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timeleft--

Thanks for the great observations. I haven't done a formal benchmark, but I built this because I saw this failure mode in several production agentic systems. The quality of the trust-gate will not be a one and done prompt, but will have to iterate on that and will most likely be application specific.

Show HN: Fava Trails – Git-backed memory for AI agents using Jujutsu (JJ)

https://github.com/MachineWisdomAI/fava-trails

Hey HN,I've been building and running autonomous AI agents (recently consulting on systems testing high on MLE-Bench), and I kept hitting the exact same architectural wall: memory poisoning.Right now, the industry standard for agent memory is to dump text into storage with very little thought about correctness. If an agent hits a transient network error and writes "this environment has no GPU"...

( Read More... | 3 comments | on HN )
It is very frustrating how "nobody has ever done comprehensive and non-biased benchmarks". All the comparisons I have seen are based on stories and anecdotes. Well, they are both very performant, I know. But, if somebody is going to publish a comparison why don't they qualify this?

PS: I find it kind of an oxymoron that the article under discussion criticizes Solr for using Zookeeper in one paragraph; "ZooKeeper is super mature, super widely used, etc. etc., but it’s still another moving part.", then on the next paragraphs it cites that one of the most dreadful thing about ES is the lack of a proven master election algorithm; "While Elasticsearch has built-in ZooKeeper-like component called Xen, ZooKeeper is better at preventing the dreaded split-brain problem sometimes seen in Elasticsearch clusters."