This is all lies. I use Claude and it's just not good enough to 100x productivity.
Update: I think this is an ad by Anthropic
Update: I think this is an ad by Anthropic
Fixing the bug required a root and stem overhaul of the class, and ended up taking more time in aggregate.
And that’s the problem: just like with self-driving cars, if it isn’t right 100% of the time you are worse off because you think it’s ok to take your hand off the wheel when it very much is not.
We’ll get full autonomy in cars before we get an LLM that can write production code reliably, and we’re still very far from that.
Have it write small chunks of code, which you code review and unit test.
The only thing where I noticed a pronounced speed up is when I use other languages I'm not super familiar with. AI can more easily help me translate concepts from languages I do know better, and then a good old Google search is often enough to fill in the rest of the blanks for me to be reasonably productive in a way that I wouldn't be without AI
For instance, say I wanted to plot a complicated Matplotlib diagram. It takes me 10+ minutes plus many context switches to get the syntax right (I don't use Matplotlib enough to have all the args at the tip of my fingers). Also I don't know everything Matplotlib is able to do -- I haven't read the entire docs. Fortunately LLMs have and they get me to the right ballpark in 10-20 seconds. I usually want to try maybe 10-15 plots before settling on something. LLMs definitely do get me there much faster.
I think if you have a clear idea of what you want to do, and how to do it, then maybe the time savings are not compelling. But if you're in space where you're ideating and groping at an idea, LLMs can significantly cut down the iteration time and even open up new channels of inquiry that you didn't know existed.
They're primarily generative assistants. Using them to implement ideas in production is probably a secondary use.
it depends on what that user wanted the code to do, and how important it is.
For example, an average, non-technical user could use this to generate a script to sort out their email, or a script for automation in MS Office VBA.
Just because it's not perfect, doesn't mean it doesn't have a good use, and won't improve. Tom Scott's video[0] makes a very good argument: we don't know where we are on the technology curve.
Nothing about production code says critical.
For one off scripts or sketching a concept quickly it’s good enough, and for language reference it’s generally useful.
However, one thing I’ve noticed with Claude in particular is it tends to overweight the top answers in stack overflow.
The problem there is top answers are rarely the best answer - rather they tend to be overly verbose and long, whereas the best answer is usually the second one that just tells you what function to call.
On multiple occasions I’ve had Claude answer a simple prompt with horribly verbose and complicated code.
Then I’ll say “what about this single call?” (Eg the type of SO answer than gets the second most votes), and it’s says “You’re right! That’s a much better answer”.
Likewise I suggest to anybody to take the domain you are most knowledgeable in and pepper your LLM of choice with lots of questions and see how much it knows.
You’ll get a feel for how much you can trust it in other domains - which is “not much”.
which is quite niche, and you'd be correct that no one would trust GPT generated code blindly for that!
But this is a spectrum, and while i think today's GPT models don't quite make it there, i argue that we're closer to success with this than auto-driving cars; mainly due to the larger tolerance for bad code, rather than actual tech improvements.
Non-technical, never built a React App, never built with Supabase, never built with Firebase (for auth). Never coded a single flow of Stripe.
100x might be an understatement. He built it from nothing with minimal knowledge of React, Tailwind, Supabase, Postgres, Stripe, and Firebase using Claude.
(He knew what all of the blocks were, but no technical coding knowledge at all)
Legit has paying customers in under a month and just running it directly via Replit (not even hosted externally).
I do similar things with code bases on a smaller scale with ChatGPT all the time. Half the time I need to make small tweaks but it’s increased my productivity tremendously.
Claude is not 100x for any typical software work, but the biggest gains come from precisely 'non-typical work', which is previously impossible.
Imagine a domain expert, who knows a niche super well with all the weird edge cases and untapped demand. Hiring a developer for it doesn't work because.
1. The communication costs are too high, the developer won't know the business niche in depth enough to make a good product.
2. The niche is not profitable enough to risk hiring a developer.
Now LLMs allows the solo non-technical founder to make a MVP app, and put it to market to test, for very little cost and risk. Sure the app is not really extendable, may have to be heavily rewritten to expand and maintain, but hiring a developer then, will be a much lower risk task.
It doesn't even reduce developer employment this way, as now there's a ton more niche use cases being opened up, and becoming profitable enough to support developers.
“It just works!”
If he can get 10 or 20 paying customers, then it's easy to find money to fix or scale the code.
This type of nonsense from Anthropic isn’t helpful whatsoever.
Death knell is not solving a problem in the first place. Almost everything is negotiable if you solve a valuable problem that people are willing to pay you to solve.
https://github.com/KolbySisk/next-supabase-stripe-starter
https://github.com/felissi/nextjs-tailwind-template
https://github.com/CriticalMoments/CMSaasStarter
https://github.com/antoineross/Hikari
I'm guessing your friend is hoping enough paying customers join and are able to fund hiring someone to fix bugs or performance issues that arise