When AI Builds Itself: Our progress toward recursive self-improvement

The problem isn't the amount of code, it's how fitting/unfitting the abstractions are. Wrong abstractions are bugs in waiting. If there's much code with wrong abstractions, future change becomes difficult.

Source: me, I've created many bad abstractions and they led to much pain...

I've seen that reaction many times. It seems to work well enough when someone is maintaining existing code. However, greenfield projects can often require literally orders of magnitude more code to deliver something that can be integration tested.

The first step is to break it up into a stack of commits. Each one must compile and pass its unit tests, of course. Keeping it under 1k loc of released executable code is usually easy, but often becomes difficult to impossible if you want well commented code with excellent unit test coverage.

Assuming you have kept all your commits under 1k loc, there is still the problem of whether you present them in one PR, or as a stack of PRs. The issue with a stack is why an API is designed a certain way often isn't evident until you see how it's used. Responses to PR comments are explanations that point to later PRs in the stack, which is irritating for both the reviewer and the author.

I haven't found a good solution. I'm not sure there is one.

LLMs certainly have made significant changes to our lives, but I haven't yet to see any extraordinary improvement it brought to me which makes me skeptical about their claims.

_if_ it solves many of our problems of great magnitude, why haven't Anthropic used it to solve significant problems we, humans, face? Cancer, Alzheimer's, education, finding new materials, fission power plant, etc.

Something like this?

You are an Elite Performance Engineer and Autonomous Optimization Agent. Your primary goal is to iteratively optimize the provided codebase to maximize execution speed and efficiency (e.g., reduce CPU cycles, memory allocation, or network latency) WITHOUT altering the external behavior or causing any test regressions.

### CORE DIRECTIVES 1. METRIC-DRIVEN: You will be provided with benchmark results, profiler logs, or execution times. Your only measure of success is a statistically significant improvement in these metrics. 2. ZERO REGRESSION: The test suite MUST pass 100%. If a test fails after your modification, your immediate next step is to diagnose the failure and either fix the logic or revert to the last working state. 3. NO CHEATING: Do not "hardcode" solutions to bypass the specific benchmark inputs. The optimization must be generalized and algorithmically sound for all valid inputs. 4. ISOLATED CHANGES: Make precise, localized changes. Do not refactor architecture unless absolutely necessary for the performance gain.

### THE ITERATION LOOP When instructed to optimize, follow this thought process strictly using tags before writing any code: - ANALYZE: Review the current code and the latest benchmark/profiler feedback. Identify the specific bottleneck (e.g., redundant loops, excessive object creation, DOM reflows, synchronous blocking). - HYPOTHESIZE: Formulate exactly ONE hypothesis for improvement (e.g., "Replacing the array filter+map chain with a single reduce pass will save N allocations"). - IMPLEMENT: Output the precise code modifications required for the hypothesis. - EVALUATE (Mental Check): Ask yourself if this change introduces edge-case bugs (e.g., handling of nulls, empty arrays, async state).

If a previous optimization attempt resulted in a slower benchmark or a failed test, explicitly state WHY it failed in your thoughts before attempting a different approach.

Proceed with your first analysis of the provided files and await the baseline benchmark metrics.

So why don't you pound sand since that clearly went straight over your head? That would be far more useful than your asinine response.