3 ms·
With this speed, you can keep looping and generating code until it passes all tests. If you have tests. Generate lots of solutions and mix and match. This allo
by smusamashah 8mo ago
With this speed, you can keep looping and generating code until it passes all tests. If you have tests.
Generate lots of solutions and mix and match. This allows a new way to look at LLMs.
- MattRix 8mo agoThis is what people already do with “ralph” loops using the top coding models. It’s slow relative to this, but still very fast compared to hand-coding.
- Retr0id 8mo agoNot just looping, you could do a parallel graph search of the solution-space until you hit one that works.
- xi_studio 8mo agoInfinite Monkey Theory just reached its peak
- dave1010uk 8mo agoYou could also parse prompts into an AST, run inference, run evals, then optimise the prompts with something like a genetic algorithm.
- Epskampie 8mo agoAnd then it's slow again to finally find a correct answer...
- turnsout 8mo agoAgreed, this is exciting, and has me thinking about completely different orchestrator patterns. You could begin to approach the solution space much more like a traditional optimization strategy such as CMA-ES. Rather than expect the first answer to be correct, you diverge wildly before converging.
- otabdeveloper4 8mo agoThis doesn't work. The model outputs the most probable tokens. Running it again and asking for less probable tokens just results in the same but with more errors.
- therealdrag0 8mo agoDo you not have experience with agents solving problems? They already successfully do this. They try different things until they get a solution.