Llm

Karpathy’s autoresearch and DeepMind’s AlphaEvolve improve things with one simple loop: propose a change, score it, keep it only if it wins. I ran the same loop three ways: on trading strategies with a backtest as the scorer, on a spec and a strategy with LLM judges as the scorer, and by hand through 56 LLM review panels. The backtest loop inflated its own results 3 to 4 times. The LLM-judged loops stalled and learned to flatter the judges. Here is what each failure teaches, and how to build a loop that works.

For months I have sent every Russian translation through Gemini, because Claude’s Russian reads like American English. I assumed Google’s search index was the reason. The data says Gemini really is ahead, but the lead is one year old, and it is not the index or the tokenizer.

The iteration trap is really a clarity trap. You iterate because you don’t know what you want.

How I added an optional LLM post-processing step to clean up voice transcriptions, with lessons on prompt engineering and graceful degradation.