alt Hacker NewsThat’s something that I have been thinking about for a while now. Even prior to LLMs, many large companies have essentially been iterating on the same half a dozen ideas over and over again and there doesn’t seem to be much new coming out even post LLMs.
The most recent new things that LLMs have unlocked (regarding software dev) for us is reverse engineering and combining old games, unearthing decade old vulnerabilities, and helping root and debloat consumer hardware. Which are all cool and useful, but none of it being a “next great thing”.
Perhaps LLMs are really doing is highlighting how the software industry was already nearing its economic maturity and this just shifted the timeline from 2040 back to 2027-2030.
Replies
Honestly I think you're on to something, as by far the most novel and interesting use cases I've seen for LLMs in general are mostly around reverse engineering things, especially if you can hit it with Ghidra, but honestly anything that can feed some sort of data and control back to a model.
> the software industry was already nearing its economic maturity
What I'm sensing is something I might describe as the Oligarchy reaching the point of significant societal pushback, like the labor rights movements that ended 80 work weeks, unsafe conditions, and child labor in factories (i.e. The Jungle).
My hope is that we can use this moment in human history to break the user tracking and targeted advertising industry. It's been so harmful to humans, but in doing so, we will likely break a major revenue source that supports our well above average salaries. I still think it should be done.
I have yet to see LLMs do well “autonomously” on anything that doesn’t fall into any of these 3 buckets:
1. well specified
2. well tested (which is a form of spec)
3. represented in the training set (i.e. not built on a corpus of human developed examples)
For example, a lot of the compiler porting efforts fall into all 3 buckets: compilers are well specified (there is a set of valid inputs and an expected behavior), they are typically well tested, and as a bonus, in a way that is implementation agnostic thanks to the clean interface (think: integration vs unit tests), and are very well represented in the training set.