know a critical one.
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