Fair point, but because I spent a year building and refining my custom tool, this is now the reality for all of my AI requests.
I prompt, press run, and then I get this flow:
dev setup (dev-chat or plan)
code-map (incremental 0s 2m for initial)
auto-context (~20s to 40s)
final AI query (~30s to 2m)
For example, just now, in my Rust code (about 60k LOC), I wanted to change the data model and brainstorm with the AI to find the right design, and here is the auto-context it gave me:
- Reducing 381 context files ( 1.62 MB)
- Now 5 context files ( 27.90 KB)
- Reducing 11 knowledge files ( 30.16 KB)
- Now 3 knowledge files ( 5.62 KB)
The knowledge files are my "rust10x" best practices, and the context files are the source files.
How do you re-evaluate your approach? I'm asking because the landscape, at least from my lens, was completely different a year ago. So I fear that as the foundation shifts whatever learnings, approaches and mental models I have risk being obsolete and starts to work against me.
The problem of evaluating is hard enough as it is without layers of indirection built on top of it.
I prompt, press run, and then I get this flow: dev setup (dev-chat or plan) code-map (incremental 0s 2m for initial) auto-context (~20s to 40s) final AI query (~30s to 2m)
For example, just now, in my Rust code (about 60k LOC), I wanted to change the data model and brainstorm with the AI to find the right design, and here is the auto-context it gave me:
- Reducing 381 context files ( 1.62 MB)
- Now 5 context files ( 27.90 KB)
- Reducing 11 knowledge files ( 30.16 KB)
- Now 3 knowledge files ( 5.62 KB)
The knowledge files are my "rust10x" best practices, and the context files are the source files.
(edited to fix formatting)