# research - started in late march after reading papers about automated design of agentic systems, ai researcher, dreamcoder, bayesian program learning, prompt breeder - realized that metalearning is the only way to make ai that adapts to the user in real time and that in-context learning, not pretraining or fine tuning, are the way to do it - realized that in-context learning and its counter part, prompt engineering, are vastly underestimated and no knows how to write system prompts, see https://github.com/x1xhlol/system-prompts-and-models-of-ai-tools - since even the best prompt engineers treat it as a wall-of-text-dump, while it needs to be treated as a program - modular and composable, while language models to be treated as interpreters - realized that ai will be better at prompting ai than humans ever could as per the bitter lesson - defined in-context computation and in-context patterns as its primitives (aka functions executed by a language model) - defined basic grammar and syntax of an in-context language, a programming language for llms - defined metapatterns as a category of pattern factories that create new task-specific patterns on the fly, aka: user query /over @metapattern --> @pattern /run by llm # prototyping - built several metapatterns as bots (prompter-3000, botmaker, metaevaluator) - generated ~30 specialized bots with them (for customers + internal processes) - designed 'agent origin' - collection of patterns / prompts that allows a new agent to be instantiated in a tool like cursor or windsurf by the seed agent - built 10+ simple agents with openai sdk to learn more about orchestration, delegation, etc # next steps - build a prototype of a self-replicating agent (@agent.maker) - recreate 3 old customer projects to verify quality - build a metalearning engine for evolutionary learning - reverse engineering commit history of open source repos to train reasoning models on process rewards ** with this in mind, let's consider why the --> [[market is full of broken stuff]] ---