https://techcrunch.com/2025/06/18/6-month-old-solo-owned-vib...
Is there any example of successful companies created mostly/entirely by "vibe coding" that isn't itself a company in the AI hype? I haven't seen any, all examples so far are similar to yours.
besides just weird or broken code, anything exposed to user input is usually severly lacking sanity checks etc.
llms are not useless for coding. but imho letting llms do the coding will not yield production grade code.
Move hand this way and a human will give a banana.
LLMs have no understanding at all of the underlying language, they've just seen that a billion times a task looks like such and such, so have these tokens after them.
It's a model. It either predicts usefully or not. How it works is mostly irrelevant.
all i know about these LLMs is that even if they understand language or can create it, they know nothing of the subjects they speak of.
copilot told me to cast an int to str to get rid of an error.
thanks copilot, it was on kernel code.
glad i didnt do it :/. just closed browser and opened man pages. i get nowhere with these things. it feels u need to understand so much its likely less typing to write the code. code is concise and clear after all, mostly unambiguous. language on the other hand...
i do like it as a bit of a glorified google, but looking at what code it outputs my confidence it its findings lessens every prompt
As a recent example of this, I was recently curious about how the heart gets the oxygen depleted blood back to the heart. Pumping blood out made sense to me, but the return path was less obvious.
So I asked chatgpt whether the heart sucks in the blood from veins.
It told me that the heart does not suck in the blood, it creates a negative pressure zone that causes the blood to flow into it ... :facepalm:
Sure, my language was non-technical/imprecise, but I bet if I asked a cardiologist about this they would have said something like "That's not the language I would have used, but basically."
I don't know why, but lately I've been getting a lot of cases where these models contradicts themself even within the same response. I'm working out a lot (debating a triathlon) and it told me to swim and do upper body weight lifting on the same day to "avoid working out the same muscle group in the same day". Similarly it told me to run and do leg workouts on the same day.
> i do like it as a bit of a glorified google, but looking at what code it outputs my confidence it its findings lessens every prompt
I'm having the exact same reaction. I'm finding they are still more useful than google, even with an error rate close to 70%, but I am quickly learning that you can't trust anything they output and should double check everything.
maybe this is the effect of the LLMs interacting with eachother, the dumbing down. gpt-6 will be a markov chain again and gpt-7 will know that f!sh go m00!
("But it works - when it works" is a tautology, not a useful model)
edit: Not a programmer. Just a guy who needs some stuff done for some of the things I need to work on.
I'm in a tiny team of 3 writing b2b software in the energy space and claude code is a godsend for the fiddly-but-brain-dead parts of the job (config stuff, managing cloud infra, one-and-done scripts, little single page dashboards, etc).
We've had much less success with the more complex things like maintaining various linear programming/neural net models we've written. It's really good at breaking stuff in subtle ways (like removing L2 regularisation from a VAE while visually it still looks like it's implemented). But personally I still think the juice is worth the squeeze, mainly I find it saves me mental energy I can use elsewhere.
We'll see how well they scale.
Instruments are not inculpable as you think they are.
aside, but I have yet to meet a single person (dev, qa, pm, exec) who doesn't do this.
A demise that in the case of a modern dysfunctional organisation would otherwise often be arriving a few years later as a results of complete and utter bureaucratic failure.
My experience is that all attempts to elevate technology to a "pivotal force" for the worse is always missing the underlying social and moral failure of the majority (or a small, but important, managerial minority) to act for the common good rather than egotistic self-interest.
Vikings with drones would endanger commercial airspace.
Vikings with nuclear weapons would be funny, from a distance.
Another claim for AI success, followed by, nothing specific
This reads a bit like "you're holding it wrong". If you need your team and organization to already be in a perfect state before AI usage will stop doing damage, what is the point of it in the first place.
Especially if AI is promoted as a "magic bullet" that will likely especially be uses by teams that are already dysfunctional.
Using it well requires a competent team, working together with trust and transparency, to build processes that are designed to effectively balance human guidance/expertise with what LLM's are good at. Small teams are doing very big things with it.
Most organizations, especially large organizations, are so far away from a healthy culture that AI is amplifying the impact of that toxicity.
Executives who interpret "Story Points" as "how much time is that going to take" are asking why everything isn't half a point now. They're so far removed from the process of building maintainable and effective software that they're simply looking for AI to serve as a simple pass through to the bottom line.
The recent study showing that 95% of AI pilots failed to deliver ROI is a case study in the ineffectiveness of modern management to actually do their jobs.