31–40 of 44 posts
“Every line of AI-generated code is a plausible-looking liability. It may pass basic tests, only to fail spectacularly in production with an edge case you never considered.” Every time I read something along the lines I have to wonder whose code these people review during code reviews. It’s not like the alternative is bulletproof code.
I find LLMs really useful on a daily basis but I keep wondering, what's going to happen when the VC money dries up and the real cost of inference kicks in? Its relatively cheap now but its also being heavily subsidized. The usual answer is to just jam ads into your product and slowly increase the price over time (see: Netflix) but I don't know how that'll work for LLMs.
you could localhost ollama, vLLM, or something like that; Open Models are good enough for simple task, With a bit of extra effort and learning, this is usually just works for most case. But in that situation, there may be no further updates, the future remains uncertain.
“Every line of AI-generated code is a plausible-looking liability. It may pass basic tests, only to fail spectacularly in production with an edge case you never considered.” Every time I read something along the lines I have to wonder whose code these people review during code reviews. It’s not like the alternative is bulletproof code.
Anything coded by an LLM risks being under-generalised.
Asking an LLM to think in a generalised way does not make it an AGI. The critical ability to generalised beyond learned patterns and to not only come up with arbitrary patterns but to use correct logic to derive them is missing from LLMs because LLMs do not have a logical layer, only a probabilistic one with learned constraints. The defect is the lack of internal logical constraints. It’s a big subject.
I say more about it here:
https://www.forbes.com/sites/hessiejones/2025/09/30/llms-are...
Aka
“layered system”
Long-term SWEs at non-tech companies will spend much of their time reviewing vibe coded features/prototypes/scripts from non-technical employees and scaling them once they become critical infrastructure. This'll eliminate jobs in the "develop CRUD app" industry but will create better jobs in security/scalability/quality expertise. But it'll take a few years as all these vibe coded business process scripts start to fa…
That is literally the exact promise of CASE tools in the 80s and the early 90s; UML code generation tools in the 2000s, and "low-code/no-code" platforms in the 2010s. It turned out to be a disaster every time, especially when the Idea Persons chucked their creations over the wall to SWEs to bash them into actual products because the Idea Persons had Far More Important Things To Do than maintain their coalesced brain…
“Every line of AI-generated code is a plausible-looking liability. It may pass basic tests, only to fail spectacularly in production with an edge case you never considered.” Every time I read something along the lines I have to wonder whose code these people review during code reviews. It’s not like the alternative is bulletproof code.
A lot of people seem to equate using AI tools and deploying code that you don’t understand. All code should be fully understood by the person using the AI tool, then again by the reviewer. The productivity benefit of these tools is still massive, and there is benefit to doing the research and investigation to understand what the LLM is doing if it was not clear up front.
Should, yeah. But that was not true even before LLMs.
Long-term SWEs at non-tech companies will spend much of their time reviewing vibe coded features/prototypes/scripts from non-technical employees and scaling them once they become critical infrastructure. This'll eliminate jobs in the "develop CRUD app" industry but will create better jobs in security/scalability/quality expertise. But it'll take a few years as all these vibe coded business process scripts start to fa…
That is literally the exact promise of CASE tools in the 80s and the early 90s; UML code generation tools in the 2000s, and "low-code/no-code" platforms in the 2010s. It turned out to be a disaster every time, especially when the Idea Persons chucked their creations over the wall to SWEs to bash them into actual products because the Idea Persons had Far More Important Things To Do than maintain their coalesced brain…
Meanwhile my mom can vibe code actual Python scripts to do parts of her job now.
“Every line of AI-generated code is a plausible-looking liability. It may pass basic tests, only to fail spectacularly in production with an edge case you never considered.” Every time I read something along the lines I have to wonder whose code these people review during code reviews. It’s not like the alternative is bulletproof code.
There's no such thing as bulletproof, but there is definitely such a thing as knowing where your vital organs are and how to tell when they've been hit.
“Every line of AI-generated code is a plausible-looking liability. It may pass basic tests, only to fail spectacularly in production with an edge case you never considered.” Every time I read something along the lines I have to wonder whose code these people review during code reviews. It’s not like the alternative is bulletproof code.
Good code is explicit about its assumptions and enforces them; good companies set hiring bars so as to filter out developers that can't write good code. There's no such thing as bulletproof, but there is definitely such a thing as knowing where your vital organs are and how to tell when they've been hit.
And the others are going to be replaced by one engineer and an AI of equivalent caliber.
Earlier quoted context omitted.
A lot of people seem to equate using AI tools and deploying code that you don’t understand. All code should be fully understood by the person using the AI tool, then again by the reviewer. The productivity benefit of these tools is still massive, and there is benefit to doing the research and investigation to understand what the LLM is doing if it was not clear up front.
> All code should be fully understood by the person using the AI tool, then again by the reviewer. Should, yeah. But that was not true even before LLMs.