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The original article does a good job of contextualizing the shifting dynamics, but yours turns that into an actionable solution. I've been wondering about this same problem too after having trouble wrangling LLMs to not make hacky solutions or go on wild goose chases.

Do you have a working implementation for this? Just a one-to-one index of files and reasoning traces? I'd like to trace these changes easily back to a feature or technical spec too (and have it change that spec if it needs to? I suppose the spec would have it's own reasoning trace)


This is compelling but it would be useful to compare upfront costs here. Investing $20,000+ in a server isn't feasible for many. I'd also be curious to know how much a failsafe (perhaps "heatable" cold storage, at least for the example) would cost.


I'm sure there are companies in this space providing private clouds on bare metal, I wonder how that would be to operate at scale though.


It could be useful if you have an embedded device that you don't want to miss data from, but for most deployments I would agree.


When using ephemeral cloud servers (Cloud Run, Lambda functions, etc), I often find myself wanting a quick and easy database to persist data. I've found cloud storage to be the best place for this but wanted a unified API between providers. I also wanted to be able to build an object on the server before persisting to the storage provider, so I came up with this approach.

What other approaches are people using for this problem?


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