Before it, "distributed computing" meant institutional grids, cluster access, gated systems. SETI@home proved that aggregating idle cycles from millions of ordinary machines was a legitimate scientific method. That proof changed what was possible.
Folding@home came next. BOINC was built to formalize the template. Distributed citizen science became a recognized mode of doing research. None of that path was obvious before SETI@home walked it first.
What's strange is that cheap cloud compute kind of ended this era not by failing but by succeeding. Why donate your CPU when AWS is a credit card away? The economics shifted. But something got lost too — the screensaver running while you slept, the knowledge that your specific machine was doing something real in the world. That personal connection to a distributed effort hasn't really been replicated.
elicash's question is the right one. Could distributed agents revive the model? Maybe. But I suspect the hard part isn't the architecture — it's recreating the feeling that your contribution matters when it's one of ten million.
Most of this data was recorded commensally at the Arecibo observatory over a 22 yr period
Interesting as Arecibo collapsed in December of 2020. It sounds like they have a lot of data to still churn through.
Those were the days
answer was really interesing: - https://github.com/PrimeIntellect-ai/prime - https://www.together.ai/
Spent hours watching the graph hoping to get triplets and some kind of confirmation that I just found ET.
Miss those days so much.
Definetly something going on here I'm not following.
>SETI@home is in hiberation. We are no longer distributing tasks. [0]
Is this paper really old or something? I would love to turn on my clients again :D
I know what you mean these types of projects inspired me to contribute as a young citizen scientist.
A different domain, but https://foldingathome.org/ is still running. Using distributed compute to study protein folding.
But to your point: No--AlphaFold is an amazing machine learning approach to predicting protein structure but Folding@Home is still immensely useful for simulating how proteins fold up over a timescale. They are/will be complimentary methods.