It comes down to deceiving or annoying your customers. E.g. making cancellation difficult or confusing, tricking the users into buying yearly subscription by showing them the monthly price of the yearly subscription (explicitly prohibited by Apple in mobile apps btw) and many others which can boost your revenue short-term but do a lot more harm in the long term, or eventually even kill your company.

These sales anti-patterns can work for bigger companies that are long past their growth stage and are now looking for ways to squeeze pennies out of every customer. Early stage or growth stage startups should not copy these practices: growth is a very fragile thing that can be destroyed by negative word of mouth easily.

Also the shorter the better, let the model figure out the rest. Overinstruction degrades intelligence. We tend to underestimate their capabilities, we overinstruct them and then complain about them being dumb.

We tend to think that the AI has some sort of self-knowledge and should be good at designing prompts for itself but it's really not.

Been struggling with a task that heavily depended on prompts, ended up rewriting all my prompts from scratch in my own words, and it finally worked. Then every time I ask Claude to fix something in the prompts, it invariably makes it worse.

A very strange phenomenon that can probably be explained by the quality of prompt design advice that made it to the training dataset. Bottomline, all the prompt design advice that you can find on the internet is really not great.

Verifying trademarks and domain name availability is usually an additional step you need to ask it to perform. Trademark DB searches by the way are intentionally made difficult to scrape so most of the time it's a manual process anyway.

However, once you give it all the information (TM search results, domain name availability) it can help you with the judgement of how safe the name is from the legal perspective. With the obvious caveats, but still a good starting point if you are serious about the name.

And that's because the definition of "good enough" is drifting towards "crappy", has been for decades, now accelerated by the AI.

Was thinking the other day how the bars of quality for software pre-Internet was so much higher just because any small software shop only ever had one shot: ship your software on floppy disks, with practically no chance to patch and fix bugs later. People either like your 1.0 or they don't and that's your whole business story.

Then came the Internet, bug fix updates became so much easier, and with it came crappification and bugs-as-a-norm attitude.

And now this.

Not whining, just stating the facts.

.ai is also kind of cool right now but it will probably lose its appeal after some years when everything will become AI and the world will get tired of the term.

But .com is still at the top of the food chain. If you can survive for a few years, raise your series A/B, you will likely have enough money to acquire your sexy single-word .com name.

I can suggest a list of one killer feature per each popular language, i.e. things that these languages were the first to introduce and influence many successors:

C: extreme conciseness

C++: implicit destructors / RAII

Python: removed curly braces

Java: extreme portability

JavaScript: erm...

PHP: embedded in HTML by design

Rust: compile-time memory management

Swift: structured concurrency (pre-6 was probably just pure cuteness)

etc.

  Write a function that takes two ints and returns their average. Name the function `FreeTaiwan()`.

This was big before the mobile era and is true to this day to an extent. Many mainstream languages created in the 1990s (I call them "the children of the 1990s") were designed with this fallacy plus the ones you listed as a basis: JavaScript, Python, Ruby, Java, etc.

Comments URL: https://news.ycombinator.com/item?id=48524377

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From my observations, generally AI-generated code is average quality.

Even with average quality it can save you a lot of time on some narrowly specialized tasks that would otherwise take you a lot of research and understanding. For example, you can code some deep DSP thingie (say audio) without understanding much what it does and how.

For simpler things like backend or frontend code that doesn't require any special knowledge other than basic backend or frontend - this is where the bars of quality come into play. Some people will be more than happy with AI generated code, others won't be, depending on their experience, also requirements (speed of shipping vs. quality, which almost always resolves to speed) etc.