Comment by bs7280
9 hours ago
In my opinion there are two main groups on the spectrum of "vibe coding". The non technical users that love it but don't understand software engineering enough to know what it takes to make a production grade product. The opposite are the AI haters that used chatgpt 3.5 and decided LLM code is garbage.
Both of these camps are the loudest voices on the internet, but there is a quiet but extremely productive camp somewhere in the middle that has enough optimism, open mindedness along with years of experience as an engineer to push Claude Code to its limit.
I read somewhere that the difference between vibe coding and "agentic engineering" is if you are able to know what the code does. Developing a complex website with claude code is not very different than managing a team of off shore developers in terms of risks.
Unless you are writing software for medical devices, banking software, fighter jets, etc... you are doing a disservice to your career by actively avoiding using LLMs as a tool in developing software.
I have used around $2500 in claude code credits (measured with `bunx ccusage` ) the last 6 months, and 95% of what was written is never going to run on someone else's computer, yet I have been able to get ridiculous value out of it.
> extremely productive camp somewhere in the middle
How do you quantify and measure this productivity gain?
These kinds of comments are so spectacularly useless. It was almost impossible to measure productivity gains from _computers_ for nearly two decades after they started being deployed to offices in the 1980s.
There were articles as late as the late 1990s that suggested that investing in IT was a waste of money and had not improved productivity.
You will not see obvious productivity gains until the current generation of senior engineers retires and you have a generation of developers who have only ever coded with AI, since they were in school.
It was not impossible to measure them. It is just that you dont like the result of the measurement - early adopters often overpaid and endes up with less efficient processes for more money.
Eventually companies figured out how to use them effectively and eventually useful software was created. But, at the start of the whole thing, there was a lot of waste.
Quite a lot of people are now paying a lot for ai that makes them produce less and lower quality. Because it feels good and novel.