> Canonical page: https://posttrainllm.com/inspiration/aster

A note of thanks · Scale boundary

# Aster AI Labs

Aster's distributed research search is useful for understanding what massive parallel experimentation buys. We study the method and boundary without trying to reproduce its compute pattern locally.

01 · The idea

## What stayed with us

Aster's distributed research search is useful for understanding what massive parallel experimentation buys. We study the method and boundary without trying to reproduce its compute pattern locally.

02 · The local translation

## What we did with it

Aster's research search makes parallel experimentation itself an object of study. We use it to ask which parts of a search loop scale with more agents and which still depend on clear objectives, independent measurement, and human judgment.

03 · The boundary

## Where the comparison stops

Aster's large compute pattern is outside this Mac lab. We have not reproduced its reported results; the retained value is a boundary map and ideas that may be useful only when an active target warrants them.

Source trail

## Follow the work

- Original project [Aster ↗](https://www.asterlab.ai/)
- Further reading [Scaling research search ↗](https://www.asterlab.ai/research/scaling_autonomous_research_to_thousands_of_agents)
- Our context [Our recorded factory stance ↗](https://github.com/PostTrainLLM/posttrainllm/blob/main/AGENTS.md#external-autoresearch-products-to-retain)

Independent appreciation. The named projects have not endorsed or affiliated with PostTrainLLM.