That's why in my workflow I don't write single monster specs. Rather, I work with the LLM to iterate on small, individual, highly constrained specs that provide useful context for what/why/how -- stories, if you will -- that include a small set of critical requirements and related context -- the criteria by which you might "accept" the work -- and then I build up a queue of those "stories" that form a, you might say, backlog of work that I then iterate with the LLM to implement.
I then organize that backlog so that I can front-load uncovering unknowns while delivering high-value features first.
This isn't rocket science.
By far the biggest challenge I experience is compounding error during those iterative cycles creating brittleness, code duplication, and generally bad architecture/design. Finding ways to incorporate key context or other hints in those individual work items is something I'm still sorting out.
> It's a logical presumption. Researchers discover things. AGI is a researcher that can be scaled, research faster, and requires no downtime.
Those observations only lead to scaling research linearly, not exponentially.
Assuming a given discovery requires X units of effort, simply adding more time and more capacity just means we increase the slope of the line.
Exponential progress requires accelerating the rate of acceleration of scientific discovery, and for all we know that's fundamentally limited by computing capacity, energy requirements, or good ol' fundamental physics.
Consider theoretical physics, which hasn't significantly advancement since the advent of general relativity and quantum theory.
Or neurology, where we continue to have only the most basic understanding of how the human mind actually works (let alone the origin of consciousness).
Heck, let's look at good ol' Moore's Law, which started off exponential but has slowed down dramatically.
It's said that an S curve always starts out looking exponential, and I'd argue in all of those cases we're seeing exactly that. There's no reason to assume technological progress in general, whether via human or artificial intelligence, is necessarily any different.
That's why in my workflow I don't write single monster specs. Rather, I work with the LLM to iterate on small, individual, highly constrained specs that provide useful context for what/why/how -- stories, if you will -- that include a small set of critical requirements and related context -- the criteria by which you might "accept" the work -- and then I build up a queue of those "stories" that form a, you might say, backlog of work that I then iterate with the LLM to implement.
I then organize that backlog so that I can front-load uncovering unknowns while delivering high-value features first.
This isn't rocket science.
By far the biggest challenge I experience is compounding error during those iterative cycles creating brittleness, code duplication, and generally bad architecture/design. Finding ways to incorporate key context or other hints in those individual work items is something I'm still sorting out.
(and yes, I use en-dashes, and no I'm not an AI)